Constructionism 2018 Conference

Constructionism 2018
Constructionism, Computational Thinking and
Educational Innovation: conference proceedings
Organizers:
Vilnius University
Faculty of Philosophy andInstitute of Data Science and Digital Technologies
in cooperation with Lithuanian Computer Society
August 20-25, Vilnius, Lithuania

Edited by
Valentina Dagienė and Eglė Jasutė

Constructionism 2018, Vilnius, Lithuania

ISBN 978-609-95760-1-5

The conference is partially supported by Research Council of Lithuania

All publications are copyright © 2018 by the authors unless specified otherwise.
Permission to make digital or hard copies of portions of this work for personal or classroom use is granted provided that the copies
are not made or distributed for profit or commercial advantage and that the full citation is included. To copy otherwise, to republish,
to post on servers, or to redistribute to lists, articles that are copyright by the author requires a written request to the authors.

CONFERENCE VENUE
Faculty of Philosophy
Vilnius University
Universiteto str. 901513 Vilnius, Lithuania
Located at the Old Campus of Vilnius University

The conference website: http://www.constructionism2018.fsf.vu.lt
Book of abstracts is available at: http://www.constructionism2018.fsf.vu.lt/book-of-abstracts
Full papers are available at: http://www.constructionism2018.fsf.vu.lt/proceedings2

Constructionism 2018, Vilnius, Lithuania

CONSTRUCTIONISM 2018 COMMITTEES
Conference Chairs
Valentina Dagienė, Vilnius University Institute of Data Science and Digital Technologies, Lithuania
Arūnas Poviliūnas, Vilnius University Faculty of Philosophy, Lithuania
Arnan (Roger) Sipitakiat, Chiang Mai University, Thailand

Scientific Committee / Reviewers
Tim Bell, University of Canterbury, New ZealandPaulo Blikstein, Stanford University, USA
Pavel Boytchev, Sofia Universoty, Portugal
James Clayson, American University of Paris,
FranceSecundino Correia, Imagina, Coimbra, Portugal
Barbara Demo, University of Torino, Italy
Vladimiras Dolgopolovas, Vilnius University,
Lithuania
Michael Eisenberg, University of Washington,
USA
Gerald Futschek, Vienna University ofTechnology, Austria
Carina Girvan, Cardiff University, Cardiff, Wales,
Paul Goldenberg, Education Development
Center, USA
Brian Harvey, University of California, USA
Bulgaria
Arthur Hjorth, Northwestern University, USA
Celia Hoyles, University College London Instituteof Education, UKEglė Jasutė, Vilnius University, Lithuania
Tatjana Jevsikova, Vilnius University, Lithuania
Anita Juškevičienė, Vilnius University, Lithuania
Ivan Kalaš, Comenius University, Slovakia
Eric Klopfer, Massachusetts Institute ofTechnology, USA
Witold Kranas, OEIiZK, Poland
Chronis Kynigos, National and Kapodistrian
University of Athens, Greece
Manolis Mavrikis, University of London, UK
Mattia Monga, Università degli Studi di Milano,
Italy

Jens Mönig, SAP, Germany
Erich Neuwirth, University of Vienna, Austria
Richard Noss, University College London Instituteof Education, UK
Mareen Przybylla, University of Potsdam,
University of Potsdam, Germany
Mitchel Resnick, MIT Media Lab, Massachusetts
Institute of Technology, USA
Ralf Romeike, Friedrich-Alexander-Universität
Erlangen-Nürnberg, Germany
Ana Isabel Sacristán, Cinvestav, Mexico
Jenny Sendova, Institute of Mathematics,
Bulgaria
Giovanni Serafini, ETH Zurich, Switzerland, Italy
Wolfgang Slany, Graz University of Technology
Institute of Software Technology, Austria
Gary Stager, Constructing Modern Knowledge,
Torrance, USA
Eliza Stefanova, Sofia University "St. Kliment
Ohridski", Bulgaria
Gabrielė Stupurienė, Vilnius University, Lithuania
Maciej Syslo, University of Wroclaw, Poland
Márta Turcsányi-Szabó, Eötvös Loránd
University, Hungary
José Armando Valente, State University ofCampinas, UNICAMP, BrazilJiří Vaníček, University of South Bohemia,
Czechia
Michael Weigend, University of Münster,Germany
Uri Wilensky, Northwestern University, Evanston,
USA
Michelle Wilkerson, University of California, USA

Local Organizing Committee
Vida Jakutienė
Aldona Mačiūnienė
Saulius Maskeliūnas
Olga Suprun

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Constructionism 2018, Vilnius, Lithuania

Photo E. Kurauskas

Photo E. Kurauskas

Welcome Message
Dear Colleagues
We would to like to welcome you to the Constructionism conference.
The Constructionism conference celebrates its fifth anniversary under this name, building on the 27year tradition of biennial Eurologo conferences established by the European Logo community. Logo is
the computer programming language for learners in which the constructionist approach was firstdeveloped. Seymour Papert, who has coined the term constructionism, in order to define its meaning,
started from the comparison with the term constructivism:
Constructionism shares constructivism's connotation of learning as ‘building knowledge structures’irrespective of the circumstances of the learning. It then adds the idea that this happens especiallyfelicitously in a context where the learner is consciously engaged in constructing a public entity, whether
it's a sand castle on the beach or a theory of the universe” ( Papert, S. & Harel, I. Constructionism. New York: Ablex Publ.
Corp., 1991).

The constructionism shares the main idea of genetic epistemology elaborated by Jean Piaget about theimmanence of the cognitive development. In Piaget’s version, the immanent algorithm of the cognitive
development includes the sensorimotor, preoperational, concrete operational, and formal operational
stages.
The pioneer of the radical constructivism Ernst von Glasersfeld has enriched the constructivist
educational discourse by introducing ideas from the Italian philosopher Giambattista Vico. In his
Magnum opus Scienza Nuova, Vico has elaborated the principle verum esse ipsum factum ("What istrue is precisely what is made"). According to Vico, “/…/ the world of civil society has certainly been
made by men, and that its principles are therefore to be found within the modifications of our own humanmind.” The world of civil society differs from the world of nature, “which, since God made it, He alone
knows” (Vico, J.-B. The New Science. Transl. by T. G. Bergin & M. H. Fisch. Ithaca, N Y: Cornell Univ. Press,
1948 [1744]: 85).
Another important idea brought from Vico is the application of tropology for the description of different
modes of consciousness or phases of its development. American post-structuralist Hayden White hasapplied tropes for the explanation of different phases of cognitive development discovered by Piaget. It
seems that it is important for the development of constructionism to track the processes in the field ofconstructivism and vice versa.
Seymour Papert has stressed that the picture of how scientists actually work should be shared withchildren but not in the way of verbally-expressed formal knowledge. Closer cooperation with the Science4

Constructionism 2018, Vilnius, Lithuania

and Technology Studies could assist in identifying more adequate forms how to communicate scientificknowledge and how to engage children into the attractive and actual scientific research.
What have we achieved until today? Has our concept of education in general and computer science(computing or informatics) education in particular changed and its quality improved? Can we use thelessons of the past to prepare for the future? In an increasingly interdependent and complex world, how
is technology and informatics changing society and affecting education through the subject areas ofhumanities, science, mathematics, and arts? The Constructionism conference has addressed thesequestions by offering experts from across the world the opportunity to exchange ideas and knowledge,
and to generate a more informed understanding of the issues of informatics and digital technologies in
education. The Conference offered a number of themes:- Constructionist epistemology- Informal learning- Innovative computing education- Learning from pioneers- Methodologies, tools and technologies- Monitoring, evaluation and research- Outreach and communication- Progressive education in national and regional contexts- Social justice, equity and citizenship- Teacher training and educational policies- Technology unplugged activities- Visual and creative arts
The Conference brings together delegates from all over the world to address pressing issues incomputing education. In addition to keynote speakers, research and practice papers, panels, posters,
demonstrations, and workshops, the Conference provides facilities and exposure for working groups forthe first time. The working groups are formed by participants with a common interest in a topic.
Participating in a working group provides a unique opportunity to work with people from different
countries who are interested and knowledgeable in the area of the working group. It is also one of thebest ways to become part of the constructivist community. Seven working groups have been accepted,
covering a broad spectrum of topics. Participants present their preliminary results to conference
attendees at a special working group presentation session, and submit a final report after the conference
concludes.
The Constructionism conference continues to be truly international with about 150 submissions from 40
countries. The accepted submissions consisted of 18 keynotes talks, 57 research and practice papers,
3 panels, 7 working group proposals, and 27 proposals for posters, demonstrations and workshops. In
addition, a special Teachers’ Day is organized before the conference: 13 workshops were proposed formore than 150 Lithuanian teachers.
Selected research papers will be published in the international peer-reviewed journals “Informatics in
Education” and “Problemos”. After the conference, there will be possibility to extend the best papers
and publish them in the international peer-reviewed journal “Constructivist Foundations”.
We are grateful to all committee members participating in any way in the conference.
Welcome to Vilnius, the capital of Lithuania! Enjoy the Constructionism conference in the country
celebrating 100 years of independence!

Conference Co-Chairs

Valentina Dagienė
Arūnas Poviliūnas

5Constructionism 2018, Vilnius, Lithuania

Program at a glance
Monday,August 20
900-1000
Registration(in front of theTheatre Hall)

1000-1200
WelcomeSpeakers(Main Building,
The Theatre
Hall*)

Tuesday
August 21
Location: Main
Building, The
Theatre Hall*TEACHERS DAY

1300-1500
Workshops
in parallel

FridayAugust 24

Saturday August
25

Location:
The Faculty ofPhilosophy**

Location:
The Faculty ofPhilosophy**

Location:
The Faculty ofPhilosophy**

Location:
The Faculty ofPhilosophy**

In parallel:
In parallel: Plenary session VII  Plenary session IX
 Plenary session VIII  Plenary session X
In parallel:
In parallel:
 Paper session 9
 Demo session 2
 Demo session 1
 Demo session 3
 Workshop 6

Coffee break

Coffee break

Coffee break

1130–1330

In parallel:
 Paper session 1
 Paper session 2
 Workshop 1
 Workshop 2

In parallel:
In parallel:
 Paper session 6
 Paper session 10
 Paper session 7
 Paper session 11
 Paper session 8
 Paper session 12
 Panel discussion III

1330–1430

Lunch

Lunch

1430–1600

In parallel: Working Group
presentations II
 Working Group
presentations III

1000-1130
30
00
Excursion to 10 –11
Old Vilnius
University I
1100–1130

1130-1300
Excursion to
Old Vilnius
University II

Thursday August23

In parallel:
In parallel: Plenary session III  Plenary session V Plenary session IV  Plenary session VI
In parallel:
In parallel: Working Group Poster session I
presentations I
 Poster session II
 Panel discussion II

830–all day
830–1030
Registration

1200-1300
Lunch(Registration in
the Faculty ofPhilosophy**)Time

Wednesday
August 221130-1200
Closing: Farewell
buffetLocation: Grand
Courtyard next to theMain Building*

Lunch(The Faculty ofPhilosophy**)

1500-1530
CoffeeBreak

1530–1700
Workshops
in parallel(The Faculty ofPhilosophy**)

1700-1800
Reflections
and Panel
Discussion(The Faculty ofPhilosophy**,
room 301)

1400-1600
Plenary
session I

Coffeebreak

1600–1630

1630-1730
Plenary
Session II
1630–1830
1730-1830
Panel
Discussion I

Coffee breakIn parallel:
 Paper session 13
 Paper session 14
 Workshop 3
1300–2000

1430–2200

Coffee breakExcursion
In parallel:
 Paper session 3
 Paper session 4
 Paper session 5

DinnerPost
Conference
Excursions(not included inconference fee)

In parallel:
 Paper session 15
 Paper session 16
 Workshop 4
 Workshop 5

1830–1930

Welcome
Reception* Main Building, The Theatre Hall: Universiteto St.3**The Faculty of Philosophy: Universiteto St. 9

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Constructionism 2018, Vilnius, Lithuania

Conference program
Monday, August 20
900–1000

1000–1200

1200–1300

1300–1500

Teachers‘ day (Pre-Conference)

Registration & Coffee (in front of the Theatre Hall)
Opening Session: Main Building, The Theatre Hall (Universiteto St. 3)
WelcomeGiedrius Vaidelis (Lithuania). Updating Educational Content: Challenges and Possibilities
Evgenia Sendova (Bulgaria). The Beauty in Science and the Science in Beauty
Rimantas Želvys (Lithuania). Future Education: New Challenges for Lithuania?
Lunch
Registration in the Faculty of Philosophy (Universiteto St. 9)
Workshops (WS) in parallel: The Faculty of Philosophy (Universiteto St. 9)
WS 1: room 106. Judith Bell (New Zealand). Dynamic Teaching Ideas for Teaching Music Theory. Target audience:
primary school teachers & music teachers
WS 3: room 204. Paul Goldenberg, Cynthia J. Carter (USA). Developing Algebraic Habits of Mind in Students. Target
audience: mathematics teachers for students ages 11–18
WS 7: room 111. Petra Enges-Pyykönen (Finland). ViLLE – Electronic Learning Path for Mathematics and Programming.
Target audience: primary school teachers
WS 8: room 201. Evgenia Sendova, Nikolina Nikolova (Bulgaria). Constructionism in Action: Do we Need to Start from
Scratch? Target audience: all teachers
WS 9: room 313. Gary Stager (USA). Teaching Coding and Physical Computing. Target audience: all teachers
WS 10: room 112. Jacqueline Staub (Switzerland). The Essence of Programming at School – Logo in a Spiral Curriculum.
Target audience: primary and lower secondary school teachers
WS 11: room 308. Carol Sperry Suziedelis (USA). How to Create and Sustain a Progressive Pedagogy in a Traditional
Setting. Target audience: all teachers
WS 13: room 306. Annalise Duca, Angele Giuliano (Malta), Sofia Nikitopoulou, Nikoleta Yiannoutsou, Chronis
Kynigos (Greece). The ER4STEM Repository for Educational Robotics. Target audience: all teachers

1500–1530

Coffee break

1530–1700

Workshops (WS) in parallel: The Faculty of Philosophy (Universiteto St. 9).
WS 2: room 106. Tim Bell (New Zealand). Computer Science Unplugged for Teachers. Target audience: primary school
teachers
WS 4: room 204. Paul Goldenberg, Cynthia J. Carter (USA). Puzzles & Programming to Develop Mathematical Habitsof Mind in 6–10-year Olds. Target audience: primary school teachers for students ages: 6–10
WS 5: room 201. Ivan Kalaš (Slovakia). Powerful Ideas in Lower Primary Programming: High Time to Recognize Them.
Target audience: educators interested in lower primary computing (pupils aged 5 to 9) and general primary teachers
WS 6: room 111. Witek Kranas (Poland). SNAP! - Beauty & Joy of Computing (visually). Target audience: informatics
teachers, lower and upper secondary schools (6-12 grades)
WS 8: room 401. Evgenia Sendova, Nikolina Nikolova (Bulgaria). Constructionism in Action: Do we Need to Start from
Scratch? Target audience: all teachers
WS 9: room 313. Gary Stager (USA). Teaching Coding and Physical Computing. Target audience: all teachers
WS 10: room 112. Jacqueline Staub (Switzerland). The Essence of Programming at School – Logo in a Spiral Curriculum.
Target audience: primary and lower secondary school teachers
WS 11: room 308. Carol Sperry Suziedelis (USA). How to Create and Sustain a Progressive Pedagogy in a Traditional
Setting. Target audience: all teachers
WS 12: room 205. Igor Verner, Khayriah Massarwe, Daoud Bshouty (Israel). Joyful Learning of Geometry in Cultural
Context. Analysis and Construction of Geometric Ornaments. Target audience: all teachers
WS 13: room 306. Annalise Duca, Angele Giuliano (Malta), Sofia Nikitopoulou, Nikoleta Yiannoutsou, Chronis
Kynigos (Greece). The ER4STEM Repository for Educational Robotics. Target audience: all teachers

1700–1800

Reflections and Panel Discussion: room 301

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Constructionism 2018, Vilnius, Lithuania

Tuesday, August 21 / Location: Main Building, The Theatre Hall (Universiteto St. 3)
830–all day Registration
1000–1130

Excursion to Old Vilnius University I

1130–1300

Excursion to Old Vilnius University II

1400–1600

Session chair: Valentina Dagienė
Opening
Plenary session I
Rimantas Želvys. One Hundred Years of Educational Development in Lithuania
James Clayson. Look Closely, Watch What Happens: Visual Modelling and Constructionism

1600–1630

Coffee break

1630–1730

1730–1830

1830–1930

Session chair: Arūnas Poviliūnas
Plenary session II
Gary Stager. Making Constructionism Great Again
Panel Discussion I
Inside the Trojan Horse – A Discussion Among the Next Generation of Constructionists
Sylvia Martinez (moderator), Gary Stager, Amy Dugré, Angela Sofia Lombardo, Susana Tesconi, Tracy Rudzitis, Brian C.
Smith, Jaymes Dec
Welcome Reception / Location: Grand Courtyard

Wednesday, August 22 / Location: The Faculty of Philosophy (Universiteto St. 9)

830–1030

1030–1100

1100–1130

1130–1330

1330–1430

1430–1600

Session chair: Gerald Futschek
Plenary session III: room 301
Carol Sperry Suziedelis. The Evolution of aConstructionist Teacher (with Reminders from Seymour
Papert)Evgenia Sendova. Back 100 000(2)Working Group (WG) presentations I: room 301
WG 2: Don Passey, Loice Victorine Atieno, Wilfried
Baumann, Valentina Dagienė. Developing
Constructionism, or a New Learning Concept, Across theAges.

Session chair: Chronis Kynigos
Plenary session IV: room 302
Celia Hoyles, Richard Noss. Scratchmaths: A Positive
Outcome for Constructionism at Scale
Ivan Kalaš. Programming in Lower Primary Years: Design
Principles and Powerful Ideas
Panel discussion II room 302
Constructionism at Scale.
Nathan Holbert (moderator), Matthew Berland, Yasmin
Kafai, Richard Noss, Celia Hoyles, Kylie Peppler, Debbie
FieldsCoffee break
Session chair: Natasa Grgurina
Paper session 1: room 301
Education and innovations
Arthur Hjorth, Corey Brady, Uri
Wilensky. Sharing is Caring in the
Commons – Students’ Conceptions
about Sharing and Sustainability in
Social-Ecological Systems
Arthur Hjorth, Uri Wilensky. Urban
Planning-in-Pieces: A
Computational Approach toUnderstanding Conceptual Change
and Causal Reasoning about Urban
Planning
Sugat Dabholkar, Gabriella Anton,
Uri Wilensky. Developing Mathetic
Content Knowledge Using an
Emergent Systems Microworld
Elmara Pereira de Souza, Luísa
Moura. Constructionism as an
Epistemological Option in Coursesof Youth Center for Science andCulture – Bahia – Brazil

Session chair: Evgenia Sendova
Paper session 2: room 302
Constructionism in Mathematics
Chantal Buteau, Ana Isabel Sacristán,
Eric Muller. Teaching in a Sustained PostSecondary Constructionist Implementationof Computational Thinking for Mathematics
Maite Mascaró, Ana Isabel Sacristán.
Assessing Learning through Exploratory
Projects in Constructionist R-based
Statistics Courses for Environmental
Science Students
Christina Todorova, Carina Girvan,
Nikoleta Yiannoutsou, Marianthi Grizioti,
Ivaylo Gueorguiev, Pavel Varbanov,George Sharkov. Visualizing Mathematics
with the MathBot: A Constructionist Activity
to Explore Mathematical Concepts throughRobotics
Einari Kurvinen, Valentina Dagiene,
Mikko-Jussi Laakso. The Impact andEffectiveness of Technology Enhanced
Mathematics Learning

Session chair: Eglė Jasutė
Workshop 1: room 111
Jacqueline Staub. The Essence ofProgramming at School – Learning
for Life
Workshop 2: room 111
Stephen Howell, Lizbeth
Goodman. Developing Body
Tracking Software with Scratch andKinectLunch
Session chair: Gabrielė Stupurienė
Working Group (WG) presentations II: room 301

Session chair: Tatjana Jevsikova
Working Group (WG) presentations III: room 302

WG 1: Gerald Futschek, Bernhard Standl, Chantal
Buteau, Andrew Csizmadia, Lilia Georgieva, Lina
Vinikienė, Jane Waite. Constructionist Approaches toComputational Thinking.

WG 3: Evgenia Sendova, Christos Chytas, Katarzyna
Olędzka, Ralf Romeike, Wolfgang Slany. Creating and
Looking at Art with Logo Eyes.
8Constructionism 2018, Vilnius, Lithuania
WG 5: Michael Weigend, Kazunari Ito, Anita
Juškevičienė, Igor Pesek, Zsuzsa Pluhár, Jiří Vaníček.
Constructionism in the Classroom: Creative Learning
Activities on Computational Thinking.
WG 6: Mattia Monga, Michael Lodi, Dario Malchiodi,
Anna Morpurgo, Oluwakemi Oduwole, Bamidele Oluchi,
Bernadette Spieler. Learning to Program in a
Constructionist Way.
1600–1630

1630–1830

WG 4: Lilija Duoblienė, Jūratė Baranova, Luc Anckaert,
Wilfried Baumann .The Constructive Strategies in Teaching
Humanities with Films.
WG 7: Ana Isabel Sacristán, Richard Akrofi Kwabena
Baafi, Lina Kaminskienė, Michaela Sabin. Constructionism
in Upper Secondary and Tertiary Levels.

Coffee break
Session chair: Mattia Monga
Paper session 3: room 301

Session chair: Jiří Vaníček
Paper session 4: room 302

Session chair: Arthur Hjorth
Paper session 5: room 306

Computational Thinking
Judith Bell, Tim Bell.
Computational Thinking and Music
Learning
Marianthi Grizioti, Chronis
Kynigos. Programming Approaches
to Computational Thinking:Integrating Turtle Geometry,
Dynamic Manipulation and 3D
Space
Marianthi Grizioti, Chronis
Kynigos. Constructionist
Approaches to Computational
Thinking: A Case of Game Modding
with ChoiCo
Anita Juškevičienė, Valentina
Dagienė. Interconnection Between
Computational Thinking and Digital
Competence

Constructionist approaches
Valentina Dagienė, Gabrielė Stupurienė.
Short Tasks – Big Ideas: Constructive
Approach for Learning and Teaching ofInformatics Concepts in Primary Education.
Miroslava Černochová, Radek Čuma,Hasan Selcuk. Forming Concepts for
Programming Conditional Statements in
the Primary School
Jean Griffin. Constructionism and DeConstructionism as Complementary
Pedagogies
Tilman Michaeli, Stefan Seegerer, Ralf
Romeike. Enabling Collaboration andTinkering: A Version Control System for
Block-based Languages
Jake Rowan Byrne, Kevin Sullivan,
Katriona O’sullivan. Active Learning ofComputer Science Using a Hackathon-like
Pedagogical Model

Reflections
Nicolas Pope, Jonathan Foss,
Meurig Beynon. Reconstructing
Constructionism by Construal
Deborah Fields, Mia Shaw,
Yasmin Kafai. Personal Learning
Journeys: Reflective Portfolios as“Objects-to-Learn-With” in an Etextiles High School Class
Evgeny Patarakin. Using Agentbased Modelling of Collaborationfor Social Reflection
Francesca Agatolio, Alfredo
Asiain, Alfredo Pina, Gabriel
Rubio, Michele Moro.
Constructive and Collaborative
Digital Storytelling for Enhancing
Creativity and Cooperation In andOut of School

Thursday, August 23 / Location: The Faculty of Philosophy (Universiteto St. 9)

830–1030

Session chair: Gary Stager
Plenary session V: room 301

Session chair: James Clayson
Plenary session VI: room 302

Gerald Futschek. Computational Thinking and Creativity
Tim Bell. CS Unplugged and Computational thinking

Uri Wilensky. Reempowering powerful ideas
Paulo Blikstein. Constructionism Won, Now What? The
Role of Constructionist Research in the Age of Ubiquitous
ComputingSession chair: Lina Vinikienė
Poster Session II: room 302

Session chair: Anita Juškevičienė
Poster Session I: room 301

1030–1100

Nalin Tutiyaphuengprasert. Applied Constructionism:
Critical Reflection and Learning Through Play in Adult
Learning
Sawaros Thanapornsangsuth, Nathan Holbert, Monica
Chan. Towards Girls’ Self-perception in Technology and
Craft: Challenges and Implications
Enric Ortega Torres, Vincent Sanjosé López, JoanJosep Solaz Portolés. Influence of Students’ Selfperceived Use of Metacognitive Strategies and Sensory
Preferences on Academic Achievement in Science andTechnology
Takeshi Watanabe, Yuriko Nakayama, Yasunori
Harada, Yasushi Kuno. Programming Lessons for
Kindergarten Children in Japan
Sayaka Tohyama, Yugo Takeuchi. Collaborative
Creative Music Activity with ICT: A Case Study for Children
in Grade Five
Yoshiaki Matsuzawa, Misako Noguchi, Issei Nakano.
Exploration of Algorithm Abstraction Process with Cubetto
and Middle Grade Elementary Kids
Aoi Yoshida, Kazunari Ito, Kazuhiro Abee. A Practical
Report on a Programming Course with “Making” Using
micro:bit
Liudmyla Kryvoruchka. Heuristic Potential of Open
Institutional Models in Researchers Education.

Michael Tan. Constructing what? Knowledge of the powerful,and powerful knowledge
Carina Girvan, Wilfried Lepuschitz, Ivaylo
Gueorguiev, Christina Todorova, Chronis Kynigos,
Marianthi Grizioti, Angele Giuliano, Annalise Duca,
Julian Mauricio Angel-Fernandez, Markus Vincze.
Educational Robotics for STEM: From Workshops toCurricula and Framework
Ivaylo Gueorguiev, Christina Todorova, Nikoleta
Yiannoutsou, Xristina Greka, Pavel Varbanov, George
Sharkov, Carina Girvan, Julian Mauricio AngelFernandez, Lisa Vittori, Annalise Duca. Towards a Generic
Curriculum for Educational Robotics in STEM: From ScientificConcepts to Technologies and Powerful Ideas
Barbara Sabitzer. Modeling Across the Subjects
Jinbao Zhang. An Experimental Exploration of theDevelopment of Design Thinking in University Maker Courses
Márton Visnovitz, Győző Horváth. The Web – A Platform
for Creation
Pekka Mäkiaho, Timo Poranen, Katriina Vartiainen.
Construction of a Project Monitoring Application Iteratively
and Incrementally
Lina Vinikienė, Valentina Dagienė. Different Cultures –Different Approaches to Reasoning and Algorithms

9Constructionism 2018, Vilnius, Lithuania
1100–1130

1130 –1330

Coffee break
Session chair: Jacqueline Staub
Paper session 6: room 301

Session chair: Wolfgang Slany
Paper session 7: room 302

Programming educationJiří Vaníček. Concept-building
Oriented Programming Education
Ungyeol Jung, Young Jun Lee.
The Direction and Possibility forSocial Justice in Informatics
Education based on Bebras
Challenge in Republic of Korea
Ken Kahn, Niall Winters. AI
Programming by Children
Elisabeth Wetzinger, Gerald
Futschek, Bernhard Standl. A
Creative Learning Sequence in an
Introductory Programming MOOC

Robotics
Julian Mauricio Angel Fernandez,
Nikoleta Yiannoutsou, Chronis Kynigos,
Carina Girvan, Markus Vincze. Towards
a Framework for Educational Robotics
Flavio Campos. Design Curriculum for
Educational Robotics: Constructionist
Pedagogical Experience in Formal
Education
Dave Catlin, Martin Kandlhofer,
Stephanie Holmquist, Andrew Paul
Csizmadia, Julian Mauricio Angel
Fernandez, John-John Cabibihan.
EduRobot Taxonomy and Papert’s
Paradigm
Karolína Mayerová, Michaela
Veselovská. How Students Struggled with
Preparation of Activities for a Leisure Time
Robotic Workshop

1330–1430

Lunch

1430–2200

Excursion & Dinner

Session chair: Ana Isabel
Sacristán
Paper session 8: room 306
Jose Armando Valente, Paulo
Blikstein. The Construction of
Knowledge in Maker Education: A
Constructivist Perspective
Panel discussion III: room 306
Constructionism across Cultures:
Commonalities and Differences ofConstructionist Implementations
Around the World
joined with papers

Jose Armando Valente, Paulo
Blikstein. Constructionism in
Different Cultures: the case of
Brazil &Deborah Fields, Paulo Blikstein.
What Is Constructionism? Views
from a Thai Perspective
Jose Armando Valente (moderator),
Paulo Blikstein, Deborah Fields,
Michael Tan

Friday, August 24 / Location: The Faculty of Philosophy (Universiteto St. 9)
Session chair: Ivan Kalaš
Plenary session VII: room 301

Session chair: Jose Armando Valente
Plenary session VIII: room 302

Paul Goldenberg. Teaching Children to be Problem
Posers and Puzzle-creators in Mathematics
Ana Isabel Sacristán. Constructionist Experiences forMathematics across Educational Levels
Paper session 9: room 301

Wolfgang Slany. Rock Bottom, the World, the Sky:
Catrobat, an Extremely Large-scaling and Long-term Visual
Coding Project Relying Purely on Smartphones
Gary Stager, co-speaker Sylvia Martinez. Turning Theory
Into Practice – Spreading Constructionism
Demo session 1: room 302

10 – 11

Tiina Partanen, Pia Niemelä, Timo Poranen. Racket
Programming Material for Finnish Elementary Math
Education

Markus Klein, Clemens Koza, Wilfried Lepuschitz,
Gottfried Koppensteiner. Hedgehog: A Versatile Controller
for Educational Robotics

1100–1130

Coffee break

830–1030

30

00

Session chair: MiroslavaČernochová
Paper session 10: room 301

1130–1330

Designing activities
Yasmin Kafai, Deborah Fields.
Some Reflections on Designing
Constructionist Activities forClassrooms
Kit Martin, Michael Horn, Uri
Wilensky. Ant Adaptation: A
Complex Interactive MultitouchGame About Ants Designed forMuseums
Michael Weigend, Fenja Göcking,Alexander Knuth, Patrick Pais
Pereira, Laura Schmidt. Media
Parkour – Experiential Learning
Activities for Media Education
Brendan Tangney, Ian Boran,
Tony Knox, Aibhín Bray.
Constructionist STEM Activities
Using the Bridge21 Model

Session chair: Márton Visnovitz
Paper session 11: room 302

Session chair: Don Passey
Paper session 12: room 306

Teacher education
Daniel Hickmott, Elena PrietoRodriguez. To Assess or Not toAssess: Tensions Negotiated in Six
Years of Teaching Teachers about
Computational Thinking
Daniel Hickmott, Elena PrietoRodriguez. Constructionist
Experiences in Teacher Professional
Development: A Tale of Five Years
Igor Verner, Khayriah Massarwe,
Daoud Bshouty. Ethnomathematics
in Teacher Education: Analysis andConstruction of Geometric Ornaments
Xiaoxue Du, Kay Chioma Igwe.
Computational Thinking in Teacher
Professional Development Programs

Methodologies
Sven Jatzlau, Ralf Romeike. How Highis the Ceiling? Applying Core Concepts
of Block-based Languages to Extend
Programming Environments
Anton Chukhnov, Sergei Pozdniakov,
Ilya Posov, Athit Maytarattanakhon.
Analysis of Constructive and Cognitive
Activities of Participants in Online
Competitions in Computer Science
Vladimiras Dolgopolovas, Valentina
Dagienė, Eglė Jasutė, Tatjana
Jevsikova. Design Science Research
for Computational Thinking inConstructionist Education: A
Pragmatistic Perspective
Aleksandra Klašnja-Milićević, Mirjana
Ivanović. Learning Analytics in
Education: Objectives, Application
Possibilities and Challenges
10Constructionism 2018, Vilnius, Lithuania
1330–1430

1430– 1600

1600–1630

1630– 1830

Lunch
Session chair: Mihaela Sabin
Paper session 13: room 301
Curriculum matters
Eva Klimeková. Curriculum
Intervention for Learning
Programming in Python with Turtle
Geometry
Carol Angulo, Alberto J. Cañas,
Ana Gabriela Castro, Leda Muñoz,
Natalia Zamora. Think, Create andProgram: Evolving to a K-9
Nationwide Computational Thinking
Curriculum in Costa Rica
Michael Weigend. Coding to Learn- Informatics in Science Education

Session chair: Carina Girvan
Paper session 14: room 302
Girls in computing
Bernadette Spieler, Wolfgang
Slany. Female Teenagers andCoding: Create Gender Sensitive andCreative Learning Environments
Caitlin Davey, Sawaros
Thanapornsangsuth, Nathan
Holbert. Making Together: Cultivating
Community of Practice in an All-Girl
Constructionist Learning Environment
Sawaros Thanapornsangsuth,
Nathan Holbert. Exploring Girls’
Values and Perspectives in Making
for Others

Session chair: Wilfried BaumannWorkshop 3: room 111

Session chair: Michael Weigend
Paper session 16: room 302
Modeling
Natasa Grgurina, Erik Barendsen,
Cor Suhre, Klaas van Veen, Bert
Zwaneveld. Assessment of Modeling
Projects in Informatics ClassKit Martin, Gabriella Anton.
Modeling Time

Session chair: Brian Harvey
Workshop 4: room 111

Corey Brady, Walter Stroup, TonyPetrosino, Uri Wilensky. Group-based
Simulation and Modelling: Technology
Supports for Social Constructionism

Coffee break
Session chair: Witold Kranas
Paper session 15: room 301
Constructionist environments
Christos Chytas, Ira Diethelm.
Designing Constructionist Learning
Environments with Computational
Design and Digital Fabrication
Kazunari Ito. Pictogramming:
Learning Environment Using Human
Pictograms Based on
Constructionism
Kazunari Ito, Aoi Yoshida,
Takashi Yoneda, Yuichi Oie.
Human Pictogram Unplugged:
Unified Learning Environment ofComputer Science Unplugged Using
Human Pictograms
Nobuko Kishi, Mari Yoshida,
Minori Yoshizawa, Aoi Yoshida.
VISURATCTH: Visualization Tool for
Finding Characteristics of Teachingand Learning Process of Scratch
Programmers

Ümit Aslan, Uri Wilensky. Agentbased Construction (a-b-c)
Interviews: A Generative Case StudyYu Guo, Uri Wilensky. Mind theGap: Teaching High School Students
about Wealth Inequality throughAgent-Based Participatory
Simulations

Ken Kahn. AI Programming in Snap!
Workshop 5: room 111
Stephen Howell, Neeltje Berger, Peter
Heldens, Kevin Marshall, Clare Riley.
Developing Affordable STEM Maker
Projects with BBC Micro:bits andMicrosoft MakeCode

Saturday, August 25 / Location: The Faculty of Philosophy (Universiteto St. 9)

830–1030

1035– 1130

Session chair: Deborah Fields
Session chair: Paul Goldenberg
Plenary session IX: room 301
Plenary session X: room 302
Chronis Kynigos. In Support of Integrated Approaches to Brian Harvey. May I Teach an Algorithm?
Constructionist Designs and Interventions: The Case of Jens Mönig. Bones, Gears and Witchcraft
ChoiCo and MaLT
Arthur Hjorth. Social Gears - a Constructionist Approachto Social Studies
Session chair: Eugenijus Kurilovas Session chair: Christos Chytas
Session chair: Ralf Romeike
Demo session 2: room 301
Demo session 3: room 302
Workshop 6: room 111
Nevin Akcay, Hulya Avci, Ali
Ken Kahn. Interpolating (andBrian Broll, Corey Brady, ÁkosGüngör, Tufan Adiguzel. The
Extrapolating) 3D Turtle Programs in
Lédeczi. NetsBlox: A
Relationship between Computer
Beetle Blocks
Constructionist Environment for
Stephen Howell. Teaching Computational
Programming and English
Creating Distributed Applications
Language Skills
Thinking with Minecraft & Microsoft
Monica Chan, Gary Lee.
MakeCode
Synthesizing the Mesh: Using
Constructible Authentic
Representations to Gain Intuitive
Understanding of BayesianReasoning

1130–1200

Closing: Farewell buffet / Location: Grand Courtyard

1300–2000

Post Conference Excursions(not included in conference fee)11

Constructionism 2018, Vilnius, Lithuania

Content
Opening / Plenaries ................................................................................................................... 19
One Hundred Years of Educational Development in Lithuania ................................................................. 20
Rimantas Želvys
CS Unplugged and Computational thinking .............................................................................................. 21
Tim BellConstructionism Won, Now What? The Role of Constructionist Research in the Age of Ubiquitous
Computing ................................................................................................................................................. 29
Paulo Blikstein
Look Closely, Watch What Happens: Visual Modelling and Constructionism .......................................... 30
James Clayson
Computational Thinking and Creativity ..................................................................................................... 38
Gerald Futschek
Teaching Children to be Problem Posers and Puzzle Creators in Mathematics ...................................... 39
Paul Goldenberg
May I Teach an Algorithm? ....................................................................................................................... 53
Brain Harvey
Social Gears – a Constructionist Approach to Social Studies .................................................................. 68
Arthur Hjorth
Scratchmaths: a Positive Outcome for Constructionism at Scale ............................................................. 69
Richard Noss, Celia Hoyles
Programming in Lower Primary Years: Design Principles and Powerful Ideas ........................................ 71
Ivan KalašIn Support of Integrated Approaches to Constructionist Designs and Interventions: The Case of ChoiCoand MaLT .................................................................................................................................................. 81
Chronis Kynigos
Bones, Gears and Witchcraft .................................................................................................................... 82
Jens Monig
Constructionist Experiences for Mathematics Across Educational Levels ............................................... 83
Ana Isabel Sacristán
Back 100 000(2) .......................................................................................................................................... 94
Evgenia Sendova
Rock Bottom, the World, the Sky: Catrobat, an Extremely Large-scale and Long-term Visual Coding Project
Relying Purely on Smartphones .............................................................................................................. 104
Wolfgang Slany, Kirshan Kumar Luhana, Matthias Mueller, Christian Schindler, Bernadette Spieler
Making Constructionism Great Again ...................................................................................................... 120
Gary S. Stager
Turning Theory into Practice – Spreading Constructionism .................................................................... 121
Gary S. Stager, Sylvia Martinez
The Evolution of a Constructionist Teacher (with Some Reminders from Seymour).............................. 122
Carol Sperrry Suziedelis
Reempowering Powerful Ideas ............................................................................................................... 123
Uri Wilensky

Research papers ..................................................................................................................... 124
Agent-based Construction (a-b-c) Interviews: A Generative Case Study ............................................... 125
Umit Aslan, Uri Wilensky
12Constructionism 2018, Vilnius, Lithuania

Active Learning of Computer Science Using a Hackathon-like Pedagogical Model ............................... 138
Jake Rowan Byrne, Kevin Sullivan, Katriona O’Sullivan
EduRobot Taxonomy and Papert’s Paradigm ......................................................................................... 150
Dave Catlin, Martin Kandlhofer, Stephanie Holmquist, Andrew Paul Csizmadia, Julian M. AngelFernandez, John-John Cabibihan
Analysis of Constructive and Cognitive Activities of Participants in Online Competitions in Computer
Science .................................................................................................................................................... 160
Anton Chukhnov, Sergei Pozdniakov, Ilya Posov, Athit Maytarattanakhon
Short Tasks – Big Ideas: Constructive Approach for Learning and Teaching of Informatics Concepts inPrimary Education ................................................................................................................................... 169
Valentina Dagienė, Gabrielė Stupurienė
Design Science Research for Computational Thinking in Constructionist Education: A Pragmatistic
Perspective .............................................................................................................................................. 180
Vladimiras Dolgopolovas, Valentina Dagienė, Eglė Jasutė, Tatjana Jevsikova
Computational Thinking in Teacher Professional Development Programs ............................................. 193
Xiaoxue Du, Kay Chioma Igwe
What is Constructionism? Views from a Thai Perspective ...................................................................... 203
Deborah A. Fields, Paulo Blikstein
Personal Learning Journeys: Reflective Portfolios as “Objects-to-Learn-With” in an E-textiles High School
Class ........................................................................................................................................................ 214
Deborah A. Fields, Mia S. Shaw, Yasmin B. Kafai
Constructionism and De-Constructionism as Complementary Pedagogies............................................ 225
Jean M. Griffin
Mind the Gap: Teaching High School Students about Wealth Inequality through Agent-based Participatory
Simulations .............................................................................................................................................. 238
Yu Guo, Uri Wilensky
To Assess or Not to Assess: Tensions Negotiated in Six Years of Teaching Teachers about Computational
Thinking ................................................................................................................................................... 251
Daniel Hickmott, Elena Prieto-Rodriguez
Sharing is Caring in the Commons – Students’ Conceptions about Sharing and Sustainability in SocialEcological Systems ................................................................................................................................. 263
Arthur Hjorth, Corey Brady, Uri Wilensky
Urban Planning-in-Pieces: A Computational Approach to Understanding Conceptual Change and Causal
Reasoning about Urban Planning............................................................................................................ 274
Arthur Hjorth, Uri Wilensky
How High is the Ceiling? Applying Core Concepts of Block-based Languages to Extend Programming
Environments ........................................................................................................................................... 285
Sven Jatzlau, Ralf Romeike
The Direction and Possibility for Social Justice in Informatics Education based on Bebras Challenge in
Republic of Korea .................................................................................................................................... 295
Ungyeol Jung, Young-jun Lee
Interconnection between Computational Thinking and Digital Competence ........................................... 305
Anita Juškevičienė, Valentina Dagienė
AI Programming by Children ................................................................................................................... 315
Ken Kahn, Niall Winters
VISURATCH: Visualization Tool for Finding Characteristics of Teaching and Learning Process of Scratch
Programmers ........................................................................................................................................... 325
Nobuko Kishi, Mari Yoshida, Minori Yoshizawa, Aoi Yoshida13

Constructionism 2018, Vilnius, Lithuania

Curriculum Intervention for Learning Programming in Python with Turtle Geometry ............................. 334
Eva Klimeková
The Impact and Effectiveness of Technology Enhanced Mathematics Learning ................................... 344
Einari Kurvinen, Valentina Dagienė, Mikko-Jussi Laakso
Constructionist Approaches to Computational Thinking: A Case of Game Modding with ChoiCo ......... 357
Grizioti Marianthi, Chronis Kynigos
Programming Approaches to Computational Thinking: Integrating Turtle Geometry, Dynamic Manipulationand 3D Space .......................................................................................................................................... 369
Grizioti Marianthi, Chronis Kynigos
Modeling Time ......................................................................................................................................... 380
Kit Martin, Gabriella Anton
Ant Adaptation: A Complex Interactive Multitouch Game about Ants Designed for Museums .............. 392
Kit Martin, Michael Horn, Uri Wilensky
Enabling Collaboration and Tinkering: A Version Control System for Block-based Languages ............. 405
Tilman Michaeli, Stefan Seegerer, Ralf Romeike
Racket Programming Material for Finnish Elementary Math Education ................................................. 415
Tiina Partanen, Pia Niemelä, Timo Poranen
Using Agent-based Modelling of Collaboration for Social Reflection ...................................................... 426
Evgeny Patarakin
Reconstructing Constructionism by Construal ........................................................................................ 437
Nicolas Pope, Jonathan Foss, Meurig Beynon
Constructionist STEM Activities Using the Bridge21 Model .................................................................... 449
Brendan Tangney, Ian Boran, Tony Knox, Aibhín Bray
Exploring Girls’ Values and Perspectives in Making for Others .............................................................. 460
Sawaros Thanapornsangsuth, Nathan Holbert
The Construction of Knowledge in Maker Education: A Constructivist Perspective ............................... 472
José Armando Valente, Paulo Blikstein
Constructionism in Different Cultures: the Case of Brazil ....................................................................... 481
José Armando Valente, Paulo Blikstein,
Concept-building Oriented Programming Education ............................................................................... 488
Jiří Vaníček

Practice papers ....................................................................................................................... 497
Constructive and Collaborative Digital Storytelling for Enhancing Creativity and Cooperation In and Out of
School...................................................................................................................................................... 498
Francesca Agatolio, Alfredo Asiain, Alfredo Pina, Gabriel Rubio, Michele Moro
Towards a Framework for Educational Robotics ................................................................................ 506
Julian M. Angel-Fernandez, Nikoleta Yiannoutsou, Chronis Kynigos, Carina Girvan, Markus Vincze
Think, Create and Program: Evolving to a K-9 Nationwide Computational Thinking Curriculum in Costa
Rica ......................................................................................................................................................... 514
Carol Angulo, Alberto J. Cañas, Ana Gabriela Castro, Leda Muñoz, Natalia Zamora
Computational Thinking and Music Learning .......................................................................................... 520
Judith Bell, Tim Bell
Teaching in a Sustained Post-Secondary Constructionist Implementation of Computational Thinking forMathematics ............................................................................................................................................ 528
Chantal Buteau, Ana Isabel Sacristán, Eric Muller
Design Curriculum for Educational Robotics: Constructionist Pedagogical Experience in Formal Education................................................................................................................................................................. 536
Flavio Campos
14

Constructionism 2018, Vilnius, Lithuania

Forming Concepts for Programming Conditional Statements in the Primary School ............................. 543
Miroslava Černochová, Radek Čuma, Hasan Selcuk
Designing Constructionist Learning Environments with Computational Design and Digital Fabrication 547
Christos Chytas, Ira Diethelm
Developing Mathetic Content Knowledge using an Emergent Systems Microworld ............................... 554
Sugat Dabholkar, Gabriella Anton, Uri Wilensky
Making Together: Cultivating Community of Practice in an All-Girl Constructionist Learning Environment................................................................................................................................................................. 561
Caitlin Davey, Sawaros Thanapornsangsuth, Nathan Holbert
Assessment of Modeling Projects in Informatics Class ........................................................................... 570
Natasa Grgurina, Erik Barendsen, Cor Suhre, Klaas van Veen, Bert Zwaneveld
Constructionist Experiences in Teacher Professional Development: A Tale of Five Years .................... 577
Daniel Hickmott, Elena Prieto-Rodriguez
Pictogramming: Learning Environment Using Human Pictograms Based on Constructionism .............. 585
Kazunari Ito
Human Pictogram Unplugged: Unified Learning Environment of Computer Science Unplugged Using
Human Pictograms .................................................................................................................................. 593
Kazunari Ito, Aoi Yoshida, Takashi Yoneda, Yuichi Oie
Some Reflections on Designing Constructionist Activities for Classrooms ............................................. 601
Yasmin B. Kafai, Deborah A. Fields
Learning Analytics in Education: Objectives, Application Possibilities and Challenges .......................... 608
Aleksandra Klašnja-Milićević, Mirjana Ivanović
Assessing Learning through Exploratory Projects in Cnstructionist R-based Statistics Courses forEnvironmental Science Students............................................................................................................. 615
Maite Mascaró, Ana Isabel Sacristán
How Students Struggled with Preparation of Activities for a Leisure Time Robotic Workshop............... 623
Karolína Mayerová, Michaela Veselovská
Constructionism as an Epistemological Option in Courses of Youth Center for Science and Culture – Bahia– Brazil ..................................................................................................................................................... 631
Elmara Pereira de Souza, Luísa Souza Moura
Female Teenagers and Coding: Create Gender Sensitive and Creative Learning Environments ......... 637
Bernadette Spieler, Wolfgang Slany
Visualizing Mathematics with the MathBot: a Constructionist Activity to Explore Mathematical Concepts
through Robotics...................................................................................................................................... 649
Christina Todorova, Carina Girvan, Nikoleta Yiannoutsou, Marianthi Grizioti, Ivaylo Gueorguiev,
Pavel Varbanov, George Sharkov
Ethnomathematics in Teacher Education: Analysis and Construction of Geometric Ornaments ........... 657
Igor Verner, Khayriah Massarwe, Daoud Bshouty
Coding to Learn – Informatics in Science Education............................................................................... 664
Michael Weigend
Media Parkour– Experiential Learning Activities for Media Education .................................................... 672
Michael Weigend, Fenja Göcking, Alexander Knuth, Patrick Pais Pereira, Laura Schmidt
A Creative Learning Sequence in an Introductory Programming MOOC ............................................... 678
Elisabeth Wetzinger, Gerald Futschek, Bernhard Standl

Posters ..................................................................................................................................... 685
Educational Robotics for STEM: From Workshops to Curricula and Framework ................................... 686
Carina Girvan, Wilfried Lepuschitz, Ivaylo Gueorguiev, Christina Todorova, Chronis Kynigos,
Marianthi Grizioti, Angele Giuliano, Annalise Duca, Julian M. Angel-Fernandez, Markus Vincze
15

Constructionism 2018, Vilnius, Lithuania

Towards a Generic Curriculum for Educational Robotics in STEM: From Scientific Concepts toTechnologies and Powerful Ideas ........................................................................................................... 690
Ivaylo Gueorguiev, Christina Todorova, Nikoleta Yiannoutsou, Xristina Greka, Pavel Varbanov,George Sharkov, Carina Girvan, Julian M. Angel-Fernandez, Lisa Vittori, Annalise Duca
Heuristic Potential of Open Institutional Models in Researchers Education ........................................... 694
Liudmyla Kryvoruchka
Construction of a Project Monitoring Application Iteratively and Incrementally ...................................... 698
Pekka Mäkiaho, Timo Poranen, Katriina Vartiainen
Exploration of Algorithm Abstraction Process with Cubetto and Middle Grade Elementary Kids ........... 703
Yoshiaki Matsuzawa, Misako Noguchi, Issei Nakano
Influence of Students’ Self-perceived Use of Metacognitive Strategies and Sensory Preferences on
Academic Achievement in Science and Technology .............................................................................. 707
Enric Ortega Torres, Vincent Sanjosé López, Joan-Josep Solaz PortolésModeling Across the Subjects ................................................................................................................. 711
Barbara Sabitzer
Constructing What? Knowledges of the Pwerful, and Powerful Knowledges ......................................... 714
Michael Tan
Towards Girls’ Self-perception in Technology and Craft: Challenges and Implications ......................... 718
Sawaros Thanapornsangsuth, Nathan Holbert, Monica Chan
Collaborative Creative Music Activity with ICT: A Case Study for Children in Grade Five ..................... 723
Sayaka Tohyama, Yugo Takeuchi
Applied Constructionism: Critical Reflection and Learning Through Play in Adult Learning .................. 727
Nalin Tutiyaphuengprasert
Different Cultures – Different Approaches to Reasoning and Algorithms ............................................... 731
Valentina Dagienė, Lina Vinikienė
The Web – A Platform for Creation ......................................................................................................... 736
Márton Visnovitz, Győző Horváth
Programming Lessons for Kindergarten Children in Japan .................................................................... 741
Takeshi Watanabe, Yuriko Nakayama, Yasunori Harada, Yasushi Kuno
A Practical Report on a Programming Course with “Making” Using Micro:bit ........................................ 745
Aoi Yoshida, Kazunari Ito, Kazuhiro Abee
An Experimental Exploration of the Development of Design Thinking in University Maker Courses ..... 750
Jinbao Zhang

Panels / Workshops / Demonstrations / Working groups .................................................... 753
Panels ...................................................................................................................................... 754
Constructionism across Cultures: Commonalities and Differences of Constructionist Implementations
around the World ..................................................................................................................................... 754
Paulo Blikstein
Constructionism at Scale ......................................................................................................................... 754
Celia Hoyles, Richard Noss, Yasmin B. Kafai, Kylie Peppler, Deborah A. Fields, Nathan Holbert
Inside the Trojan Horse – A Discussion Among the Next Generation of Constructionists ..................... 755
Gary Stager, Sylvia Martinez, Amy Dugré, Angela Lombardo, Susana Tesconi, Tracy Rudzitis,
Brian C. Smith, Jaymes Dec
Workshops............................................................................................................................................... 756
WS1: The Essence of Programming at School – Learning for Life......................................................... 756
Jacqueline Staub

16

Constructionism 2018, Vilnius, Lithuania

WS2: Developing Body Tracking Software with Scratch and Kinect ....................................................... 759
Stephen Howell, Lizbeth Goodman
WS3: Group-based Simulation and Modelling: Technology Supports for Social Constructionism ......... 761
Corey Brady, Walter Stroup, Tony Petrosino, Uri Wilensky
WS4: AI Programming in Snap! ............................................................................................................... 765
Ken Kahn
WS5: Developing Affordable STEM Maker Projects with BBC Micro:bits and Microsoft MakeCode ..... 767
Stephen Howell, Neeltje Berger, Peter Heldens, Kevin Marshall, Clare Riley
WS6: NetsBlox: A Constructionist Environment for Creating Distributed Applications ........................... 769
Brian Broll, Corey Brady, Ákos Lédeczi
WS7: The ER4STEM Repository for Educational Robotics .................................................................... 773
Annalise Duca, Angele Giuliano, Sofia Nikitopoulou, Nikoleta Yiannoutsou, Chronis Kynigos

Demonstrations ....................................................................................................................... 777
The Relationship between Computer Programming and English Language Skills ................................. 777
Nevin Akcay, Hulya Avci, Ali Gungor, Tufan Adiguzel
Synthesizing the Mesh: Using Constructible Authentic Representations to Gain Intuitive Understanding ofBayesian Reasoning ................................................................................................................................ 781
Monica Chan, Gary C. F. Lee
Teaching Computational Thinking with Minecraft & Microsoft MakeCode .............................................. 786
Stephen Howell
Interpolating (and Extrapolating) 3D turtle Programs in Beetle Blocks ................................................... 788
Ken Kahn
Hedgehog: A Versatile Controller for Educational Robotics .................................................................... 791
Markus Klein, Clemens Koza, Wilfried Lepuschitz, Gottfried Koppensteiner

Working groups ....................................................................................................................... 794
WG1: Constructionist Approaches to Computational Thinking ............................................................... 794
Bernhard Standl, Gerald Futschek, Jane Waite, Andrew Paul Csizmadia, Lina Vinikienė, Janne
Fagerlund
WG2: Developing Constructionism, or a New Learning Concept, across the Ages ............................... 838
Don Passey, Loice Victorine Atieno, Wilfried Baumann, Valentina Dagienė, Arūnas Poviliūnas
WG3: Creating and Looking at Art with Logo Eyes ................................................................................. 855
Evgenia Sendova, Christos Chytas, Katarzyna Olędzka, Ralf Romeike, Wolfgang Slany
WG4: The Constructive Strategies in Teaching Humanities with Films .................................................. 868
Lilija Duoblienė, Jūratė Baranova, Luc Anckaert, Wilfried Baumann
WG5: Constructionism in the Classroom: Creative Learning Activities on Computational Thinking ...... 884
Michael Weigend, Zsuzsa Pluhár, Anita Juškevičienė, Jiří Vaníček, Kazunari Ito, Igor Pesek
WG6: Learning to Program in a Constructionist Way.............................................................................. 901
Mattia Monga, Michael Lodi, Dario Malchiodi, Anna Morpurgo, Bernadette Spieler
WG7: Constructionism in Upper Secondary and Tertiary Levels ............................................................ 925
Ana Isabel Sacristán, Lina Kaminskienė, Mihaela Sabin, Richard Akrofi Kwabena Baafi

Teachers’ Day .......................................................................................................................... 940
The beauty in science and the science in beauty or mathematics, informatics and science teaching as an
eye-opener of the beauty of ideas ........................................................................................................... 941
Evgenia Sendova
Ateities švietimas: naujos galimybės Lietuvai? ...................................................................................... 944
Rimantas Želvys
17Constructionism 2018, Vilnius, Lithuania

WS1: Dynamic Teaching Ideas for teaching Music Theory .................................................................... 945
Judith Bell
WS2: Computer Science Unplugged for Teachers ................................................................................. 945
Tim Bell
WS3: Developing Algebraic Habits of Mind in Students ......................................................................... 945
Paul Goldenberg, Cynthia J. CarterWS4: Puzzles & Programming to Develop Mathematical Habits of Mind in 6–10 year Olds ................. 946
Paul Goldenberg, Cynthia J. CarterWS5: Powerful Ideas in Lower Primary Programming: High Time to Recognize Them ......................... 946
Ivan Kalaš
WS6: Snap! - Beauty & Joy of Computing (visually) ............................................................................... 947
Witek Kranas
WS7: ViLLE – E. Learning Path for Mathematics and Programming...................................................... 954
Mikko-Jussi Laakso, Petra Enges-Pyykönen
WS8: Constructionism in Action: Do we Need to Start from Scratch? .................................................... 954
Evgenia Sendova, Nikolina Nikolova
WS9: Teaching Coding and Physical Computing ................................................................................... 955
Gary S. Stager
WS10: The Essence of Programming at School – Logo in a Spiral Curriculum ..................................... 955
Jacqueline Staub
WS11: How to Create and Sustain a Progressive Pedagogy in a Traditional Setting (Roundtable
Discussion) .............................................................................................................................................. 956
Carol Sperrry Suziedelis
WS12: Joyful Learning of Geometry in Cultural Context. Analysis and Construction of Geometric
Ornaments ............................................................................................................................................... 956
Igor Verner, Khayriah Massarwe, Daoud Bshouty

18Constructionism 2018, Vilnius, Lithuania

Opening / Plenaries

19Constructionism 2018, Vilnius, Lithuania

One Hundred Years of Educational Development in
Lithuania
Rimantas Želvys rimantas.zelvys@fsf.vu.lt
Vilnius University, Lithuania

Abstract
This year we celebrate the 100th anniversary of declaring the independent Republic of Lithuania, andthe main focus of our attention is the development of education since 1918, which can be divided into
at least four different periods.
1918-1940. The period of building. After declaration of the independence in February 1918, the Ministryof Education was established. After the devastating World War I the Ministry discovered in the territoryof the newly founded state 8 functioning gymnasiums (upper secondary schools) and 11
progymnasiums (lower secondary schools) with 360 teachers. Besides that, 1232 teachers worked inprimary schools, so the whole teachings corps in the country was 1592 teachers. There were no
institutions of higher education as the only university in the country – Vilnius University – was closed
down in 1832. The system of education had to be built practically from nothing. Lithuanian university
was re-established in 1922. Compulsory primary education – four years – was introduced during theperiod of 1928-1931. In two decades of intense work the fully-functioning system of education was
created and the illiteracy rate in 1940 dropped down to 2 percent.
1940-1944. The period of destruction. The Soviet occupation in 1940 and the World War II had anenormous destructive effect on our educational system. Just in a single day – June 14, 1941 – 11
percent of all Lithuanian teachers were deported to Siberia. Many of the teachers died during the war,
perished in exile or fled to the West in fear of repressions when the Red Army was approaching. In 1943
the Nazi German authorities closed down Vilnius University as an act of revenge for non-cooperatingwith the occupational administration. The system of education met the end of the World War II withdemolished schools and few remaining teachers.
1944-1990. The period of adaptation. After the war the country had to adapt to the imposed Soviet
model of education. Mass education was one of the essential elements of the model. In 1949
compulsory seven-year education, and in 1958 – compulsory eight-year education was introduced. In
1986 the eleven-year long general secondary education was extended to twelve-year long generalsecondary education. Massification of general secondary schooling was accompanied by thecentralization, monopolization, unification, and, most important, strong ideologization of education.
1990-2018. The period of transformation. There are many challenges facing our education. We have todeal with an ideological challenge – what will substitute the previously imposed communist ideology?
Do we accept the new “global ideology“ – neoliberalism – or shall we look for something else? We facea strategic challenge – what are the long-term goals and mission of our education? Do we accept theprevailing outlook that education is a service which has to supply the global labour market with anecessary workforce, or is it something else? We face a structural challenge – due to demographic
reasons is shrinking instead of expansion. We came to the understanding that closing down schools is
more difficult than building the new ones. We face the challenge of economical efficiency – how toachieve the desired level of quality and equity in education at the costs which are both available andacceptable to our society? We acknowledge that the current state of our education is far from perfectand that there are still many questions to be answered and many solutions to be found. However, when
we look back at the starting position we had one hundred years ago, there is no doubt that we can bereally proud with what we have achieved.
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Constructionism 2018, Vilnius, Lithuania

CS Unplugged and Computational thinking
Tim Bell, tim.bell@canterbury.ac.nz
University of Canterbury, New Zealand

Abstract
The CS Unplugged activities (csunplugged.org) provide a scaffolding for a constructivist approach to
introducing topics in computer science, without the need to learn programming first. It has been widelyused to support Computational Thinking in school curricula. This paper discusses the connection
between CS Unplugged activities and one of the (many!) definitions of Computational Thinking, anddiscusses how it should be used in this context based on research into using the Unplugged approacheffectively in education. In addition to considering the value of an Unplugged approach for teaching
students, we will look at broader applications, including supporting teachers who are new to the subject,
and using it in integrated learning where computational thinking is exercised as part of other curriculum
areas.

Keywords
CS Unplugged; computational thinking; teacher PLD; integrated learning

Introduction
Computer Science Unplugged (CSUnplugged.org) was originally intended as an outreach tool to explain
computer science to young students, without the overhead of having to learn programming first.
However, it is now used in a variety of contexts, and with the recent adoption of elements of computerscience into school curricula around the world (Heintz et al., 2016), the Unplugged approach has often
found a role in the classroom. The new curricula are commonly based around the idea of Computational
Thinking, an idea that came to prominence after the publication of a paper by Jeanette Wing in 2006
(Wing, 2006), although the term was used by Papert as early as 1980 in his widely read “Mindstorms”
book (Papert, 1980), and the general concept predates Papert’s work (Tedre and Denning 2016).
Here we look at what Computational Thinking is, how it relates to Computer Science Unplugged
activities, and how this connects to previous research on the use of CS Unplugged for teachers andstudents.

Computational Thinking
The heading of “Computational Thinking” (CT) is commonly used in the context of introducing computerscience into primary and high school education. There are varied views on what Computational Thinking
is actually about, and its broad value outside of teaching computer programming, although there is
general agreement that it relates to the kind of thinking students need to develop in order to effectively
be able to program digital devices, that there are particular skills that are relevant, and that it has anenormous impact on the world.
To understand CT, it is helpful to focus on what computation is. The notion of computation andcomputability has been explored in depth over the years, with fundamental ideas being based on
Turing’s work in the 1930s about what he called an “automatic machine” (Turing, 1937), which is now
commonly referred to as a Turing machine. The limitations of a Turing machine still define theboundaries of digital computation today (with the possible exception of the new developments inquantum computing), so both the power and limitations of computing are reflected in any Turingcomplete programming language, which includes many widely used educational languages such as
Scratch and Python. From this point of view, it could be argued that CT is centred around learning touse such languages to their full extent (Denning, 2017), since the machines dictate exactly what canand can’t be done in computation. It is reasonable to be concerned that analogies to computation (suchas cooking recipes) are imperfect and might even teach against some of the deeper principles, but we21

Constructionism 2018, Vilnius, Lithuania

need to acknowledge that beginners in any subject are often given simplified models to help scaffold
their learning (such as the Rutherford-Bohr model of the atom, Newtonian physics, or the division ofhistory into discrete periods).
However, it is also possible to enforce computationally authentic elements of computing without using
a digital device. For example, the CS Unplugged resources (Bell et al., 2009; Bell, Rosamond and Casey2012) have an activity on sorting algorithms (https://csunplugged.org/sorting-algorithms), which puts a
simple rule in place that only two values can be compared at once using a balance scale, and thecomparison is done by a third party so that there is no memory of previous comparisons other thanplacement of the weights. This forces students to explore the same kinds of algorithms that a digital
device would have to use when sorting by comparison, so it isn’t just an analogy, but an alternative
physical implementation of the kind of computation that is possible if one could program a digital device.
It has the advantage that students don’t need to learn about programming first before engaging with thealgorithm, although ultimately a computer program is needed to see all of the limitations encounteredwhen implementing the algorithm.
Broader definitions of Computational Thinking have been derived based on the general skills needed toreason about computation. For example, Wing (2010) uses the definition of Computational Thinking as“… the thought processes involved in formulating problems and their solutions so that the solutions are
represented in a form that can be effectively carried out by an information-processing agent.” This leads
to a more general view of programming, where CT includes knowing how to: describe a problem, identify the important details needed to solve this problem, break the problem down into small, logical steps,
 use these steps to create a process (algorithm) that solves the problem, and then evaluate this process.
The underlying skills needed to achieve these steps have been presented in various forms, but common
elements generally include some sort of variation on a list such as algorithmic thinking, abstraction,
decomposition, generalization, evaluation and logic. Such lists have been produced by nationalorganisations supporting new curricula, including Computing at School in England (Csizmadia et al,2015) and the CSTA (CSTA, 2011).
Denning (2017) warns against overgeneralising such underlying skills; for example, decomposition canbe applied to many situations, such as breaking down a large (non-computer) project into components,
but in a computational context such an activity has constraints imposed by the nature of a computing
environment, as well as good practice (such as decomposing a large program into modules withmeaningful functions). Here we focus on applying these concepts in the context of computer science,
which helps us to connect the general ideas with what they mean in a computational context.
Figure 1 shows the skill list that we have chosen to use to connect CS Unplugged to CT. They are basedon a combination of lists commonly found in the literature mentioned above.
Rather than try to define these here, we will illustrate them with examples from CS Unplugged activities.

Figure 1. Computational Thinking skills

22Constructionism 2018, Vilnius, Lithuania

Computational Thinking and CS Unplugged
The CS Unplugged material was developed from around 1992 (Bell, Rosamond and Casey, 2012),
while the concept of Computational Thinking became widely promoted after Wing’s work around 2006;
since CS Unplugged was intended to convey ideas from computer science, and CT has been describedas “thinking like a Computer Scientist” (Wing, 2006), matching Unplugged activities with CT ideas serves
as a way to show how they are both serving a similar purpose.
Here we will use three contrasting activities: the binary numbers demonstration (about data), searching(about algorithms), and sorting networks (applying a parallel algorithm to data). Note that the Unplugged
activities are intended to be used in a constructivist manner, so that students are discovering patterns
and rules for themselves based on a very short description of a challenge, rather than being told therules and then applying them. This means that they are exercising the CT skills themselves as theysolve the challenges that they are given.
In the binary representation activity, students manipulate cards that represent the powers of two (Figure2). They follow the simple rule that the dots on a card may either be completely visible or not, and are
given exercises around finding ways to display a given number of dots, counting, and exploring patterns
in the representation, always with the constraint that a card is entirely visible or not (this is enforcing a
constraint that a physical computing device would have). The constructivist approach means that thestudents are given little more instruction that the rule that each card is either visible or not; even thenumber of dots on each card is deduced by the students after being shown the first three.

Figure 2. The CS Unplugged binary representation activity

This activity is exercising the CT skills as follows; note that it could also be used to evaluate how well astudent is applying the skills.







Algorithmic thinking: Although this activity is about data, students are applying algorithms to thebits. Working out the representation of a decimal value can be done using a greedy algorithm
working from left to right (“Do you want to include the 16-dot card?” etc.) Initially students may takea haphazard approach, but by scaffolding the idea of working from left to right, it becomes clearthat the decisions are easy. Other algorithms that come up are incrementing by one (students canbe scaffolded to find the pattern that this can be done by flipping cards from right to left until awhite card comes up), doubling a value (shift left), and determining if a number is odd or even(simply check the right hand bit!)
Abstraction: Although binary representations are commonly said to be made of zeroes and ones,
there are no such physical digits on a computer, only abstract representations. Students canexperiment with a variety of abstraction; we commonly start with “yes” and “no” for each card, but
can then ask the students to be creative with other binary symbols, such as using two different
musical pitches, dance moves, or even animal sounds.
Decomposition: the problem of working out a number representation can be overwhelming at
first, but when decomposed into a left-to-right algorithm (“do you want this card?”), it becomes a
lot simpler to comprehend.
Generalising and patterns: There are many patterns to be explored here; the first generalisation
is working out the number of dots on the nth card, but students can also discover many otherpatterns, for example, that the maximum value that can be represented with k bits is one less than23

Constructionism 2018, Vilnius, Lithuania

the value of bit k+1, or that when counting, each card is being flipped with half the frequency ofthe one to its right.
 Logic: There are several rules that students can deduce using logic. One useful one is theuniqueness of a binary representation. For example, suppose they have found the representation
01001 for the number 9. The students can then be asked “is it possible to have a representationof 9 where the first bit is 1?” They will argue that it’s not possible because you would have 16 dots
– too many. The first bit must be 0. Then ask if the second bit could be a 0. Students will soon
realise that there are only 7 dots left in that case, and can argue themselves that the second bit
must be 1. This reasoning can be applied to all the bits of any binary representation, and students
will have created an informal proof that a particular value has a unique representation.
 Evaluation: Being able to convert between decimal and binary numbers isn’t a widely used skill,
but being able to evaluate the limits of a representation is. For example, students can evaluate thelargest number possible with, say, 5 bits, and then with 6 bits, and with scaffolding, realise thateach extra bit doubles the range of possibilities. This can lead to reasoning about the effectivenessof, say, a 256-bit security key vs. a 512-bit key; or an 8-bit character representation vs. 16-bit. In
both cases the increase in representation is considerably more than the factor of 2 that might
appear on the surface.
In the searching algorithms activity, students are given hidden values to search in several contexts: it
could be hidden “treasure chests” held by a friend who will reveal the contents only one chest at a time
(Figure 3), or cups that have values hidden under them, which can only be turned over one at a time.
In both cases, the goal is to find a value without looking at more items than necessary. At first the values
are unsorted, but students are later given a series of values that are sorted, which they canconstructively use to apply a form of binary search to avoid frustratingly long searches.

Figure 3. The CS Unplugged treasure hunt searching activity

Computational thinking appears in the searching activities as follows.






Algorithmic thinking: Students constructively discover a (variation of) the binary search algorithm
motivated by minimising the cost to them of finding a given object or value.
Abstraction: The values being searched (keys) are usually an abstraction of some item that needs
to be found, such as a person’s name being searched to find some information associated withthem. Working with the search key is sufficient to understand the algorithm.
Decomposition: Each key comparison is decomposing the solution space into smaller parts; inthe case of a sequential search it is a small gain (the solution space is reduced one by one), but
students can explore the power of divide-and-conquer through binary search, where a half of the
problem space is eliminated in one step.
Generalising and patterns: The different guises of searching (treasure chests, cups, envelopes
and so on) are all the same problem with the same possible solutions, but presented in different
ways. This enables students to recognise what the general algorithm is, rather than just a specificapplication of it.
24Constructionism 2018, Vilnius, Lithuania



Logic: There are a number of ideas that can be reasoned about here: for example, that binary
search can only work on a sorted list, and that binary search is guaranteed to find what the student
is looking for even though many items are never inspected.
 Evaluation: This is a key reason for students to explore algorithms; sequential and binary search
have quite different performance, and although students may not use mathematical language like“logarithms”, they can appreciate that even billons of items can be searched in a very small timewith binary search, and that sequential search becomes arbitrarily worse than binary search as
the size of the list increases.
In the sorting network activity, students traverse a network drawn on the ground, making a simple
comparison of values at each node and taking the left or right exit based on the comparison (Figure 4).
Again, the instructions given are very simple (compare values and go left or right), but the activity allows
the students to construct a range of understandings based on their experience.

Figure 4. The CS Unplugged sorting network activity



Algorithmic thinking: The students are physically engaging with a parallel algorithm, and seeing
how a complex outcome (sorting) can be achieved by the combination of many simple steps (inthis case, comparisons of pairs of values). They also have the opportunity to design their own
parallel algorithms for smaller sorting networks.
 Abstraction: The sorting network is a physical representation of what happens inside a computer.
The values being compared (keys) are also an abstraction of some item that is being sorted, which
may have more data than just the sort key.
 Decomposition: A key aspect of this activity is that the complex task is decomposed into a very
simply described task. When comparing words or large numbers, that is further decomposed intoa character-by-character comparison to determine which value comes first.
 Generalising and patterns: The sorting network can be used to sort any values that can becompared for order; numbers are in order of increasing value, while words are in alphabeticalorder, but the comparisons can also used for other types of data, such as musical notes (higher
and lower), and stories (which plot element comes before another?) Logic: Students are able to reason about the correctness of the configuration by applying logic(an exhaustive test would take n! time). A first step is to apply logic to reason that the smallest item
must end up in the correct place, regardless of where it starts. A full proof of correctness is likely
beyond students, but for small sorting networks there is a lot of opportunity to reason about what
will happen.
 Evaluation: A sorting network can be evaluated in terms of the number of nodes required (threeat a time in the case of a 6-way network), but also in terms of the number of parallel steps required(the length of the network, and therefore time required). Two different networks for the same
number of inputs can be compared based on these metrics.
These activities have been used as examples; on the CS Unplugged website these CT skills are madeexplicit for every activity to help teachers see the bigger picture of why a particular activity is relevant,and also to appreciate which finer details of an activity are important to fully engage students in CT.

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Integrated learning
Computer science isn’t an end in itself, and is used in many practical contexts. When taught in schools
it can be used effectively in integrated contexts, where the concept is applied to other subjects to support
learning in both at the same time, even with an Unplugged approach.
For example, the binary representation activity includes the possibility of threading beads of two colours
into bracelets, necklaces or bag-tags. Making up chains of beads gives students the chance to think
about language, and what they would like to communicate with the beads; it also happens to exercises
fine motor skills. More generally, the activity can be extended to art, where two symbols or images areused to embed information in a picture; or music, where the two values can be used as note pitches orlengths. Both of these can be used to introduce the idea of steganography, where a message iscommunicated in plain sight through an artefact that appears to have a different purpose. Topics likebinary numbers can also be integrated with history and writing – where did the idea come from, andhow have people communicated in the past over distance? Looking into representations like Braille andMorse code can reveal how communication has influenced history, but also how it is natural for humans
to develop codes for communication over distance or for efficient storage. Students can construct theirown codes based on their new understanding, and this provides a richer experience than simply learning
standard codes (such as ASCII and Unicode), as they will face the questions that arise for themselves,
such as special characters, using digits as text, and so on.
Sorting networks can be integrated naturally with other curriculum topics, and might be used to compare
dates in history, words in alphabetical order, note pitches in music, or numbers written in a foreign
language. They provide motivation for students to repeatedly compare the values that they are learning,
and to see them in situations other than the sequence normally presented. At the same time, they are
becoming familiar with a computational model.
Searching algorithms can also be explored in terms of history – how did people look up information in
pre-computer times, and who had access to such information? Who are the people who developed
these computer algorithms, and what motivated them? There is also the possibility of acting out suchalgorithms; and a binary search can even be used to compare an unknown pitch with the notes on the
piano to determine what it is.

Applying CS Unplugged
An important element of this style of teaching is to give minimal instructions, and allow students toconstruct the knowledge for themselves. Once they have done this, it is important to then relate what
they have done to the broader context of computing, and what happens on physical devices. Two earlystudies discovered that without this connection “the program [based on CS Unplugged] had no
statistically significant impact on student attitudes toward computer science or perceived content
understanding” (Feaster et al., 2011) and that “the students’ attitudes and intentions regarding CS did
not change in the desired direction” (Taub et al., 2012). In terms of conveying knowledge using thisapproach compared with a more conventional approaches, Thies and Vahrenhold (2013) found that “…
it is indeed possible to weave Computer Science Unplugged activities into lower secondary Computerscience classes without a negative effect on factual, procedural, or conceptual knowledge”, and that it
could have some benefit in that “the Computer Science Unplugged materials can prove helpful for ability
grouping within a class, since, on average, more students are enabled to reach a higher operational
stage.”
Gains from using CS Unplugged were reported by Hermans and Aivaloglou (2017), who combined itwith teaching programming for one group, while having a second group spend the same total amountof time learning only programming; they found that “…the group taught using CS Unplugged material
showed higher self-efficacy and used a wider vocabulary of Scratch blocks.”
Looking at these different contexts, we see that CS Unplugged is best used in combination with “plugged
in” work. This is not surprising, given that getting a program to work correctly is an excellent way for a
student to show that they have understood the computational concepts they are working with, since thecomputational agent (the computer running the program) will do exactly what the program says to do.
26Constructionism 2018, Vilnius, Lithuania

Based on this, the CS Unplugged website now offers a range of “Plugging it in” exercises to provide
follow-up activities that allow students to link their Unplugged learning with computation on a digital
device.
An Unplugged approach seems to have promise for helping student learning if used effectively, butanother important value of it is for teachers. It is important for teachers to be confident in a topic so thatthey can build student confidence, and given that the new computing curricula appearing around theworld are often taught be people new to the subject, ways to build teacher confidence will be important(Gutiérrez and Sanders, 2009). CS Unplugged has been used in a variety of teacher professional
learning and development (PLD) initiatives, and the research available on this is reporting positive
outcomes. For example, Curzon et al. (2014) report on teacher professional development that had asubstantial “Unplugged” component, and noted that it was “inspiring, confidence building and gave [theteachers] a greater understanding of the concepts involved.” An important feature of the constructivist
approach of Unplugged activities is that they allow very quick wins, where teachers can understand a
new concept (such as binary numbers) very quickly, without the overhead of having to learn to program
first. Smith et al. (2015) reported that teachers who were training other teachers (thought the UK Masterteachers system) commonly included CS Unplugged when providing professional development forcolleagues, and both Morreale and Joiner (2011) and Sentance and Csizmadia (2017) found that after
attending their workshop, CS Unplugged was widely adopted by teachers.

Conclusion
Computer Science Unplugged activities support computational thinking, although for this to be effective
they should be used in a context where they will be linked to implementation on a digital device. By
using Unplugged early to introduce concepts, both students and teachers new to the subject can haveearly success without the overhead of becoming proficient enough at programming to engage properlywith ideas that can have an impact in our digital world. This then provides a useful platform to motivate
learning the skill of programming, but also a way to connect computer science with other subjects.

References
Bell, T., Alexander, J., Freeman, I., and Grimley, M. (2009). Computer science unplugged: school
students doing real Computing without computers. New Zealand Journal of Applied Computing andInformation Technology, 13(1), 20-29.
Bell, T., Rosamond, F., and Casey, N. (2012). Computer Science Unplugged and related projects inmath and computer science popularization. In H. L. Bodlaender, R. Downey, F. V Fomin, and D. Marx
(Eds.), The Multivariate Algorithmic Revolution and Beyond: Essays Dedicated to Michael R. Fellows
on the Occasion of His 60th Birthday, LNCS 7370, pp. 398–456. Springer-Verlag.
Csizmadia, A., Curzon, P., Dorling, M., Humphreys, S., Ng, T., Selby, C., and Woollard, J. (2015).
Computational thinking: a guide for teachers. Available fromhttp://computingatschool.org.uk/computationalthinking.
CSTA. Operational Definition of Computational Thinking. 2011;https://c.ymcdn.com/sites/www.csteachers.org/resource/resmgr/CompThinkingFlyer.pdf
Curzon, P., McOwan, P. W., Plant, N., and Meagher, L. R. (2014). Introducing teachers to computational
thinking using unplugged storytelling. Proceedings of the 9th Workshop in Primary and SecondaryComputing Education – WiPSCE ’14, 89–92.
Denning, P. (2017) Remaining trouble spots with computational thinking, Communications of the ACM.
60(6): 33-39, June.
Feaster, Y., Segars, L., Wahba, S. K., and Hallstrom, J. O. (2011). Teaching CS unplugged in the highschool (with limited success). In G. Rößling, T. L. Naps, and C. Spannagel (Eds.), Proceedings of the
16th Annual SIGCSE Conference on Innovation and Technology in Computer Science Education,
ITiCSE 2011, Darmstadt, Germany, June 27-29, 2011 (pp. 248–252). ACM.
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Papert, S. (1980) Mindstorms: Children, Computers and Powerful ideas. Basic Books.
Gutiérrez, J. M., and Sanders, I. D. (2009). Computer Science education in Perú: a new kind of monster?
ACM SIGCSE Bulletin, 41(2), 86–89.
Heintz, F., Mannila, L., & Färnqvist, T. (2016). A review of models for introducing computational thinking,computer science and computing in K-12 education. In Proceedings – Frontiers in Education
Conference (FIE) (pp. 1–9).
Hermans, F., and Aivaloglou, E. (2017). To Scratch or Not to Scratch?: A Controlled ExperimentComparing Plugged First and Unplugged First Programming Lessons. In Proceedings of the 12th
Workshop on Primary and Secondary Computing Education (pp. 49–56). New York, NY, USA: ACM.
http://doi.org/10.1145/3137065.3137072
Morreale, P., and Joiner, D. (2011). Reaching future computer scientists. Communications of the ACM,
54(4), 121.
Sentance, S., and Csizmadia, A. (2017). Computing in the curriculum: Challenges and strategies froma teacher’s perspective. Education and Information Technologies, 22(2), 469–495.
http://doi.org/10.1007/s10639-016-9482-0
Smith, N., Allsop, Y., Caldwell, H., Hill, D., Dimitriadi, Y., and Csizmadia, A. P. (2015). Master teachersin computing: What have we achieved? In Proceedings of the Workshop in Primary and SecondaryComputing Education (pp. 21–24).
Taub, R., Armoni, M., and Ben-Ari, M. (2012). CS Unplugged and Middle-School Students’ Views,
Attitudes, and Intentions Regarding CS. Trans. Comput. Educ., 12(2), 8:1–8:29.
http://doi.org/10.1145/2160547.2160551
Tedre, M., and Denning, P. J. (2016). The Long Quest for Computational Thinking. In Proceedings ofthe 16th Koli Calling Conference on Computing Education Research, pp. 120–129.
Thies, R., and Vahrenhold, J. (2013). On Plugging “Unplugged’’ into CS Classes. In SIGCSE ’13:
Proceedings of the 44th ACM technical symposium on Computer Science Education (pp. 365–370).
Turing, A. M. (1937). On computable numbers, with an application to the Entscheidungsproblem.
Proceedings of the London mathematical society, 2(1), 230-265.
Wing, J., (2006) Computational Thinking, Communications of the ACM. 49 (3)Wing, J. M. (2010). Computational Thinking: What and Why?
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Constructionism Won, Now What? The Role ofConstructionist Research in the Age of Ubiquitous
ComputingPaulo Blikstein, paulob@stanford.edu
Stanford University, USA

Abstract
Logo was created almost exactly 50 years ago. It might be the right time to take stock on theaccomplishments of the last five decades, and the possible directions for the future. The Constructionist
community, having rebelliousness in its DNA, has grown used to say that “the revolution has not comeyet.” We are still far from realizing Papert’s vision, but in the last 20 years there has been impressivechange in schools and in the discourse around educational innovation.
The first change is on computing itself. Several high-profile initiatives have brought coding into themainstream of education, with several cities and countries advocating and implementing programming
as a mandatory topic in pre-college education. The scale of these initiatives is impressive, and eventhough they are still in early stages, they represent a clear recognition that coding has finally been
accepted as a school topic at the highest levels of policy making.
Makerspaces and fab labs are a second phenomenon that has reached surprising popularity in schools
in just a few years. Thousands of schools already have well-equipped makerspaces, and even though
access is not equitable in many of them, their mere presence in schools point to a crucial recognition of
the value of constructionist pedagogies, creativity, student agency, and construction.
Third, even traditional disciplines and national standards are being “infected” by the constructionist virus:
many science and math curricula around the world now employ constructionist-inspired pedagogies andprinciples, and some go as far as incorporating tools such as computational modeling and sensing toscience classes. And this is also happening at the national level: for example, in the United States, theNext Generation Science Standards made engineering and design mandatory in basic education. And
finally, Constructionist tools such as Scratch, NetLogo, Lego Robotics, GoGo Boards, and the Lilypad,
have become much more robust and been use by millions of children worldwide.
Given all the good news, what is the right reaction from this community? Claim “mission accomplished”
or double down our efforts? It seems that the main challenge for the next 50 years will not anymore
convincing schools that many of these technologies and approaches are useful and effective but will beconcentrated in two clusters: (a) Making sure that those new learning opportunities are offered to
students with equity, and (b) Battling the forces of trivialization, that for economic or ideological reasons,often try to overly simplify the technologies and methods of Constructionism. To overcome thesechallenges in the next 50 years, we will have to find innovative forms of doing research in learning, newavenues for public advocacy, and novel ways to reach students.

Keywords
Logo; constructionism; computing education; maker education.

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Constructionism 2018, Vilnius, Lithuania

Look Closely, Watch What Happens: Visual
Modelling and Constructionism
James Clayson, james@clayson.org
American University of Paris, France

Abstract
The core activity in my approach to visual modelling is a series of exercises that encourage students toembark on individual computational explorations of shape, placement, color and texture themes bylooking closely at physical objects meaningful to them. In this paper, I will describe the journey that led
me to designing and teaching courses around this central notion and how such modelling works,
encouraging students to integrate a wide range of technical and non-technical skills into their work. I willtalk about why I think this activity is important for students in all disciplines, including the liberal arts. I
will talk specifically about the effects visual modelling has had on my students and I will also describe
how the teaching and practice of visual modelling have informed my own development and learningover the past 40 years.
My reasons for integrating greater visual imagery into computational activities are summarized in theform of 13 arguments.

Keywords
constructionism; transformational objects; visual modelling; visual thinking; computational tools;
liberal arts; Logo; Python

Example: Look, record, see what happens

Look closely, watch what happens: visual modelling andconstructionism
I believe that the image is the great instrument of instruction. What a child gets out of any
subject presented to him is simply the images which he himself forms with regard to it.
I believe that if nine tenths of the energy at present directed towards making the child learn
certain things, were spent in seeing to it that the child was forming proper images, the workof instruction would be indefinitely facilitated.

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Constructionism 2018, Vilnius, Lithuania

I believe that much of the time and attention now given to the preparation and presentationof lessons might be more wisely and profitably expended in training the child’s power ofimagery and in seeing to it that he was continually forming definite, vivid, and growing
images of the various subjects with which he comes in contact in his experience.
John Dewey... concreteness is not a property of an object but rather a property of a person’s relationshipto an object. … The more connections we make between an object and other objects, themore concrete it becomes for us. The richer the set of representations of the object, themore ways we have of interacting with it, the more concrete it is for us.
Uri Wilensky

My personal visual modelling journey
After graduate school I worked in industry for ten years as a manager of operations research (OR).
Then I moved to France where I discovered both teaching and the power of imagery and visual literacy
for constructionist learning. I managed an art school in the south of France for five summers. There, I
took many of the foundation studio courses in art and design which eventually informed my wholeteaching approach and led to a radical reexamination of pedagogical styles and cognition. I was alreadyin my early forties when I was hired by the American University of Paris (AUP) to teach OR.
I found my AUP students to be highly motivated, extremely verbal in several languages, and street wise.
But they had great trouble identifying appropriate ways for exploring large and messy problems whoseparts and relationships were not precisely knowable. Of course, this wasn’t too surprising. My operations
research students – who were economics, business and computer science majors - had taken scoresof separate courses but few truly interdisciplinary ones. They had difficulty integrating the quantitativewith the qualitative and breaking large problems down into the smaller parts that could provide a starting
point for coming to grips with the whole. They didn’t know how to diagram relationships since they had
no visual vocabularies. Few of them had ever taken a studio arts class. They weren’t yet in possessionof personal repertoires of problem-solving techniques. They didn’t know the value of speaking withothers to help clarify ambiguous tasks and, when this did happen, they didn’t yet appreciate theimportance of recording verbal clips of shared insight. They were totally unaware that just talking could
become a starting point for figuring out difficult problems and that often they didn’t need new tools to doso.
One day I was speaking to my colleague, Roger Shepheard, who was director of Parsons School of Art
in Paris. Roger taught painting and drawing. Unlike my students, he said, his students had no trouble intalking about ambiguity and multiple viewpoints. They all kept journals in which they recorded talk, ideas,
diagrams, sketches … their work process. They loved deconstructing and reconstructing, cutting andpasting. But, he said, they were distrustful of and resistant to the idea that conforming to a morestructured plan could be useful to their work. For them everything seemed open for further exploration.
But Roger wanted these art students to see some closure, to produce more individual pieces of workthat were, if not “finished”, then at least a summary of their author’s current state. Many of these art
students did have some math experience but felt ill at ease with it. And they would often, almost
ideologically, reject using whatever math they did know.
That’s when Roger and I had the fun idea to put our students together and watch what happened! We
paired my economics, business and computer science majors with Roger’s art students. We called ourexperimental course, Problems in Visual Thinking. The central constructionist twist and ideologicalengine for this course was model building with Logo. The class went on for 5 years and was the basisof my book on problems in visual thinking (Clayson 1988). After this stimulating initial experience, I later
revised and extended my visual modelling ideas for a team-taught course with AUP colleague and artist,
Ralph Petty, that would be open to AUP students from any discipline. Our collaboration lasted 10 years
from 2000 through 2009. (Clayson 2007, 2008).
Most recently I translated all my Logo materials from these earlier years into Python and I wrote a newtext for a course I gave at Deep Springs College in California where I was a visiting scholar in 2015.
31

Constructionism 2018, Vilnius, Lithuania(Clayson 2015). I built, with the help of colleagues from Comenius University (Slovakia), a number of
Python modules that let me be “Pythonic” in my own visual and constructionist style. The translationfrom Logo to Python was a fascinating and thought-provoking exercise that I hope to describe
elsewhere.

A closer look at what happened and what I learned
For thirty-five years I encouraged students to look more closely at favored objects in their lives and torecord what happened and what they felt when they did this. Computational methods were thenecessary catalysts for this visual activity, but they were not sufficient. In the end, it was the criticalmixture of qualitative, visual and quantitative methods that lead to students’ seeing more clearly. Seeing
clearly provided them an intense emotional and intellectual satisfaction that lasted long after the course
was over.
Having kept my own teaching journal throughout these years, I have been able to distill my experience
into 13 arguments for adopting this visual approach to modelling. In the constructionist spirit of putting
ideas into the public space, I now share them with you. I hope my personal journey as a constructionist
teacher with visual affinities might inspire others to look more closely at nearby objects and carefully
watch what happens.

Arguments for doing visual modelling
1. The aesthetic argument. When we model an object in order to bring it down to a size that we canhold, to view it from all sides, this can give us enormous aesthetic pleasure. Whether the model is aminiature flashing Eiffel tower, a ship model, an embroidered flower or a stage set, we can sense its
wholeness, its complexity, without dwelling on individual parts. The model, of course, will be an
abstraction, a simplification of the whole. Model authenticity can be measured in the pleasure it gives
us. The simplicity of our model illustrates a restructuring of the modelled thing; we can feel it. LeviStrauss talks about turning physical dimensions into “intelligible dimensions”. (1966). We could also callthem meaningful dimensions. Seeing one object more meaningfully through our interaction with it is askill that can easily be transferred to other objects. (Bateson 1972)
2. The design literacy argument. Two-dimensional graphic design explores the aesthetic, emotionaland communication inherent in compositions of lines, shapes, typography, signs, symbols, color, textureand depth cues placed on a canvas, page or website. Typically, basic design courses introduce design
theory through a structured set of exercises resembling my own constructionist approach (Wilde 1991).
Unfortunately, most university students do not have the opportunity to learn anything about art or designconcepts, nor to gain insight into how design might be useful if applied to other fields. Visual modellingoffers a crash course introduction to the language and tools of design.
3. The situated computational thinking argument. There is a public domain book called “How toThink Like a Computer Scientist” (Wentworth 2018) that is considered to be a handy introduction to
Python programming. The question is, handy for whom? It is useful to me because I trained as anengineer, have studied computer science, have learned to program in many different languages andhave taught applied mathematics. In other words, it is useful because I already know how to think like
a computer scientist. This is not the case, however, with most of my visual modelling students. This
book, while useful to me, is therefore totally useless and inappropriate for most liberal arts
undergraduates. My students already have a personal epistemology. They want to find and use toolsthat they feel comfortable with that can immediately help them to get on with their own idiosyncraticmodes of inquiry (Minsky 1988, Kelly 1955). They don’t want to have to become someone else first.
And why should they?
What are some of the simpler more accessible computational tools these students might find useful?
Design manipulations like deconstruction/reconstruction, replication, scaling, generating random
components, perspective and color operations cry out for computational constructs and I introduce thesein class via my solutions to specific design problems. Each suggested computer construct is situatedwithin a series of structured visual tasks. Afterwards, some students may be intrigued enough to lookat the programming text mentioned. But for most, reading a text of new material can be extremely hard32

Constructionism 2018, Vilnius, Lithuania

going unless they already have some familiarity with the content. Guide books, as we all know, generally
make more sense after the trip than before.
Over the years I have found that recursion, for example, appeals greatly to some non-technical students,
especially artists, who want to immediately play with it. Recursion seems to strike at something they
feel emotionally at ease with. Linking design and illustration tasks with computer constructs that
operationalize these tasks extends the meanings of both design and computational tools. New tools cansuggest new design approaches.
There is one big idea from computer science that does stand out in my mind as being personallyempowering for most students: simulation. Simulation is the act of turning ideas into computer code thatcan be manipulated and experimented on. Students can actually watch what happens when they dothis and are therefore motivated to continue their explorations. I have found that working in anenvironment of visualization makes simulation even more potent. Students see what a program, what
an idea means by watching what it does. And they don’t have to be computer experts to do so.
Proponents of computational thinking often talk about the “power of abstraction” that is implicit incomputer programming. I have found that this powerful idea resonates best when abstractions from one
medium are compared with those from another. The sketch of an object, or a poem based on it, after
all, are abstractions. But it is the comparison between the drawn and the spoken and the programmed
abstraction that is the essence of visual modelling.
4. The body syntonicity argument. The word syntonicity was coined in the 1800s to describe
alternative musical tuning systems. When two instruments were heard to be in harmony with each other,
they were judged to be syntonic. This meaning was later extended to describe individuals whose
emotions were in tune with their environment. Freud extended syntonicity’s use to his system: a person’smanner was ego-syntonic if it supported the needs and desires of their egos.
Papert’s notion of body-syntonicity (1982) assumes that all people have a number of separate areas forrational, psychological and physical reasoning. Parts of the body have, in effect, their own reasoning,knowledge and skills to which the mind in the head may or may not have access. Papert’s incorporationof turtle graphics into his Logo language was intended to open young programmers to affordances intune with their body languages.
Papert hoped that turtle geometry would radicalize the way geometry is taught. This has not happened.
Nevertheless, I have found that basing my visual modelling classes on turtle geometry does radicalize
the way geometry is remembered and rethought for use in physical tasks. The real value added seems
to be the pleasure of being able to do this, to remember, to rethink and use something only vaguely
remembered. To make known what was not yet known.
There are, of course, other body sources of knowledge that Papert did not seem much interested in: theeye, ear, hand and voice (Arnheim 1969). In visual modelling these resources all have a role to play,
and all must be called upon in order to look closely.
5. The meaning-making bricolage argument. The anthropologist Levi-Strauss used the term
“bricolage” to describe a direct approach to problem solving, repair work and thinking (1966). LeviStrauss studied pre-modern societies, but his ideas are remarkably contemporary. The bricoleur acts
quickly using notions, improvisations and tools that are already at hand. Speed is often important. We
can think of the bricoleur as a repair person who carries around a bag of tools that can be used on the
spot. The repair plan often emerges from the doing itself, through iteration.
Visual modelling uses bricolage tools often overlooked in higher education: learning to talk and write
rapidly about what we see, making quick diagrams of the structures we observe and the ideas we haveabout them. I discovered that when students act like the bricoleur, and have to improvise quickly, pieces
of remembered algebra, geometry and trigonometry often pop out. They will test if and how thesemathematical notions might help in solving some visual task. Often this means relearning the halfremembered math. Often it means learning the math for the first time, but in a context more meaningful
to the student.

33Constructionism 2018, Vilnius, Lithuania

6. The thinking journal argument. Papert has warned us that the phrase “thinking about thinking” is
not very useful. Rather, he advises, we should think about a specific person thinking about specifictasks. In this regard, constructionists suggest not only that teachers should focus on individual learners,
but that students, in turn, should focus on their own thinking processes in specific contexts. How
students learn to do this, to watch themselves in the act of meaning-making, is a critical part of theconstructionist project.
Unless meaning-makers can watch themselves in action, see how they dialogue with themselves, view
how they share their own meaning-making activities with others, how can they study themselvesformally? Visual modelling requires that modellers keep a journal of their modelling activities: the words,
sketches, codes, code play/code change. Journaling then is the trace of these activities, a trace of
thinking. We need to catch and record these acts of thinking so that we and others are able to reflect
upon them later. (Clayson 2015)
7. The extreme graphical distinguishability of visual modelling as a constructionist toolargument. Perhaps the most obvious feature of visual modelling – the sheer physicality of the target
subject – is not fully appreciated. The target is viewable; the modelling methods are viewable; theimages generated by the modelling process are viewable. Visual comparisons between the target
subject and the model are easy to make without a lot of abstract analysis. This viewability is not so truein more abstract math courses. Tangibility encourages fuller emotional and intellectual participation fromstudents having different skills and interests
8. The not-like-other courses argument. Papert warned about the difficulties of inserting Logophilosophy into traditional academic courses, especially math ones. He knew how hard it is to change
educational institutions and therefore advocated for doing something totally new instead. In the early
1980s when I first taught formal computational modelling, I deliberately refrained from labelling it eithera math or a computer science course. Instead I focused on the design aspect of visual modelling, a
non-traditional academic approach. I structured and marketed these courses to appeal to a variety ofdifferent student majors and interests: from studio arts, though humanities to the social sciences andphysical sciences.
9. The emotionally comfortable vocational argument. Visual modelling is a multidimensional
introduction to design theory, computational programming, formal reflection on thinking, careful andclear observational techniques, and effective journaling. I think It is important to note that a visualmodelling approach also seems to help many students who have suffered unhappy experiences withmath courses in the past. With visual modelling they gain math agency because they are able to use
mathematical notions to see and find new meaning in their own physical worlds. The freedom and easeof mixing personal math knowledge with other disciplines is an extremely useful vocational outcome.
10.The power of computational explorations of patterns seen in concrete objects argument. It is
obvious that turtle graphics cannot depict objects like a photo would. All media have this limitation andthe “modern” development of abstraction thrives on it. Different media facilitate the showing of different
aspects, different themes seen in objects, as well as indicating how the modeller feels about looking at
those assemblages of themes.
Visual modelling introduces the notion of looking closely at works of art, as well as other objects aroundus, by modelling their themes algorithmically. But finding characteristic themes requires learning a new
vocabulary appropriate for describing and interpreting art. In my classes we show each other specificexamples, often using museum postcards, from different abstractionist schools of painting and learn todescribe them. Various methods for deconstructing or reconfiguring objects into themes are portrayedand discussed. Alternative approaches to modelling similar objects taken by different artists are
examined. Using cubist, impressionist, fauvist, pointillist, supremacist or abstract expressionist
techniques can suggest different and surprising ways to configure and display even the most mundaneof objects as well as seeing the surprising complexity of all shapes. (Clayson 1985, 2007, 2008, 2013).
I argue that looking closely at works of art, both abstract and realistic, can affect how we look at andrelate to objects around us. The reverse is also true.
11. The tricking the ego argument. We see what we expect to see and usually that is what we havealready seen before. We scan fields of things, but often do not look closely at individual items, especially34Constructionism 2018, Vilnius, Lithuania

if they are thought to be already familiar. We may hesitate from taking a visual arts class because wethink “I can’t draw” because we have never learned how to slow down enough to look at things closely
and carefully. Yet everybody is capable of drawing, so why are we so hesitant? It’s because we hear
the little voice of our ego warning us that the experience might be embarrassing.
Visual modelling offers an alternative and tricky means of settling down and looking at concrete things
that is not like other visual arts. Paradoxically, it shows us that in order to slow down we have to work
faster. We are drawn-in to the object-subject without realizing it: by talking very quickly about what wesee, writing about it, sketching, coding and playing with that code. Fast, without reflection. Something
surprising always happens during this experience. Ironically, using such a simple and limited medium
as turtle graphics, actually gives us a freedom and willingness to draw that more sophisticated tools
often inhibit. It is exciting and empowering, like the child’s uninhibited use of crayons on blank paper.
The slowing down trick is accomplished by working fast with what we have at hand. Sketch fast, talkfast; then code it; not the reverse. The trick is to break the pattern of seeing only what we anticipate
seeing. Visual modelling is full of tricks and surprises, so the results cannot be anticipated. It’s fun.
12. The looking for new transformational objects argument. In his book “Mindstorms”, Papert
concentrated on telling us about his own transformational experiences with model car gears and how
he used Logo to seek out other transformational occasions. But he didn’t say why it might be importantfor each of us to recall our own transformative events and how we also might draw energy from them.
(1982) Christopher Bollas (1987,1992) argues convincingly that people are designed to continue
searching for transformative objects over their entire lifetime. Unfortunately, the frenzy of our adult lives
inhibits us from coming upon them naturally. Visual modelling helps restore students’ ability to find andexploit transformative objects in their local surroundings for personal pleasure and development.
Sherry Turkle is another person who has documented the primal importance of transformative moments
in human existence. She has assembled several collections of evocative personal narratives abouttransformative objects that all constructionists should read (2007, 2011).
13. The different modes and different points of view argument. Good problem solvers know how to
structure and then restructure situations in alternative ways. Statisticians know that multiple approaches
each based on unique methods is the creative way to explore a data set. Art students know to movearound a figure or still life, sketching and evaluating the model at each location before they settle down
to work in more detail. But then they go back, change position, and look at other possibilities; nothing isreally definitive, and each perspective offers its own rewards. Using different drawing tools – pencil,
pen, chalk, charcoal – helps expand their ability to reconceptualize. So can art students’ weird and nonconventional formulations. They paint it yellow; turn it upside down; push the arguments beyond therealistic; put a cloth over their subject and draw that; change the music; turn off the lights, use a flashlight… and watch what happens!

Meta-texts versus texts
Constructionists talk a lot about how we learn: is knowledge transmitted or is it constructed? Most of theconstructionists I know favor a far more nuanced approach to this discussion. Whatever knowledge
building/acquisition is, it is certainly not explainable in binary terms. Such reductionism just doesn’t suithuman diversity. Thus, a variety of learning modes is always preferable. Throughout my teaching career
I have found that a combination of instructionist and constructionist approaches works best. This seems
natural to me but is not always natural to my students – especially because of the dominance of
instructionist education in the world and resulting aversion to ambiguity and risk. To address this student
anxiety, I try to show by example why and how different approaches to a single problem may all be
useful, each in its own way. For every course I teach, I write what I call a meta-text – as opposed to a
standardized text. My meta-text is designed to show how I have gone about learning the material in ourcourse and suggests that students use it as a starting point to create and record their own experiences
too.
The most important text in many subjects is the one written by the students themselves in their own
language and style. As a constructionist I know that agency is best gained by having formallyconstructed a written document so that it can be read and modified over time. The student as author is35

Constructionism 2018, Vilnius, Lithuania

free to make additions and changes to their own text, and in my classes, they are required to share thisrecord with others who are also writing their own texts.
This record keeping is what I mean by “journaling”. But how do students get started with such ajournaling exercise? Not, I think, by following a fixed set of rules, but rather through examples in thecontext of the course material being explored. We talk in class about the different ways of doing this,and we check in throughout the semester to compare notes. Therefore, my meta-texts include my own
journal entries of how I explored the same task that I set for my students, along with illustrations of otherapproaches that may also represent student work.

Conclusion
This paper has described my own approach to constructionist practice in the classroom in the form of
thirteen arguments. The approach and lessons learned have emerged over 40 years of teaching. The
introduction of visual thinking and journaling as essential elements of learning may make me a bit of an
outlier here in a community mostly addressing math and science teachers, not those in the humanities
and liberal arts. It seems only right, therefore, that I propose some assessment of my radical projectand its outcomes.
I offer four criteria:
1) Carry-over agency: the students who had taken my visual modelling class were far more
effective in my traditional applied mathematics courses than those who had not done so.
2) Student testimonies: students have written to me years later to say they identify my visualmodelling course as a major learning event in their undergraduate life.
3) Student feedback: I won many teaching awards for my work.
4) My own thinking process: I look at the world far differently because of the work done invisual modelling. I can actually see the changes in the journals that I have kept for those many
years.
Looking back, I see that my journey has been surprising, taking unexpected turns and new directions. I
certainly wasn’t planning to become a teacher. At each new juncture there was a transformative eventor object – teacher, book, conversation or conference – that pointed the way forward. And always, it
seems, there was a constructionist thread: a need to explore, to learn, to model and to build a common
project along with my students. The relationship has been reciprocal: we have all learned from one
another, but each in our own way.
In today’s world there is a tendency to look without seeing. We are too busy, moving too fast, too
overstimulated by digital interfaces and gadgets. This is such a pity. Seeing is not only one of life’s great
pleasures, but also a powerful instrument for learning and understanding. We need to remind ourselvesthat in human evolution, the eye, the hand and the brain developed together. This paper has proposed
one way that visual literacy can inform, augment and enrich the constructionist enterprise. If you takethe trouble to look closely you’ll be amazed at what you see. It’s magic!

References
Arnheim, R. (1969). Visual thinking, University of California Press, London. 13-36
Bateson, G. (1972). Deuterolearning, Steps to an ecology of mind, U. of Chicago Press, London.
Bollas, C. (1987). The shadow of the object: psychoanalysis of the unthought known, Free Association
Books, London, 15ff
Bollas, C. (1992). Being a character: psychoanalysis and self-experience, Hill & Wang, New York.
Clayson, J. (1985), Visual modelling with Logo: a structured approach to seeing, MIT Press, Cambridge.
Clayson, J. (2007), Radical bricolage: making the liberal arts coherent, Plenary, EuroLogo, 2007,
Bratislava.

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Clayson, J. (2008), Radical bricolage: building coherence in the liberal arts using art, modelling andlanguage, International Journal of Education through Art, 4:2, 141-161
Clayson, J. (2013), Talking statistics/talking ourselves: some constructionist lessons from the work ofGeorge Kelly, Technology, Knowledge and Learning. 18, 181-199
Clayson, J. (2015), A computational eye: visual modelling with Python, Deep Springs College. Bishop,
California.
Dewey, J. (1897), My pedagogic creed, The School Journal, Volume LIV, Number 3, 77-80
Kelly, G. (1955) The psychology of personal constructs. Vol. I, II. Norton, New York.
Levi-Strauss, C. (1966), The savage mind, Weidenfeld and Nicholson, London. 1-33.
Papert, S. (1982), Mindstorms: Children, Computers and Powerful Ideas, Perseus Books, Jackson.
Minsky, M. (1988). Chapter 10: Papert’s principle, The Society of Mind, Simon & Schuster, New York.
98–107. (Some of the most crucial steps in mental growth are based not simply on acquiring new skills,
but on acquiring new administrative ways to use what one already knows).
Turkle, S. (2007). Evocative objects: things we think with, MIT Press, London.
Turkle, S. (2011). Falling for science: objects in mind, MIT Press, London
Wentworth, P., Elkner, J., Downey, A., Meyers, C. (2018), How to think like a computer scientist,
https://media.readthedocs.org/pdf/howtothink/latest/howtothink.pdf
Wilde, J. and Wilde, R. (1991). Visual literacy, Watson-Guptil, New York37

Constructionism 2018, Vilnius, Lithuania

Computational Thinking and Creativity
Gerald Futschek, gerald.futschek@tuwien.ac.at
Vienna University of Technology, Austria

Abstract
Creativity is one of the characteristics in constructionist learning. Creativity is frequently connected tolearning by doing and needs a high amount of freedom in choice of activities and learning steps.
Basically, Computational Thinking denotes thinking processes that are related to problem solving known
from computer science. Computational Thinking does not only involve algorithmic thinking skills that are
useful in programming and algorithm design but also integrates skills like abstraction, decomposition,
generalization and evaluation that are used in problem definition, system modelling and systemevaluation.
Since Computational Thinking has become part of informatics school curricula in many countries, theparadigm of competence orientation has led to very detailed curricula that describe a large variety ofdetailed competences. The high amount of details can lead to a kaleidoscopic teaching practice.
Although, from a constructionist viewpoint it seems more promising to learn with learning settings thatallow creativity, fun and sense of achievement. The role of creativity in Computational Thinking Learning
is not only in creating a creative output but also in finding new ways of thinking to find solutions toproblems.
We show several examples of learning settings and didactic projects that allow creativity, fun and senseof achievement in learning Computational Thinking.

Students creating a new gamefor Computational Thinking Learning

Keywords
computational thinking education; problem solving; creativity

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Constructionism 2018, Vilnius, Lithuania

Teaching Children to be Problem Posers and Puzzle
Creators in Mathematics1
Paul Goldenberg, pgoldenberg@edc.org
Education Development Center (EDC), Waltham, MA, USA

Abstract
Seymour Papert’s 1972 paper “Teaching Children to be Mathematicians Versus Teaching About
Mathematics” started with the summary statement “The important difference between the work of a childin an elementary mathematics class and that of a mathematician is not in the subject matter…but in thefact that the mathematician is creatively engaged….” Along with “creative,” a key term Papert kept using
is project rather than the common notion of problem. A project is not simply a very large problem. It
centrally includes a focus on sustained and active engagement. The projects in his illustrations were
essentially research projects, not just multi-step, fully-prescribed, build-a-thing tasks, no matter how
nice the end product might be. A mathematical playground with enough attractive destinations in it draws
children naturally to pose their own tasks and projects—as they universally do in their other personaland group playgrounds—and to learn to act and think like mathematicians. They even acquireconventionally taught content through that play. Physical construction was always available, andappealed to such thinkers as Dewey, but for Papert computer programming, newly available to school,suggested a more flexible medium and a model for an ideal playground.

Figure 1. Two puzzles that introduce algebra’s logic and then its notation. Children can invent their own.

A fact about playgrounds is that children choose challenge. In working and playing with children I’ve
seen that puzzles tap some of the same personally chosen challenge that a programming-centric
playground offers. Children are naturally drawn to intellectual challenges of riddles and puzzles (onesthey learn and ones they invent); and adults are so lured by puzzles that even supermarkets sell booksof them. So how do real puzzles and school problems differ? What’s useful about creating a puzzle orposing a problem? How might puzzles and problem posing support mathematical learning? And what’s
constructionist about this? This plenary will try to respond to these questions, invite some of your own
responses, let you solve and create some puzzles, and explore how problem posing in programmingand puzzling can support mathematics even in an age of rigid content constraints.

Keywords
Problem posing; puzzles; mathematics; algebra

1

Funding for doing and reporting the work described in this paper was provided in part by the National Science Foundation, grants 1441075,
1543136 and 1741792. Views expressed here are those of the author and do not necessarily reflect the views of the Foundation

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Constructionism 2018, Vilnius, Lithuania

Teaching children to be problem posers and puzzle creators inmathematics
Papert’s early work and the origin of constructionism largely were outside of the school setting. The
current school environment is even more rigidly constrained than it used to be. The question is “Is there
any hope for this kind of constructionist thinking and teaching in a school setting, not as a pull-out forwell-resourced schools and with the best of their students, but as part of the regular program?” I want
to share some ideas that, to me, exhibit the essential elements of constructionism and could easily becore to even moderately conservative school practice.
I, too, love playing with kids outside the classroom. There’s more freedom and it’s easier. But we allknow that if we really want to touch many children’s lives, we need to find a way to find them where theyare. They are in school. I think it’s possible.

Children choose challenge
Not all children; not all the time; but mostly. Children are often pretty adventurous on the playground.
Tiny ones climb the monkey bars higher than their parents are totally happy with. When climbing getstoo easy, they hang upside down. Children walk on five-inch-wide retaining walls two to three feet above
sidewalk level when the get a chance; they hop across the street on one foot; when bicycle riding feels
easy, they try letting go of the handlebars. Even with games, they up the ante if the game feels too easy,changing rules fluidly to add extra challenge.
For a toddler, there’s enough challenge fitting the boat-shaped piece into the boat-shaped hole and themoon-shaped piece into the moon-shaped hole, but when that’s no longer a challenge, kids seek more.
Kindergarteners like fitting together the two-dozen jigsaw puzzle pieces of a large picture of a dinosaur.
And when that gets too easy, some try putting the pieces together face down, some try jigsaw puzzles
with smaller and more numerous pieces, and some just move on to totally different activities.
Children also put effort into figuring out how things work. Schulz & Bonawitz (2007) showed preschoolers a box with two levers and two different toys that popped up when the levers were pressed.
One group of children were shown that each lever caused one toy to pop up. The other group saw onlythat when both levers were pressed simultaneously, both toys popped up. The first group’s information
was complete and unambiguous, with nothing left to figure out. The second group’s information wasincomplete: either lever might have controlled both toys, with the other doing the same, or nothing, or
raising just one toy if pressed by itself. Or the two levers might be totally independent, one for each toy.
When the children were then given the toy to play with (or ignore) on their own, children in that second
group played longer, spontaneously exploring to puzzle out the cause and effect relationship. It’s
tempting to relate the first group’s experience to the situation children often experience in schoolmathematics, where common pedagogy (at least in the U.S. and UK) shows exactly how each thing isdone, leaving no evidence that there is anything to figure out, and taking little advantage of children’s
built-in curiosity.
Kids also love riddles, challenges to logic, interpretation or perception. And just as they spontaneously
add challenge to their playground activities or jigsaw puzzles, they’ll add to the repertoire of riddles bymaking up their own, sometimes creations that they, at their age, find funny (illogical) because thechallenge “works” for them, and that we adults find simply ludicrous (illogical) because the challenge nolonger works.
The point is the challenge. When it’s not there, children are bored. When they’re bored, they invent
challenge. In school, that inventiveness can be to the dismay of their teachers, whose response may
dismay the children, but that won’t stop the drive for the challenge.

Why puzzles?
Kittens stalk and pounce to make their hunting skills sharp and they scratch to keep their claws sharp;that’s because sharp claws and hunting skills are among the particular adaptations that make their
species successful. Our species’ special adaptation is not sharp claws and pouncing but a mind thatlets us adapt to nearly any environment, which is how we wound up populating city and farm, blazing
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Constructionism 2018, Vilnius, Lithuania

heat and frigid cold, arid and tropical jungle. Keeping our minds sharp is what makes our species
successful.
That has implications for learning. In our species, it is adaptive for the young to be a bit distractible andnot to focus too narrowly. Because we live in such varied environments we cannot have “built inknowledge” about which features will matter most for survival. As children, we watch social behaviour(whom should we copy, whom should we stay close to, whom should we stay away from?), animals(food or danger?), artefacts (how do they work?), math lessons (who knows?, maybe they’re important)and everything else.
Some distractibility and lack of focus are assets to a child, but less so for adults who must earn theirliving, whether by blow-darting the rabbit (while avoiding the tiger) or by generating research papers or
by teaching children. But adults still have to keep their minds sharp. Adults argue the merits of ideas—
politics, religion, co-workers—even when the practical value of the argument is near zero. It’s a mental
exercise. Puzzle books for adults are sold not just in academic bookstores but also in supermarkets;
puzzles appear in newspapers and in airplane magazines. Boredom is painful; enforced boredom is
torture.

Puzzles and surprise in mathematics learning
In 1964, Sawyer (2003) seeded the ideas for a wonderful textbook series for primary schoolmathematics (Wirtz, et al., 1964) and for our own curriculum materials (see, e.g., Goldenberg, et al.,2008). He took a very algebraic approach to teaching elementary arithmetic, with a major emphasis onplay and surprise. On the surface, the content was exactly what one expects for the grade level but witha twist that included research, puzzles for children to figure out, all foreshadowing the algebra thatchildren would learn later.
For example, as a way to give seven-year olds practice with addition and subtraction they startwith a piece of mathematical research. A child is asked to suggest some
addition equation like 4 + 1 = 5 or 1 + 1 = 2, and the teacher would write
it at the board. Another child is asked to suggest a new equation, which
the teacher carefully lines up directly underneath the first. Then theteacher has the children add vertically, displaying the results like this.
Do these three new numbers make a true addition equation? The teacher
completes the bottom row of numbers to read 5 + 4 9. Surprise! Will thisalways happen, or did they just get lucky? Children are given thechallenge of finding a pair of addition sentences that don’t work. Sevenyear-olds are sure they can find some and set off busily, getting lots ofpractice.
Of course, they will find some (they think) and report them excitedly, but the preponderance of casesthat do work will get even seven-year-olds to doubt the counterexamples and check to see if they’ve
made a mistake. This research is hardly a project in Papert’s sense. The problem may not even lastmore than one classroom period or so. But it does generate curiosity, the creative engagement that
Papert referred to as the experience of the mathematician.
Their research convinces them of a result, but if we don’t leave it as magic and instead help expose thelogic inside the puzzle, children get even more excited. They have a tool they can and do use, first to
figure out for themselves why the puzzle works and then to invent new
puzzles for themselves and their friends! Exposing the logic involves small
reminding children of reasoning they developed in Kindergarten and first
grade. Given a collection of buttons differing by two attributes, colour and large
size, kindergarten children naturally sort, though sometimes their sorting is
idiosyncratic—two large buttons and a small one, for example, to make a“family.” They learn to respond to “show me a small button” and “how manysmall buttons do you have?” And they can learn to respond to “show me a41Constructionism 2018, Vilnius, Lithuania

blue graylarge grey button” and “how many small blue buttons do you have?” Aftersorting by a single attribute, they can learn to sort by two attributes.
small
large
blue gray

Now, when we ask how many small buttons and how
many large, we are summarizing the rows, and we can small
write that summary.

6

large

4

We can similarly summarize the number of buttons bycolour (columns).

7

3

Once children really have and can use cardinality, it is clear that the number of blue and grey must be
the same as the number of large and small—either way, it’s all the buttons. Second grade students
comfortably replace buttons with numbers and then use that structure as part of their reasoning.
blue grayblue gray
small

6

small

4

2

6

large

4

large

3

1

4

7

3 10

7

3 10

Figure 2a, 2b. Actual buttons replaced by the number of buttons.

Reading across, children see 4 + 2 = 6 and 3 + 1 = 4; adding down the columns, they get 7, 3, and 10,
which must make a true addition statement.
Subtracting down isn’t always possible for 7 year olds—depending on the situation, it might
require negative numbers, and the meaning changes, too (it doesn’t yet
make sense to subtract the number of large buttons from the number ofsmall ones)—but with numbers that they can subtract (as is the case in Fig.
2b), the arithmetic still works and produces a true addition statement.
Subtracting to see how many more small buttons than large, we get
1 = 2 – 1, and that exact same logic will be essential in algebra a few years
later!
The format isn’t just a trick, or a school artefact; it’s the structure of any
spreadsheet that subtotals the columns and rows and has a grand total.
Wirtz, et al. (1964) used this format as a puzzle (right), and as a route into
multi-digit addition and subtraction (fig. 3).

6

11
3
18

Which cell might
you fill in first?

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Constructionism 2018, Vilnius, Lithuania

5 45
7 37

40
7 37
82

Figure 3. The same arithmetic presented in 3a as an addition puzzle 45 + 37, with the grey square as the sum,and in 3b as a subtraction puzzle 82 – 37, with the grey square as the difference.

For 9- or 10-year olds, this structure also models the multiplication algorithm. Instead of colour and sizelabels, we label the width and height of the columns and rows, and imagine cells filled with unit squares
instead of buttons. How many squares are in the four regions? In the most concrete image, everythingis to scale (fig. 4a).
With a smaller array, say 3  4, we can see why multiplication gives the answer and we can count tocheck. But with numbers like 37  26, we certainly don’t want to count! Instead, we use an abstraction(fig. 4b), ignoring scale, but maintaining a sense of the logic of multi-digit multiplication, not a set ofmemorized steps that often wind up feeling arbitrary. Of course, the steps involved in this logical model
map perfectly onto the abbreviated notation often taught in school, and fully explain that notation. In
fact, it is worth delaying the abbreviated notation until children are so secure in the logic of the arraymodel that they can easily extend it to three-digit multiplication, because exactly this method—four
separate multiplications and only then a possible summing up—will be required when the students study
algebra.

30

7

20

600

140

6

180

42

30 7
20 600 140
6 180

Figure 4. An array model of multiplication true to scale (left, figure 4a) and abstracted (right, figure 4b).

Sawyer suggested other ways that even very elementary content like addition and subtraction of small
numbers could be learned or practiced in a puzzle-like context that both builds curiosity andforeshadows later ideas and methods. Figure 5, for example, shows what a standard worksheet mightpresent as 16 unrelated addition/subtraction practice exercises for 7-year olds, but structured in a way
adds a bit of intellectual challenge—how-do-I-do-this?—and foreshadows systems of equations that thechildren will meet several years later.

Figure 5. A practice exercise for 2nd grade, foreshadowing systems of equations. (Wirtz, et al., 1964)

Again, it’s not a “project” in Papert’s sense, and not “creative” in the most familiarly used sense of that
word, but especially the last two columns pull for children to be mathematically creative.

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One of the most powerful introductions to algebra that I’ve seen is Think-of-a-number tricks, also fromWirtz, et al. (1964): Think of a number. (Yes, you! Please think of a number.) Add 3. Double the result.
Subtract 4. Cut that result in half. Subtract your original number. Aha! I can read your mind! You got 1
at the end!!!
For 9- or 10-year-olds, this is wonderful magic. They want to do it over and over, but also want to know
how it works. I say that I picture the secret number as that many marbles or whatever, tucked in a bagor bucket
where we can’t see them—only the secret keeper knows the number inside. When I
give the instruction “add 3,” I know about those grapes, so I draw them outside the bag. I ask the children
what the next instruction is (they almost always remember) and what the picture should be like (theyalmost always say “two bags and six marbles”). Then I continue, each time asking the children todescribe the next picture. At the end, “subtract your original number” gets rid of the bag. So the numberof grapes in it doesn’t matter! There’s one grape left, and we can see it!
Even after the usual huge smile and the cry “I get it!!,” seeing it once isn’t enough. The understanding
evaporates until children see the generality, not just the way this particular trick worked. To create that
abstraction for themselves, children need research time: practice drawing pictures to match instructions,applying instructions to specific numbers, and variations on the trick from which to generalize and learn
to invent their own tricks.
They also need chances to study the trick inside out and backwards, starting, for example, with the 16that Suri had in mind after the instruction “double that” and figuring out what secret number she musthave started with. To do that, a child might note that the picture corresponding to Suri’s 16 shows six
grapes, so ten grapes must be hidden in the two bags. Suri’s secret number—the grapes in one bag—must have been 5.

Figure 6. Using bags and grapes to introduce 3rd graders to algebraic notation and solving equations. (Problem
from Wirtz, et al., 1964, reworked for 3rd grade based on Mark, et al., 2014.)

I’ve recently been introducing a new crop of 8- and 9-year olds to algebra this way and told them thatthey’d soon know how to invent new tricks of their own. After two days of playing with the puzzle, Lucysaid “I really get it, but I still don’t know how to make up my own.” So we played. I said “OK, I’ve thoughtof a number” and I drew. “Just make up one instruction, anything you like, and I’ll draw the next
picture.” She said “add 5?” I said “OK,” drew
, and asked “What next?” She said “double
that?,” still with the question in her voice. I said “whatever you’d like me to do… Is that what you want44

Constructionism 2018, Vilnius, Lithuania

me to do?” She nodded and I said “you draw the picture.” She drew two buckets and 10 dots. She thentold me to subtract 2 (no question in her voice, and she drew the picture), then subtract 7 (she drew thepicture). That change in tone—no question in her voice—was because she now understood something
new, not about the mathematics of this trick but about mathematics, itself. She could just make up arule, any rule, and it was then up to her to figure out its implications. That is so like watching a childprogram, see the effect, decide whether that effect is desired or not, and then decide what to do next.
I asked, “OK, what can you do in order to know my number?” Long pause. Then Lucy commanded“subtract your original number” (and drew the picture). After another pause, she said “Oh!! Subtract youroriginal number again!” Her smug smile showed clearly that she knew what she had done but I wanted
to check, so I prompted her to “read my mind.” Instantly, but with excitement and what also sounded
like surprise in her voice, she said “Oh! One! You got one!” as if understanding the trick for the first time
all over again. The joy of “getting it” is far more magical than any grade, praise or prize could be.
These are five to fifteen minute events. By the end of a week of them, instead of drawing the picturesthat the children describe, we write the words with which they describe the pictures. “Two bags and six
marbles” is a lot to write, so we abbreviate it: 2 b + 6. No discussion of variables; no explaining about
letters standing for numbers; 2 b + 6 is brief, but the language the children themselves used, and they
fully understand it. For now, that’s enough. Later, when they formalize algebra, the bag or bucket image
is useful to return to: a variable is a container for a value.
Containing a value (or being a pointer to it) is the programmer’s image; representing a value is themathematician’s image. The underlying idea common to both images is that a value can be referred toby a name and that this abstraction is useful. In practice, nearly all children love the think-of-a-number
tricks, so they become a natural, appealing and compelling way to acquire that value-naming idea. Partof the power of the “trick” is that it is faithful to the mathematics, even though it is limited.2 But part of its
power, I’m sure, is what Schulz & Bonawitz (2007) saw: children play longer and more curiously when
there’s something they don’t understand and they believe that they can figure it out.3
This was not a classroom assignment. The children didn’t have to do this and wouldn’t be tested on it.
But they put effort and attention into the think-of-a-number trick because they want to know how it works.
The intensity of Lucy’s interest, even readily admitting what she couldn’t yet do and asking for help
doing it, was because there was a genuine mystery left to solve—one that she saw as hard—but she
was so tantalizingly close that she was convinced she could reach that goal.

Why have students invent puzzles?
Four reasons come immediately to mind; perhaps there are more.
First, the construction of a workable puzzle is a creative act, making the student a creator and not justa consumer of mathematics. We who call ourselves constructionists easily accept making as a goodthing, but it’s useful to say why. What you make is yours; creating gives ownership. Mathematics is often
perceived—except by mathematicians—as the antithesis of creativity, a subject in which rules rule andwe obey. It’s very possible to learn mathematical content that way, and some people like that order andsimplicity. But mathematical thinking can’t work that way because genuinely new problems could thennever be solved. For new problems, one must create new ideas and approaches. Young students’mathematical creativity can’t be at the leading edge of mathematics, but it can be at their leading edge.
Puzzles are not the only opportunities for students to be creative in mathematics but they’re good ones,
especially for younger students.

This imagery doesn’t represent “divide by 2” well unless the numbers of bags and marbles are both even. The imagery is adaptable to“negative grapes,” but frankly awkward. So we need to be clear that the imagery is not the goal, not a “new method” for algebra. But it’s anextremely effective entry to algebra.
3This qualification is important. Nobody—no corporation, no person—puts time/money/effort into an endeavor that they believe has no chanceof success. Students who have been convinced they are “no good at math” often don’t put effort into study that we believe would make thembetter. But they don’t share that belief, so from their perspective, it is wiser to aim their efforts in a direction that seems more likely to pay off.
That is an adaptive, economical choice. That is why it is so important to show (not tell) them that they are capable by hooking their interest on
something they perceive as hard but attainable.
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Second, constructing a good sharable puzzle is a balancing act—easy enough to be solvable and hardenough to be fun. To be solvable, a puzzle must also be well specified—enough clues to derive a uniquesolution (or a limited class of solutions)—without having so many clues that only the arithmetic is left.
Determining when one has given enough clues to derive a solution is quite a challenge.4
That challenge, and also the act of being a creator, may be part of why construction of a sharable puzzle
appeals to kids, but the appeal is yet another reason to have kids create.
And fourth, construction of a sharable object helps reveal the child’s thinking to both the child andteacher, supporting refinement of that thinking, and discussion and analysis.
SolveMe.edc.org is a puzzle world with three kinds of puzzles aimed at developing algebraic reasoning.
Each puzzle type also lets students create their own puzzles and share them on line. The Mobiles appcollection begins with relatively elementary puzzles like the ones in figure 7, and even simpler ones forreal beginners.

Figure 7. Two relatively simple mobile puzzles.

The mobile’s total weight might be given (above, left) and players must figure out how much the blue
red objects must weigh in order for this mobile to balance. Or (above, right), no total weight might begiven, but the weight of one of the hanging objects might be specified. Again, the player must puzzle
out the weights of the other objects.
Players often just work these out in their heads, but the app offers them other options:they can scrawl annotations on the screen, like this. →They can also create equations by dragging off a copy of a horizontal beam,or the entire mobile, and substitute these into other equations (or themobile) to derive new information, like
. The app also lets them factor 2 out ofequations liketo derive new

equationsand to drag a common element out of both sides of an equation like—to see—and to get. Otten, et al. (2017) describe

how 11-year-olds used explicitly algebraic correct reasoning in the context of informal notation andmanipulations of a physical hanging mobile.
The mobile puzzles are essentially systems of equations. Some students are intrigued by the fact thatthey can get those equations and see what those equations mean. In class, that is an advantage, but
informally, even the students who like the fact that they can get equations mostly don’t work with theequations, instead inventing informal methods equivalent to the formal manipulations that algebraclasses teach and name. They also see, early on, that the “weights” can be fractional and even negative.
Some of the puzzles are quite challenging, like the ones in figure 8a, without being required to, students
really persevere because they’re sure they can solve the puzzles if they keep at it.

4This is especially true in creating a good MysteryGrid puzzle or Who Am I puzzle, not described here, but part of the SolveMe suite of puzzles
mentioned below.

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Constructionism 2018, Vilnius, Lithuania

Figure 8a. Two mobile puzzles at a more advanced level.

As I’d said, we felt it important to provide a tool with which students could create their own puzzles andeven share them with friends or with the entire SolveMe community. The sheer variety of users’
contributions is fascinating. Some are genuine puzzles, like those shown above. Others seem to beintended more as works of art, like these.

Figure 8b. Two mobile “puzzles” invented by users, apparently intended only as art.

Manousaridis (2018) regularly encourages students in grades 2 and 3 to create their own puzzles as
posters after solving some on line. Part of her goal is, of course, the ownership that comes from buildinga puzzle. But it is also clear that the task naturally leads children to work at the frontier of their ability,
partly because they take special pride in pushing (and displaying) what they can do.
The 9-year-old who created the puzzles shown in figure 9 was clearly proud of the arithmetic she didbut especially proud of having created a puzzle that required such fancy arithmetic. The puzzle, not justthe artwork on the poster, is a highly personal and creative act. This child is what Papert (1972)
described as “the mathematician… creatively engaged.”

Figure 9. A mobile puzzle invented by a nine-year-old to challenge her classmates to use fractions.

Programming in mathematics
The examples and contexts described above have been very far from the programming-centric proposalthat Papert made in (1972) but, as I’ve tried to show, well in line with the mathematical creativity,
exploration, and research projects that he regarded as doing mathematics rather than learning about
it—creating and solving one’s own problems versus learning mathematical facts and solving problems
47

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created by others. While nobody would claim that programming is the only (or even always best) venue
for creative expression and exploration in mathematics, I and others believe it can be an enormous help
if it can become a natural part of learning mathematics. For it to be “a natural part,” it would need to belearned along with the mathematics, growing over time just as the mathematics does, and used in waysthat support the mathematics and don’t compete with it by seeming to be a separate venture—fun stuff
but disconnected—or by creating excessive overhead or distraction. If that can be achieved, then theflexibility and expressive ability of programming can give it a central role in children’s mathematicallearning and creativity.
Noss & Hoyles (2018) focused especially on that expressive ability:
Maths is difficult in part because of the language in which it is expressed. Can we find adifferent language—and set of ideas and approaches—that is more open, more accessible
and more learnable. And can we find it without sacrificing what makes mathematics work?
Our tentative answer is “yes”—the language of programming might, if we design it right, bejust such a language.
Mathematics really needs three languages. Two are already used universally in school: natural
language for semantics (context, explanation, and some of the logic) and conventionalarithmetic/algebraic notation. Unfortunately, if used inappropriately, both can also get in the way. In
particular, mathematical notation is too often used as the entry point to new ideas rather than a concise
way to record ideas that are already well understood. Consider that the third graders knew intuitively
that doublingproduced, and six-year-olds, when asked verbally (notin writing) what five eighths plus five eighths might be, are happy to respond “ten ayfs,” then ask what
an ayf is. They never answer ten sixteenths. The distributive property is built in to our logic early. But
when the word “distributive property” is introduced in third grade, it is often taught with a written string
like 8  7 = 8  (5 + 2) = (8  5) + (8  2) = 40 + 16 = 56 that is opaque and daunting to a beginner.
Despite your mathematical literacy and knowledge, probably even you zipped past the string of symbols
without reading closely enough to see if it was correctly typed. Processing such a string of symbols
takes focus and effort, and therefore can’t be the optimal way to introduce the distributive property toan eight-year-old. Too much cognitive space is taken up just decoding the long string; not enough is left
for thinking about the idea.
And neither natural language nor mathematical notation is particularly good at expressing process or
algorithm. That’s what a good programming language can provide. Also, unlike a string of symbols orwords that sits on paper—correct or incorrect—and gives no feedback without the reader (re)readingand (re)processing it mentally, a programming language is a notation that can be run and gives direct
feedback on what it does.
ScratchMaths (Noss & Hoyles, 2018) is one beautiful example of infusing programming directly into
grade-level-required mathematics for 9- to 11-year olds. At EDC, we are building a full-year
mathematics+programming curriculum for 7- to 8-year olds as a stepping stone to doing the same forall of the first six grades of school, using the playful and puzzle-centric ideas of Sawyer (2003) andWirtz, et al. (1964) as redeveloped in our Think Math curriculum (Goldenberg & Shteingold, 2007a and2007b). First programming experiences for young children can be quite open—moving a robot around,or just code-streams of interesting effects—but if we are explicitly intending to show 7-year-olds how
they can use programming as a language to experiment with and express the mathematics they are
currently learning, the first coding experiences need to be rather simple while, at the same time, leaving
plenty of room for puzzling and exploring.
Here is one environment EDC is currently exploring with children. The computer displays a number line,
optionally settable for any range, with only one number labeled. The ticks just mark regular intervals,
but interval size is completely settable (consecutive integers, consecutive eighths, skip counting by any
amount, starting at any arbitrary number). For the youngest children, the ticks indicate consecutive
integers, with only 0 labeled (and intentionally chosen not to be the leftmost mark on the line). The 7year-olds are given a palette of tools from which they can draw. For example, they may have,,,
and, later, a repeat block.
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Figure 10. A simple number line with ticks representing consecutive integers.

Clicking a tool performs the indicated arithmetic, shows the corresponding movement on the line, andlabels the result. For example, if the sprite is at 3, clicking the +5 block moves the sprite 5 spaces right(arc optional) and marks 8 there (figure 11).

Figure 11. A move of

from 3 to 8.

Children explore the tools with very open puzzles like “Try to label all the numbers 0 to 10.” When they
have learned how blocks can be snapped together to create a script, they get more focused puzzles ofincreasing challenge that require experimenting, planning, mental arithmetic, and prediction of results.
Two puzzles are shown in figure 12, as they might appear to children.

Figure 12. Two programming puzzles.

More challenging problems can follow: Create two different scripts that…. Children can also be asked
in class discussion to analyze and explain. For example “explain why this script moves
from0 to 1. If you start at 4, where will this script move you? Is there a way to move from 0 to
1 inexactly 2 moves? What’s the shortest script that…?”
Because activities like this “have legs” mathematically, they can grow with the child and be used in later
grades. At the simplest, the very same activity can be used on a“zoomed in” view of the number line, to explore fractions. The
children’s tools now include,, etc.; similar tasks would
1
2
challenge them to mark /4, /4, etc. Still other variants—like changing the tools to ±6 and ±9 andchallenging children to label all the numbers they can—show how versatile this format is, capable ofaddressing later grade-level standards (e.g., factors, multiples, common factors, analyzing patterns,
building fluency with multiplication facts) and foreshadowing in grade-appropriate ways ideas children
will make explicit later. Other elementary school projects involve functions (e.g.,)
and let students build and compose their own. And creating code that generates squares from repeatedmoves, rows from repeated squares, and arrays from repeated rows—and the similarities of thealgorithms inside draw row and draw array—illustrates the meaning and value of “abstraction.”
Abstraction includes both generality, and “hiding complexity”—suppressing details or identifying theimportant characteristics for a particular purpose—by creating a single new command/function to
replace a longer collection of instructions that would otherwise have to appear in several places. This
example also shows how the activity directly supports mathematical content mandated for schools. The
draw array block depends on two parameters, the dimensions of the array. A draw rectangle block isa further (though simpler) abstraction, using the same two inputs but drawing only the border of thearray. For either of these, children might invent a playful quiz, having their program draw a random49

Constructionism 2018, Vilnius, Lithuania

sized array and ask about area (how many tiles it has) or perimeter, using their own reasoning about
those inputs in order to teach the program how to calculate the correct answer.
And, at the high school level, programming allows students to build the mathematical objects andprocesses that they are studying: relatively easily, they can build functions that manipulate polynomials,
transform points with matrices, render a set of points in space in a convincing projection on the screen(Lewis, 1990), and study algebraic structures (Cuoco, 1990). And tools such as Geometer’s Sketchpador Cabri—not programming in the usual sense, but construction with the computer rather than just useof the computer to manipulate pre-designed models—allow students studying geometry to build models
of mathematical objects and ideas, and to explore the consequences of manipulations of those models.

Programming in general
Secondary students, too, need places to be mathematically creative. The mission of Beauty and Joy ofComputing (BJC, 2017) is broadening participation in computer science with a focus on letting students
see not just the joy of creation and beauty in the objects they can produce through programming, butalso beauty in the programs themselves. With this as one goal, it introduces the elegance of recursion
and higher order functions. It manages to make these reputedly “difficult” topics accessible by virtue ofthe lucid visual imagery of the Snap! visualprogramming language, a block-based languagethat is reasonably characterized as essentially
Scheme disguised as Scratch. Two BJC excerpts
involving recursion were used in a computerscience elective with sixth graders. They wrote
recursive code to draw a complex tree, and here
they and their teacher are giggling at the result of agossip-producing program with a randomly invokedrecursive step that, in this case, generated quite along sentence. Other students in this elective
created a program to conjugate Spanish verbs
properly so that they could generate sentences in
Spanish.
They tested the work of their programs by using map, a higher order function, to apply their conjugation
block to a list of verbs.
Initial funding for BJC required it to be an Advanced Placement course with a framework dictated by theCollege Board. Even so, except as constrained by AP requirements, BJC is largely project based withexperience before formality; the explorations through which programming is learned include projects set
in contexts like art and graphics, linguistics, mathematics, and games. While BJC is not a math course,
its activities naturally touch—and help teach—many conventional mathematical content topics, and ourapproach to programming is consistently focused on mathematical/computational thinking.
The point of introducing various contexts—the arts, linguistics, etc.—is partly to meet the varied interestsof students, but much more to show how broadly they can allow themselves to wander, how much they
can tailor their independent projects, for which even the AP framework allocates time, in their own
personal direction.

Playgrounds
Giving even very young students a way to think algebraically using bags and grapes lets them invent
mathematical tricks they love. It prepares them for algebra but more importantly, it lets them feel smart
and pose problems and play with their own algebraic ideas. More broadly, treating mathematics asserious intellectual play, puzzling things out by searching and researching, and gaining the intellectual
tools for posing one’s own challenges teaches children to be mathematicians. Papert suggested
programming as a medium for that, but the essential ingredient remains the promotion of serious
intellectual play. Programming taught just as a skill or to meet new standards may well not serve that
purpose. But if a programming environment lets students explore and create, provides good tools for50Constructionism 2018, Vilnius, Lithuania

doing that, and gives students the “third language of mathematics” so that as their ideas and thinking
grow in sophistication they have a language for expressing and honing those ideas, such anenvironment does add a new playground consistent with Papert’s vision of children being creatively
engaged as mathematical thinkers.

A question, based on wild final thoughts, pure opinion that I might disown
tomorrow
A few states, including Massachusetts (the one that I live in) have begun to develop frameworks forcomputational thinking (CT) across the grades (DESE, 2016). CT is variously defined but always
includes elements like abstraction, algorithm, modelling and simulation, programming, and data (withan implication, not reflected in all implementations, that “data” means big data). Not surprisingly, there
has also been a proliferation of on-computer and “unplugged” activities, not involving programming, tohelp develop this thinking.5 The difficulty of adding anything else to an already jam-packed school day
has led to a lot of talk about integrating CT activities into the existing content areas, particularly scienceand mathematics (e.g., EDC STEM+CT, 2018), but also language. In my opinion, some of thesuggestions are shallow, but that should be no surprise at a time when the whole effort is so new. 6 Still
it got me to thinking about why my own inclination has been toward programming, not away, and toward
abstraction, and algorithm rather than modelling and simulation, whenever the aim is explicitly tointegrate with other subjects.
I think it’s largely bias. I tend to think more about elementary and middle school, and more aboutmathematics than about science. At the elementary school level, modelling and simulation are easier
to integrate with science than with mathematics; programming, along with abstraction and algorithm, iseasier to integrate with mathematics than with science.
Modelling, for example, is something that mathematics (and mathematicians) can do, and sincemathematics can build models of mathematical ideas, modelling is also something that mathematics
uses. But, at least as far as I see at the elementary school level (especially in the early grades) modellingwith mathematics—creating mathematical models of phenomena—is very limited. And fairly abstruse,in the following sense. While every mathematical statement (like “there are seven cows”) is an example
of an abstraction (the cowness is reduced to irrelevancy) and just a model of the reality, no kid in the
known universe thinks of such a statement as an abstraction or a model. That level of abstraction is sonormal to them that it is totally “invisible”—it’s just what language does. By contrast, modelling is anatural place to focus in science—the core of experimentation and the form of many scientific claims—
and simulation (at least as generally used) is an automation/extension/elaboration of modelling.
Programming is exactly the opposite, easier to integrate into (early) mathematics than into science. (Ofcourse, take this with a cup of salt, as I’ve not given scientific programming nearly as much thought. As
I advertised, these are wild final thoughts that I might disown tomorrow.) That may be partly because
the kinds of statements one makes in early mathematics tend to be about relationships and about simple
processes. “Writing a program” that enacts a function, like doubling its input or adding 10 to its input, iseasy programming. In fact, it’s easier to write in a general way as a program (a Snap! block) than as apaper-pencil scrawl, because a program is an active notation; it will enact the action and give feedback,which paper-pencil scrawl do not. It is also a structured notation, imposing a bit of order on what young
students typically scatter over a page in a way that, even if totally correct, does not reveal their logic.
Similarly, writing a program that pairs elements of two sets, writing a program that draws simple shapes,
or creates arrays or paths to study, is mathematically on task and easy programming. By contrast, mostscientific phenomena are too complex for young children to model by writing a program (often pretty
complex even for adults).

5

Just as I was completing this paper, I received a copy of Bebras (Dagiené, et al., nd), a set of activities, many puzzle-like, that I found quite
appealing, all designed to develop various elements of computational thinking in students.
And, clearly I, myself, am being a bit shallow in using the vague quantifier “some suggestions.” Of course, in any situation, some suggestions
will be shallow.
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Constructionism 2018, Vilnius, Lithuania

I’d love to get reaction to this last, very spur-of-the-moment rumination. What genuine programming
activities, at the elementary school level, can be really well integrated with science? And what modellingor simulation activities, again at the elementary school level, can be really well integrated withmathematics?

Acknowledgments
Funding for doing and reporting the work described in this paper was provided in part by the
National Science Foundation, grants 1441075, 1543136 and 1741792. Views expressed here
are those of the author and do not necessarily reflect the views of the Foundation.

References
BJC. (2017) The Beauty and Joy of Computing. http://bjc.edc.org
Cuoco, A. (1990) Investigations in Algebra. Cambridge, MA: MIT Press.
Dagiené, V., Stupuriné, G., Vinikiené, L., and V. Kincius. Bebras. Creative Commons AttributionShareAlike 3.0 Unported License (CCC BY-SA 3.0). http://www.bebras.lt
DESE. (2016) Massachusetts Department of Elementary and Secondary Education. 2016
Massachusetts Digital Literacy and Computer Science (DLCS) Curriculum Framework. Accessed at
http://www.doe.mass.edu/frameworks/dlcs.pdf, 16 September 2016.
EDC STEM+CT. (2018) http://go.edc.org/elementary-ct The Broadening Participation of Elementary
Students and Teachers in Computer Science project is directed by Joyce Malyn-Smith. Accessed March
29, 2018.
Goldenberg, E.P., Mark, J., Kang, J., Fries, M., Carter, C. and Cordner, T. (2015) Making Sense of
Algebra: Developing Students’ Mathematical Habits of Mind. Heinemann: Portsmouth, NH, USA.
Goldenberg. E.P. and Shteingold, N. (2007a) Early Algebra: The MW Perspective. In Algebra in the
Early Grades, Kaput, J.J., Carraher, D.W. and Blanton, M.L., Eds. Erlbaum: Hillsdale, NJ, USA.
Goldenberg. E.P. and Shteingold, N. (2007b) The case of Think Math!. In Perspectives on the Design
and Development of School Mathematics Curricula, Hirsch, C., Ed. NCTM: Reston, VA, USA.
Lewis, P. (1990) Approaching Precalculus Mathematics Discretely. Cambridge, MA: MIT Press.
Manousaridis, T. (2018), personal communication.
Mark, J., Goldenberg, E.P., Kang, J., Fries, M. and Cordner, T. (2014) Transition to Algebra.
Heinemann: Portsmouth, NH, USA.
Noss, R. and Hoyles, C. (2018) The ScratchMaths project is directed by Richard Noss and Celia Hoyles,
with Ivan Kalaš, Laura Benton, Alison Clark Wilson, & Piers Saunders. See
http://www.ucl.ac.uk/ioe/research/projects/scratchmaths. Accessed March 29, 2018.
Otten, M., van den Heuvel-Panhuizen, M, Veldhuis, M., Heinze, A. and Goldenberg, P. (2017) Eliciting
algebraic reasoning with hanging mobiles. Australian Primary Mathematics Classroom, Vol. 22, No. 3,
pp. 14–19.
Papert, S. (1972) Teaching Children to be Mathematicians Versus Teaching About Mathematics, Int. J.
Math Ed in Science and Tech., Vol. 3, No. 3, pp. 249–262.
Sawyer, W.W. (2003) Vision in Elementary Mathematics. Dover: New York, NY, USA.
Schulz, L.E. and Bonawitz, E. B. (2007) Serious Fun: Preschoolers engage in more exploratory playwhen evidence is confounded. Developmental Psychology, Vol. 43, No. 4, pp. 1045–1050.
Wirtz, R., Botel, M., Beberman, M. and Sawyer, W.W. (1964) Math Workshop. Encyclopaedia Britannica
Press: Chicago, IL, USA.

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May I Teach an Algorithm?
Brain Harvey, bh@berkeley.edu
University of California, Berkeley, USA

May I Teach an Algorithm?
BRIAN HARVEY, bh@berkeley.edu
University of California, Berkeley

Abstract
After all these years, I'm still not sure what Constructionism entails. Here's an example taken from ourwork on the Beauty and Joy of Computing curriculum: We have a yearlong Tic-Tac-Toe project. Earlyin the year, students draw the board and use mouse clicks on the board to let two human players
alternate moves. A month later, they start building data structures that will let the program analyze theboard. At that time, they check whether either player has won, or whether the game is tied. Two months
after that, they return to the project, letting the program be one of the players, and determining the
program's move with rules such as "if I can win on this move, do it." A Tic-Tac-Toe program is an
obvious project, one of the things many learners do spontaneously. But I had an ulterior motive in thedesign of the project: I want students to practice using higher order functions (MAP, KEEP, COMBINE).
The data structures in the program are built to accommodate that.

We provide the overall structure of the strategy procedure.

The question is, because I have this motive and I impose this design, am I being hopelessly
instructionist? Is it my project rather than the kid's project? How much flexibility is required for the kid
to "own" the project? And, is the kid owning the project what makes it Constructionist?

Keywords
curriculum; ownership; constructionism; instructionism

What does “Constructionism” mean?
About 10 years later, I was working with Sherry Turkle and John Berlow at theLamplighter School in Dallas, TX, the first elementary school where there were enoughcomputers for children to have almost free access to them. The first space shuttle wasabout to go up, and the tension of waiting for it appeared in many representations onscreens all over the school. "Even the girls are making space ships," one girl told us.
But we noticed that although everyone had space ships they did not make them thesame way. Some programmed their space ships as if they had read a book on"structured programming," in the top-down style of work that proceeds through careful
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planning to organize the work and by making subprocedures for every part under thehierarchical control of a superprocedure. Others seemed to work more like a painterthan like this classical model of an engineer's way of doing things. The painterprogrammer would put a red blob on the screen and call over her friends (for it was more
often, though not always, a girl) to admire the shuttle. After a while someone might say:"But it’s red, the shuttle is white." "Well, that's the fire!"—came the reply—"Now I'll makethe white body." And so the shuttle would grow, taking shape through a kind ofnegotiation between the programmer and the work in progress.
—Seymour Papert and Idit Harel, “Situating Constructionism,” in Constructionism (Ablex
Publishing Corporation, 1991)
The first thing to notice about this story is that no teacher assigned the project of making a space ship.
Teachers did give assignments at Lamplighter, but there was also time for kids to do what they wanted.
The outside stimulus of the Space Shuttle led to Seymour being able to compare student-initiatedprojects that happened to be depictions of the same thing in two different styles. My question is, whichof the different characteristics of this anecdote make it constructionist?







If a teacher had assigned drawing a spaceship, would that matter?
How about if the teacher had said “make it as realistic as you can”?
What if the common stimulus had been something abstract rather than a physical thing?
The kids in this story were already familiar with Logo. What happened in the first week of school?
Could a constructionist teacher start with the usual “Draw a square”? Or must we just sit the kids
down in front of Logo and have them explore? (That would have been hard with Logo, really. But
it can definitely be done with Scratch.)
What if this week’s class topic had been subprocedures, and the teacher had said “make whatever
project you want, but try to use subprocedures”? Still constructionist?

The minimal definition of constructionism is that people learn best by making something, preferably aphysical thing, that is available for public critique. But most people who call themselves constructionists,
including Papert, connect the approach with the broader progressive education movement, in which
locus of control is a crucial issue. That’s why these questions don’t have obvious answers.
I wasn’t at Lamplighter to ask these questions. But somewhat analogous questions have come up forme in the development of the Beauty and Joy of Computing secondary school curriculum (BJC).

The Beauty and Joy of Computing
About a decade ago was the beginning of an ongoing campaign in the United States to increase thenumber of women and minorities studying computer science. The National Science Foundation (NSF)
made a strategic decision to concentrate on high school (ages 14-18) students, reasoning that it’s inthose years that young people make career choices. My colleague Daniel Garcia and I decided toredesign our CS breadth course for non-majors, with the idea that the same course would serve as ahigh school course for all students. A pilot version of the course was offered in 2008; since 2009 it has
been offered to more than 300 students every semester.
The United States doesn’t have a central education authority; curriculum decisions are made by eachstate or by local school districts. It’s very difficult, therefore, to introduce a new topic to the high school
curriculum. But there is a non-governmental de facto national curriculum organization: the College
Board, a consortium of colleges and universities7 that creates standardized tests that students take aspart of the college application process. One project of the College Board is a set of 38 Advanced
Placement exams in college-level curricula. The first 37 AP curricula were set by surveying freshmancourses actually taught at colleges and universities, but the NSF partnered with the College Board tocreate a new course, “AP Computer Science Principles,” in a subject that was, at the time, taught at

In the United States, a “university” is an institution that conducts research as well as teaching undergraduates (ages 18-22) and graduate
students (over 22). A “college” is an institution that primarily teaches undergraduates, although there is no hard rule separating the twocategories.
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very few colleges. (There was already an “AP Computer Science A” exam, a traditional introductory
programming course.8) AP courses are typically one semester long (about five months) at a college oruniversity, but take a full school year at the high school, so that students are not deterred from AP
courses by the density of college-level work.
Because CS Principles wasn’t a well-established college course, the College Board couldn’t use its
usual approach of surveying colleges to determine what topics should be tested. Instead, they set up
a committee of university and college faculty to invent ab initio a curriculum framework for a computerscience course appropriate for non-majors. (College Board 2017)
Berkeley’s course, The Beauty and Joy of Computing (BJC), was initially designed before the CS
Principles framework was written; we designed the course that we thought would best serve Berkeley
undergraduates. One consideration was that the course should prepare students for our first course for
CS majors, based on the brilliant text Structure and Interpretation of Computer Programs by Hal Abelson
and Gerald J. Sussman. (Abelson 1996) At the same time, BJC was one of the pilot projects for CS
Principles, and once the framework was written, we added content to BJC to satisfy it.
In the early years of BJC, we partnered with individual high school teachers who heard about the
curriculum and wanted to try it. These early adopters tended to be either at private schools or in wealthy
suburban school districts. But in 2014 the NSF solicited proposals for partnership grants proposed
jointly by partnerships including a university and a secondary school district, and we decided it was timeto jump into the deep end: New York City, a majority-minority school district (that is, most of its students
are nonwhite) with 500 high schools. In New York, many students are learning English as a second
language, and even some of the native speakers have below-grade-level reading skills. We made thecrucial decision to include in the partnership an NGO, Education Development Center (EDC), with longexperience developing curricula for K-12 education, to rewrite the curriculum specifically for high school.
The EDC team for this project is headed by Paul Goldenberg and June Mark. Our partnership was
funded for four years starting in January, 2015.

Tic-Tac-Toe
A program to play tic-tac-toe is a classic programming project. We decided to use this example as an
ongoing project throughout the year, starting early in the curriculum, when students have written
graphics programs but have no experience with data structures; this first part of the project was aprogram to draw the tic-tac-toe board and then display moves by two human players. A month later,when students have experience with list processing, they add code to detect and announce wins andties. Two months later, they take the big step of writing a strategy procedure so that the computer canbe one of the players.
We are now at the point where the connection with Seymour’s story from the Lamplighter School should
be clear. We are, I guess, constructionists; but we are writing a project into a curriculum, with specificmilestones based on specific programming techniques that we want students to practice. The students
are making an artifact: a game program. Is this Constructionism?
That question was not just of theoretical interest to us. As it turned out, each step in the project design
involved some disagreement among the curriculum team (six people at EDC, and me, spending half ofeach year at EDC, in Massachusetts, and the other half at home in California but working with EDC
remotely). At each of these points of disagreement, we had to make a decision.

Drawing the board
The first point of disagreement came right at the beginning, with the drawing of the board. One of ourteam members, who had extensive Logo experience, wanted students to use their square procedure
repeatedly, but instead of building a fixed 3x3 board into the code, parameterize the number of squares
so that the same code could be used to draw a chess board or a sudoku board. Another team member,

CS A is taught using Java, probably the hardest programming language to learn because of its Byzantine syntax. That’s one reason a new
course was needed to encourage non-geeks.
855Constructionism 2018, Vilnius, Lithuania

with prolific experience creating Scratch projects, wanted to give a sprite a square costume (three
costumes, actually: an empty square, a square with an X inside, and a square with an O inside) andclone it nine times.
Each of these proposals was good in one way and less good in another way. Drawing the squares
programmatically was a better fit with the rest of what was happening at that point in the curriculum,and in particular gave an opportunity for students to practice the use of Cartesian coordinates to position
the squares. But using sprites made the next step, detecting users’ moves in the form of mouse clicks
inside a square, much easier, because the click would be captured by one of the nine sprites. If thesquares were just lines drawn on the stage, students would have to use nested if commands or docomplicated arithmetic on coordinates to determine the square into which the user wants to move.
The use of layered abstraction and modular arithmetic was part of the original intent of the project, but
we learned visiting classes that students were having much more trouble figuring out how to handle themouse coordinates than we’d anticipated. (See Figures 1–3.) We then had two choices: Debug our
project instructions by treating only the 3x3 case and adding enough scaffolding so that students could
complete the task, or abandon the original curricular goals by using the “when I am clicked” feature of
Snap! to solve the coordinate problem below an abstraction barrier. (The silver lining would be adding
something new, parallel processing, to the curriculum.)
Perhaps the most constructionist solution would have been for us to describe the desired result without
making any suggestion at all about the algorithm to use. But students had never cloned a sprite at thispoint in the curriculum, so they would be unlikely to discover that solution (see Figure 4); and we alreadyknew that the Logo-like “draw nine squares” solution made capturing mouse clicks too hard for many
New York high school students without scaffolding.

Figure 1. Teaching abstraction was a goal of the original version.

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Constructionism 2018, Vilnius, Lithuania

Figure 2. So was teaching Cartesian coordinates and conditionals.

Figure 3. We had to provide a lot of hints about converting mouse coordinates to board position.

Figure 4. We had to teach what cloning means and how to do it.

57Constructionism 2018, Vilnius, Lithuania

We first attempted to solve the dilemma by compromising: We wrote two versions of the project
instructions and let students (or teachers) choose which to use. This had the virtue that nobody on theteam felt rejected, but it turned out to have few other virtues. In particular, the choice in this first part ofthe project affected later work, so that we would have to maintain two versions of the project throughout
its stages. (Consider the problem of determining whether a player has won the game. The “draw nine
squares” solution is structured as one program, with a single flow of control, and full access to the historyof moves. The “nine sprites” solution, however, has this information distributed among the nine sprites,
so the programmer must make extra effort in the second part of the project to communicate the moves
to a central database. See Figure 5.)
So we all agreed that we had to make a choice. I preferred the computer science ideas in the “drawnine squares” version, which fit better (as they were designed to) with the overall story line of thecurriculum. But I finally changed my vote, breaking a tie, when I convinced myself that many of ourstudents will have used Scratch in elementary school, and will therefore have the same expectation as
our Scratch-using colleague that clicks are to be caught by sprites. They would reasonably ask why weare asking them to do it with a much harder technique. We did edit the “nine sprites” curriculum pageto call attention to its computer science ideas, although they’re not the ones we had been planning for.
(See Figure 6.)

Detecting wins
There are only eight ways to win a tic-tac-toe game: three horizontal, three vertical, and two diagonal.
This is a small enough number that it wouldn’t be too painful for students to check for a winning movewith eight if commands, each checking explicitly for three squares with the same player in them, usingsquare numbers built into the code. (See Figure 7.) This is certainly the solution that most students
would invent, left to themselves.
But I had a curricular motive for this project that I was not willing to give up: teaching the use of higher
order functions (map, keep, combine). BJC is my third time using this project for this purpose; I’ve
done it before in Logo (Harvey 1997) and in Scheme (Harvey 1999). The fundamental idea is to keep
the information about eight ways to win in a list of lists (Figure 8). Then detecting a win is just mapping
the actual board position over this list of wins, and looking for a list of three Xs or three Os in the result.
This can be done in a one-liner (Figure 9), but for pedagogic reasons we break the nested map calls
into separate functions (Figure 10). The resulting curriculum page is shown in Figure 11.

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Figure 5. Creating global board knowledge from distributed-computing sprites.

Figure 6. If life gives you lemons, make lemonade.

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Constructionism 2018, Vilnius, Lithuania

Figure 7. Detecting wins the “obvious” or “natural” way.

Figure 8. Moving the knowledge of eight wins from the code into data.

Figure 9. The one-liner version with nested map calls.

Figure 10. Checking for a win using map.
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Figure 11. Guiding students through the code.

Using this approach was controversial in the curriculum team. Higher order functions are exotic topeople who’ve learned to program in conventional languages, such as Python, Java, or Scratch. It’s
only people who learned in functional languages (mainly Lisp dialects such as Logo, Scheme, or Snap!)
who find this approach more natural. Also, there’s nothing about higher order functions in the CS
Principles framework; we’re not required to teach it, and the biggest complaint teachers have madeabout BJC is that they don’t have enough time to cram it all into the year. But at Berkeley, higher orderfunctions and recursion are the two most important ideas that make BJC special. (A third is that we
spend much more time on the social implications of computing than the framework requires.)
The central idea in computer science is abstraction, and the CS Principles framework reflects that.
Higher order functions form a potent abstraction over lists; instead of the conventional loop over list
items, with an index variable holding the position of the current item in the list, higher order functions
allow the programmer to deal with the list as a single entity, without singling out individual items. The
TicTacToe wins list is best understood not as a list of lists, and certainly not as a two-dimensional
array, but rather as a list of triples, where “triple” is an abstract data type consisting of three squarenumbers in the range 1-9. So TicTacToe wins is an eight-item list of all the winning triples. Statusof triple takes a triple as input, reporting a new triple in which the square numbers have been
replaced by the player who owns each square. Status of winning triples maps status of
triple over the eight winning possibilities, reporting a new list of triples. Then we’re almost done; we
ask whether that new list contains the list {X, X, X} or {O, O, O} depending on who moved last.
Contains is a primitive block that answers that question.
We seem now to be very far from the space ships at Lamplighter. The project (tic-tac-toe) is assignedby the curriculum writers; students are told to use a particular method that even some of the curriculum
team view as “unnatural.” It’s in a good cause, furthering abstraction, and I can testify that after a while
it becomes natural. (I learned to program in Fortran, but the solution in Figure 9 is truly the first thingthat occurred to me while working on this project.) And the code is beautiful; we have “Beauty” in thename of our curriculum and we take that seriously.
And kids do need to learn skills. Even in maker spaces, the current paradigmatic example of “Do It
Yourself” learning, they don’t leave kids to discover for themselves how to use a soldering iron or a table
saw safely.

61Constructionism 2018, Vilnius, Lithuania

Again, it was proposed that we offer students two ways to write this procedure. But why? If we ask
students to pick one, they have no basis for choice until they’ve tried both. So we’d have to have them
do it both ways. And, if we’re going to have them do it in the way we want, how does it help to havethem also do it a different way? I think that proposal was more about trying to avoid conflict among theteam than about the needs of students.
But what’s important for our story is how much weight the word “project” carries in a curriculum. If
something is an “exercise” then everyone easily keeps in mind what purpose the exercise is supposed
to satisfy in the overall curriculum. But once something is a “project,” completing the project becomesa purpose in itself, and the needs of the curriculum are easily seen as getting in the way of that.
And really, tic-tac-toe in our curriculum barely deserves the name “project.” We want students to do ita particular way, for particular reasons, and we carefully guide them along that way. (Review Figures
5 and 11 if you need a reminder of what I mean by that.) At the end of each unit is a collection of about
half a dozen optional projects, and those really are projects: We don’t care how the student does them,or even whether the student does them, because they do not bear the weight of teaching the curriculum.
They’re for the student who finishes early and wants more to do. We try to pick projects that are
somewhat relevant to the unit in which they appear, but even that isn’t really important. They just haveto be beautiful and/or joyous.

Tic-tac-toe strategy
The final stage of the project is allowing a person to play against the computer. This requires a strategy
procedure to generate the computer’s moves. But once we have status of winning triples
showing what’s where on the board organized by winning triples, this is pretty much just a matter ofpattern matching.
We start by asking students to discuss in small groups how they play tic-tac-toe. (Figure 12.) (A slight
digression: The CS Principles framework asks us to teach about “pseudocode.” I don’t believe that any
programmer really thinks in pseudocode—certainly not the ones who have an expressive programming
language to work with. What students do in this exercise may look like what the framework authors
have in mind, but it’s very different. The students are not thinking about a computer program in
handwavy English. They’re thinking about themselves, for which English is an appropriate language.
People think in English; computers think in programming languages. Nobody thinks in pseudocode.)
By the way, the claim that people play tic-tac-toe using rules was controversial in our group, as was theclaim that the first two rules are the same for all people. Some of us worried that students who had adifferent way to think about tic-tac-toe might feel excluded. So, take a minute to think about how you
play tic-tac-toe (or how you played it as a child; you’ve probably graduated to Sudoku and its variantsnow). If you use rules, think about what your second rule is. (We give the first rule in Figure 12.)
The first rule is, “If you can win on this move, do so.” If the computer can win on this move, it must be
by filling one of the eight winning triples. What must be true of a triple for the computer (which is playingO) to be able to win using it on this move? The triple must contain two Os, and no Xs. (The third squareof the triple will be empty, which is represented by the square number.) Figure 13 shows an excerpt
from the curriculum in which students are asked to answer that question, and then use the answer towrite the strategy procedure. Here is an excerpt from the program:

62

Constructionism 2018, Vilnius, Lithuania“find first item (of the list of triples) such that the number of Os is 2 and the number of Xs
is 0.” Even if you aren’t familiar with the notation for higher order functions (and the students are, by
this point in the curriculum), you can see how the structure of the code matches the structure of the
problem you’re trying to solve.
Note that this is not the same as saying that the language is “like English.” It isn’t, except in the sensethat all programming languages are like English: the names of primitives are meant to suggest what
they do. But the arrangement of those primitives—the syntax of the language—is quite different fromEnglish, as it should be. Rather, the point is that the higher order functions, like all abstractions, let you
talk in terms of the problem you want to solve (triples, Xs, Os) rather than in terms of computer hardware(index variables, looping, memory addresses).
Figure 13 shows the excerpt from the curriculum in which students are asked to express the first rule interms of triples, and then use that to guide writing the code. Figure 14 shows the complete solution,
apart from a couple of helper procedures.

Figure 12. How do human beings play tic-tac-toe?

63Constructionism 2018, Vilnius, Lithuania

Figure 13. Guiding the student to convert a rule to a program.

Figure 14. The complete strategy procedure.
64Constructionism 2018, Vilnius, Lithuania

Efficiency
Note that in our solution, can __ win now? calls winning square for __, and so next movefor computer ends up calling winning square for __ twice for a rule that succeeds. Of course
we could use a local variable to save the value from the first call and avoid the second, but we areadamant that in a first course that aims to attract nontraditional students to computer science, efficiency
doesn’t matter. What matters is promoting abstraction all the time.

Differentiated instruction
The directions we give students are narrow, because we have specific purposes in mind and also
because we are under pressure from teachers to ensure that even ill-prepared students succeed in theactivities. We do have “Take It Further” activities, for more advanced students, with much lessscaffolding. (Figure 15.)

Figure 15. Enrichment activities.

We stole the twisty-road signs from Donald Knuth’s TE XBook (Knuth 1984). A double-twisty-road
exercise could be a month of hard work.
In the other direction, we provide for students who need even more handholding than we expect withthe “click for a hint” links.

What does “Constructionism” mean? (reprise)Or, getting to the heart of the matter, am I a constructionist?
The most pessimistic answer would be that I crossed the line as soon as I decided to be a curriculum
writer instead of directly working with kids. Actually, I’m less worried about violating Seymour’sphilosophy than that of Martin Buber, who writes about the human relationship between student andteacher as the most important part of education. I guess a more positive way to say this is that teachers65Constructionism 2018, Vilnius, Lithuania

and curriculum writers are both important, and curriculum writers do have a big influence, but I shouldn’t
be surprised that the front lines in education are in classrooms and not in curriculum team meetings.
But that’s not a very good answer, because it lets me off the hook. It means that I can write any old
curriculum, without worrying about whether it’s progressive or not, artifact-creating or not, just whether
it contains the right ideas and information.
Another answer that appeals to me is that the duty of a constructionist curriculum writer is not to holdstudents’ hands, but rather to leave space for them to create something new. I fear that we haven’t
done that enough. The original Berkeley version of BJC, aimed at college students, was better about
this. If our purpose is to improve access and equity in computer science, we have to succeed with all,or at least most, of real high school teachers and students, in classes in real schools, not all of which
will be progressive, let alone constructionist.
A big part of our problem is that we are constrained by a standardized test that we didn’t write. Teachers
are terrified of getting to the exam in May with unprepared students. To avoid that, students not only
have to succeed at our lessons; they have to do it quickly. When I was a high school teacher, I had noauthority to answer to, no standardized test, and so I could be relaxed about how fast students worked.
We have been considering a version of BJC that would not be labelled as an AP CS Principles course.
This would let us leave out many things in the framework that aren’t part of the BJC story line, so,ironically, it could end up having more and deeper computer science than the CS Principles version.
But most importantly, teachers could teach their classes at whatever pace seems right for that particulargroup of students. That would let us leave out some of the scaffolding, or at least bury more of it underclick-for-hint links.
By the way, a student’s AP score is based not only on the sit-down exam in May, but also partly on a
programming project that the student does in class and submits to the College Board for grading.
(Teachers are required to set aside 12 hours of class time for this project; it can’t just be homework
because some students don’t have computers at home. If a class period is 45 minutes, that’s three
weeks of instructional time gone—one more reason the teachers feel rushed. There is also an 8-classhour “computational artifact” addressing the social implications of computing.) The required
programming project makes this class at least slightly constructionist: There’s one artifact createdentirely by the student.
As I write this, I am leaning toward not feeling guilty about wanting students to build programs in specificways. Ideally, I think, there would be more variety about this in the curriculum; sometimes we’d insist
on them doing it our way, but other times we’d let them work without guidance and then have groups ofstudents compare their solutions, and then show them our solution. But we are deterred by timepressure from doing this.
There’s a lot more curriculum I want to write. I’ve mentioned the non-AP BJC. Some schools haveasked for a semester-long subset of BJC. The project that most excites me, although maybe nobody
would actually use it, is “BJC year 2,” a course that would introduce object oriented programming, dive
deeper into functional programming, and perhaps write an interpreter for a programming language.
We’ve also talked about mix-and-match modules for schools that have robots, 3D printers, and otherprogrammable toys.
On the other hand, I really miss teaching kids!

Acknowledgments
This work was funded by the National Science Foundation under grant number
1441075. Any opinions, findings, and conclusions or recommendations expressed inthis material are those of the author and do not necessarily reflect the views of the
National Science Foundation.

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References
Abelson, H. and Sussman, G. J. (1996) Structure and Interpretation of Computer Programs 2/e. MIT
Press, Cambridge.
College Board (2017) AP Computer Science Principles: Course and Exam Description. College Board,
New York.
Harvey, B. (1997) Computer Science Logo Style 2/e. MIT Press, Cambridge.
Harvey, B. and Wright, M. (1999) Simply Scheme: Introducing Computer Science 2/e. MIT Press,
Cambridge.
Knuth, D. E. (1984) The TE XBook. Addison-Wesley, Menlo Park.
Papert, S. and Harel, I. (1991) Situating Constructionism. In Constructionism. Edited by I. Harel and S.
Papert. Ablex Publishing Corporation, Norwood, NJ.

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Constructionism 2018, Vilnius, Lithuania

Social Gears – a Constructionist Approach to Social
Studies
Arthur Hjorth, arthur.hjorth@u.northwestern.edu
The Center for Connected Learning and Computer-Based Modeling, Northwestern University, USA

Abstract
The Gears of Seymour Papert’s childhood have been a persistent analogy for our work in theConstructionist community for decades. It is a productive analogy that has helped concretize many
central themes in Constructionism: It frames our view of knowledge as understanding the inner workingsof a system; it forefronts the existence of an external representation of some domain knowledge; and itemphasizes that learning happens through the manipulation of this external representation by alignmentof an internal, mental model with an external, physical model. Gears are powerful, maybe because theircause-and-effect is simple: one cog moves another cog which moves another cog which moves anothercog. Always at the same ratio, and at a rate pre-determined by whichever cog we apply torque to.
Importantly to our work as an educational research community, it makes studying thinking about cogs
relatively straight forward.
But what if the gears were social? What if they have inner lives, mood swings, wants and desires, andwork under the constraints of social pressures and modern family life that they must negotiate,collectively and individually, in order to organize their turn ratios? Does it change how we should design
Constructionist learning environments and activities? Does it change how we should study students’
thinking? I will not claim that the deterministic nature of the Gears-analogy caused Constructionism tofocus on deterministic subjects. But maybe the focus on STEM and programming have led us to not
explore and interrogate potential shortcomings of the analogy?

Students reasoning with different representations of Urban Planning

I address these questions based on my experience with designing, implementing, and studying
Constructionist learning in social studies classrooms at the high school level. I present “Complex Social
Systems Thinking” (CSST) as a guiding framework for designing learning activities and for studying
students’ thinking. I then present a set of Constructionist learning activities that I have designed on
Urban Planning, and present data to exemplify what CSST looks like “in the wild”. Finally, I discuss therelationship between Constructionism and CSST and present my hopes for the future ofConstructionism in social sciences education.

Keywords
complex systems thinking; social studies; design; qualitative analysis

68Constructionism 2018, Vilnius, Lithuania

Scratchmaths: a Positive Outcome forConstructionism at Scale
Richard Noss , r.noss@ucl.ac.uk
University College London, UK

Celia Hoyles, c.hoyles@ucl.ac.uk
University College London, UK

Abstract
From September 2014, all primary schools in England have been required to teach the national
computing curriculum, which includes designing and building programs. This presentation will discuss
some of the challenges in implementation as part of describing the two-year ScratchMaths (SM) project.
SM designed a comprehensive curriculum for Year 5 and 6 pupils (aged 9-11 years) that maps directly
to the computing curriculum, seeks to develop pupils’ programming skills as well as exploit these skills
to explore key mathematical concepts. The SM curriculum comprises detailed student activities forabout 20 hours per year over two years, with teacher support materials and professional developmentdays. SM has been implemented in over 100 schools across England. The project was evaluatedqualitatively through design, survey and observational research. In addition, SM was evaluated by anindependent research team employing a randomised control trial.
We will report a surprising result on the constructionist path from the RCT, namely that:
ScratchMaths led to a small increase in computational thinking as measured by the test
used at the end of the first year of the trial. (The independent report on SM project).
This is surely good news for the constructionist community, not least as it was shown that this increase
was significantly greater among ‘disadvantaged students’ as measured by their eligibility for free school
meals (a standard proxy measure in UK). We will have plenty to say about the substantive issues
concerning this finding and the quantitative methods employed. While it is relatively straightforward to
identify performance in clearly pre-defined skills, it is much more difficult to do the same for necessarily
open questions: How does programming (in Scratch) engage students in ways that supports them seelearning as worthwhile? How do learners express themselves using Scratch? What can a Scratchaware learner do that he/she couldn’t have done without Scratch?’. And of course, what was meant bycomputational thinking.
We will also report the second major finding of the SM project, namely that:…even though a positive correlation was found between computational thinking andmathematics, ScratchMaths did not increase mathematics attainment during the trial
period, as measured by Key Stage 2 tests. (The independent report on SM project)
Again, there is much to say to seek to explain this outcome: it raises questions again of the validity ofthe measures, but also of teacher confidence and the ‘fidelity’ of the intervention as implemented in thesecond year. this is a problem for researchers in the scientific as well as social domains. For example,
medicine – routinely regarded as the arena in which the most rigorous research is carried out, the ‘gold
standard’ for research methodology – is facing much the same challenge. For example, the Medical
Research Council in the UK, acknowledges that when dealing with complex interventions, one must beaware that complex interventions are built up from a number of components, which may act both
independently and inter-dependently. The components usually include behaviours, parameters of
behaviours (e.g. frequency, timing), and methods of organising and delivering those behaviours (e.g.
type(s) of practitioner, setting and location). It is not easy precisely to define the “active ingredients” ofa complex intervention. (Medical Research Council, UK April 2000.)
Educational interventions are certainly no less complex than this. So even before we reach the problemof knowledge and change, we have to admit that it is difficult to be clear what are the key components
69Constructionism 2018, Vilnius, Lithuaniaof a given complex intervention. In conclusion, we admit to be quite surprised at the above outcomes,
they give us all we think food for thought and we note we would have missed them if we had closed thedoor on methodologies that have, up to now, been relatively taboo in constructionist circles.

Keywords
Scratch; computational thinking; mathematics; methodology; randomised control trial

References
Benton, L. Hoyles, C., Kalas, I & Noss, R. (2017) Bridging Primary Programming and Mathematics:
preliminary findings of design research in England Digital Experiences in Mathematics Education, pp
1- 24
Benton, L. Kalas, I; Saunders, P; Hoyles, C; Noss, R. (in press) Beyond Jam Sandwiches and Cups ofTea: An Exploration of Primary Pupils’ Algorithm-Evaluation Strategies" J of Computer Assisted
Learning

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Constructionism 2018, Vilnius, Lithuania

Programming in Lower Primary Years: Design
Principles and Powerful Ideas
Ivan Kalaš, kalas@fmph.uniba.sk
Comenius University, Bratislava, Slovakia

Abstract
Latest national computing education strategies – often installing the beginning of the new mandatory
subject into the lowest years of primary education – embrace programming as a key instrument ofcomputational thinking. National curricula usually set ambitious requirements for primary computing
education, listing essential computational constructs and practices to be mastered by primary pupils(e.g. use sequence, selection, and repetition in programs; or design, write and debug programs that
accomplish specific goals...). While the research findings within our recent ScratchMaths projectsuggest that the intervention which we developed is a viable strategy to meet the expectations ofnational curricula in years 5 and 6, the question remains how to implement them in years 1 to 4 (i.e.
with the age group of around 5 to 10, depending on educational system).
There are numerous portals and on-line resources claiming to have the answer to that question. Our
main concern, however, is that those resources and the expertise behind them often originate fromafter-school experience, secondary or higher education practise or individualised home “edutainment”– focusing on isolated flashes of learning, often neglecting complexity of important basic computational
constructs and practices, plus failing of advantages of primary education.
In our on-going research and development, we strive to better understand what distinguishes afterschool programming environments and approaches (in the code.org style) from systematic andappropriate pedagogies for lower primary computing. In my plenary I will present our emerging approachfor transforming so called “basic” computational constructs into thoroughly constructed and iteratively
verified gradation of short units of tasks which the pairs of pupils – and then the entire class – try to
explore and solve, envisage and discuss, compare, share and explain, exploiting the 5Es pedagogicalframework of the ScratchMaths project. I will give reasons for our decision to develop new set ofprogramming environments and I will formulate our key design principles and explain which powerfulideas we want pupils to experience and explore so that they get the opportunity to gradually build deep
understanding of essential computational constructs and practices in appropriate progression.

Keywords
primary programming; programming environments for primary pupils; developmental appropriateness1 Background
Latest national computing education strategies – often installing the beginning of new mandatory subject
into the lowest years of primary stages9 – embrace programming as a key instrument of computational
thinking. Renewed national curricula usually set ambitious requirements for primary computing
education, listing essential computational concepts, procedures and processes to be mastered byprimary pupils (e.g. understand the concept of abstraction, logic, algorithms and data representation…,
use sequence, selection, and repetition in programs;… design, write and debug programs that
accomplish specific goals... etc.). Besides that, new national strategies also emphasise building
productive connections between computing and other subjects, mathematics in particular. Naturally,
new situation increases demand for new learning content and interventions – systematic, complex andconsistent, and new programming tools – inspiring and powerful yet developmentally appropriate for9

in some countries starting at the age of 6 or 7, in some others at 5 or even 4

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Constructionism 2018, Vilnius, Lithuania

pupils and the needs of formal education. For years 5 and 610, Scratch (Maloney et al., 2010) is
considered to be such environment, with functionality and affordances which inspire educators,
researchers and developers to exploit it as a new means for cultivating computational thinking andcontributing to the development of mathematical thinking as well.
This is exactly the trajectory we have recently followed in the UCL IoE ScratchMaths project, however,while our research findings in the ScratchMaths project (see Benton et al, 2016; 2017, 2018a; 2018b;
Kalaš, Benton, 2017) suggest that the intervention we developed might be a viable answer to meet therequirements of national curricula for years 5 and 6, the question remains open how to implement theserequirements in years 1 to 4 (i.e. approximately with the age group of 5 to 10, depending on different
educational systems).

2 Analysis
Indeed, years 1 to 4 of the primary education are a real challenge for researchers and educators in thefield of computing. While there are numerous portals and on-line resources addressing that challenge,
our main concern is that those resources – and the expertise behind them – often originate from afterschool experience, secondary or higher education practise or individualised home “edutainment”,
focusing on isolated flashes of learning, exploring bits and pieces of computer science essentials in
unspecified order, often neglecting complexity of important basic computational constructs, failing mostof advantages of primary learning environment and lacking consistent curriculum design and complex
pedagogical framework.
That is why we have recently focused on lower primary years and set out for studying cognitivedifficulties of computational constructs11 which are traditionally considered essential, easy, and thus
appropriate for that age group. In doing so we are encouraged by the authorities of 70s, 80s and 90s,
referring here e.g. to Papert (1980), Pea (1985) or Perlman (1976) who observed ... children becoming
overwhelmed when introduced to multiple new concepts… through her Tortis Slot Machine system.
Inspired by those observations she started considering the cognitive difficulties behind some aspects ofprogramming (see Morgado et al., 2006, p. 4). More recent research literature – if focused at the lower
primary age group – often studies the potential of programming in the context of teaching mathematics(see e.g. Clements, 2002; or Lewis and Shah, 2012). Those that explicitely study the development ofprogramming and computational thinking in the lower primary age group, like (Futschek and Moschitz,2011), (Touloupaki et al., 2018), (Wilson et al., 2013) or our own earlier research (Moravčík and Kalaš,2012), are still very rare.
Let us note however that it is not possible to study cognitive demand and appropriateness of any
computational constructs without selecting a programming tool or designing and developing one, andin paralel with studying the constructs also study the relevance of that tool for the age group of ourinterest. Therefore we conducted an analysis of existing programming tools and environments12 in which
we decided to identify and exclude from the consideration:
tools which we regard as ‘closed programming quizzes and puzzles’13,

tools which support computational constructs that we a priori consider too complex orinconsistent for the lower primary age group,that is, for pupils of the age group around 9 to 11 – in some countries last years of primary (ISCED 1), in others first years of lower secondary(ISCED 2)
11
As explained in (Kalas, Benton, 2018), we prefer to think about computational constructs rather than computational concepts. In our
understanding, computational constructs comprise computational concepts (like procedural abstraction, variable or iteration) andcomputational procedures associated with exploiting that concept, for example interpreting a sequence of commands, modifying it,transforming it by reducing some of its steps etc.
12
we will report on that aspect of our work in depth elsewhere13
as aptly nicknamed in (Grizioti, Kynigos, 2018)
10

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Constructionism 2018, Vilnius, Lithuania

tools which do not accommodate with our conception of appropriate constructivist pedagogy of
programming14 for the lower primary years, nor allow to serve the whole and systematic learning
process,tools which do not comply with our technical requirements15.

As this selection process excluded all tools presently known to us, we decided to design and develop a
new programming enviroment (in fact, a compound interface with several closely connectedenvironments or microworlds arranged in a gradation) for primary years 3 and 4 (i.e. for the age groupof app. 8 to 10).

3 Method
In summer 2017 we started the development of Emil, a virtual robot-like character, positioning theintervention into the ‘middle‘ of the lower primary years – to win more flexibility for later possible
alterations and fine-tuning. At present (summer 2018) we have completed the development of thecontent and programming tools for year 3. It is designed for about 12 lessons; however, several activities
may easily be expanded, based on the learning goals, erudition and experience of the teacher16. Also,
we have already launched parallel development for all other years of lower primary computing education– addressing pupils aged 5 to 10.
The interventions for years 1, 2 and 3 are being inspired by our previous development of Thomas the
Clown, see Moravčík, Kalaš (2012), enriched with our recent experience from ScratchMaths project,
Emil development and all other valuable experience gained in the recent years. The plan is to develop
and support around 10+ programming lessons per year, supported by three or four new programming
environments (to be designed and developed by our team in the next 18 months). All lessons (andprogramming environments) will acknowledge our understanding of the role of programming as an
instrument for learning by exploring and solving problems. Thus, the whole intervention will offer richconnections (bridges) to other subjects and areas.
In parallel with this we continue the development of Emil for year 4: The plan is to build year 4 (i.e. preScratch) programming intervention of 16 to 20 lessons, where most of the lessons will address multiple
components of the new computing curricula17. In Emil year 4 intervention we plan to create more
opportunities for open constructionist learning by designing one of the environments as open (although
restricted) programming tool.
Emil intervention for year 3 includes:
 three programming applications or microworlds,


gradations of thoroughly designed units of tasks, sometimes with one solution, with no solution
or several ‘possibly good’ solutions – so that important discussions are being ignited to consider
arguments for or against pupils’ strategies. All tasks themselves are open for informed content
developer and thus may be either rebuilt or redesigned18 to fit the specific needs,a workbook for pupils with series of worksheets to use in the lesson and other tasks to solve as
extra activities or as homework,teacher materials of (a) basic and (b) advanced levels (where basic materials are designed forteachers implementing our interventions for the first time),

14for example, with the possibility to first build the need for a computational construct, and then discover it by oneself, thus constructing its
understanding
15
which are multi-platform application, with offline and/or online versions
16
Recently one of our design teachers commented one of the tasks as being rich enough to be extended to a full lesson – of mathematics –by itself. This matches our plans and we expect teachers to make similar transformations by themselves – into mathematics, traffic education,
art and design, technology etc. We also want to stimulate natural and self-motivated extensions to after school activities and explorations athome.
17
speaking from the broad international point of view, see e.g. Webb et al. (2018)18
although we do not expect teachers to develop their own tasks or modify the ones we designed and trialed for them73Constructionism 2018, Vilnius, Lithuania

strategy and content for the professional development, andsome supplement tools and materials to support additional activities.

In the project we apply the design research strategy (as presented e.g. in Nieveen, Folmer, E. 2013),
proceeding in numerous short iterations. We closely collaborate with three design schools and five of
their classes. These schools represent wide range of schools in Slovakia, including a modern big stateschool in a small town, a school belonging to a network of schools governed by a church, and a small
and new private modern constructivist school. Regular visits to the classes inform our development by(a) observations – after several initial months when each lesson was conducted by a member of ourteam we completely handed the teaching process over to the generalist class teachers themselves, (b)talking to pupils (in the lesson) and teachers (after the lesson), and (c) by collecting and analysing pupils’
worksheets – as we use one or two worksheets in every lesson as we want pupils to:(I) record their work, sometimes developing their own notations for that,(II) read the assignments from the paper (sometimes these are given in the screen, sometimes onlyin the worksheets),(III) design and share similar tasks for their classmates etc.
We also use some extra worksheets to be solved by the pupils after the lesson to assess theirachievement. Figure 1 illustrates a task from such worksheet. The scene on the left shows the situation
and a plan (a program) for Emil’s next journey. He will fly over the coins and buttons, on his way
collecting everything into a box (as if he was ‘buying’ buttons). Pupils are to choose one of three possible
outcomes of that journey. Note that solving this problem means reading a plan and interpreting it,
considering dynamically changing situation in the scene: when flying from position marked with 3 to 4
Emil will collect two coins and only two buttons as one of the three in the line will have been collectedalready.
In our visits to schools we pay special attention to the wording of the assignments, as we often observethat pupils are not sure what to do even if we think we managed to post the task in a very simple andstraightforward way.
Pupils are always working in pairs, which for the most of our schools is rather uncommon scheme ofwork. Traditional „lecturing“ is still a frequent form of teaching – even in primary school, therefore
teachers were – at the beginning of our collaboration – often uncertain about the pedagogy, in which
there is no space for lecturing, pupils work in pairs (but in parallel individually fill in their worksheets)
and every now and then are invited to meet on the carpet with the worksheets in their hands to discuss
the tasks (at least once for each unit of tasks). The focus of our teacher materials is on what to discusswith the pupils and when. These common discussions play several important roles in our interventions:pupils learn from each other by explaining and comparing their solutions, and listening to others,pupils discover by themselves how important the worksheets are and the way they haverecorded their solutions as they later use those histories to argue and discuss their results andstrategies19,

by asking the questions and organizing the discussion with demonstrations (usually by the pupils
themselves) using the teacher laptop projected on the screen, teacher has important opportunity
to assess the group and everyone’s achievement.

19

They started writing down the movements (commands) of Emil even before we proceeded from the direct drive style of control tocomputational control (programming). We study different level of control in (Kalaš, Blaho, Moravčík, 2018).

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Constructionism 2018, Vilnius, Lithuania

Figure 1. Emil is going to ‘buy’ buttons again. Check the box to show which shopping Emilwill get if he follows the plan as indicated on the left?

Note that our programming environments do not give any feedback to pupils – a big surprise for themand their teachers at the beginning. Instead, we encourage the pairs – and then the whole group – todiscuss each solution and decide whether they deem their solution correct. Note also that some of thetasks have several solutions, sometimes disputable ‘could be solutions’, sometimes no solution at all –situations which are surprising and very rare in our schools. Our observations prove however that after
one or two lessons this approach is smoothly accepted both by the pupils and the teachers.

4 Findings so far
The strategy of short and frequent iterations in our design research has helped us move from mostly
experience- and intuition-based development to more systematic design and better understanding ofwhat the basics of primary programming comprise. While we agreed on several design principles fromthe beginning of the development for year 3, some others have appeared and crystallised later in theprocess. At present we can briefly formulate our design principles as follows:i.

the intervention must be an opportunity and formative experience for every pupil in the class,clearly illustrating the perception of programming as an instrument for exploring the world,
experiencing powerful ideas20, and controlling an agent, in harmony with what Ackermann (2012)
identified as the most appealing role of programming for very young children,ii.

the learning process must be structured into gradations of units of a small number of tasks (4 to6, as we see it now). The lead principle of this organisation must be a coherent arrangement ofcomputational constructs structured by the increasing cognitive demand (as advocated e.g. by
Perlman, 1976). These steps of increasing complexity must be small and intuitive21,

iii.

the tasks should exploit natural intrinsic motivation of pupils, the tasks and programming tools
must be developmentally appropriate,iv.

the pupils work in pairs, but individually fill in their worksheets,v.

there is no space for lecturing, the whole learning process is implemented by pairs of pupils
collaboratively solving the tasks, then the whole group discussions (explaining, reading,demonstrating, arguing, listening…) about the solutions and problems22 – scaffolded by theteacher,

For the origin of the concept of powerful ideas in learning, see (Papert, 1980) but consider also Kay’s recent definition from the Thinking
about Thinking about Seymour symposium in 2017: An idea is powerful if it changes the context in which we think.
21
not requesting any additional explanations
22
Beside our own observations of this principle in the design schools, we are also supported by literature: Harel and Papert (1990) highlightthe cognitive benefit of generating verbal explanations, which helps to clarify ideas. Noss and Hoyles (1996) assume that the process ofreflection and explicit articulation required to generate these explanations is a key part of the constructionist learning.
20

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Constructionism 2018, Vilnius, Lithuaniavi.

the programming environments give no explicit feedback other than – in the computational
control stage of the work – running the program and showing each step of Emil in the screen,thus displaying the final state of the process23,vii.

the intervention should be delivered by the generalist class teacher, rather than engaging acomputing specialist. Why is this principle so important for us in the primary education? Webelieve that the intervention must be accepted by the teacher and integrated in the wholelearning process of the pupils, making explicit connections and bridges to all other subjects
whenever possible and productive. We find this – at this stage of school – more important thanhaving a formal computing education.

Recent revisions of several national curricula set ambitious requirements for primary computing
education, listing essential constructs and practices to be mastered by primary pupils. Whether thosepractices and constructs are developmentally appropriate for lower primary years, how to interpret themand how to arrange the intervention so that it helps pupils ‘discover’ them by themselves in a true
constructivist way remains the hardest and most crucial question.

abcFigure 2. When pupils directly control Emil in the scene to solve a problem (the task here is: Light allhouses and come to the blooming cherry tree) by clicking or touching the commands, a record of thesteps is automatically collected in the panel above the scene (illustrated by a). However, the panel has
limited number of positions thus the length is a real issue. Later in the succession, identical cards are
cumulated into ‘piles’ (see b above), thus “saving” more positions in the panel. Then pupils start tonotice repeating pairs of cards and start connecting and cumulating them up into piles of ‘double cards’or ‘triple cards’ (see c above).

In our experience, the complexity of so called ‘basic’ computational constructs24 is often trivialised, oreven not recognized nor properly understood. That is the reason why the most important designprinciple for us is to discover and respect their complexity and identify possible gradations of ‘micro
steps’ leading to their deep and sustainable understanding. In Emil year 3 intervention we mostly focus
on three such constructs, namely sequence (or more broadly, order), pre-constructs25 of repetition andpre-constructs of abstraction. Figure 2 illustrates (in a simplistic way) some of the steps leading todiscovering the construct of simple repetition (note however that the process of full understanding andadopting of this construct will continue into higher years of primary and lower secondary education).
Beside the design principles we are also constantly trying to clarify and reformulate the powerful ideas
of computing which pupils are experiencing while solving the tasks. Towards the end of the first year of
our iterative design and assessment of the environments in the design schools we identify the key
powerful ideas of the Emil year 3 intervention as follows:

23

explicit feedback, however, would mean that the application itself evaluated the final state and communicated to the user whether the solution
was correct or not
24
i.e. concepts and related procedures
25
by pre-concepts or pre-constructs, we mean thoroughly identified simple ideas or an ‘atomic’ operation which in small steps cumulatively
and gradually lead to deep understanding of the concept or the procedure connected with that concept76Constructionism 2018, Vilnius, Lithuania

Experiencing order
Pupils start discovering the order in things by collecting the items from the scene either into a box (i.e.
a container with no internal order) or in a shelf (i.e. a container with explicit order from left to right) – byindirectly manipulating26 Emil. Later, when the level of control gets to higher levels, every command isrecorded in the panel above the scene27. Thus, pupils start perceiving and discussing the order in steps(of a process) as well. The other form of order present in many tasks is the inherent order of the items
themselves, as with numbers or letters. Throughout the whole year 3 intervention we then cultivate thesense of order by recognizing, building, reading and discussing different patterns in data and in
programs, i.e. in external records of steps or in external plans for future steps.

Coping with constraints
Pupils encounter several kinds of constraints, static and dynamic, implicit and explicit, such as (a)constraints in driving Emil – where to click, which command can be applied, how many clicks orcommands can be run etc.; (b) constraints in the stage where objects serve as static or dynamic
obstacles28 (collect all coins but nothing else…) or later in the succession when one position cannot beclicked twice; (c) constraints in the box (collect only …, how many buttons will Emil get if he runs a given
program etc.); (d) constraints in the shelf where some positions have predefined content, or when theorder of items to be collected is bound by a rule (collect some ‘good’ words…, collect a repeating patternof apples and pears…); or (e) constraints in the panel with program when pupils are limited by a
restricted number of steps (or piles of steps as illustrated in Figure 2).

Learning to control
In (Kalas, 2016) we referred to three levels of control in educational primary programming: direct
manipulation, direct control and computational control (i.e. when the steps to be taken by an actor are
all planned in advance), in a way related to increasing “distance” between an actor to be controlled anda pupil to control it. In our current design research, however, we have identified one more level – indirect
manipulation, sitting between direct manipulation and direct control in the hierarchy. Pupils exploit thislevel of control when they cannot drag or click Emil but can click somewhere else in the scene to makehim move there – if such move is possible.
Besides, we have also realized that the levels of control must be studied in relation with the way therecord of these steps or the plan of the steps is represented, see Figure 3. This two-dimensional
categorisation of control provides more complex instrument for designing and analysing tasks andprogressions of pupils. We discuss these aspects of control in more detail in (Kalas, Blaho, Moravčík,
2018).

Learning to think with program, learning to think about program
Programs – first as records of the steps being taken, later also as plans for future steps (future
behaviours) – become the objects to think with and think about in the course of Emil year 3 intervention.
Pupils learn to read them, analyse and compare, modify, match and simplify, they learn to discuss andthink about their properties and structure – in activities of Emil, in common discussions or in theunplugged tasks in the workbook. Our goal is to build pupils’ (and teachers’) perception of a programas an object which represents certain process and is an interesting object to work with, an instrument
to explore a problem, experience and share an idea, or create a product.

Learning to create and abstract
In Emil the Artist, the last of the three environments in year 3 intervention, pupils create different visual
patterns, repeating textile motifs or other regular geometric structures. It serves as an opportunity forthem to build several elementary compositions, then abstract from the details and plan massive

26

the reader will find the explanation of this category of control later in this paper
see a, b and c in Figure 2
28
Think of pupils interpreting (i.e. reading and envisaging) Emil’s move from mark 3 to 4 in Figurer 1: will the button in the middle column be
still there?
27

77

Constructionism 2018, Vilnius, Lithuania

repetitions and combinations of those bits. We consider this to be a workshop for preliminary steps thatwill later lead to the construct of user defined procedures and infinite and conditional iteration.

Figure 3. The task on the left: For sleeping Emil plan a journey to collect all 0’s (and no 1’s) in no morethan six clicks. Then wake Emil up to run your plan. (In the picture the solution – a plan – is alreadyshown as well.) When this task is analysed within our two-dimensional categorisation of control wearrive at computational control where the program is represented as an internal plan (i.e. inside thescene, not externally as a sequence of explicit commands).

5 Discussion and conclusion
Increased interest in primary programming creates enormous need for better understanding primary
programming as a complex phenomenon comprised of properly selected educational objectives,computational constructs (or sometimes pre-constructs) and practices, programming environment(s),and systematic learning content with properly developed pedagogies – suitably delivered to primaryteachers. All of these must be studied in parallel as they are interconnected in multifold ways.
As we decided in our project to contribute to deeper understanding of the complexity of elementarycomputational constructs at the level of lower primary, we need to be in possession of an adequate
programming tool. As we failed to identify such tool we set out for designing and developing one. This
demanding but productive constellation is giving us unique opportunity to get deeper in studying thecomputational constructs by designing and developing new programming environments and, in parallel,
design and implement appropriate programming environments by studying the learning processes ofpupils when building their understanding of the elementary computational constructs, exemplary
citcumstances for adopting design research strategy. We believe that this situation provides us alsowith valuable opportunity to design modern computing programme of study for lower primary educationand thus support systematic and sustainable learning processes in primary programming29 – somethingrather new and much needed in the primary school.
Our design and development is continuously informed by frequent small assessment probes composedof a selection of tasks. These are the tasks included in the learning gradation, but also some additional
assessment tasks we prepare for teachers and pupils. The assessment task of Figure 1, for example,
contains a program represented as an internal plan of moves (i.e. moves indicated inside the scene).
Pupils were asked (working individually this time) to identify the box of buttons and coins which will
result from Emil running given program. In the class of 21, four pupils wrongly marked the third
alternative, probably failing to notice that the button in the second row, third column will have alreadybeen collected when Emil later moves from mark 3 to 4. All other 17 pupils correctly identified the firstalternative as the correct result. Such small probes – regularly inserted into our design and assessment

29

inspired e.g. by complexity and consistency of the mathematics programmes of study

78

Constructionism 2018, Vilnius, Lithuania

iterations – help us observe and understand the progression of each pupil in the group and about thedevelopmental appropriateness of the units of tasks as well.
Naturally, this is the point where deeper research must start. We believe that programming
environments which are already resulting from the project, will provide interesting instrument for a followup systematic research of the complexity of primary programming.

Acknowledgements
First, I would like to thank other core members of our Emil project team, Andrej Blaho and Milan
Moravčík. I am also grateful to Indicia, npo. for funding this work. Finally, I want to thank the pupils andteachers from our Slovak design schools for their dedication, hard work, enthusiasm for programmingwith Emil and continued engagement with our team throughout the project.

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Constructionism, 2016. Suksapattana Foundation, Bangkok, pp. 26-33
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Some Findings of Design Research in England. Digital Experiences in Mathematics Education, August
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Benton, L., Saunders, P., Kalas, I., Hoyles, C., Noss, R. (2018a) Designing for learning mathematics
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Blackwell, A. F. (2002) What is Programming? In 14th Workshop of the Psychology of Programming
Interest Group, pp. 204-218
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Futschek, G., Moschitz, J. (2011) Learning Algorithmic Thinking with Tangible Objects Eases Transition
to Computer Programming. In: Kalaš I., Mittermeir R.T. (eds) Informatics in Schools. Contributing to
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Lewis, C. M., Shah, N. (2012) Building upon and enriching grade four mathematics standards with
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Children and Microcomputers: Research on the Newest Medium. Sage, pp. 193-212.
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(eds.): De 0 a 1 ou l’informatique a l’ecole. Actes du colloque Didapro 7 – DidaSTIC, Lausanne 2018.
Peter Lang, Bern, pp. 303-323
Webb, M. E., Bell, T., Davis, N., Katz, Y. J., Fluck, A., Sysło, M. M., Kalaš, I., Cox, M., Angeli, C., MalynSmith, J., Brinda, T., Micheuz, P., Brodnik, A. (2018). Tensions in specifying computing curricula for K12: Towards a principled approach for objectives. IT – Information Technology, Vol 60 (2), pp. 59-68,
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Wilson, A., Hainey, T., Connoly, T.M. (2013) Using Scratch with Primary School Children: An Evaluationof Games Constructed to Gauge Understanding of Programming Concepts. Int J of Game-Based
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In Support of Integrated Approaches toConstructionist Designs and Interventions: The
Case of ChoiCo and MaLT
Chronis Kynigos, Kynigos@ppp.uoa.gr
National and Kapodistrian University of Athens, Educational Technology Lab School of Philosophy,
P.P.P. Dept, Greece

Abstract
Constructionism is now a 50 year-old theory of learning, a theory of educational design and a frameworkfor pedagogical action. In this time, society, educational challenges and the abundance of digital media
have brought a diversity of frames, focal points, viewpoints and interventions. In many places siloconstructionist perspectives are seen as obsolete or at best ultimate frames for meaning-making
through individual and social bricolage. In my talk I will argue for perspectives integrating
constructionism in a wider landscape of educational paradigms, theories, affordances and intervention
strategies. I will do this by showing what students and teachers have built with two web-based
constructionist expressive media, one very different to the other, MaLT – turtle sphere and 'Choiceswith consequences' games (ChoiCo). In my context these are proving to be powerful means for a
proximal approach to infusing constructionist perspectives in wide scale initiatives.

MALT2 - programming, 3D, Dynamic Manipulation, http://etl.ppp.uoa.gr/malt2. ChoiCo: GIS, Programming, BD,
http://etl.ppp.uoa.gr/choico

MALT is a Logo programmable 3D simulator including Turtle Graphics and most importantly dynamic
manipulation of variable values with DGS - like effects on the graphical output from variable procedures.
ChoiCo is a tool for game modding, supporting a socio-scientific paradigm of diverse consequences
games involving complex issues like dietary choices or environmental issues and embedding powerfulideas in various ways. The examples will show how avenues for powerful ideas uniquely made available
and embedded in multifaceted issues present exciting challenges for design.

Keywords
Integrations; affordances; educational paradigms; programming81

Constructionism 2018, Vilnius, Lithuania

Bones, Gears and Witchcraft
Jens Mönig, jens@moenig.org
Principle Investigator SAP, Germany

Abstract
Who doesn't like a good story? I never get tired playing with stories kids animate with Scratch or Snap!
To me, storytelling is at the heart of the current digital literacy movement. But it's not just about kids.
I've been surprised by the culture of storytelling nurtured in big industrial companies. Designing the rightstory can be crucial for a project, a program or a promotion. The opposite is also true: The wrong story
has the ability to compromise funding and even thwart a career. And then there are stories that convince
for all the wrong reasons, and success that feels like defeat.
I will share a few of my stories for children, corporate management and government officials. Amongthem, how Katharina Kepler's witchcraft trial has been a turning point for computing, and how machinelearning can be used to illustrate a business proposal, before examining a particular terrible instance ofconstructionism gone wrong in the German state of Baden-Württemberg. Expecting this to spark some
controversy I will close by opening up a discussion with the audience about favorite stories, inviting
examples of "good" and " bad" specimens..

Keywords
Scratch; Snap!; ldigital literacy82Constructionism 2018, Vilnius, Lithuania

Constructionist Experiences for Mathematics
Across Educational Levels
Ana Isabel Sacristán, asacrist@cinvestav.mx
Centre for Research and Advanced Studies, Cinvestav, Mexico

Abstract
Constructionism, in the research literature, is seen predominately in contexts where it is used as aparadigm for promoting learning at K-12 levels. However, it can apply at all levels, as well as connectlevels. In this paper I deal with the issue of classroom implementations for mathematical learning of
constructionist experiences at different educational levels, but with particular focus on higher education
implementations. I begin by revisiting some of the fundamental principles of constructionism. Then, I
look briefly at how constructionism can provide early access to powerful ideas; that is, where younger
learners can access mathematical ideas perceived as more advanced. For that, I mention my work in
the design of two different microworlds that enabled young students to explore and engage withmathematical infinity-related ideas.
I then share some of my experiences in several attempts of constructionist implementations andmicroworlds for mathematical learning at university level. I present two examples that involved building
and exploring computer models and simulations of real phenomena: the first in a distance-learning
course conducted as a virtual mathematics laboratory; the second, of videogame construction byengineering students (see figure below). A third example is of computer programming R-based tasks
for the learning of statistics in environmental science students. I finish by presenting a fourth example
from a university in Canada where mathematics university students are required to program digitalmathematical exploratory objects or microworlds. In all the examples presented, students engage inconstructing models or programs, and in doing so, engage in doing mathematics.

A students’ videogame (left), with his table of physical characteristics of the objects involved (top-right) that hewould need to program into the game engine (bottom-right).

Keywords
Constructionism; computer programming and expressive media; collaborative learning; modelling;higher education.

Introduction
Since the 1980s, there have been many projects attempting to implement the constructionist paradigm
in schools, in order to enhance mathematical learning. Most of the first ones were based on Logo
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Constructionism 2018, Vilnius, Lithuania

programming, and were at primary or middle-school levels. There have been, however, fewer
constructionist implementations for mathematical learning at university level reported in the literature,
relative to those reported at the K-12 levels, despite: several excellent proposals for college of quiteadvanced mathematical discovery ideas with Logo –e.g. as in Abelson & diSessa’s (1986), Turtle
Geometry–; some of the work of members of the Center for Connected Learning and Computer-Based
Modeling (http://ccl.northwestern.edu), particularly related to concepts such as proof or probability (e.g.
Wilensky, 1993 & 1995) or for STEM learning using NetLogo; and some advocates for rethinking
university education through technology-enhanced learning and collaboration (e.g. Laurillard, 2002).
Why the lack of constructionist implementations in higher education, is beyond the scope of this paper,
although it is clear that implementing constructionist exploratory learning environments in school
cultures has been problematic and complex, as has been discussed elsewhere (e.g. by Laurillard,2002).
In this paper, I would like to present some examples of constructionist experiences and implementations
in which I have been involved. The first examples are meant to illustrate how constructionism can help
bridge educational levels, by giving younger learners access to powerful and advanced mathematicalideas. In the second part, I focus on examples of constructionist implementations for mathematicallearning in higher education.
But first, I would like to begin by revisiting some of the main ideas of constructionism.

Some fundamental ideas of constructionism, and the value of computer-based
expressive activities
The fundamental premise of the constructionist paradigm, as stated by Papert and Harel (1991), is thatit shares “constructivism's connotation of learning as ‘building knowledge structures’ […] then [adds] theidea that this happens especially felicitously in a context where the learner is consciously engaged inconstructing a public entity” (p. 1), that is, something shareable.
In Papert’s (1980) vision, one particularly valuable means of achieving the above is in programming thecomputer because, in doing that, the student “establishes an intimate contact with some of the deepest
ideas from science, from mathematics, and from the art of intellectual model building” (p. 5); and “inteaching the computer how to think, [students] embark on an exploration about how they themselves
think” (p.19). Computer programming also involves debugging and, as Papert (1980) explained, errors
are of benefit because they lead to the need to understand what went wrong, and through that
understanding, to fix them.
Papert’s theories of computer programming as a way for learners to engage in “Mathland”, wasconfirmed in a relatively recent study by Rich, Bly and Leatham (2014) which showed that programmingand solving programming problems, can: provide a context for many abstract concepts; illustrate thedistinction between understanding the application of mathematics in a specific situation, and theexecution of a procedure; help divide complex problems into more manageable tasks; provide
motivation and eliminate apprehension; and give context, application, structure and motivation for the
study of mathematics. In fact, programming can be an engaging problem-solving activity where students
can explore mathematics in different representations and generate and articulate mathematical
relationships. Nevertheless, as has been learned, these programming activities need to be carefully
designed and structured within a learning environment.
In terms of learning environments, Papert (1980) first proposed that with the versatility of computers,
one could create microworlds: “self-contained world[s], […] each with its own set of assumptions andconstraints”, where learners “get to know what it is like to explore the properties of a chosen microworld
undisturbed by extraneous questions [… and] learn to transfer habits of exploration from their personal
lives to the formal domain of scientific theory construction” (p.117). He called these incubators forknowledge and powerful ideas; “places” where these ideas (including certain kinds of mathematicalthinking) can easily hatch and grow. Moreover, Papert (1980) placed emphasis on the entire learning
culture (a different kind of culture) conceiving educators as support for learners to build their own
intellectual structures; creating conditions for construction and invention (rather than providing readymade knowledge), giving students objects-to-think and materials drawn from the surrounding culture,84

Constructionism 2018, Vilnius, Lithuania

including “emotionally supportive working conditions [that] encourage them to keep going despitemathematical reticence” (p. 197), with students having creative and personally defined end-productsthat they can genuinely be excited about. Hoyles and Noss (1987) considered that microworlds had to
be designed taking into account the characteristics of the specific learners; having a careful pedagogicaldesign with materials (e.g. worksheets) and appropriate teacher interventions; and a social environment
fostering collaboration and where products can be shared and discussed in small and whole groups.
A way to summarise a constructionist learning environment (where the italicised words describe mostof the main elements of constructionism) could be as: student-centred learning situations wherestudents consciously engage in constructing (e.g., program) shareable, tangible objects, throughcreative personally meaningful projects (e.g. computer-based) where they have objects-to-think-with,access/develop powerful ideas, and have opportunities for explorations, and thinking about their own
thinking (analysing) through debugging (fixing).
I consider that constructionist implementations ideally should have the following characteristics:

There has to be a medium (e.g. digital tools) for an exploratory and expressive activity (such as
computer programming/coding, or building/describing models or structures using an expressive
medium or software).
Students need to be actively involved and mostly in charge of their constructions and explorations.
Activities should take place within a structured microworld and collaborative learning environment
with the characteristics described above.

In the following sections, I present examples of projects in which I have been involved, that have fulfilled
those characteristics.

Constructionism across educational levels: Early access topowerful ideas
Although my main focus, in this paper, is on tertiary educational level constructionist experiences, in thetitle of the paper I refer to experiences across educational levels because an important aspect of theconstructionist philosophy is that the experiences and learning can transcend pre-established(institutional) and assumed educational hierarchies and categories, by enabling learners to accessand/or develop powerful ideas. So I would like, therefore, to delve briefly into the idea of how learners
can have early access to powerful ideas.

Connectedness and webbing for enhancing mathematical meaning-making
There are many examples of how, through constructionist approaches, advanced mathematical andscientific ideas can be made accessible to younger learners. As described above, Papert talked in termsof microworlds as incubators for powerful ideas. For example, many of the mathematical ideas
presented in Abelson and diSessa’s (1986) Turtle Geometry book, although some are very advanced,
are powerful ideas that become more accessible through their Logo-based explorations.
Wilensky (1993) talked of “connected mathematics” where mathematical meanings are enriched byconnecting ideas and representations; thus mathematical knowledge becomes more concrete (i.e.
accessible) through the built relationships. His ideas were precursors to what Noss and Hoyles (1996)
later called “webbing” where, from webs of familiar connections, further connections are built outwards
along lines of one’s own interests, with “the presence of a structure that learners can draw upon andreconstruct for support – in ways that they choose as appropriate for their struggle to construct meaning
for some mathematics” (p.108). It is through that scaffolding of microworlds or constructionist digital
environments, that learners can more readily build connections and access powerful and advanced
ideas. It is worth noting that Wilensky later expanded the “connected mathematics” to “connected
learning”, in order to include all disciplines and, as I understand it, more forms of connectedness, suchas connecting people in collaborative learning and through collaborative and networking technologies.
In my past work, I have been involved in several projects where constructionism was used for early
access to powerful ideas. Some were related to trigonometry and called the Painless Trigonometry85Constructionism 2018, Vilnius, Lithuania

Projects, where students explored and used trigonometry in personally meaningful year-long school
projects, in grades below those where most of the trigonometric ideas they used are first formally
introduced (see Sacristán & Jiménez-Molotla (2012). Others projects, which I present next, related tomathematical infinity.

Programming-based microworlds for creating meanings of the infinite
The examples that I present here involve a couple of microworlds designed to make mathematicalinfinity-related ideas accessible to younger students. The first example is a Logo-based microworld (seeSacristán & Noss, 2008), that enabled students to explore and develop concepts of infinite processes
and objects. In this microworld, students of different ages (some as young as 14), by programming in
Logo, constructed and investigated graphical models of infinite sequences of the type {1/k n} (such as
spirals and bar-graphs), as well as fractal figures (such as the Koch curve), conceived as "limit-objects"
of infinite graphical sequences. Students gave meaning to the processes under study by coordinating
the visual and numeric outputs with the symbolic code contained in the procedures that they themselves
had written. In this way, the microworld supported students in the coordination of hitherto unconnected
or conflicting intuitions concerning infinity, based on a constructive articulation of the different
representations.
Another example was the design and implementation of computer programming activities using theinnovative ToonTalk infrastructure (http://www.toontalk.com), aimed at introducing young students (9–13 years old) to the idea of infinity, and in particular, to the cardinality of infinite sets (see Kahn, Sendova,
Sacristán & Noss, 2011). Via carefully designed computational explorations within an appropriately
constructed medium, students in several European countries built computational models and programs(for example, through programming robots in the ToonTalk animated world – see 0), which they sharedand discussed through distance communication (through web reports): students in each country
challenged the constructions of the others, thus fostering deep mathematical inquiry and introducing thereal spirit of mathematics to school classrooms. In this way infinity was approached in a learnable way
without sacrificing the rigour necessary for mathematical understanding of the concept.

Figure 1. Part of a student’s web report showing ToonTalk robots generating the set of integer numbers

Constructionist implementations for mathematics in higher
education
In terms of higher education, I have been involved in several constructionist projects where university
students engage in computer programming and/or expressive activities for mathematical exploration orlearning. I present some of these below. These follow the constructionist principles outlined in the firstpart of this paper, including providing students with objects-to-think and materials from the real world or
surrounding culture; as well as having students building shareable objects, discussing and collaborating.
In that sense, I follow Laurillard (2002) who advocates for constructionist and collaborative technologybased learning environments in higher education and considers that “the aim of university teaching is
to make student learning possible […] not simply impart decontextualised knowledge, but must emulate
the success of everyday learning by situating knowledge in real-world activity” (p. 42) helping students
reflect on their experience of the world and ways of representing it. Thus, in all the projects presented
below, students were or are involved with topics and data related to real-life phenomena and that canbe meaningful for their area of study.

86Constructionism 2018, Vilnius, Lithuania

A distance-learning virtual mathematics laboratory
A first project (see Olivera, Sacristán & Pretelín-Ricárdez, 2013) involved a distance-learning
environment (a virtual laboratory) where university students (mostly adults pursuing continuing
education) engaged in exploratory modelling activities of various types of real-life mathematicalproblems (e.g. related to linear motion; gravity and free-fall; population growth; cryptography; andinvolving statistical analysis). This project used Lesh’s et al. (2000) idea of model-eliciting activities(MEAs), where tasks centre on building models, cycling through models and sharing these. MEAs sharesome of the conceptions of constructionism in that “the products that students produce […] involve
sharable, manipulatable, modifiable, and reusable conceptual tools (e.g., models) for constructing,describing, explaining, manipulating, predicting, or controlling mathematically significant systems”(Lesh and Doerr, 2003, p. 3).
In our virtual laboratory, the tasks required exploring the proposed situations; building models throughcollaboration; sharing, discussing and reflecting; and proposing new explorations. Some of the mostinteresting activities involved the analysis of videos (which led to extensive discussions on determining
the scale of the videos), developing models to reproduce the behaviours and phenomena shown on thevideo, and analysing and discussing which proposed models best fit the real data (see 0 further below).
The explorations, modelling and collaborations were carried out through various digital means andinteractive tools for learning through exploration (following the definition of a virtual laboratory proposedby Jeschke, Richter & Seiler, 2005). The various digital tools included different materials (e.g. real-life
videos) and complementary expressive software, such as: frame-by-frame video analysis software; avirtualruler
foron-screenmeasurements(JR
ScreenRuler,
http://www.spadixbd.com/freetools/jruler.htm); tools for finding mathematical equations to fit the data,such as spreadsheets or CurveExpert (http://www.curveexpert.net); and modelling software (Modellus,
http://modellus.pt) for building mathematical models and comparing them to the real data. Since
students were at a distance, they needed to collaborate and share their conjectures and findings online;
for this, we mainly used a web-based discussion forum. The online exchanges constituted additional
means for learning, since they forced students to express their ideas, constructions and conjectures asclearly as possible to others in written form (aided by screen captures and even photos of theirhandwritten work), thus helping them clarify their own understandings (while at the same time acting as
windows, for teachers and researchers, into their meaning-making, as described by Noss & Hoyles,
1996).
In most activities, as explained above, students collectively worked on a problem (or part of it), and later
proposed new problems. For example, one activity involved analysing and modelling free-falling
dropping objects on Earth; after the initial explorations, some students proposed analysing the gravity
on the Moon by analysing a NASA video of an astronaut jumping on the Moon (0). Because that video
is not of a free-falling object, it generated discussions on what kind of movement it is (with students
concluding it is a type of parabolic shoot with a nearly vertical angle). They then proposed mathematicalequations that they implemented in Modellus and compared the resulting model to their real data (0).
The construction of models and simulations helped students identify and discern the importantmathematical elements in the situation under study and that help model it. Furthermore, as describedabove, the construction of meanings was also supported by the social structure created by the online
community.

87Constructionism 2018, Vilnius, Lithuania

Figure 2. A student proposes, in the online forum, to analyse the gravity on the Moon through the video of anastronaut jumping on the Moon.

Figure 3. The student shares her comparison of the real data video (left), with the model she constructed using
Modellus (right), where the astronaut is represented by the green dot (centre).

Videogame construction by engineering students
Another higher educational constructionist project in which I was involved in recent years, was one
where senior university engineering students created videogames (see https://tesisapretelinr.blogspot.mx/) which required using and adapting physico-mathematical models. This was a
project inspired by the work of Kafai (1995). The programming of videogames is a motivating activity
that engages students in producing working models of certain real-life behaviours but in a context thatis meaningful to them. Some of the videogame topics involved: physical phenomena (e.g. modellingand simulating water behaviour; see Pretelín-Ricárdez & Sacristán, 2015; or bouncing objects, e.g.
balls, such as in basketball or tennis games – see 0 and 0); virtual robots navigating mazes (which
required using and designing digital systems, i.e. combinational logic circuits; see Sacristán & PretelínRicárdez, 2017); or simulated mechanical systems (e.g. robotic arms). One of the aims of this project
was for students to develop know-how for their future profession as engineers on how to apply
mathematical knowledge and modelling.
The videogame constructions (using GameMaker Studio – http://www.yoyogames.com) were structured
through sequences of model-building tasks that involved several stages combining or alternating paperand-pencil work; individual and/or collaborative programming work; and whole class discussions. As in
the project described above, this project used Lesh et al.’s (2000) Model-Eliciting Activities (MEAs)
theory; in particular, the design of the model-building tasks took into consideration the six principles of
MEAs: reality, model construction, model documentation, self evaluation, model generalisation, andsimple prototype.
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Having to build models of phenomena and then program these models into the GameMaker videogame
engine, helped students gain a deep understanding of all the elements involved in each model (e.g. see
the Figure in the abstract; and 0 and 0, which show some of the elements that needed to be programmed
into each videogame depicted in the figures). For example, for the videogames involving waterbehaviour (Pretelín-Ricárdez & Sacristán, 2015), students needed to produce first a mathematicalmodel for that behaviour: they usually came up with complex models of fluid mechanics, addressing thewater model either as a molecular model or as a continuous model. They then realised that these models
could not be programmed as such into the videogame engine, so they were forced to analyse anddiscern the most important elements present, in order to produce, and program, simplified models into
the videogame engine. They did this through collaboration and discussion.

Figure 4. A couple of students’ Tennis videogame (left). In order to program the ball’s
movement (see 0) the students’ analysed, and mathematically depicted its behaviour, such as its up-and-down
movement when hit from the right, through a diagram (right).

Figure 5. Script of the tennis ball’s up-and-down movement when hit from the right.

89Constructionism 2018, Vilnius, Lithuania

As recognised by the students, the videogame construction activities provided an opportunity to apply
their theoretical knowledge in real-life projects and for experiencing how such real-life projects could be
carried out. In this way, students gained insights and expertise on how to apply their knowledge in
different realistic projects of contexts related to their engineering profession.
Another important result, is that students appropriated the proposed videogame construction tasks as
their own, turning them into meaningful personal projects, which motivated them highly. This wasevidenced by the deep commitment that they manifested in the way they integrated the physics andmathematical models with game-playing mechanics and aesthetic aspects (such sprites, backgrounds
and sounds), thus turning the games unto attractive ones (e.g. 0).

R-based tasks for the learning of statistics by environmental sciences students
In another project (see Mascaró, Sacristán & Rufino, 2014, 2016), directly inspired by Logo
programming microworlds, we have designed sequences of constructionist and collaborative, computerprogramming tasks in the R programming language (see the R Project for Statistical Computing –http://www.r-project.org) for the learning of probability, statistics and experimental analysis concepts.
These tasks are the core content of courses for college and graduate environmental sciences andbiology students –who tend to have strong aversions to mathematics and statistics. The aim is forstudents to develop statistical reasoning, rather than applying blindly statistical tests; build statisticalmodels for research; apply and understand statistical computing software (in this case, R) to carry out
calculations in experiments; and learn how to interpret the results given by the software. All the tasks
are presented through R-code “worksheets” with instructions, guidelines, examples (using data adaptedfrom real research situations), programming tasks, questions for reflection and comments (see 0).

Figure 6. Part of an R-based task, including on the left-hand side some worksheet questions, with typed
commands for generating graphs (i.e. the histograms presented at the right).

The understanding of statistical models is facilitated by creating objects in R, to represent them (e.g.
graphs, lists of data, statistical values, etc.). Tasks are carried out through collaborative work leading toreflective interactions, explanations and evaluations. By typing R-commands, students draw andinterpret graphs (i.e. visualise models) relating numerical data to graphical representations, as well as
to mathematical formulae. They need to predict what a change in the programming code would produce.
In this way, students go back and forth in the analysis of the data, and suggest changes for obtaining
different representations. In this way, they can develop deeper understandings of how statistical
functions and graphical representations can help create richer meanings for the data.
Over the course of eight years of design research and iterative processes, we have refined the R-basedtasks, and implemented these in over a dozen courses at university and post-graduate levels, More90

Constructionism 2018, Vilnius, Lithuania

recently (Mascaró & Sacristán, 2018, in these conference proceedings), we have also developed
constructionist-compatible ways to assess students’ learning, in order to grade them for the university
courses in which they are enrolled. Through both course observations and assessments, we haveobtained encouraging results, particularly in the affective dimension (see Mascaró, Sacristán & Rufino,2016): many students lose their fear of statistics, with most of them actively engaging in the activities;
furthermore, several students have appropriated themselves of the software (e.g. building their own R
scripts) for their own research with an apparent clearer understanding of statistical concepts.

Brock University’s MICA program
A final example of a higher education constructionist implementations that is worth mentioning, is the
MICA (Mathematics Integrated with Computers and Applications) program at Brock University in
Canada (Muller, Buteau, & Sacristán, 2015; Buteau, Sacristán & Muller, 2018). That program waspromoted by Eric Muller, who was a participant and author of the very first ICMI study held in 1985,
researching the influence of computers and informatics on mathematics and its teaching (see Cornu &Ralston, 1992). The MICA program stands out as a complete curricular implementation that has beenfunctioning since 2001 –rather than being just a limited-scope project– and that integrates computer
programming activities in the pure and applied mathematics syllabi: In the MICA program, first- andsecond-year university students, in addition to traditional mathematics courses, have non-traditionalcourses where they engage in programming their own interactive mathematical digital environments or
microworlds, also called Exploratory Objects (or EOs). (The roles of the MICA instructors are also nontraditional, as well as demanding, as discussed in Buteau, Sacristán and Muller, 2018; in these
conference proceedings.) Through the construction of their EOs, students explore their own statedmathematical conjectures, theorems, or real-world situations, and need to engage in a combination ofmodelling, simulation, optimization and visualization of the mathematical ideas they explore andprogram. As such, they learn mathematics by doing and creating mathematics. The premise of the
program is that students’ abilities and potential to do mathematics are both enhanced as they program
their own interactive computer environments (Muller, Buteau, & Sacristán, 2015). Although I was not
involved in its design nor implementation, I have been fortunate to collaborate for the past 4 years with
Muller and some of his colleagues at Brock University in researching the impact of that program on
students’ learning and appropriation of computational thinking and programming.

Concluding remarks
In this paper I have summarised what I consider the main characteristics of constructionism. I thenpresented examples of either microworlds that helped bridge educational levels by giving young learners
access to advanced mathematics; or examples of an area that is scarce in the research literature: thatof constructionist implementations for mathematical learning, in higher education.
Some of the higher education projects were experimental, while others are programs implemented inreal courses: The virtual laboratory, although implemented in experimental courses during several
years, could not be continued due to the differences with the established curricula (confirming thecomplexity of implementing constructionist approaches in institutional settings). It is an example of how
implementing such projects in a sustained and non-experimental way, is challenging. In contrast, thevideogame project was so successful that it is now being considered as a regular course in theengineering program where it was first given as an experimental course. The statistics program has
been integrated into regular courses for several years, with much success, even though, in thosecourses, students are initially more guided than in other constructionist implementations. And the MICA
program has been held and implemented successfully for over 15 years.
In any case, the latter examples illustrate the possibilities for integrating constructionism in higher
education. All of those projects meet the constructionism characteristics that I outlined at the beginningof the paper: at the core of each project are tasks that give students a central active role for explorationand construction, where they have to engage in some type of expressive activity (programming and/or
modelling) using technology; and most also involve collaborative work and group discussions, where
products are shared and analysed. I consider the latter social aspects to be fundamental for reflecting
on the knowledge put into practice, and generating more stable meanings for that knowledge.
91Constructionism 2018, Vilnius, Lithuania

Acknowledgements
I would like to thank and acknowledge the work of all my co-authors in the projects described in thispaper. I also acknowledge the financing from DGAPA-UNAM with the PAPIME PE204614 andPE207416
grantsfordeveloping
the
tasks
in
the
statistics
project.
I note, also, that parts of this paper were previously published in Sacristán (2017).

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Back 100 000(2)Evgenia Sendova, jenny.sendova@gmail.com
Institute of Mathematics and Informatics at the Bulgarian Academy of Sciences, Bulgaria

Abstract
I am sharing memories of the first conference Children in Information Age, held in Varna (Bulgaria) 32
years ago – both personal as well as extracted from the proceedings and two documentaries featuring
the vision of eminent scientists and educators on computers in education from different continents.
Based on discussions with researchers, teachers, parents, representatives of industry, discussed herein
are their questions and visions in today’s context. Below are some of the questions posed then, still
relevant today:
In what way should the students be trained as to the future problems of rational use of computers? How
can we make the best use of the computer in the future?







Shall we use computers to make the educational process more technical or more human?
What does “computer literacy” mean in fact? How should we define literacy?
Which statements made today would be obsolete in a few years, if not months?
Should the computer be seen as an attraction in itself holding a child indoors with its artificial
simulations, however realistic they might be?
What are the new roles of the educators?
What might we really like to see?

After considering the views of the pioneers of the information age I am sharing some concerns basedon a recent experience in observing uses of technology dictated mainly by the market models in the
educational institutions.
The views of representatives of various stakeholders of education are presented as shared in a recent
panel discussion on the future education we would like to see happen.
After considering some finding and recommendation published in a document of the European
Parliament on the teaching and learning in the Digital era I conclude with an optimistic perspective
based on the work with teachers and students in the frames of national and European educational
projects in which I have been involved.

Keywords
teaching and learning in the information age; digital era

Introduction – How close is 32 years ago?
This paper reflects some of the ups and downs of my professional life, the length of which as far as myinvolvement in education is concerned, coincides with the number in the title.
In 2017 we celebrated 32 years of the first issue of Children in Information Age – a series of 3international conferences held in Bulgaria (Varna – 1985, Sofia, 1987 and 1989) dealing with the
problems of introducing computers in education. (The number 32 is more impressive in binary system,
hence the title of the paper.) The subtitle of the Varna conference was Tomorrow’s Problems Today.
The foremost objective of the Conference was to enable an exchange of opinions and concrete resultsfrom research and applied work of scientists and experts in using computers in the education of children
at school, in extracurricular activities and at home. In order to outline a more complete picture of the
problems to be faced by the teachers, psychologists, programmers and hardware designers, as well as
by the strategists of educational policies, the organizers invited both supporters and specialists withreservations about the massive introduction of computers from the earliest years of education (Sendov,Stanchev, 1986, v).
94Constructionism 2018, Vilnius, Lithuania

The large number of outstanding researchers and specialists from the East and West (350 participantsfrom 40 countries and representatives of UNESCO, IIASA, WHO, IFIP and UNICEF) contributed tosummarizing the experience of the most advanced countries in implementing computers in educationand taking into account the positive and negative effects when planning the future work.
I would slightly rephrase a remark of André Gide: Everything has been said before but since people
prefer to start from scratch we have to keep re-reading what the pioneers were wondering about…
In this case I decided to browse through the proceedings of the first two issues of Children in Information
Age and was surprised and at the same time not very surprised to find wonderfully formulated ideas
and questions which appear to be still relevant and challenging for the educators and the policy makers.
Let me share some of them with you and challenge you to think which of them have already been solved
and which (if any) sound obsolete. (During the late 40s it was considered that the first computersdesigned exclusively for high speed calculations, because of their price and difficulty to use, had nocommercial future and their potential use was only for a few large military or industrial laboratories.) To
speak of educating children with computers at the time would have been pure nonsense (Hebenstreit,
1986, p. 29). Even the first implementation of Logo was on a machine that, in today’s terms, cost about$1,000,000, and many thought the project would never be brought to any practical consequences(diSessa, 1986, p. 97).
Tomorrow’s Problems Today (as seen in 1985)
A general question considered by most of the speakers was as follows:•

In what way should the students be trained as to the future problems of rational use ofcomputers?

In his plenary talk (Velikhov, 1986) the speaker (then Vice-president of the Academy of Sciences of theSoviet Union) shared the idea of introducing computers on a large-scale basis gained from theexperience of the Siberian Department of the Academy of Sciences of the Soviet Union, headed byAcademician Ershov. He argued that the training of the student should be based on the principle of unity
in model building for the task to be solved, creating of algorithm for its solution and coding this algorithm
for a computer. Thus – he claimed - it is necessary to teach not only hardware and programming
languages, but also techniques for the practical problem solving…. This process embraces threestages: (i) comprehension stage (covering the age of 7-11), during which the children conceive thecomputers as a new assistant and a new friend. A Logo-type language (called by Ershov ROBIK) canbe used for about 2-3 hours weekly; (ii) second stage (covering the age 11-14), during which thestudents get acquainted with more professional use of the computer and learn: creating the model ofthe problem, the problem solving algorithm and finally, the program itself; (iii) third stage (the last two
grades) – using the computers for productive work, based on acquainting the students with application
program packages, so that they could use them in typical practical situations.
Related to this question is how to make the best use of the computer in the future. Richardson suggeststhat as this attractive, useful and co-operative machine improves while the child grows, so must his
attitude about how to use it be guided by his parents, teachers and his playmates (Richardson, 1985).
A variation on the same question is considered in (Hebenstreit, 1985):
Which scenario of introducing computers in education is the most promising: (i)computer awareness/literacy; (ii) computer as an intelligence amplifier, or (iii) computerassisted activities?

Sub-questions he reflects on include: Which aspects of programming could be useful in education? In
what programming language (if at all) should we train the children today when in 10 years we might
program in natural language and it might be Japanese? Does the concept of problem solving havesemantic content and if so, is it a method?
Hebenstreit claims that the general act of problem solving cannot be reduced to the specific task of
automating procedures and the teaching of programming and/or algorithms is thus of no use except tospecialists in informatics. The real challenge for the educators according to him should be to prepare
the children to solve problems which we don’t even know today, firstly by developing insight, intuition
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and imagination based on a solid understanding of the basic paradigm of science, and secondly – byhelping them to identify which part of the problem requires their own intellectual effort and which partcan be assigned to the computer as an assistant.
One of the most interesting open questions he poses (open till today to the best of my knowledge) are
the following:What is the impact of a text-processing package on the speed of learning to write and speak?
According to Piaget the child becomes progressively conscious that s/he is a performer
compared to the outside objects. A computer with a program can become a performer while still
being an object. Does this change the way a child understands the outside world and his/her
relationship to the world? If it does, what does it change?
How are we going to change our whole system of education to take into account the existenceof tools, so powerful that they would have been unthinkable 10 or 20 years ago? Furthermore,
how are we going to integrate these in a new set of coherent curricula at all levels, so as toeducate everyone from elementary school through university to make the most efficient use of
these tools?

The ultimate purpose of computing as formulated by Hebenstreit should not be to turn people into
servants of the computer but to develop in people those qualities which are unique in men, i.e. to help
people to become more human (ibid, p. 45) – a wonderful paraphrase of Richard Hamming’s
statement that the purpose of computing is insight and not numbers.
Talking about becoming more human let me bring an interesting analysis of the impact of the emerging
technologies on the educational system in Japan as presented in (Shiba, 1985). The author of thisanalysis claims that the real problem in introducing computers in school does not lie in the hardware or
the software but in what is called the human ware and heart ware, since the human element requires along period of time to accept any recognizable changes. Shiba expresses his concerns about thecompetition among the children founded on intellectual ability, student violence both at school and at
home, bullying among students. His pessimistic view at the time concerning a rapid progress in theJapanese educational system was due to the increasing number of teachers who regard the work simply
as a means of earning money in exchange for a given number of hours’ work. An essential reason forthe lack of vitality in the Japanese educational system according to him had been the limited freedomof teachers to change the content of what is taught influenced by the emphasis on assessing bothschools and students by means of standard deviation.
Shiba concludes that for the use of computers to be truly effective it is necessary (i) to create a new
type of educational institution outside of the scope of the traditional schools, and (ii) employ a new
management system which will enliven this new type of institution and promote the educational reforms
necessary for the coming society.
The need of educational reforms expressed by practically all the participants in the Conference comesalong with the need of answering the question:How should we define literacy?

The limited interpretation of literacy as the condition of being literate, i.e. able to read and write should
be broadened to the condition of being educated (closer but not too precise).
It was at the IFIP congress in 1981 when A. P. Ershov talked about programming as the second literacy
but serious reservations existed even then as to whether the content of the new literacy should be based
solely on a knowledge of programming (Ershov, 1981).
The discussion about what literacy will be needed in future society and what cultural change would haveto be the consequence of this need was reflected in a number of papers in both – the 1985- and 1987issues of Children in Information Age.
Several speakers in the first one shared the opinion that the definition of computer literacy is still not
clear and that in the US “computer literacy” could mean anything – from learning to use the keyboard,
to a short lecture on the history of computers, to an introduction into a programming language such as
Basic… (White, 1986, p. 48). Sylvia Charp notes in her paper (Charp, 1986, p. 189) certain trends
96Constructionism 2018, Vilnius, Lithuania

though, viz. Programming is becoming decreasingly important. The emphasis is how to best use
computers in a variety of applications…Though courses in Computer Literacy are still being given acrossthe US, questions are being asked: What happens after Computer Literacy? What technological andconceptual tools are needed to process a continuing flow of new information?
The metaphor of computer literacy as second literacy and the analogy with printing was used in(Sendov, 1986, p. 197) to give an idea of the level of the information technology at the time and how it
stood in relation to the printed word. As Bl. Sendov had seen it, these were the times of Gutenberg.
The theme of defining literacy was continued by Kurt Kreith (Kreith, 1987), who related the search for
new meaning of literacy to problem solving. His concerns are that mathematics, improperly applied, canserve to distort or interfere with rational thought. Thus, in the future, mathematical skills and knowledge
may be needed not only to search for societal problems, but also to protect our civilization against
inappropriate attempts to represent it in mathematical terms.
Interesting questions concerning the new literacy are raised by Pamela Fiddy in (Fiddy, 1987). How do
we recognize the New Literacy and the New Non-literacy? What is the difference between New literacy
for children, and New literacy for their teachers, parents and other adults? What New Literacy skills do
adults need? What are the implications for the development of New Literacy Skills in schools? Herinterpretation of literacy was that people who have achieved New Literacy use IT with confidence,
appropriately and effectively and realize the potential of IT to apply it in innovative and creative ways.
The software designers were mainly concerned with the principles behind an integrated computational
environment for education relevant to the needs of Tomorrow’s society. The main question consideredin (diSessa,1986) is formulated as follows:What should we do beyond Logo and other present educational uses of computers with
the increment of power that will come affordable within the next 5 years or so?

Although aware that it is difficult to deal with disruption of the routine and expected practice diSessa
shares the importance of experiencing the having of a wonderful idea of your very own during thelearning process. His message is that feelings of achievement and personal satisfaction of the learner(even when they flow from rare events) can influence the whole educational process. But designing
such events in the educational system is exceedingly hard (ibid, 1986, p. 98).
One of the most important questions considered at the Conference was:

What are the new roles of the educators?

A pilot study in Egypt (Owais, 1985) brings the attention to some related questions which had been
overlooked concerning the use of computers in education: (i) What is the objective of training pupils –to prepare them as future producers or to help them increase their knowledge; (ii) what is the objectiveof training teachers – to ease their job or to help them to become more innovative and creative; (iii) tomake the teacher able to contribute to the adaptation of packages written in foreign languages or to get
him able to design and perform programs to alleviate the illiteracy…
The need of a more relevant teacher education was expressed in (Wibe, 1985, p. 856) who states thatduring the last years a lot of teachers have been qualified to teach informatics but very few have learned
how to use it as an educational tool in the different school subjects.
There was a general consensus (Sendov, 1986) that (i) no computer can replace a teacher; (ii) thecomputer is only a tool; (iii) students should be taught how to learn and teachers should encourage anddevelop the desire and the need for continuing study.

Are we passing the Constructionism torch smoothly to the next
generations of educators?
A number of recent events dealing with demonstrations of new educational software and hardware
provoke disturbing feelings not only for the constructionism community.
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At an international exhibition marketing innovations in education technology attended by more than 40
thousand visitors there were (to me at least) more examples of how NOT to use technology than of how
to use it.
A program generating a sentence of randomly chosen words was called “Poet”. Similarly, a program
generating a sequence of randomly chosen notes was called “Composer”. The authors had not
implemented any concepts related to the structure of a poem or a musical composition. Not surprisingly,
they had not heard of books such as Exploring Language with Logo (Goldenberg, Feurzeig, 1987).
At a very recent conference embracing researchers, educators and teachers (held in Bulgaria) there
was a workshop led by young representatives of firms promoting programming courses for young
children. They were demonstrating a couple of robots moving in a maze on the floor. After showing thecommands needed to move the robots (not RIGHT and FORWARD as you might expect) a mother fromthe audience asked: What would you do after a child who has already learned these commands getsbored? The answer was: No, problem at all! We have five other programming environments to offer…
There are teachers who believe that just because something is modern and attractive (e.g. the computer
video games), we should try to implement it in the classroom without being sure about the effect, about
what we gain as teachers/learners…
In a chapter on Virtual reality in education, I had to review a project on Dante for 4 th-graders in which
the authors claimed that a simplified and filtered text has been adapted for the use of kids in primaryschool, allowing a better and clearer understanding of Dante's Inferno. To the best of my knowledge themysterious tercet of Dante in his Inferno 9, 61-63 urges the reader to consider the doctrine concealed
under these strange verses (Fowlie, 1981). The video-clip demonstrating the class atmosphere showed
kids reading, nobody was looking at the screen showing a fire (possibly to represent the hell). The
authors of the proposed Chapter did not discuss what the kids had gained in terms of knowledge which
would not be doable without a virtual reality. What if they were just drawing their ideas about the hell,
discussing the pictures of Gustave Dore (who was not even mentioned), and if they would have justorganized a play themselves instead of using an avatar with the name of Dante on his back…
Just for a comparison with much older “new technology” I reread a paper (Tabov, Muirhead andVassileva, 1999) in which the authors present a course for interdisciplinary and integrated education
dealing with Dante and his world. In particular, the birth and death dates of one of Dante’s ancestors is
calculated by means of the Geomland software. Astronomy, mathematics, history, and literature(including the difficulties of translating Dante) were highlighted in teaching. Finally, it is suggested thatthe dates normally presented by editors of Dante's Divine Comedy may be incorrect. The authors
discuss the problems and questions relevant to this sort of education and especially those involved
when teaching mathematics and the humanities together.
Today, it is troubling to see how often educational software designers and producers trivialize the notionof creativity by claiming that children can easily become composers, poets, artists, filmmakers - just byusing their fantasy and rearranging the elements of a story, a poem, a famous painting or the bars of amusical piece.
In a nutshell, when I hear people telling me: O-o-oh, Logo, the little turtle drawing squares… But isn’t it
old fashioned? or even worse: Constructionism? How many papers dealing with this notion have youread recently which are not part of conference proceedings? I feel bitterness. This is not people’s fault
though. We owe to the educators, teachers and children the powerful educational ideas we have been
nurtured with thanks to the constructionism and we should not remain indifferent if these ideas are often
darkened by shiny new technology used per se.
And to quote the pioneer of constructionism, Papert, this claim is not based on an arrogant belief thatthe inventors of this educational philosophy are smarter than the rest. It is based on the belief that theconstructionism was not invented at all, that it is rather the expression of liberation lf learning from theartificial constraints of pre-digital knowledge technologies (Papert, 1999).

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Constructionism 2018, Vilnius, Lithuania

What education do we need for the future (seen as of today)?
In a recent panel discussion on what education we need for the future (I had the honor to moderate) the
panelists were colleagues and former students of mine representing various stakeholders of education.
What follows are fragments of their statements transcribed from an audio recording.
M.B. (parent): Todays culture in a number of schools is for the children to stay quiet, not to touchanything so as to avoid damages, to be disciplined. It is difficult for the teachers to cultivate and develop
the soft skills of their students since they follow the model by which they themselves have been
educated. A modern pre-service education should include learning to learn so that it could bedemonstrated later in class setting.
V.T. (teacher): Teachers motivation is crucial. It is not sufficient though for a school to have a single
teacher ready to spark the curiosity for science, the love for knowledge. Communities of teachers who
act like researchers are needed.
D.P. (teacher) – Few teachers are ready to leave their comfort zone. We are time-poor and even when
I manage to achieve an enthusiastic atmosphere I feel like single swallow. The need of more time andefforts discourage even teachers who have been hooked at the beginning.
T.A. (teacher) I was professional programmer and musician. My goal is to participate in the foundationof schools of new type. Today’s economy does not need just STEM specialists; people who are built
personalities are needed. The style of teaching should be constructionism, but the key question is how
to prepare teachers who act as masters, designers of experience.
S.H. (researcher and mentor) The main problem of today’s society is the lack of patience – people
want immediate results. And the innovations require time and risk-taking with no guarantee for ultimate
results. The IBL in its highest level, open inquiry, is very important idea to be implemented in school.
Experiments, observations, explorations should be in the core of education so that the students feelthey are participating in the creation of something new. The students’ mentors at that level are rarely
among the teachers – there is gap between the traditional teacher education at the university and what
the modern teaching strategies require. A lot of competences could be acquired in any subject provided
the students are active participants in the learning process.
T.K. (software company founder): We have to learn to work not for the others but with the others, tobuild connections and become stronger. We have to have the will of formulating our goals – no matter
how powerful, the machine cannot set the direction. We have to become very good in setting goals.
Dreyfus Model of Skill Acquisition could be used in any profession. The role of the teacher should be
the one of the master: I’ll teach you how to become master vs. I’ll show to you what master I am. As
mentors we can help students raise their intuition, learn how to learn. Recently I used my high school
mentee to teach me about block-chain technology – by teaching me he became better.
K. D. (researcher, involved in gifted education) Since we can’t predict the future professions, we aretalking about key competences and transfer of knowledge. In its kernel the education should be oriented
to wide range fields. The competence of selecting and analyzing information is crucial.
O.K. (University Professor) The more important skills required in the workplace will increasingly be
communication skills. I do not think computer training can make much of an improvement in personalcommunication skills. If it is in the form of training with games - yes, it would evolve thinking towards
empathy. On the other hand, analytical skills can be acquired through computerized learning. Perhaps
the "truth" is in the balance. In the near future, the notion of "computer literacy" will disappear completely.
It is already out of use - today we are talking about "digital competences", which includes communication
and basic problem solving in all aspects of life. It is believed that digital skills today help to master otherkey competences such as communicative, language skills or basic math and science skills. The futurerole of the educators will be increasingly in the aspect of mentoring and supervising.
B.S. (researcher involved in educational reforms) The real question for me is not so much what
education but rather what upbringing we need for the democratic society. Education, especially in the
public schools and universities, must be engaged with the formation (upbringing) of responsible andloyal citizens… We could declare that the digital technology is an excellent tool for education; the99Constructionism 2018, Vilnius, Lithuania

problem is how to use this tool for the upbringing of the pupils… This problem is especially difficult inthe so called new democracies. The use of market models in the educational institutes is not aconsequence of the democracy but rather the result of the weakness of the democracy and theaggression of the market. After decades of successful use of digital technology for education, it is clearthat the quality of education depends mostly of the quality of the teacher. All teachers have to bepotential researchers.
Finally, the panelists agreed that we all as teachers have to be guides for our students’ development,
but at the same time we have to be ready to learn with them and learn from them

What to do (and not to do) in education in the digital era
Let us cast a glance at a recent document dealing with the education in digital era (Lonka, Cho, 2015),
which states that so far the digital revolution has not transformed most schools or most teaching andlearning process in classrooms. The research of the authors indicates that the students with the best
skills in technology are also the ones who are most bored and disengaged at school. Thus finding
meaningful ways of using technology not only for learning but also for collaborative knowledge creationis needed.
Some of their key findings and recommendations include: (i) It is important to base our conclusions on
perceiving learning as knowledge creation, rather than emphasizing mere knowledge acquisition; (ii)
Well-being and Social and Emotional Learning (SEL) are at least as important as other 21st century
skills (such as media literacy, cultural awareness, and complex problem solving); (iii) Instead ofcomputer-supported learning, it would be advisable to talk about new forms of Socio-Digital Participation(including media literacy, such as using social media and search engines); (iv) Systematic developmentof flipped and inquiry-based learning programs with meaningful use of technologies would be advisable;(v) We need constant reforms in schools and teacher education so as to follow the importantdevelopments of society. Perhaps too much time has been spent looking at test results, such as PISA.
A recent Call to Action concerning (i) students and learning with ICT; (ii) professional development ofintegrating technology, and (iii) educational system policies for infusing technologies, appeared as aresult of the work within EDUsummIT 2017 Rethinking Learning in a Digital Age (Voogt, Knezek, Lai,
2017, pp. 16-17). EDUsummIT (International Summit on ICT in Education) is a global knowledge
building community of researchers, educational practitioners and policy makers committed to supporting
the effective integration of research and practice in the field of ICT. Approximately 90 leading
researchers, policy makers and practitioners spanning all continents, attended EDUsummIT in
Borovets, Bulgaria, September 18-20, 2017 to discuss the research, policy, and practice challenges
faced in 9 thematics groups and come up with recommendations for the researchers, teacher educatorsand policy makers. The summary report of the work of the thematic groups together with cross-analysisof their recommendations was prepared by Voogt, Knezek, and Lai (ibid).

Instead of conclusion: So…what do we do now?
Baring in mind the importance of the continuous professional development of teachers expected to
implement inquiry based learning approach in their practice a research team at the Institute ofMathematics and Informatics at the Bulgarian Academy of Sciences took the initiative to provide
scientific, methodological and technical support in inquiry based mathematics, informatics and IT
education (IBMIE) at all levels and forms in a national context (Chehlarova, Kenderov, Sendova, 2017).
This support is carried out by organising various types of PD courses and by developing open accesslearning environments that enhance IBMIE with a focus on the acquirement of key competences.
The inquiry based learning supported and enhanced by digital technologies in my country has its roots
in an experiment of the Research Group on Education (RGE) founded by the Bulgarian Academy of
Sciences and the Ministry of Education) in 1978 (Sendov, 1987).
RGE - a model for a technology-prompted curriculum100Constructionism 2018, Vilnius, Lithuania

The guiding principles of RGE were learning by doing and making and integration of the school subjects(Nikolov, 1987). The educational resources developed for the 29 experimental schools included
textbooks, teacher guide-books, and unified (Logo-based) computer microworlds tuned to specificsubject domains. The newly developed curriculum enabled students to pass gradually from constructing
controllable models, through various problem-oriented microworlds for explorations in natural
languages, music and art to a fully programmable microworld for explorations in Euclidean geometry.
The tasks dealing with modeling pieces of art, music, poetry, and writing programs to generate suchworks were meant to motivate students to learn about structures and use their programs as materialized
hypotheses of their observations (Sendova, 2001). The experiment lasted for 12 years starting from1979. Although it did not lead to essential changes in the Bulgarian educational system as a whole, theRGE model set a good ground for a series of European educational projects involving ICT enhanced
inquiry based mathematics and science education (e.g. DALEST, InnoMathEd, Math2Earth, DynaMAT,
Fibonacci, Mascil, KeyCoMath, STEM PD Net, Scientix).
Some recent promising educational practices in Bulgarian setting
Thanks to the system for PD of teachers in mathematics, IT and informatics developed by IMI-BAS, a
network of such teachers acting as multipliers of the IBL ideas has been established. The current
activities of IMI-BAS include various types of PD courses and events, as well as open access learning
environments related to STEAM (science, technology, engineering, arts and mathematics).
•

Professional development (PD) courses for teachers in mathematics and IT

The main goal of the courses is in harmony with the most recent educational strategies for updating themath and science education in the EC countries: the development of key-competences by implementing
the inquiry based learning in integration with the world of work. These PD courses are based on a teamwork (of the lecturers and the participants alike) and implement educational models adaptable to various
school settings. The teachers work on pedagogical problems related with: formulating their own mathproblems reflecting real-life situations, not solvable with the current math knowledge of the students butallowing for explorations leading to a good enough approximation of the solution; studying andproposing methods for tackling problems which are unstructured, or whose solutions are insufficient or
redundant; solving “traditional problems” with “non-traditional” data, for which the use of a computing
device is necessary; formulating more relevant evaluation criteria for the students’ achievements;
assessment of learning resources in terms of formation and development of IBL skills; project-based
work with presentation of the results (Zehetmeier, Piok, Holler et al., 2015).
Professional development courses in which teachers and students work collaboratively on real-life
problems have turned out to be especially fruitful. The format of such courses is typical for the Summer
Research School organized annually by the High School Student Institute (HSSI) of Mathematics andInformatics one of the goals being to help teachers improve their mentoring skills (Sendova, 2014).
•

Learning scenarios in support of cross-curricular integration

A good repository of digital resources is the Virtual School Mathematics Laboratory (VirMathLab) being
developed by IMI-BAS (Kenderov, Chehlarova, Sendova, 2015 a), which contains about thousand
scenarios with dynamic files transparent for the users (http://www.math.bas.bg/omi/cabinet). The design
and the implementation of these scenarios is just an element of a more ambitious goal − we expect ourstudents to look for manifestations of geometric congruences, discover them and use them in various
activities, and thus – to be able to find patterns and relationships deepening their knowledge andunderstanding of the surrounding world.
•

New types of mathematics contests

Mathematics with a computer and Theme of the month are new type of contests based on the
VivaCognita computer platform (Kenderov, Chehlarova, Sendova, 2015 b; Chehlarova, Kenderov,
2015; Kenderov, Chehlarova, 2016). Students (3-K12) are invited to work on a chain of problems inincreasing difficulty. Some of the problems in both competitions are accompanied by GeoGebra files
which facilitate the exploration of the mathematical essence of the problem.

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Constructionism 2018, Vilnius, Lithuania

Conclusions
With all our efforts we are trying to help teachers create an atmosphere where the students would nottell themselves: I am a good student, because I got so many points on the test, I’ll take the exam, I’ll
enter the university… But they would rather think about the excitements the genuine learning offers:
How interesting, I wonder what will happen if… I feel like a real scientist! I am not afraid to try something
nobody has tried before…And even to express certain disappointment that Archimedes had preceded
them with his discoveries… (a genuine remark by a 6th grader in the context of constructing
Archimedean solids by means of plastic straws). As for the teachers they should not feel embarrassed
when they don’t know the answer but would demonstrate how they are looking for it (possibly together
with their students).
For such an attitude to education to be cultivated however the whole society should be adapted to thedigital era and learn how to learn throughout life. This is the hope for the society to become even morehuman in the digital era…
Can we agree now with Paul Valéry that the future is not what it used to be…?

References
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Chehlarova T., Kenderov, P. (2015): Mathematics with a computer—a contest enhancing the digital andmathematical competences of the students. In: Kovatcheva, E., Sendova, E. (Eds.). UNESCO
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Fiddy, P. (1987) Welcoming people to the Age of Information Technology: Teaching experiences leading
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Fowlie, W. (1981) A Reading of Dante's Inferno, University of Chicago Press
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(2015) Concepts for In-Service Mathematics Teacher Education: Examples from Europe. In: Gehring,C., Ulm, F. (Eds.) Developing Key Competences by Mathematics Education. ISBN 978-3-00-051067-0
University of Bayreuth, Germany pp. 23-33
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Rock Bottom, the World, the Sky: Catrobat, an
Extremely Large-scale and Long-term Visual Coding
Project Relying Purely on Smartphones
Wolfgang Slany, wolfgang.slany@tugraz.at
Kirshan Kumar Luhana, kirshan.luhana@student.tugraz.at
Matthias Mueller, mueller@ist.tugraz.at
Christian Schindler, cschindler@ist.tugraz.at
Bernadette Spieler, bernadette.spieler@ist.tugraz.at
Institute of Software Technology, Graz University of Technology, Austria

Abstract
Most of the 700 million teenagers everywhere in the world already have their own smartphones, but
comparatively few of them have access to PCs, laptops, OLPCs, Chromebooks, or tablets. The freeopen source non-profit project Catrobat allows users to create and publish their own apps using only
their smartphones. Initiated in 2010, with first public versions of our free apps since 2014 and 47
releases of the main coding app as of July 2018, Catrobat currently has more than 700,000 users from180 countries, is available in 50+ languages, and has been developed so far by almost 1,000 volunteers
from around the world (“the world”). Catrobat is strongly inspired by Scratch and indeed allows to import
most Scratch projects, thus giving access to more than 30 million projects on our users’ phones as ofJuly 2018. Our apps are very intuitive (“rock bottom”), have many accessibility settings, e.g., for kids
with visual or cognitive impairments, and there are tons of constructionist tutorials and courses in many
languages. We also have created a plethora of extensions, e.g., for various educational robots, including
Lego Mindstorms and flying Parrot quadcopters (“the sky”), as well as for controlling arbitrary external
devices through Arduino or Raspberry Pi boards, going up to the stratosphere and even beyond tointerplanetary space (“the sky”). A TurtleStitch extension allowing to code one's own embroidery
patterns for clothes is currently being developed. Catrobat among others intensely focuses on including
female teenagers. While a dedicated version for schools is being developed, our apps are meant to be
primarily used outside of class rooms, anywhere and in particular outdoors (“rock bottom”, “the world”).
Catrobat is discovered by our users through various app stores such as Google Play and via social
media channels such as YouTube as well as via our presence on Code.org. Sharing, remixing, andcollaboration is actively encouraged and supported. Catrobat has a very long term perspective in that it
is independent of continuous funding and actively developed in a test-driven way by hundreds of probono volunteers from around the world. Our aim is to grow by a factor of thousand and reach a billion
users by 2030. We warmly welcome new contributors in every imaginable field and way with open arms.
Please join us and contact me via wolfgang@catrobat.org today!

Keywords
Pocket Code, Game Design, Gaming, Gender Inclusion, Coding, Mobile Learning, Social Inclusion,
Constructionism, Girls, Teenagers, Apps, Smartphones, Tinkering

Introduction: Background, mission, and history
Knowledge in Computer Science (CS) is essential, and industries have increased their demand for
professionals that have technical experience. The next generation of jobs will be characterized by new
standards requiring employees with computational and problem solving skills in all areas, even if they
are not actual technicians (Balanskat and Engelhardt, 2015). However, the number of young people,
and women in particular, choosing to study and work in Information and Communication Technology(ICT) fields is decreasing dramatically (NCWIT, 2015; NCWIT, 2017; European Commission, 2016a).
In the last decade, European technology employment has grown three times faster than all employment
104Constructionism 2018, Vilnius, Lithuania

in total. The continuous improvements of technology and the numerous advancements in industrial
p r o c e s s e s m a d e i t p o s s i b l e t o d e v e l o p a u t o n o m o u sFigure 1: Pocket Code's UI

vehicles, robotics, 3D printing, genetic diagnostics, or Internet of Things (IoT) technologies. These
technologies are already part of everyday life, and there is a corresponding growing worldwide need for
qualified scientists, engineers, and technicians. For these reasons, society and governments havemandated that teenagers should acquire computing and coding skills (European Commission, 2016b),
or even a new way of (critical) thinking and problem solving skills (Wing, 2006; Kahn, 2017; Tedre andDenning, 2016; Mannila et al., 2014). Presenting coding as a range of diverse skills which can belearned by adapting ideas from games is a generally applicable concept. Thus, a gamified andconstructionist concept should hold teenagers’ focus to actively participate by activating intrinsic andextrinsic motivators (Ryan and Deci, 2000). Games can be played everywhere, including on
smartphones, tablets, and other digital devices. Moreover, the mobile game market continues to growfaster than other game industries, e.g., the number of game apps on Google Play grew by 28% in 2017
(Jingli, 2017; Takahashi, 2017).
Catrobat’s approach is inspired by Piaget’s Constructivism theory 1948 (Piaget and Inhelder, 1967),
starting with first computer programming courses at the MIT in 1962 (Greenberger, 1962), and refinedwith Papert's Constructionism concept in 1980 (Papert, 1985). Since then, different approaches wereused to motivate kids for coding. With our free open source non-profit project Catrobat our goal is toprovide computational thinking skills for everyone, especially teens from less developed areas whereother computational devices such as PCs are almost non-existent.
Catrobat’s apps and services have been immensely influenced by MIT’s Scratch30 project, and we
consider Catrobat to be Scratch’s little sister project for smartphones. Scratch itself has been strongly
shaped by Papert’s powerful ideas, and extends Papert’s “Low Floor” but “High Ceiling” for the Logo
programming language for kids (i.e., easy to start, but allowing to develop more complex projects aswell) by adding “Wide Walls”, emphasizing that Scratch supports a wide variety of projects as well asways to learn and play, according to the needs and interests of its users (Resnick 2017). Going beyond
Logo and Scratch’s metaphor of the room, Catrobat’s smartphone based approach allows to literally
break down the walls of the room and move outdoors, thus inspiring Catrobat’s mission statement,
which is “Rock Bottom, the World, the Sky”. “Rock Bottom” because on the one hand we aim at building
upon and going beyond Scratch’s focus on making the first experiences in coding as easy and satisfying
as absolutely possible for our teenage user group, e.g., through a physics engine that is much easierand intuitive to use that similar concepts in other game making environments. On the other hand,because of our reliance on smartphones, it also is meant to evocate that our users can and, to a large
percentage, do leave the “room” to code outside or create outdoor projects, in some cases literally “onthe rock”. Many of our users are indeed developing their projects while on the go, far from classrooms
and their homes, and have developed apps that take advantage of the various sensors such as the30

Scratch MIT: https://scratch.mit.edu/

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Constructionism 2018, Vilnius, Lithuania

cameras, GPS, compass, or acceleration sensors that are built into smartphones and that allow tocreate, e.g., augmented reality, geocaching, or dynamic outdoor sports games. Additionally, today’s
teenagers all over the globe have, to an already very large degree and also increasingly, their own
smartphones readily available and permanently connected to the Internet, even in rural areas in Africa
and other regions in the world where there may be no central power supply at home and kids charge
their phones via solar panels in a central community facility of their village. Catrobat also has begun to
become available in languages that are not supported by the phones makers themselves, such as
Swahili, Gujarati, or Sindhi. Catrobat thus strives at reaching out to all corners of “the World” in an
effective and efficient way that is indefinitely sustainable. The reliance on already existing smartphones,
as well as the free open source character of Catrobat, which lets it thrive with little to no funding, also
are significant aspects that allow Catrobat to scale up by avoiding the high costs and logistics that have
hampered the success of similarly motivated projects in the past. Regarding the third part of our mission
statement, Catrobat allows already since 2017 to program drones flying autonomously in “the Sky” (inparticular, the popular Parrot Augmented Reality Drone 2.0), with a real-time video being transmitted to
Pocket Code’s screen, under full programming control by the user. While coding with Pocket Code has
been done in airplanes, for the future we plan on going even higher. Because phones are small,lightweight, and portable, off-the-shelf Android phones have already been used to control balloons up
to the stratosphere, and our extensions via Bluetooth and local WiFi connections to battery powered
Arduino and Raspberry Pi allow to control any hardware of these flying computational systems, as longas the isolation keeps the harsh environment at bay and there’s enough energy. On a further note,
PTScientists’ private enterprise Moon rover mission project31, scheduled to lift off in 2019 and sponsoredby, among others, Vodafone, Nokia Bell, Audi, and Red Bull, has several experiments on-board that
rely on regular Android phones to lower the costs of otherwise extremely expensive “rocket science”
hardware, with Vodafone and Nokia Bell sponsoring the installation of a 4G data network based onstandard phone transmission technology from the rovers to the base station on the Moon and from there
back to Earth. One of our dreams is that we will empower kids to use Pocket Code to design experiments
and to program autonomous robots on the Moon, on Mars, and possibly even farther away. There is a
thriving worldwide PhoneSat32 community led by NASA that has launched a large number ofnanosatellites based on 10x10x10cm cubesats using unmodified consumer-grade off-the-shelf Android
smartphones and standard Arduino boards, which both would immediately work with Catrobat’s apps.
The sky has no limit, both in the concrete as well as in the metaphorical sense. Regarding the latter,our goal is to allow every kid to create complex, high resolution, and high performance apps using ourtools, which they will be able to offer to other users, even commercially. Indeed, we allow our users tocompile their apps into real Android apps, sign them with their own developer key, optionally add ads
via their own AdMobs account, and sell them on Google Play for real money, thus directly empowering
them to leave the limitations of the metaphorical “room” and bring their creations to the outside world.
On a historical note, one of the initial motivations for starting the Catrobat stems from Neal Stephenson's
science fiction book “The Diamond Age: Or, A Young Lady's Illustrated Primer: a Propædeutic
Enchiridion in which is told the tale of Princess Nell and her various friends, kin, associates, &c.”. In thebook, human tutors are hired anonymously on demand by a very affluent industrialist and aristocrat, toremotely educate a young girl, initially a toddler, living under gruesome conditions, by mistaking her,because of circumstances described in the book, for a princess for whom the primer was actuallycreated. The illustrated primer, which is highly portable and has a voice- and touch sensitive interface
that allows her to communicate with her tutors, accompanies Nell throughout her adolescence up to
young adulthood, when she becomes a leading force and changes the fate of millions of the mostunderprivileged kids on Earth. One of the main story lines spanning a large part of the book is theacquisition of computational thinking skills by Nell through the Illustrated Primer. The book has won the
Hugo and Locus science fiction awards, and was also cited by the developers of the One Note per Child
Project as well as of Amazon’s Kindle as a motivational inspiration. In 2017, Catrobat won the “Closing
the Gender Gap” prize for a new subproject called “Remote Mentor”, in which we have started to
implement the necessary technical infrastructure and study the required social aspects to realize

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Stephenson’s Illustrated Primer. We have partnered with sociology scientists focusing on genderaspects for the research part. In November 2017 we have begun to conduct initial remote mentoring
experiments under real conditions, first with sponsoring from Google as a Google Code-in mentoringorganization at the end of 2017, with several hundreds of teenagers from all over the world being
remotely mentored by a pool of 38 Catrobat mentors over a period of seven weeks, followed by the“Remote Mentor” project itself, with initial funding from the Internet Foundation Austria until the end of
2018. Because of Stephenson’s book, Catrobat has focused from its very beginnings to aim at
empowering female teenagers in less affluent regions such as rural areas in India, Tanzania, or Brazil.
Teens have reacted very positively to these first remote mentoring experiments, and we plan toeventually integrate the remote mentoring features into our tools and services so that mentors andmentees will be anonymously and automatically matched on demand, internationally in all languages,
in a large scale, long term, and continuously further developed way.
This paper is organized as follows: First, we emphasize two trends that have emerged in the last decade(both were important in developing our app Pocket Code): block-based and visual coding, and anincreased use of mobile devices among our main target group (teenagers from 13 to 19 years). Second,
the focus lies on the Catrobat project and the educational app Pocket Code. Third, we describe ourplanned next steps and subprojects that are being developed, followed by a summary and conclusion.

Computational Thinking Skills for All
Computational Thinking promotes the importance of coding and computer science activities, thus
delivering concepts that are more applicable and highly essential to prepare teenagers for the future(Barnett et al., 2017; Tetre et.al. 2016). After 2006, there was a rapid increase in the number of publishedarticles about learning to code (Wu and Wang, 2012). The ongoing movement of promoting coding
through visual programming languages has its origin at that time.

Trend 1: Block-based and Visual Coding
In the last decade, a number of block-based visual programming tools, e.g., Scratch, have been
introduced which should help teenagers to have an easier time when first practicing programming.
These tools have all had very similar goals: they focus on younger learners, support novices in their firstprogramming steps, they can be used in informal learning situations, and provide a visual/block-based
programming language which allows teenagers to recognize blocks instead of recalling syntax (Tumlin,
2017). Unlike traditional programming languages, which require code statements and complex syntax
rules, here graphical programming blocks are used that automatically snap together like Lego blocks
when they make syntactical sense (Ford, 2009).
Another critical improvement of visual programming systems over classic text based programming
languages is the fact that all elements of the programming environment and also the programming
language itself, including the formula elements, are translated to the human language of the youngusers. Especially for human languages that are not written with the Latin alphabet, this is a hugeadvantage for users, as they are not used to think in English and very often have difficulties to evenread Latin scripts. In case of developing countries, usually only a small percentage of the population
understands English, in which most user interfaces are exclusively available, thus implicitly excluding alarge part of the world’s population. Localization of a software can revolutionize E-learning, resulting in
more educated workforce and improved economy (Ghuman, 2017). Pocket Code supports localizationand internationalization on the application level. The app’s language and locale can be changed withoutchanging the smartphone’s interface language on the system level. Languages such as Sindhi and
Pashto, which are yet to be supported by operating systems, can thus be seamlessly used by our users(Awwad, 2017). Catrobat shares this feature, which improves accessibility and inclusiveness to users
from all regions of the world, e.g., with Scratch and Snap!, and this certainly contributes in a major way
to the positive worldwide reception of these visual programming environments.
Thus, visual programming languages provide an easier start and a more engaging experience forteenagers. The ease of use, and simplicity of such programming environments enables young people

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to become game makers, and by the seamless translation of their user interfaces, to collaborate withother users on a worldwide scale.

Trend 2: Smartphone Usage
With mobile games, more people can engage who were previously limited to use other platforms suchas PCs or consoles. Further, children nowadays grow up with mobile devices and feel comfortable using
them. Considering current prices, and the forecast of the user penetration of smartphones in Austria,
France, Germany, and the United Kingdom from 2014 to 2021 (Statista Market Analytics, 2016), we canconclude that smartphones will be used significantly more by teenagers in the future than tablets,
laptops, and desktop PCs. Smartphones and the use of apps are already a part of our culture and arechanging the way in which many people, particularly teenagers, act in social situations. For mostadolescents the smartphone performs several functions in their daily lives, and it contributes, e.g., toidentity formation through self-presentation on the Internet. In addition, the smartphone is used a lot
during spare time (most games are played in the evening (Verto Analytics, 2015)) or for just killing sometime, e.g., when commuting. In addition, online games and mobile games play an important role in thedaily lives of teenagers (Bevans, 2017). A recent study which examined American female players’
experiences found that 65% of the Android users who play mobile games are women (Google and
NewZoo, 2017).
Teenagers increasingly have mobile devices on their own, which enables them to creatively express
themselves at any time and to use apps that bring their ideas and creations to life. With a moremeaningful use of mobile devices, teenagers worldwide can acquire powerful knowledge that will makethem into better problem solvers, thinkers, and learners.

Catrobat and the Pocket Code App
The Free Open Source Software (FOSS) non-profit project Catrobat33 was initiated 2010 in Austria at
Graz University of Technology. The multidisciplinary team develops free educational apps for teenagers
and programming novices. The aim is to introduce young people to the world of coding (Slany, 2014).
With a playful approach, teenagers of all genders can be engaged, and game development can bepromoted with a focus on design and creativity. A first public version of our free app was published in2014, with 47 releases of the main coding app as of July 2018. Our app currently has more than 700,000
users in 180 countries, is natively available in 50+ languages (including several languages not directly
supported by the underlying operating system), and has been developed so far by almost 1,000
volunteers from around the world.
These volunteers are implementing software, designing educational resources, translating the app, or
provide other services that help to advance the project. The contributors work together in a cooperative
way, having the chance to engage in a field they like and create something within a community that
follows the same shared vision. Besides attracting students, educators, and other interested
contributors from all over the world, Catrobat also benefited from being part of Google’s Summer ofCode and Code-In initiatives. These initiatives promote open source worldwide and motivate teenagers
and students to get involved in projects such as ours. Our openness towards international contributors
helps us to represent different cultures, bring in various viewpoints, and generate new ideas how the
project can further develop. Catrobat’s project management as well as development is done in an agile
way, allowing our contributors to adopt new technologies, respond to user feedback, and embrace
upcoming ideas quickly. An example where a feature request issued by users was implemented is our
web based automatic APK (Android Package) generation. It is currently being extended for users to beable to sign their apps and add AdMobs based ads, so that they can publish their Catrobat projects on
Google Play and other app stores and earn money with them. Another example is of technical nature
and affects our deployment workflow, which was redesigned and fully automated to enable us to deploy
to Google Play including the localized screenshots and app descriptions in 47 languages (not all our
languages are supported on the Google Play store) with only two mouse clicks. All this helps to provide

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a motivating user experience for our young target group, support them in their learning process, andfoster collaboration.

Figure 2: Pocket Code Alice themed program

Pocket Code: Creating your own GamesThe app Pocket Code34 is an Android-based visual programming language environment that allows thecreation of games, stories, animations, and many types of other apps directly on phones or tablets,
thereby teaching fundamental programming skills. This app consists of a visual Integrated Development
Environment (IDE) and a programming language interpreter for the visual Catrobat programming
language. The IDE automatically translates the underlying code parsed by the XML file into visual brickelements and vice versa. With the use of simple graphic blocks, users can create their own game,colorful animations, or extensive stories directly on the mobile phone without prior knowledge.
The drag and drop interface provides a variety of bricks that can be joined together to develop fully
fledged programs. The app is freely available for Android on Google’s Play Store and soon will beavailable on Apple’s iTunes Store for iPhones. Figure 2 shows Pocket Codes’ UI and an example projectwith “Alice in Wonderland” characters.

Pocket Code: the Mobile Integrated Coding Environment
Projects in Pocket Code follow a similar syntax to the one used in Scratch and are created by snapping
together command bricks. They are arranged in scripts that can run in parallel, thereby allowing
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concurrent execution. To communicate between objects, to trigger execution of scripts, or scripts
beyond objects, broadcast messages are used. By means of this mechanism, sequential or parallel
execution of scripts is possible, either within the same object or over object boundaries. In addition to
the basic control structures, Pocket Code offers event triggering building blocks for event-driven
programming. Familiar concepts, such as variables, lists, or Boolean logic, are included as well.
In addition to Scratch, Pocket Code has a 2D physics engine which enables the user to define certain
physical features of objects and the stage (collision detection, velocity, gravity, mass, a bounce factor,
and friction) to create from simple up to complex simulations of the real world. An example thatshowcases both the physics engine as well as the use of inclination sensors is a simulated wooden
maze through which a metal ball needs to be navigated from a starting position towards the winningend position, all while avoiding a number of holes on the floor of the maze, by tilting one’s phone, as if
it were a real, physical wooden maze. The wooden walls initially execute a physics brick named “Setmotion type” with the pull-down option “others bounce off it”, and the ball executes the same “Set motion
type” brick with the option “bouncing with gravity”. The movement of the ball by tilting the phone isrealized by a “Set gravity for all objects to X: -3 x inclination_x Y: -3 x inclination_y steps/second2” brickthat is executed in a “Forever” loop. Voilà, that’s all that is needed. To increase the realism of thesimulated maze, there is an additional “When physical collision with anything” brick followed by a“Vibrate for 0.02 seconds”. Even more, the ball’s metallic reflection sheen is oriented always in the same
direction using the magnetic compass sensor of the phone, thus giving the impression that the lightalways comes from the same side. It would be easy to also influence the gravity vector using theacceleration sensors built into the phones, which would make the ball not only react to tilt, but also to
shaking and quick moves in any direction. The rest of the scripts handles the animations when the ball
“falls” into one of the holes, and also shows the amount of holes that have successfully been avoided
so far in the top left corner. Note that all objects that execute one of the two variants of the “Set motion
type” brick mentioned above, automatically have their convex hull computed for the physics collisions,based on the visible parts of their current look. No other game engine to our knowledge does thisautomatically: in other gaming frameworks, the bouncing box is either quadratic, or has to be manuallyspecified by a professional developer. Catrobat’s use of convex contours makes it extremely intuitiveand simple for the user to create complex games using the physics engine. Note that arbitrary 2D forms
going beyond the convex hull, e.g., patterns of the form “U” or “8”, are much more complex to handle
automatically, and it is probably easier for users to handle special cases on an individual basis, e.g., byforcing several objects to move in synchronicity. In contrast, for all practical purposes it is impossible torealize a physically correct collision and bouncing of objects from each other for arbitrary shapes inother visual programming languages such as Scratch. This aligns with the “rock bottom” metaphor of
Catrobat corresponding to the “low floor” metaphor of Logo and Scratch, making complex games easy
and intuitive to realize, with a very low entry threshold, with physics being a concept everyone is
intimately familiar with. At the same time, the maze project also has a resolution of 1920 x 1080 of which
every pixel is fully used, giving it a very high-resolution and also highly realistic look, on par withprofessionally created game apps. This exemplifies Catrobat’s “the sky” metaphor corresponding to the“high ceiling” of Logo and Scratch, since with Pocket Code, there literally is no upper limit in realism andperformance of the created games: a project’s resolution is only limited by the capabilities of the phoneon which it is created, so, e.g., a project with a 3840 x 2160 pixels resolution can be created using aSony Xperia XZ Premium smartphone. Figure 3 shows the stage as well as some of the scripts
mentioned above. The project can be found under the name “Tilt maze 1.0” on Catrobat’s sharing sitefrom within Pocket Code.

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Figure 3: Partial screenshots from “Tilt maze 1.0” that relies on Pocket Code’s physics engine, where thephysical behaviour of objects is set through its “motion type”, e.g., “others bounce off it” for the wooden walls of
the maze (middle script), and a short vibration when the ball touches a wall (script on the right). The movementof the ball by tilting the phone is realized by a “Set gravity for all objects to X: -3 x inclination_x Y: -3 x
inclination_y steps/second2” brick that is executed in a “Forever” loop (not shown here). Direct link to the “Tiltmaze 1.0” project on the web version of Catrobat’s sharing site https://catrob.at/TiltMaze

With Pocket Code’s intuitive merge functionality, the new parts of two projects can be seamlessly
merged together into one larger project – parts of the two initial projects that exist in both are not
duplicated. This makes programming cooperatively much easier. Modern smartphones are equippedwith a large number of sensors, although most mobile games only use few or none of them (Kafai and
Vasudevan, 2015). Within Pocket Code, users can create games using the device’s sensors, such as
inclination, acceleration, loudness, face detection, GPS location, or the compass direction, which makes
user input easy and engaging. With Pocket Code it is also possible to connect via Bluetooth to Lego®
Mindstorms robots or Arduino™ boards. The following extensions are available: Lego Mindstorms
NXT/EV3, Parrot AR.Drone 2.0 and Parrot Jumping Sumo Drone, Arduino, Raspberry Pi (via WiFi),
NFC tags, Phiro robots35, and Chromecast. These kinds of computational construction kits makecreating programmable hardware accessible to even novice designers and combines coding andcrafting with a rich context for engaging teenagers (Kafai and Vasudevan, 2015). In the context ofrobots, being able to program a smartphone makes much more sense, as the smartphone can bemounted on the robots, thus allowing to give it a face, a voice and other sounds, and additional sensors
such as acceleration, inclination, magnetic field, GPS, voice recognition, computer vision. Also, since
only a smartphone is needed with Catrobat, the programming can be done on the spot, outside, e.g.,when using one’s land-based robot or flying drone outdoors. With Catrobat no laptop or PC is necessary,thus, coding can take place anytime and anywhere, and in particular can be widely made available evenin less affluent communities around the world. In addition, Catrobat released a Scratch Converter toallow the conversion of existing Scratch projects to the Catrobat language directly within the app, so
there are, in fact, now more than 30 million projects available for remixing and inspiration to our users.
The Pocket Code interface consists of several very distinct areas: First, the app itself with a main menu
and the collection of downloaded or developed-by-oneself projects, second a community sharing36
platform, which is integrated into the app as a web-view, and which serves as a learning, sharing,
remixing, cooperation, and publishing place, third the “stage” where projects get executed on the phone,
and additionally a sophisticated graphical editing program that allows to draw and edit the looks of allactors, objects, and backgrounds of one’s projects. This community website provides an online platform
for users to download and upload programs, share them with other users, search for programs, and toprovide feedback, e.g., write a comment to a project or rate a project. In addition, tutorials and starter
projects are provided. In the community website’s project overview, users can execute the project
directly in desktop browsers (HTML5 web player), download the project to the Pocket Code app, or
download the project as a standalone Android app. In addition, the tool automatically creates statistics
from Pocket Code projects and provides an online code overview. The project details page is illustrated
in Figure 4. Figure 5 illustrates the options within the main menu.

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Figure 4: Pocket Code web share: project details page with code statistic and code view

If the user starts first with a new and empty program, it initially only contains one empty backgroundobject. With the “+” sign users are able to add objects, looks, or sounds (depending on which activity
he or she is in). The background object itself can be assigned to several backgrounds, which can beexchanged during runtime. The background can also have its own scripts. Every project can consist ofmultiple objects and at least one background (which is a special kind of object). Every object can holda.) scripts that define the behavior of the object, b.) looks which can be changed and used, e.g., forobject animation, and c.) sounds to make the object play music, other sounds, or recorded speech.
Scripts can control the looks and sounds. Looks can be drawn and edited with Pocket Paint. Pocket
Paint37 is a second app of Catrobat available on Google Play, which allows users to create their own
objects with a pencil or different shapes (note that we currently work on integrating this second app
completely in Pocket Code in order to simplify the installation for our users). Distinctive features of
Pocket Paint include the ability to use transparency, to zoom in up to pixel level, to change thedimensions of the looks, and to use layers, the latter being particularly interesting to create consecutive
looks from an animation series. In addition, users can add looks with their camera, from their phone’s
memory, or use Catrobat’s Media Library with a collection of predefined graphics. To add a new soundthe user can either record a sound directly in Pocket Code, add a sound from the Catrobat Media Library,or add a sound from the phone’s memory. This workflow is illustrated in Figure 6.

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Figure 5: Pocket Code main menu: within the settings menu you can find, e.g., the accessibility preferences or
the Scratch Converter; 2) create a new project by starting with an example game or with an empty game; 3)project overview: tap on one to execute or modify it; 4) find help: videos, tutorials, step-by-step tutorials,
education page for teachers and students or google groups forum; 5) download and play games from otherusers; 6) upload your game to the sharing platform.

A script is a collection of code blocks that contain the logic of programming and define the operationsof the object. Thus, it is possible to move the object and access its properties and change them. For
adding scripts there are seven different brick categories (see Figure 7): a.) The Event category in darkorange that contains hat-bricks or broadcast bricks. Hat bricks are special kinds of bricks that,
depending on certain circumstances such as a tap on an object, start the attached script; b.) The Control
category in orange contains if-then-else bricks, loop-bricks to control the ﬂow of the script, bricks toswitch between scenes, clone bricks, etc; c.) The Motion category in blue color contains bricks tomanipulate the object’s position, orientation, or movements; d.) The Sound category in purple contains
bricks to start and stop sounds, manipulate the volume, or accept spoken input; e.) the Looks category
in green contains bricks to change the graphical appearance of the object, e.g., set/change size,brightness, transparency or hide/show the object as well as set a certain look to animate the object, toshow speech and think bubbles, or to ask for written user input; f.) The category Pen in dark green holds
bricks for drawing lines (a pen that follows the object) and the option to leave stamped marks of theobject on the background, g.) The Data category in red contains bricks to manipulate variables and lists,e.g., to set/change variables, maintain lists, add/insert/replace items, and show variable content on thestage. This color scheme makes is possible to understand scripts more easily through of the bricks’
color which supports readability. By activating extensions in the settings menu, additional categories
appear for Lego (yellow), Drone (brown), Arduino, the Phiro robot (both in cyan), etc.

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Figure 6: Pocket Code’s UI: add a look/object or add a sound with the “+” sign.

In contrast to Scratch and Snap!, Pocket Code does not use bricks for formulas. Instead, there is aformula editor that looks like a calculator and allows the creation and execution of mathematical andlogical formulas that can be used in bricks. In (Harzl et al. 2013) we compared of blocks-based (like in
Scratch and Snap!), text-based (like with traditional programming languages such as Python), andhybrid ways (such as Pocket Code) to enter and edit formulas, and showed that teenagers can createand debug complex formulas using a hybrid editing mode faster and with less errors than with theblocks-based as well as purely text based approaches. All advantages of block-based interaction
modes, such as easy discoverability of features, translation into many human languages, avoidance ofsyntax errors, and immediate feedback about current values (including dynamic sensor values), are not
only present in Pocket Code’s formula editor, but are additionally enhanced by the familiarity of how
formulas are written, changed, and read by users in other contexts, as well as by the familiar interface
of an electronic pocket calculator (app). Pocket Code’s formula editor is shown in Figure 8. It consistsof an input field to show and compose the formula, a keyboard, and a compute button to display thecurrent result. On the keyboard, five categories for various values, functions, and operators areavailable. a) Object: a collection of values of the current object, e.g., values for the X- and Y-coordinate,or the current speed, b) Functions, such as sin or cos, a random number generator, or list and string
functions, c) Logic is used to compare values or to combine logical expressions, d) in Device there isinformation that the smartphone or tablet records, e.g., inclination, loudness, or GPS data, and e) Data
stores created variables and lists and shows their last value.
With a tap on the play button the program starts. The objects are shown on the stage and the scripts
are executed. To stop or to pause the program, the user has to tap on the back button of the phone. A
stage menu appears which can be seen in Figure 9. The stage is organized in a logical coordinate
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system with an X- and Y-axis, which allows an exact positioning of the objects. This axis can bedisplayed in the stage menu (see Figure 9c).

Figure 7: Script categories:
choose bricks from the sevenbasic available categories.

Figure 8: Formula editor: the value for the
direction can be deﬁned as a constant or, e.g., asensor can be chosen by tapping on "Device".

Figure 9: Stage; a.) tap the play button to start the program, b.) tap the back button of the phone to pause thegame, c.) in the stage menu the user has five options: 1. tap back again to stop the game and switch back to
editing of project, 2. restart the game, 3. resume the game, 4. add a new preview picture to the project (this will
be shown, e.g., on the sharing platform), and 5. display the x/y axes on the device screen.

Projects and Further Work
This new and forward-thinking approach to code on mobile devices received national and internationalrecognition. The Catrobat project has won a number of awards, including 2016 two Lovie Awards ex
aequo with Red Bull and Doctors without Borders, evaluating the best European digital projects in
London, and the Reimagine Education Award for innovative educational projects at the Wharton
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Business School of Pennsylvania38. Additionally, in March 2017, Catrobat won the “Platinum Award” inBest Mobile App Awards Best Educational App category and 2016 the “Internet for Refugees”39 award
for a Right-to-Left language implementation of Pocket Code, which supports several RTL languages,e.g., Arabic or Farsi, and particularly focuses on refugees and teenagers in crisis or development areas.
A new project was started in January 2018 which promotes remote mentoring by connecting female rolemodels with female programming beginners. This idea was awarded with the “Closing the Gender Gap”
prize of the Austrian NetIdee in November 2017. During the European H2020 project “No One Left
Behind” (NOLB), the team developed a special flavoured school version of the app with the name“Create@School”. This version compromises, e.g., the gathering of analytics data for visualization, theintegration of accessibility preferences, and the development of pre-coded templates. Currently a new
flavored version customized for female teenagers is in development. This version with the name
“Luna&Cat” promotes special content for girls, like featured and user-contributed programs, media
assets, and tutorial videos.

Figure 10: “Stitched” patterns in Pocket Code. Picture on the right with kind permission from Andrea MayrStalder, www.TurtleStitch.org project.

One new feature which is currently under development is an extension to program embroiderymachines. Once available, self-created patterns and designs can be stitched on t-shirts, pants, or evenbags or shoes. With Pocket Code, the embroidery machines will be programmable, similar to theexisting TurtleStitch40 project, which realizes this concept on a PC (while with Pocket Code only asmartphone is needed). As a result, teenagers have something they can be proud of, something they
can wear, and they can show to others. This feature has proven to be especially engaging for female
teenagers and shows them new ways of expressing themselves creatively through coding. Figure 10
shows an example of an embroidery pattern made with Pocket Code.
Another new beta feature allows registered users to sign and release the Catrobat project as apps on
Google Play. Users optionally also add mobile ads to earn money. In developing countries, mobile
technologies are playing an important role in developing economies (Alderete, 2017). This is because
mobile phones and the mobile internet require considerably fewer financial resources in comparison toa traditional desktops and laptops (Stork, 2013). Due to lack of alternate employment opportunities in38

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developing countries, the low cost of investment is a critical enabling factor for new entrepreneurs(Alderete, 2017). The economic advantage of releasing an app on Google Play or integrating AdMob
within apps can motivate many people to learn programming and solve issues digitally. This is especiallybeneficial for under-privileged user groups who have access to limited resources like computers andcontinuously available electricity. They can benefit by sharing their creativity with others and serve a
global market with minimal resources such as a low-cost smartphone and mobile internet, which are
increasingly available everywhere. Figure 11 shows an example of apps with AdMob integration.

Figure 11: Android apps with AdMob banner advertisement created with Pocket Code.

Conclusion and Discussion
The aim of this paper was to provide an overview about the Catrobat project and the Pocket Code app
as of July 2018. Pocket Code provides an easy way to start coding. It is not intended to allow thedevelopment of standard applications, but to promote understanding of the logic behind coding andfoster computational thinking skills, thus following a constructionist approach in learning by doing andthe creation of sharable artefacts. Creative and artistic talents can be recognized and learning can occur
in a user-centered, project-based setting with the use of new media. Users of Pocket Code are mostly
teenagers who can learn from each other and share their ideas to create new games and other apps
together. The community sharing platform allows users to give and receive feedback, support, andassistance from others around the world, thus allowing our users to stand on the shoulders of their peers
and learn from each other. They can try out new ideas and realize the projects they define forthemselves, aided and inspired by likeminded others in a user-friendly and social environment. Catrobat
fosters diversity and learning in a worldwide community. Our goal is to empower teenager all over theworld to realize their potential and express themselves creatively with today’s and any future technology.

References
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119Constructionism 2018, Vilnius, Lithuania

Making Constructionism Great Again
Gary S. Stager, gary@stager.org
Constructing Modern Knowledge, USA

Abstract
The Constructionism community is at a crossroads with the passing of Seymour Papert, the uncertain
future of Logo, and the emergence of simpatico movements outside of the academy. This session will
explore unfinished work in addition to the challenges and opportunities faced by the next generation of
constructionists. What will it take to sustain its relevance and make constructionism great again?
Topics explored include:








The Premature Death of Logo
Seeing the “Entire Elephant”
Piaget versus Popular Coding Curricula
Twenty Things to Do with a Computer Today
Agency and the Lost Art of Teaching
Know Who Your Friends Are
The Progressive Imperative

Keywords
Progressive education, constructionism, Logo, Seymour Papert, Scratch, physical computing, CS4All

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Constructionism 2018, Vilnius, Lithuania

Turning Theory into Practice – Spreading
Constructionism
Gary S. Stager, gary@stager.org
Constructing Modern Knowledge, USA

Sylvia Martinez, Sylvia@inventtolearn.com
Constructing Modern Knowledge, USA

Abstract
In age marked by ascendant instructionism, the presenters have led two initiatives that introduced
constructionism successfully to preschool – high school educators around the world. Withoutcompromising the powerful ideas of Papert or his learning theory, the presenters have madeconstructionism accessible and resonant among practicing educators through publishing andprofessional development efforts. Countless educators have been inspired bring constructionism to life,
often accompanied by the use of cutting-edge technology, without government, foundation, or academic
support.
Five years ago, the presenters published Invent To Learn: Making, Tinkering, and Engineering in theClassroom. This book is quite possibly the most popular text ever written about constructionism andhas been translated into multiple languages. Invent To Learn sought to situate the emerging makermovement in a theoretical context of constructionism and historical context of progressive education
while building a bridge between the informal learning movement outside of schools and soundclassroom practice. Constructionism pervades the text explicitly and tacitly. The success of Invent To
Learn led to the publication of ten other books by constructionist educators. During this plenary session,
the authors will reflect upon lessons learned about learning-by-making, teaching, and school change
since the time of publication.
Nearly twenty years ago, Gary Stager and Seymour Papert engaged in multiple conversations aboutbuilding a different bridge; one between our progressive education colleagues suspicious of modernity
and an educational technology community that, in Papertian terms was increasingly “idea averse.” While
Papert was never able to convene such a summit, Gary Stager created the annual Constructing Modern
Knowledge summer educator institute. Over eleven years, Constructing Modern Knowledge has createdan immersive learning environment modeling constructionism and pedagogical strategies developed
collaborative with Seymour Papert during their “prison project.” Papert’s unique emphasis on thecompetence of educators, the absence of coercion, computer as material, powerful ideas, technology
as prosthetic, project-based learning, and the centrality of the learner – especially when the learner is ateacher - create the conditions for countless educators to not only develop exceptional computational
fluency, but construct personal lessons for creating productive contexts for learning in their personalschool contexts.
At the Constructionism 2018 Conference, we will share the unique structure of Constructing Modern
Knowledge, along with learning stories and project vignettes supporting the efficacy of constructionism
by and for educators willing to take off their teacher hats and put on their learner hats.

Keywords
Constructionism; Logo; Seymour Papert; maker movement; coding; fabrication; professional
development; progressive education

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Constructionism 2018, Vilnius, Lithuania

The Evolution of a Constructionist Teacher (withSome Reminders from Seymour)
Carol Sperry Suziedelis
Millersville University, USA

Abstract
Many teachers who are aligned with the organic, creative, and dynamic ideas of Constructionism find it
difficult to navigate in traditional waters. The obstacles are many: rigid curriculum, excessive testing,lack of resources, few allies, inability to articulate philosophy to the satisfaction of the powers that be. It
takes courage, and this paper attempts, through anecdote and narrative, to offer ways and means todevelop a Constructionist mindset and the tools and attitudes to effect changes. We hope to inspire
discussion of topics such as what it means to learn, thinking about thinking, the importance of teacher
engagement, relationships, relevancy, aesthetics, gender issues, and project-based learning.
Along the way, we will remember Seymour Papert, his dedication to the possibilities of “learning as adimension of life,” and resurrect some of the ideas” he used to inspire us.

KeywordConstructionist teacher; dynamic

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Constructionism 2018, Vilnius, Lithuania

Reempowering Powerful Ideas
Uri Wilensky, uri@northwestern.edu
Northwestern University, Chicago, IL, USA

Abstract
In Mindstorms, Papert expressed many ideas foundational to Constructionism. Among these are how
children programming can be transformative for learning, the empowerment children gain by creative
expression with the computer, and powerful ideas of computation. In the nearly four decades since
Mindstorms, there has been great progress in realizing these potentials. In particular, there has been
wide recognition of the importance of coding and of computational thinking for all and many initiatives
have arisen to galvanize these efforts. These new efforts are exciting for constructionists and we haveseen public and academic vindication for these ideas.
In this paper, I point out that, in addition to these ideas, there is a part of Papert’s vision in Mindstorms,
that is more neglected. That is the more general notion of powerful ideas, not solely ideas inherent incomputation such as variables, procedures and recursion, but also powerful ways of thinking from otherdisciplines that are made more powerful and accessible through computation. Mindstorms expoundedon turtle geometry, making it accessible both through programming the turtle and engaging with turtle
microworlds. In either mode, the turtle added to the Euclidean point one other property, that of aheading, and therefore angular velocity. This change results in a new definition of a circle, one that is
intrinsic and so connects geometry to the powerful ideas of calculus. This change in the Euclidean pointis a case of what Wilensky and Papert called a restructuration, a change in the representational
infrastructure used to encode knowledge. Restructurations throughout history have increased thepower, usability and learnability of formerly difficult knowledge. Classic examples are therestructurations of arithmetic from Roman to Hindu-Arabic representation and the restructuration ofkinematics from natural language to algebra. Both of these dramatically democratized access to thesepowerful ideas. Wilensky & Papert argued that, like in turtle geometry, computational representations
can serve as the basis for significant restructurations -- restructurations that increase the power andlearnability of powerful ideas in science. Like the turtle, a powerful means of creating restructurations is
to add agency to primitive elements. This can be achieved through agent-based computational
approaches. In this paper, I’m going to show examples of the many different restructurations thatmembers of the CCL lab have constructed in the past decade including NetLogo-based restructurationsof powerful ideas of biology, materials scienceand economics -- and invite the constructionist
community to increase its efforts in creating
computational representations of powerfulideas. These ideas are accessed throughmulti-turtle programming and emergent
systems
microworlds.
Agent-based
representations provide “objects-to-think with”
that facilitate powerful ideas of discrete
mathematics, probability and network theory.
As the world increases in complexity, citizens
Agent-based models of a) artificial selection ofsunflowers b) predator-prey ecosystemof a society increasingly require use of thesepowerful ideas to make sense of their naturaland social worlds and to be empowered to make meaningful changes in society.

Keywords
powerful ideas; restructurations; agent-based modeling, NetLogo

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Research papers

124Constructionism 2018, Vilnius, Lithuania

Agent-based Construction (a-b-c) Interviews: A
Generative Case StudyÜmit Aslan, umitaslan@u.northwestern.edu
Learning Sciences, Northwestern University, USA

Uri Wilensky, uri@northwestern.edu
Learning Sciences, Computer Science and Complex Systems, Northwestern University, USA

Abstract
We propose agent-based construction (a-b-c) interviews as a new research methodology specificallydesigned to expose patterns of reasoning about emergent phenomena and complex systems. In an ab-c interview, the researcher acts as an active mediator between the participant and an agent-basedmodeling environment. As the participant describes the model, the researcher tries to write thecorresponding code and probes the participant about his or her reasoning. In this paper, we present a
generative case study in which an adult participant constructs a NetLogo model of aging with the helpof a researcher. We conduct a preliminary grounded analysis of this case study and trace the evolutionof the participant’s model throughout the hour-long interview. Our findings show that the act of mediation
between the participant and the agent-based modeling environment can potentially afford, at times evenobligate, the researcher to continuously make on-the-fly hypotheses about the participant’s thinking,present these hypotheses through writing the model’s code, and get immediate feedback from theparticipant. Our findings also show that a-b-c interviews can potentially expose more fine-grained,
spontaneous, and connected reasoning processes that cannot easily be studied through traditional taskbased or verbal clinical interviews.

Keywords
agent-based modelling; complex systems; emergence; knowledge; reasoning; research methodologies;
clinical interviews

Introduction
Many of the world's pressing issues can be conceptualized as emergent phenomena. That is, they aremacro level observable patterns emerging from micro level interactions between numerous individual
entities (Johnson, 2006; Mitchell, 2009; Wilensky, 2001). Some examples are climate change,
migration, and epidemics (United Nations, 2016). Research suggests that learning about complex
systems and developing relevant reasoning skills would greatly benefit those who engage with theworld's pressing issues in any formal or informal manner such as democratic participation, personal
choices, or policy making (e.g., NRC, 2012; Sterman, 1994). However, studies have shown that it isdifficult for most people to make sense of emergent phenomena, and the complex systems suchphenomena arise from, because they are non-linear, non-deterministic, decentralized, and multi-leveled(e.g., Chi, 2005; Wilensky and Resnick, 1999). More importantly, even though lots of research has been
done, there is controversy about the nature of reasoning processes that cause such difficulties (e.g.,
Chi and Roscoe, 2002; Jacobson, 2001; Hmelo-silver, Marathe and Liu, 2007; Levy and Wilensky, 2008;
Penner, 2000; Sengupta and Wilensky, 2011).
Almost all of the studies on reasoning about emergent phenomena and complex systems rely on clinicalinterviews focusing on relatively simple, well-understood emergent phenomena such as diffusion ofliquids (Chi, 2005) or ants foraging for food (Jacobson, 2001). During a clinical interview, a participant
is presented with tasks, cases, or verbal questions and is expected to produce verbal answers (Clement,
2000; Ginsburg, 1997). We argue that the expectation of verbal articulation might be a limiting factor
when it comes to emergent phenomena because it may be too difficult to form coherent on-the-fly
explanations about phenomena or systems that include many actors, interactions, levels, and125

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stochasticity. It may be necessary to support participants with an infrastructure that helps offload someof the more difficult aspects such as randomness and hypothesis testing.
We propose a new research methodology that emerged from our previous research on learning andthinking about emergent phenomena through agent-based modeling (Aslan and Wilensky, 2016a;
2016b). We tentatively call this approach agent-based-construction (a-b-c) interviews. As
the name suggests, a-b-c interviews ask for participants to construct an agent-basedmodel of an emergent phenomenon. A-b-c interviews incorporate agent-based modelingbecause it has been shown to be a particularly powerful methodology in learning and reasoning about
emergent phenomena (e.g., Klopfer, 2003; Wilensky, 2001; Wilensky and Reisman, 2006; Wilensky andPapert, 2010; Wilkerson-Jerde and Wilensky, 2015). We hypothesize that observing people's reasoning
as they are trying to construct an emergent phenomenon through agent-based modeling can offer us
insights that may otherwise not be possible through traditional clinical interviewing. We also hypothesizethat a productive avenue would be for a researcher to act as an active mediator between the participant
and the agent-based modeling environment. This way, it would be possible to work with participants
who do not have any prior experience in computation or agent-based modeling. It would also enable
the researcher to observe and model the participants’ reasoning on a moment-by-moment basis (Sherin,
Krakowski and and Lee, 2012).
In this paper, we attempt to formulate a working definition of a-b-c interviews through presenting agenerative case study with an adult participant who constructed a model of aging with the help of a
researcher over the course of an hour. We analyze the interaction between the participant, theresearcher, and the agent-based modeling environment and reconstruct a timeline of the evolution of
the participant’s model. We discuss the results of our analysis and determine main features, as wellas major challenges, of a-b-c interviews.

Theoretical underpinnings
Our proposal of a-b-c interviews as a research methodology is founded on two paradigm shifts inthe way we study knowledge and reasoning, and two paradigm shifts in the way we make sense of theworld around us: (1) the theory of constructivism, (2) the methodology of clinical interviewing, (3) the
field of complex systems, and (4) the practice of agent-based modeling. We hypothesize that a fruitful
way to study people's ways of reasoning about the world could be through studying their reasoning
about emergent phenomena and complex systems, so we propose a new research methodology
specifically designed for the intersection of these four paradigm shifts. In this section, we review eachof these topics briefly and explain our reasoning on why they are important for our proposal.
We begin with the emergence of the theory of constructivism, which fundamentally changed ourunderstanding of the nature of human knowledge and reasoning. Piaget (1972) and his colleagues
successfully demonstrated that knowledge is not readily acquired from an outside source but is actively
constructed by the learner. Underpinning our proposal are two specific theories that extend Piaget's
theory of constructivism: (1) constructionism and (2) knowledge-in-pieces. Constructionism is a theoryof learning that takes constructivism's connotations of "learning as building knowledge structures" and"to know an object is to act on it" (Piaget, 1972, p.20), and adds the idea that this "happens especiallyfelicitously in a context where the learner is consciously engaged in constructing a public entity" (Papert,
1980; Papert and Harel, 1991). Knowledge-in-pieces (KiP), on the other hand, is a constructivist theory
that builds on artificial intelligence researchers’ attempts to model knowledge and learning (e.g.,Anderson, 1983; Minsky, 1986; Newell and Simon, 1972). KiP challenges conventional notions ofknowledge as monolithic self-contained structures and conceptualizes it as a loosely organized networkof primitive elements that are activated spontaneously and continuously reconfigured as the reasoning
process is happening (diSessa, 1993).
The second paradigm shift is the methodology of clinical interviewing, which was invented by Piaget in
the process of developing his theory of constructivism (Ginsburg, 1997). In contrast to standardized
tests, which cannot go beyond behavioral manifestations of knowledge in pre-determined boundaries,
clinical interviews give researchers the freedom to design object manipulation tasks, ask clarificationquestions, make on-the-spot hypotheses about subjects' reasoning, and test these hypotheses with126

Constructionism 2018, Vilnius, Lithuania

follow-up questions. Thanks to these strengths, clinical interviews provide opportunities to study morenaturalistic forms of reasoning and to uncover hidden structures and processes (Clement, 2000;
Ginsburg, 1997). Even though the original clinical interviews conducted by Piaget and his colleagues
were mostly based on simple tasks for children, the methodology has evolved considerably over theyears and became more reliant on verbal question-answer sequences. In addition, researchers haveshown that participants’ reasoning about a subject may evolve and even develop further during thecourse of a clinical interview (Sherin, Krakowski and Lee, 2012).
The third paradigm shift that we incorporate in our proposal is the emergence of the field of complex
systems due to the way it dramatically changed our understanding of the world around us (Bar-Yam,
2004; Mitchell, 2009; Waldrop, 1993; Wilensky, 2001). A complex system is defined as "a group ororganization which is made up of many interacting parts" (Mitchell and Newman, 2001, p. 1). Our lives
are embedded in many complex systems such as the internet, economies, the brain, ecosystems, theweather and the immune system. Within these systems, many simple entities — often called
components or agents — organize themselves without any central controller and the interactions
between them result in a "collective whole that creates patterns, uses information, and, in some cases,
evolves and learns" (Mitchell, 2009, p.4). The macro-level patterns which arise out of micro-levelinteractions between the parts of complex systems are called emergent phenomena (Wilensky, 2001).
As a result, many real-world phenomena are non-linear, non-deterministic, stochastic and multi-leveled(Mitchell, 2009; Wilensky, 2001; 2003).
Lastly, agent-based modeling (Epstein, 2006; Wilensky & Rand, 2014) is a practice that emerged fromthe field of complex systems that "makes use of simple computational rules as the fundamental
modeling elements" (Wilensky and Papert, 2010, p. 7) Hence, it is a paradigm shift from traditional
aggregate-level models that are built using methods such as linear algebra or differential equations.
The main reason that makes agent-based modeling compelling for our proposal is the fact that it offersa better epistemological match to our intuitive notions of parts that make up complex systems as distinct
individuals or entities instead of aggregate populations (Wilensky and Papert, 2010). Thus, it is possible
to teach the basics of agent-based modeling to a research participant and have them describe a realworld phenomenon through characteristics of entities such as people, organizations, or objects. In our
initial formulation of the a-b-c interviews, we use the NetLogo agent-based modeling environment(Wilensky, 1999) as the construction tool because is the most widely used agent-based modelingenvironment and it has its roots in both the field of complex systems and the field of learning sciences(Wilensky, 1999; 2001). It is a direct descendant of the Logo programming language (Papert, 1980) and
it is designed to be a "low threshold, high ceiling" programming environment. Research has shown thatstudents as young as in upper elementary school level can learn to develop models (e.g., Wilensky,2003), but it is also used by professional scientists in cutting edge research (e.g., Maroulis et al., 2010;
Pumain and Reuillon, 2017).
We argue that, much like the wood blocks used in Piagetian interviews on conservation of volume(Piaget, Inhelder and Szeminska, 1960), agent-based modeling offers material affordances that match
well with real world phenomena. Actively constructing an agent-based model would require a participant
to explicitly think about the constituents of an emergent phenomenon, making it possible to observe theparticipants’ reasoning processes at more fine-grained levels as they unfold over the course of aninterview. Being an active mediator between the participant and the agent-based modeling environment
enables researchers to work with non-programmer participants on complex phenomena through anactive dialogue that involves making and testing on-the fly hypotheses about the participant’s reasoning.

A generative case study
In this section, we present a preliminary form of an a-b-c interview as a generative case study(Clement, 2000). The data presented here is taken from a previous study that was designed as anintervention to introduce the basics of agent-based modeling to adults with no prior experience incomputational practices, and then help them develop a NetLogo model with the specific goal of exploring
the effects of such an experience on their reasoning about stochastic phenomena. The intervention
consisted of three one-on-one meetings with each participant. In the first meeting, the participant was127Constructionism 2018, Vilnius, Lithuania

shown the basics of the NetLogo agent-based modeling environment. In the second and third meetings,
a researcher helped each participant develop a model of a real-world issue of their choice. The
participant described the model he or she wanted to develop and the researcher wrote thecorresponding NetLogo code.
As we were analyzing the video data from this study, we came to notice the rich interaction between the
researcher and the participants during the model construction process and decided to recalibrate our
focus on the ideas presented in this paper. The case study shared in this section is one such interview
conducted with Karina (pseudonym), who works as a special education paraprofessional in a publicschool in a large city in the U.S. We chose this case because it captures a single-meeting that starts
with Karina expressing her idea and ending with her being satisfied with the model she developed withthe help of the researcher.

Methodology
The interview with Karina lasted 50 minutes and 7 seconds and it is recorded in video. We analyzed thevideo by watching it through a qualitative video analysis software and marking instances in the video
during which the participant was actively talking. We discarded in-between instances during which theresearcher either just worked on writing the NetLogo code or explained how the code works to theparticipant. After this initial round of data reduction, we ended up with 22 short episodes. Out of these22, we determined 9 of them as main episodes for the purposes of this paper. Then, we transcribed andanalyzed each episode in detail by marking the parts of transcripts that highlighted Karina's reasoning.
Finally, we built textual and visual representations of Karina's model for each episode (i.e., Sherin,
Krakowski and Lee, 2012). We are going to present transcripts from these 9 episodes with visual
snapshots of the NetLogo model, as well as our visual reconstruction of her model.

The topic of the interview: population decline
The emergent phenomenon presented in this section is chosen by the participant herself. Before theinterview, she mentioned to the researcher that she read some news about declining populations inJapanese villages and she wanted to build a model of this issue. Population decline, or ageing, is listed
as one of 18 global issues that "transcend national boundaries and cannot be resolved by any one
country acting alone" by the United Nations (2016). It exhibits itself in many levels of the society and it
is a constant headline in news, especially in developed countries, due to its implications for global
economy in terms of workforce and health care systems (e.g., Anderson and Hussey, 2000; Rowe etal., 2016). It is a very suitable topic for the purposes of this paper because it is an emergent phenomenon
embedded in greater complex systems in various levels such as local populations, global economies
and healthcare systems.

Karina’s model of population decline
The interview starts with Karina telling the researcher that she wants to work on a completely new ideathat she came up with after coming across a story about the issue of ageing in Japanese villages in thenews. The researcher welcomes her decision and asks her to articulate her idea on the record (seeTable 1). She briefly talks about her idea and stops. The NetLogo model shown in Table 1 only contains
two conventional buttons, setup and go, but no code.
Table 1. Karina's description of her original idea
Screenshot

Transcript

Diagram model(K)arina: So, the idea is based on a fact,which is, in Japan 40% of the population isover 60 years old if I’m not mistaken. So,
they are coming across, umm, they areexperiencing, what, what’s my word? what
did I say? I don’t, I can’t remember …

128

Constructionism 2018, Vilnius, Lithuania(R)esearcher: Deaths and child birth andaging …
K: Well, that the population is, no-one there
will be, the percentage of young people isless, therefore there is a lot less people
procreating. So, in some instances villages
are dying out because of the, umm, big
number of elderly versus young people andso for example, umm, a village of 300 is
now down to 30 because of such a highpopulation of old people.

After Karina's initial explanation, the researcher proceeds to create a simple model in which there areold people and young people. He stops before creating any people in the model and asks Karina about
how to visualize people in her model. As seen in Table 2, this question prompts Karina on not only
deciding how the people in the model should look like but also talk about actual agent behavior. She
also briefly mentions how she wants to be able to manipulate this model. We update our visual andtextual representation of her model accordingly.
Table 2. Karina's first description of the agents
Screenshot

Transcript

Diagram model

R: So, the turtle shapes, which one doyou prefer for people?
K: The people, oh, the old people
should be unhappy.
R: Old people should be unhappy?
OK.
K: Yeah, just because they can’t,yeah, sad face. Just for the fun of it(laughs).
R: Let’s start with the natural face,…,no no no, but they will get older
maybe. Or are we gonna just put older
people initially and leave it like that?
Or do you wanna create people likeyoung ages, get them older, makethem reproduce? Stuff like that?
K: My idea was, increase anddecrease the number of old people
and
how
that
affects
thepopulation.
R: Oh, I see. So, then people will be
old and young, right?
K: Yes. Well, old people will beconsidered people that can’t havekids, obviously. Or they are not
reproducing.

Once again, the researcher proceeds to write the code that, he thinks, will produce the model thatreflects the model in Karina's mind. He adds a number-of-people slider to the interface that determines
the size of the population's village and an elderly slider that determines what percentage of this village
is elderly people. When the setup button is clicked, the people of the model are created. Each person
is designated as either young or old and then they are placed on random locations in the model's two129

Constructionism 2018, Vilnius, Lithuania

dimensional world. Once he finishes writing this code, the researcher clicks the setup button and asks
Karina what she wants to do next. Table 3 shows Karina's response to this question.
Table 3. Karina's first description of agent behaviour
Screenshot

Transcript

Diagram model

R: OK. So, I have these people, they are in random
places. They are the residents of this village. Umm,
what’s next? What are these people gonna do?
K: Well, I’m assuming, umm, that’s what I’m trying towrap my head around. Umm. I guess if two people
meet, they are gonna reproduce one person. And
then, then now I’m thinking, yeah, we’ll start there.

After hearing Karina's idea, the researcher starts writing the code so that each person continuously
moves around randomly. When two young people touch each other, they produce a new young person.
Once he writes this code and shows it to Karina, they run the model and notice that the number of young
people grows exponentially in a very short time. In Table 4, Karina reacts to this outcome and notices
that the model is not complete. She adds that she wants the parents to become immediately old when
they make a baby.
Table 4. Karina's update of agent behavior
Screenshot

Transcript

Diagram model

R: Let’s see if this works, … oh yeah …
K: Wait, what about my old people?
R: They are, they are in there but we aremaking so many babies randomly …
K: (laughs) …
R: Maybe we should like decrease the…
K: So, my thing is now, now the green
people, the parents should be oldnow!
R: They should be gr, oh, wha? …
K: When they make one baby, yeah,
let’s just, yeah.

Once the researcher modifies the code accordingly, they run the model and they see that the numberof young people decrease very quickly but the number of elderly people keeps increasing. Here, Karina
thinks the outcome is interesting but her reaction indicates that she is not content with her model yet.
The researcher senses her hesitation and asks her to elaborate. She hesitantly deliberates on whether
the old people in the model should eventually die and what would be the implications of such an additionon the size of the village

130Constructionism 2018, Vilnius, Lithuania

Table 5. Karina's further updates of agent behavior
Screenshot

Transcript

Diagram model

K: I think it is interesting still, to see …
R: Yeah, almost everybody is old red, yeah …
K: But …
R: No, say it.
K: I was just gonna say, not that it would affectour findings, but the old people have to[expletive] die …
R: Wanna do that?
K: But that doesn’t really matter, you know what I’msaying?
R: Yeah! They just turn red and they cannot
reproduce again.
K: Right, exactly. But what does it tell about thesize of the village? Do you know what I’m saying?
R: Yeah! Do you wanna do that?
K: (Nods approvingly).

After this change, the researcher runs the model and Karina expresses excitement with the model
because everyone dies. This point is marked as the completion of her initial model.
Table 6. Karina's reaction to the first completed version
Screenshot

Transcript

Diagram model

R: Let’s see … So?
K: [expletive]! Everyone’s [expletive] oh damn! Lookat that!

After the addition of the mechanism of death, they run the model again and Karina now displays
excitement about her model. The researcher probes her about the model's outcomes. He alsointervenes with his own ideas. He first suggests adding a plot that shows the number of all the people
in the village and also the number of elderly over time. When they add this plot and run the model, they
see that the percentage stays relatively stable even though the village continuously declines andeventually dies. The researcher probes her about this outcome. She asks to change the model in a waythat young people may get old even if they never made babies. The researcher intervenes and suggests
adding the possibility of making more than one baby, too. When the researcher asks this question,
Karina decides that she wants each family to have between 1 to 3 kids.

131

Constructionism 2018, Vilnius, Lithuania

Table 7. Researcher’s intervention with his ideas
Screenshot

Transcript

Diagram model

K: I think it is interesting still, to see …
R: I don’t know what to make of this because thedifference between the elderly and, didn’t changemuch.
K: Yeah.
R: There was always similar proportion of elderly
and young people.
K: Hmm, but then the young people don’t stay, they
can’t go on forever until they make a partner, until
they meet a partner, right? Young people also die!
R: Yeah. Young people, if they make just one baby
they die out.
K: Right. That’s the only reason why they die. They
don’t die because of natural causes.
R: (Nods) They turn to old and they die. I think weshould also add the more than one baby thing here…
K: Yeah, yeah, yeah.
R: Because it’s like, of course this is gonna die because they don’t
make more than one baby, so like they keep decreasing.
K: Yeah, yeah, yeah. OK!

The idea of having multiple babies in Episode 7 causes confusion between the researcher and Karina,
probably due to the fact that the researcher introduced the idea himself. Episode 8 starts when the
researcher implements a mechanism for multiple babies. Karina pushes back because she wants toknow how this model works. Once the researcher explains how the current version of the model works,
she realizes that the model does not do what was on her mind and explains exactly how she wants themodel to work. Even more, she explains why she wants the model to work that way.
Table 8. Karina's first implementation of the idea of fertility rate
Screenshot

Transcript

Diagram model

R: So, we, we give them some initial
fertility. You know what that means?
K: OK. But, but, how many babies can bemade after the initial interaction? Is there arange or are they all automatic? OK there
is a …
R: Yeah. Right now, every single time I
create a person, I give them a baby count.
So, I have 3, you have 2, and after making
2 babies you turn old, after making 3
babies I turn old.
K: Oh! I see.
R: Minimum is 1, maximum is 3 and it’srandom.
K: OK. I see. But I guess, I guess what I’msaying is, from you with, you and I
132

Constructionism 2018, Vilnius, Lithuania

reproduce, how many babies are we gonnahave?
R: Just one. Every single time …
K: Oh! See, that’s where, that’s where I
wasn’t wrapping my head. I wanted it tobe where they can either make 1 baby to
3 baby, the family size.
R: Ah! That’s so much better!!!
K: Yeah. Because I’m thinking 3, because
3 is like the average in US.

In the final episode of the interview, the researcher and Karina run the version of the model where eachpair of parents procreate either 1 or 2 or 3 young people randomly and then turn to old people, as well
as some young people eventually becoming old even if they cannot find a partner to make babies. Whenthey run this version of the model, they notice that the population grows exponentially. Once again,
Karina probes the researcher about the way the model works. She asks whether a couple can have 0
babies. When she realizes that it is not the case, she tells the researcher that she actually wanted somecouples to have 0 babies because there are "some sterile people". After this episode, Karina wassatisfied with the model and the interview ended.
Table 9. Karina's reaction to the first completed version
Screenshot

Transcript

Diagram modelResearcher: It goes crazy.
Karina: And they can have 0 babies,
right?
Researcher: No!
Karina: Yeah, they can have 0 babies,
too!
Researcher: Let’s make it …
Karina: I thought that’s what we have
said, Umit! [expletive]! ….
Researcher: Let’s do …
Karina: Because I mean there is somepeople that are sterile.
Researcher: Yeah. So, …, this time thepopulation died out. Let’s make it 200,
maybe … Now it is like, interesting, …
Karina: (yelling) I’m [expletive] smart!!!
When am I gonna get accepted to U of C,(yelling) when? (laughs)

Discussion
Although it was an early form of a-b-c interviews, this case study provides us valuable insights tohypothesize about the main features of a-b-c interviews, as well as the major challenges in
conducting such interviews systematically. We summarize our preliminary findings in two arguments:(1) the interaction between Karina, the researcher and the model afforded us to observe patterns in
Karina's reasoning about this emergent phenomenon that may not have been possible through asking
only verbal questions, (2) the interview highlighted a reasoning process that resembles models or mini
theories that Karina constructed on the fly and continuously reconfigured drawing on many smaller133

Constructionism 2018, Vilnius, Lithuania

pieces of her knowledge from many different contexts and levels. We also discuss the researcher's
moves as an interviewer in two arguments (1) the researcher's active participation ended up
problematizing the validity of the findings but also provoked fruitful reasoning as seen in episodes 6
through 8, (2) the researcher failed in taking full advantage of Karina's model in probing her to elaborate
her reasoning in greater detail.

Karina’s reasoning
Episode 1 starts with Karina explaining the phenomenon she chose to model: the decline of the
population in Japanese villages. Within itself, this episode resembles a short verbal interview with her.
She mentions a positive feedback loop mechanism when she says "therefore there is a lot less people
procreating", brings up two variables when she utters "the number of elderly versus young people", andfinally speculates on the cause of this emergent phenomenon with her utterance "because of such ahigh population of old people". As the interview proceeds, we observe Karina develop a more and moresophisticated model. She first adds a mechanism for procreation and then adds a mechanism for aging,both through definitions of how young people behave and how old people behave. She also describes
how she envisions using the model by "increasing and decreasing the number of old people and howthat affects the population". Her reasoning goes beyond a simple causal relationship between thepercentage of elderly people and her final model incorporates the idea of fertility rate as the major
factor, although rather accidentally and implicitly. We argue that without the construction element of theinterview, we would not have been able to observe her reasoning about the individuals that make up
this complex system and we could have even concluded that she holds misconceptions (or naive
theories) about the phenomenon of population decline such as assuming deterministic mechanisms(Wilensky and Resnick, 1999).
Constructing an agent-based model with her enables exposition of her reasoning about an emergent
phenomenon at a fine-grained level including some less salient ideas. For instance, we notice thatshe draws from a number of ideas from her personal knowledge about how people behave andhow people procreate. Even though she is specifically thinking about the population of a Japanesevillage, she justifies her design decisions based on ideas from various resources in various levels suchas making old people look like sad faces because "they can't" procreate, making families have up to 3
babies because "it is the US average", and making it possible for some families to have no babies
because "some people are sterile". Taken together, these episodes show us that she is not just throwing
random ideas at the model. Quite the contrary, she has a specific explanation for each of her modelingdecisions.
Lastly, her modeling decisions expose a non-monolithic reasoning process about this emergent
phenomenon. For example, she decides to make the model so that "two people produce only one baby
and immediately get old" but with the caution that this is only where she wants to "start from". We seeanother such instance in Episode 4, when she hesitates whether the old people in the model should
eventually die or not. In both cases, we see her bringing together many pieces of her knowledge
about people, aging and procreation, but also having difficulty in figuring out how all these pieces fit
together when it comes to population decline. This finding is consistent with diSessa’s (1993)
knowledge-in-pieces theory, but we are cautious about such straightforward associations because we
do not have any data that could highlight the exact nature of this process, and further research needs
to be conducted.

Researcher moves
We notice a number of potentially problematic interventions from the researcher throughout theinterview. This is mainly due to the fact that his goal was not to interview Karina but to help her createa NetLogo model. The first of these interventions come at the very beginning of the interview. When
Karina asks the researcher to remind her of what she said about population decline, the researcher
mentions "deaths and child birth and aging". We observe all these ideas in Karina's utterances in thefollowing episodes. It seems like Karina does not immediately pick up these ideas and keeps talking
about her original idea but it is still unclear whether the researcher's move implicitly impacted Karina's
reasoning. Hence, a potential argument against the validity of our preliminary findings would be that134

Constructionism 2018, Vilnius, Lithuania

such moves might have led her towards specific ideas. We acknowledge that this is a major challenge
in conducting a-b-c interviews; much attention must be paid to prevent the interview from turning
into a teaching intervention. On the other hand, we argue that it is important to position the researcher
as an active mediator not only because the participant is assumed to be a novice in agent-basedmodeling, but also because the researcher attempts to model the participant’s reasoning through writing
the model’s code and gets immediate feedback from the participant. This is a direct extension of a major
strength of clinical interviews; the researcher is expected to formulate on-the-fly hypotheses based on
the participants’ responses and ask follow-up questions (Ginsburg, 1997).
In this specific case study, the researcher's interventions also end up triggering fruitful episodes
that, we argue, may not have happened otherwise. Such an intervention happens in Episode 6, when
Karina brings up the idea to make young people old after some time. The researcher suggests adding
an initial mechanism of "multiple babies" and she agrees. However, instead of probing Karina on how
to implement this idea in the model, the researcher proceeds to implement his own mechanism, which
ends up being quite different from what was in Karina's mind. She eventually catches the researcher'sintervention and forces the researcher to correct the model. This episode affords us to notice that Karina
wants to configure procreation in her model as a process that only happens in monogamous families.
This observation may or may not reflect her actual reasoning but implies that she assumes otherpossibilities as negligible. Unfortunately, the researcher does not ask follow-up questions about suchimportant points. This brings us to our last point that the researcher's moves fail in asking Karina
questions that probe her to elaborate her ideas in greater detail. We end up not being able to certainly
assert whether she actually changed her focus from the percentage of elderly to fertility rate. We also
cannot know whether she left out some factors, such as polygamy, nutrition and migrations, deliberately
or she did not think of those.

Concluding Remarks
It has been shown that people, young and old, struggle greatly in making sense of emergent phenomena(Chi, 2005; Wilensky and Resnick, 1999) although our lives are embedded in such phenomena in many
levels (Mitchell, 2009; Wilensky, 2001). Agent-based modeling has been shown to be effective at
progressing learners in their understanding about emergent phenomena (Wilensky, 2003; Wilensky andReisman, 2006). We proposed a new research methodology, tentatively named agent-based
construction (a-b-c) interviews, specifically to study people’s reasoning of emergent phenomena andcomplex systems. We situated our methodology at the intersection of four major paradigm shifts: (1) thetheory of constructivism (Piaget, 1972), (2) the methodology of clinical interviews (Ginsburg, 1997), (3)the field of complex systems (Mitchell, 2009), and (4) the practice of agent-based modeling (Wilensky,2001). We then presented a generative case study, which was originally designed as an intervention,but offered a first glimpse on potential affordances and challenges of a-b-c interviews. In the case study,
an adult participant, Karina, described a model of aging in a Japanese village and the researcher wrote
the code for her. We argued that the interaction between Karina and the researcher exposed complex,
spontaneous patterns of reasoning, which could not have been observed through verbal questions or
simple tasks such as drawings. We also argued that the main challenge of a-b-c interviews is also themain strength of them. The act of mediating is simply the act of hypothesizing about the participant’s
reasoning, much like traditional clinical interviews (Ginsburg, 1997), but testing these hypotheses
through writing the code and getting feedback from the participant instead of only asking verbal
questions. Yet, if not done carefully, this can easily evolve into intervening rather than mediating. We
end this paper by offering a very first working definition: a-b-c interviews are a special class of clinicalinterviews that are conducted through an open-ended agent-based modeling task that is actively
mediated by a researcher.

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Active Learning of Computer Science Using a
Hackathon-like Pedagogical Model
Jake Rowan Byrne, jake.byrne@tcd.ie
The Trinity Centre Research for IT in Education, School of Education and School of Computer Science& Statistics, Trinity College Dublin, the University of Dublin, Ireland

Kevin Sullivan, kevin@bridge21.ie
The Trinity Centre Research for IT in Education, School of Education and School of Computer Science& Statistics, Trinity College Dublin, the University of Dublin, Ireland

Katriona O’Sullivan, Katriona.OSullivan@mu.ie
Department of Adult and Community Education, Maynooth University, Kildare, Ireland

Abstract
Despite extensive literature on Computer Science (CS) education, there are few pragmatic pedagogicalmodels that support active and project based learning. The new Irish CS course for post-primary schools
advocates such an active learning approach through a series of “Applied Learning Tasks” (ALT). This
paper explores the use of a constructivist/constructionist 21st Century pedagogical model, delivered asa weeklong “Hackathon-like” activity, targeting the learning outcomes for the embedded systems ALTfrom this new CS course. Twenty-one students participated in the workshop and completed pre andpost surveys to assess their confidence in CS topics and their associated learning outcomes. Analysis
revealed that students were more confident in almost all the learning outcomes surveyed. There wereparticularly significant increases in the embedded systems learning outcomes, which was the focus of
the students’ projects. The findings suggest that the combination of a “Hackathon-like” event and aconstructivist/constructionist learning model can be effective in increasing student motivation,
confidence and learning of Computer Science concepts and skills at post-primary level.

“Froasties”: Participants created an automated food processor, specializing in French toast.

Keywords
constructivism; constructionism; hackathon; computer science education; creativity; design thinking;problem solving; prototypes; teamwork

Introduction
This paper builds on prior work (Byrne, O’Sullivan, & Sullivan, 2017) that explored students aspirations
for careers in Computer Science to examine how a similar intervention can be used to support Ireland’s138

Constructionism 2018, Vilnius, Lithuania

new Leaving Certificate examination in Computer Science (NCCA, 2017a). The Leaving Certificate (LC)is a specialized terminal exam for post-primary schools used to determine university admissions in
Ireland. The LC in computer science is being introduced in September 2018, so there is a need toestablish effective teaching methodologies which will lead to success for students preparing for theexam.
This work outlines the rationale for the design and delivery of a 21st century learning experiencemodelled around a hackathon-like event. Reportedly, hackathon attendees go with the hope of“learning” something new (Briscoe & Mulligan, 2014). This is the most popular rationale for attendance,
which implies that hackathons provide a viable context for learning. Additionally, the fact that hackathons
are most commonly technology-oriented creates a persuasive argument for incorporating a hackathonlike activity into the classroom to promote a “learning by doing” constructionist approach to learning(Kafai & Resnick, 1996).
The Bridge21 model promotes project-based and “learning by doing” values inherent in constructionist
learning (Kafai, Lee, et al., 2014). The model also emphasizes the importance of peer-to-peer learning
through a social constructivist philosophy and technology mediated learning (Byrne et al., 2017; Lawlor,
Conneely, Oldham, Marshall, & Tangney, 2018). All students attending the learning experience will have
had prior experience with the Bridge21 model.
Through their previous exposure, they learned to work in teams and to program with Blockly and
Scratch. These skills will be called upon during the learning experience when the participants are divided
into teams and guided in the creation of their product designs. They had four days to generate an idea
in the realm of IoT and wearables, prototype it, and prepare a pitch to “sell” the product. They had
Arduino, Python, Scratch and an assortment of sensors at their disposal throughout the process. The
collaboration and creativity required is expected to enhance their confidence and abilities with thesetechnologies.

Background
Introduction of Computer Science at post-primary level in Ireland
The Irish government has recently announced the introduction of a new curriculum/specification for
Computer Science as an optional subject as part of the Leaving Certificate (LC) at post-primary level(NCCA, 2017a). The Leaving Certificate is a high stakes set of exams which takes places at the end of
a two year cycle, equivalent to 11th and 12th grades in K-12. Leaving Cert results are used to determine
entrance to third level education. The new course hopes to develop traditional CS content knowledge
as well as project and team management skills in the students, in line with the “broad, balanced”
educational aims of the Leaving Certificate (NCCA, 2017b).
There are three strands to the current draft specification (NCCA, 2017a): Practices and Principles,
Cross-Cutting Concepts and Computer Science in Practice (Table 1).
There are a total of 66 learning outcomes spreads across these three strands. The aim is to use strand
3 projects to iterate through the learning outcomes from strands 1 and 2. This means not every learning
outcome will be addressed in each project, but after the students have completed all five projects they
will have covered all 66 learning outcomes.
This is the first time that Computer Science has been introduced as a dedicated subject in Irish schools
so potential teachers will have limited experience and practice in delivering such courses. This suggests
a need to develop practices and activities that can both support the learning of traditional content while
also supporting the development of team and project management skills.

Approaches for Enhancing STEM Education
There is a lot of discussion about various approaches to STEM education. This varies from approaches
that explore STEM education as a general phenomenon (Johnson, Brown, Cummins, & Estrada, 2012;
Sanders, 2009; J. Williams, 2011) to practical projects that explicitly describe STEM learning
experiences (Barker, Melander, Grandgenett, & Nugent, 2015; Fee & Holland-Minkley, 2010; Nugent,139

Constructionism 2018, Vilnius, Lithuania

Barker, Grandgenett, & Welch, 2014; Tangney, Oldham, Conneely, Barrett, & Lawlor, 2010). Despite
differing views on pedagogy, e.g. enquiry based, integrative or problem based learning, the need formore STEM graduates is generally agreed upon.
Table 1. Leaving Certificate Computer Science Course Draft Specification
Strand 1

Strand 2

Strand 3

Practices and Principles

Cross-Cutting Concepts

Computer Science in PracticeComputers & Society

Abstraction

User Centered Design

Computer Systems

Information Systems

Data

Analytics

Algorithms

Modelling & Simulation

Evaluation/Testing

Embedded Systems

Computational Thinking

Designing & Development

Supporting the approach of the new Leaving Certificate course, Roberts (Roberts, 2012) suggests thatboth domain knowledge and 21st century “soft” skills such as, teamwork, creativity, problem solving andinquisitive thinking are all important in STEM education. Roberts also suggests that domain knowledge
should be learnt through “authentic problem solving in rich social, cultural and functional contexts”.
These approaches would provide students with the rich learning experience that the new curriculum is
aspiring to promote.

Hackathons
A hackathon usually involves the prototyping of some digital artefact and pitching or presenting that
prototype as part of a team-based “problem-focused computer programming” activity (Briscoe &Mulligan, 2014) over a 24-48 hour period. Hackathons generally aim to combine both team dynamics
and the “authentic problem solving in rich social, cultural and functional contexts” that Roberts suggests.
This is a common motivating element of hackathons, which tend to focus on some cultural or social
issue that the participants can identify with.
There have been some attempts to explore which pedagogical approaches are used in hackathon-like
activities (Duhring, 2014; Nandi & Mandernach, 2016; Skirpan & Yeh, 2015). These studies fall short asthey have limited practical and theoretical basis and are generally exploratory in nature. However, they
do suggest that hackathon-like activities might be useful when considering the design of contextualized
and practical learning experiences.
Contextualizing STEM topics is seen as important in broadening participation so it is recommended tostructure activities so that they emphasize creativity, design and problem solving while working on realworld problems (Cooper & Heaverlo, 2013). Hackathon-like approaches include these elements andhave been used with pre university students in STEM subjects (Kafai, Rusk, et al., 2014). This suggeststhat hackathons could make for a viable framework to develop effective and inclusive STEM activities
but there is limited research on how to structure such a hackathon-like activity for learning.

21st-Century Learning
In addition to the introduction of Computer Science as its own subject there are efforts being made tointegrate 21st century skills across the curriculum (Dede, 2010; NCCA, 2014; Rychen & Salganik, 2005).
These involve the development of key skills such as effective communication, thinking critically andteamwork. The Bridge21 Model (Tangney et al., 2010) has been used to teach Computer Science using
a social constructivist pedagogical approach (Byrne et al., 2017). This approach focuses on team based
activities that are designed to develop both traditional content knowledge and 21st century skills.

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Materials and Methods
Bridge21 Pedagogy and Model
The Bridge21 learning experience emphasizes teamwork, learning by doing and a technology-mediatedapproaches (Lawlor et al., 2018). The model has been adapted for use in a wide range of subjects suchas history (O'Donovan, 2015), mathematics (Tangney & Bray, 2013) and computer science (Byrne etal., 2017; Tangney et al., 2010). It is a social constructivist approach that moves away from traditional
teacher-led learning as it is designed to foster intrinsic student motivation and learning potential (Lawlor,
Marshall, & Tangney, 2015). The teachers’ goal in this model is to facilitate the activities, by encouraging
students to think for themselves and model problem-solving processes. In line with the literature on
STEM and CS education, this approach promotes collaboration, discovery learning and problem
solving. Vygotsky’s (Vygotsky, 1978) “more able other” informs the team formation and facilitation topromote peer learning and mentorship. Constructivist and constructionist approaches are increasingly
being used to develop learning experiences especially when looking for students to be creative whileworking with computing technologies (Brady et al., 2017; Kafai & Resnick, 1996; Przybylla & Romeike,2014).

Bridge21 Activity Model
The Bridge21 activity model (Byrne, Fisher, & Tangney, 2015) follows 7 phases that can be
implemented over the course of an hour, a day, a week or as needed depending upon the scope of theactivity. The 7 phases are: Setup, Warm up, Investigate, Planning, Create, Present and Reflect. It
incorporates many elements known to be conducive to teamwork; self-directedness, creativity, andpositive self-driven experience. The activity model is a largely linear structure, with some iteration tocater to the personalized needs of the students, as well as the demands of a particular project. Thismethod also mirrors the Agile model of software development (Kastl, Kiesmüller, & Romeike, 2016; Rico
& Sayani, 2009; Romeike & Göttel, 2012). To maximize the educational outcomes, the structure of thehackathon follows the Bridge21 activity model.

Before the “Hackathon”
The attendees have previously taken part in introductory Bridge21 Computer Science workshops. Fromthese experiences the students develop a working knowledge of relevant technologies. For example,
they have been introduced to Scratch, Blockly, and LEGO Mindstorms through Bridge21. They’ve also
learned computing essentials such as conditionals, looping, initialization, variable instantiation. Thismeans that there is a consistent elevated baseline throughout all attendees and simulates a base levelof experience, equivalent to rudimentary learning that might occurs in early projects as part of theLeaving Certificate coursework. The students, having volunteered for this programme, have expressed
an interest in computer science, similar to the demographic who would choose to study CS as a subject
at LC.

Hackathon Challenge
Internally the event is referred to as “Invent Week” and is adapted from the popular hackathon model
already employed at levels ranging from hobbyist to professional settings. Traditionally, thesehackathons are held over the course of a consecutive 24-72 hours during which the participants are
nourished, housed, and free to work according to their own (often frantic) schedule in productive teams.
This breakneck pace and high-pressure environment would be inappropriate for many of the students
considering their age (15-17 years) and alternative commitments they may have. The same limitations
would apply in any secondary school trying to implement our hackathon-like model. As a result, we’vetailored the structure to be more accommodating.
Rather than leaving all of Invent Week as a solid block of time over the course of 1-3 days, the in-classhackathon-like event is run as a 4-day project where the students work from 9:30 am to 3:00 pm eachday. This allows them to tend to their other commitments, and maintain a healthy lifestyle without the
restraints imposed by a typical hackathon. This helps to make the model replicable at other institutions.
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Each of the 4 days will contain a specific focus as the project builds towards a final deadline at lunchtime
on the fourth day.
The goal of the week is to target one of the four Applied Learning Tasks (ALT) on the new LC CS course.
The Embedded Systems ALT was chosen as it was well suited to a hackathon-like event. The learning
outcomes from this project dealt with a number of topics: digital and analogue inputs, controlling digital
outputs, storing analogue input data, automating processes and designing a program that utilizes all ofthese elements. Invent Week was designed to explore these topics experientially, rather than throughtraditional content delivery.
Day one will correspond with the first 3 steps in the Bridge21 activity model, and partially the fourth.
This day will consist of the teams coming together for the first time (set up) and performing some ice
breakers including divergent thinking (warm up) about computers in everyday life. Students also learned
about the technologies they used including Python, Arduino, and Raspberry Pi through the completionof a number of set tasks (investigate).
The concept/idea planning phase should conclude early on day 2 as team members are assigned roles,
then production begins. They will be loosely monitored as they progress, but a mainstay of social
constructivist/constructionist education and the Bridge21 model is the experiential learning that comesfrom working through a process with peers using real world artefacts and programs. They will spenddays 2, 3, and part of 4 completing their project (create) before concluding the week by presenting theirproject on day 4.

Technical Infrastructure
In terms of embedded systems hardware the teams were provided with a Raspberry Pi running
Raspbian OS with an Apache webserver, PHP and Python; Arduino Uno boards; a motorized robotic
chassis with Grove shield/hat adapter add-ons for Arduino and Raspberry Pi; and an array of input
sensors (heart-rate detector, light detector, microphone etc.) and outputs (LEDs, servos, motors, etc.).
Additionally each team had access to a video camera, sound recorder, open source software (Audacity,
Scratch, etc.) and 2 PCs running Windows. Limiting the 4 person teams to 2 PCs is intended toencourage paired programming (L. Williams, Kessler, Cunningham, & Jeffries, 2000). The students
were encouraged to think beyond the confines of the provided hardware and software, as approaches
can be taken to simulate a desired outcome. They were supported by the mentors with help in finding
such approaches.

Mentoring and Facilitation
The learning experience was facilitated by one lead coordinator and 4 mentors, proffering a 4:1
student/staff ratio and a 1:1 team/staff ratio. The mentors were not assigned to specific teams. They
moved from team to team, monitoring progress and stayed out of the way when not needed but werealways open to query from any team. This was done intentionally to encourage the students to dissect
their problems as thoroughly as they could before requesting external assistance. This reflects the realworld practice of reaching out for expert advice from a technical consultant.
The lead coordinator had a background in mechatronic engineering, while the mentors each had
backgrounds in computer science. When called upon, the mentors would help the students to reach asolution, while taking care to not simply tell students the answer. This required a question-based or“Socratic” teaching approach where small but relevant bits of information are revealed until the student
could form their own solution. The aim is for students to develop their computational thinking skills,
which ideally helps them to grow in the LC learning outcome areas and become more independentproblem solvers.

Methodology
Twenty-one students participated in Invent Week. Ten of the participants were female, and eleven weremale providing a gender-balanced cohort. Ethical approval was sought from and granted by the Schoolof Computer Science and Statistics at Trinity College Dublin.
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The data collection consisted of a pre and post questionnaire design, each of which was completed at
the students’ own discretion. None of the questions were mandatory, and the students were made awarethat they could skip any part they did not feel like answering. Every question was personally evaluative,
asking things such as “I feel confident in my ability to evaluate different solutions to one problem” or “I
feel confident in my ability to describe what an algorithm is and give an example”. Every answer was
based on a 5-point Likert scale (with 5 being strongly agree, and 1 being strongly disagree). The totalof 63 questions were drawn from two sources.
The first 34 questions came from a questionnaire used in previous instances of Invent Week (Byrne etal., 2017) These queries establish the student’s interest and knowledge about pursuing careers incomputer science, through university and as a realistic future. This data is valuable for tracking what
information is resonating with the students, even when it’s not necessarily the central focus of the weekor this paper. The following 29 questions were drawn from the learning outcomes for the LC CS course(NCCA, 2017a). These address much more specific skills like confidence with embedded systems or
I/O operations. It is very important that a student has these mastered in order to pass their exams at theend of secondary school.
By providing a pre and post questionnaire oriented towards the learning experience, we assess thestudents’ self-perceived learning during Invent Week. The level of complexity and technical-knowledge
behind the prototype creation also helped us to evaluate the learning occurring during the program. This
did not provide quantitative data, but it helped us to tailor our lessons according to the students’ needs.
This adaptability is replicable in a typical classroom setting.

Results
Research Questions
Can a Bridge21 style Hackathon improve students’ motivation, confidence and learning of computerscience concepts and skills, particularly in the area of embedded systems?

Prototypes
Each of the five teams participating in Invent Week created an embedded systems prototype in areasof Internet of Things, wearables, home automation, and robotics. A heavy emphasis was placed onincorporating internet connectivity into the devices, considering the increasing trend of “smart”
technologies and the important skills gleaned from working with internet communication. Furthermore,
every team created a website for their product and many went on to add social media pages and videos
illustrating the needs addressed by their product. The artefacts and code related to each group has
been compiled into folders and saved for later review.
“Poc Doc” was a wearable medical device intended for use in cases ranging from minor injuries to life
threatening situations. The product is worn on the arm and links to a medical API that can offer treatment
suggestions based upon the symptoms it detects in the user. Detection is completed with an array of
Arduino sensors which track heart rate, body temperature, and sudden changes in acceleration indicating a fall or concussive force. The device will automatically call an ambulance to its location if theuser doesn’t interact with it for a certain time period following a concussive event. This team also madeattempts at calling a Google voice API to respond to medical queries.
“Froasties” was an automated food processor, specializing in French toast. It is designed for industry
use in hotels and restaurants. Users are given the option to log into a website and activate the machinewith the press of a button. This was accomplished through a crossover of HTML, PHP, and Arduino
scripts working through an embedded Raspberry Pi. The physical prototype was created with Lego
Technic.
“Benny the Bin Buddy” was a smart attachment that a user could attach to their bins for automatic sorting
between recyclables and rubbish. This was accomplished by simulating a spectrometer with colored
LEDs and an Arduino light sensor, in tandem with Arduino Servos that could dump the objects into theappropriate bin depending upon their colors. The detected colors were checked against a website before
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selecting if it was recyclable or not. Further development would lead to linkage with a database thatwould be updated to successfully sort more object types over time.
“iWindow” was an automated home technology and a smart window. It had internet connectivity,
allowing a user to remotely open or close it or lock it with a button on their website. It was also outfittedwith sensors that tested UV levels, air quality, and temperature both inside and outside the window. It
then made the decision to automatically open or close the window with an Arduino Servos based upon
the inputs.
Lastly, “Emo-Tee” was a smart clothing device employing a wearable LCD and internet connectivity. It
was a t-shirt with connectivity to a web-app which allowed the user to select which image, from a rangeof emoji, would appear on the front of the shirt. The images were preset by the team as a form ofcensorship, but it was theoretically possible to display any image on the clothing.
Table 2. Statistically significant questions from the Invent Week survey.
Question
Number

Question18

I am confident that I can program a computer to detect a button push and turn on an LED.

21

I am confident that I can create a program to control outputs over the internet.

23

I am certain that I can design a Wearable/IoT technology

24

I am confident that I can effectively communicate a Wearable/IoT product25

I am confident that I can create/prototype a Wearable/IoT technology

26

I use a step by step process to solve problems.

30

In order to solve a complex problem, I break it down into smaller steps.

37

I feel confident in my ability to evaluate different solutions to one problem.

47

I feel confident in my ability to think about the technology design process.

49

I feel confident in my ability to use data types that are common to procedural high-level
languages: Boolean, integer, real, char, string, date.

50

I feel confident in my ability to describe what an algorithm is and give an example.

57

I feel confident in my ability to describe the difference between digital and analogue inputs.

58

I feel confident in my ability to use and control digital inputs and outputs.

59

I feel confident in my ability to measure and store data returned from an analogue input.

60

I feel confident in my ability to develop a program that utilizes digital and analogue inputs.

61

I feel confident in my ability to design automated applications.

Pre- and Post-Workshop Comparison
The students were administered one survey before the week began, and a follow-up survey after theirpresentations on the final day. All of the questions were the same, except that the follow-up asked thestudents for their group names and roles that they played within the groups. Apart from these shortanswers, all of the other questions were evaluated with a 5-point Likert Scale where 1 indicated a lower
confidence on the given topic, and 5 indicated a higher confidence. There were a total of 63 questions.
After conducting a t-test on the data, although all questions showed increases, only 16 of the questions
showed a statistically significant improvement from the beginning to the end of the week, as displayedin Table 2.
Questions 37 to 61 in Table 2 were drawn directly from the learning outcomes section for the Leaving
Certificate Draft Specification (NCCA, 2017a). Of interest, questions 35 to 63 have statisticallysignificant improvements for the students, which is 9 of the 29 questions that address those learning
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outcomes. These are encouraging results, as this was the focus for Invent Week. Figure 1 illustrates
the improvements.

Leaving Certificate Outcomes

5
4,5

4
3,5
3
2,5
Q37

Q47

Q49
Q50
Q57
Q58
Q59
Pre-Invent Week
Post-Invent Week

Q60

Q61

Figure 1. The statistically significant improvements in learning outcome areas for the Leaving Certificate.

Strand Averages

Rating Scale

5
4,5
4

3,5
3
2,5
Strand 1
Pre-Invent Week

Strand 2
Post-Invent Week

Strand 3

Figure 2. Displays the increases in the mean score of questions related to each Strand.

Furthermore, the Leaving Certificate learning outcomes are broken into 3 sections, or “strands” which
are intended to be intertwined throughout learning activities to form a metaphorical “rope” ofinterconnected skills and abilities. These strands are classified as follows: Strand 1 is “Overarching
Practices and Principles”, Strand 2 is “Cross-Cutting Core Concepts”, and Strand 3 is “Computer
Science in Practice”. Different learning outcomes are targeted in each strand. This strand includes four
different and highly specific projects, such as information systems, analytics, modeling/simulation, andembedded systems. Invent Week focused on the embedded systems aspect of Strand 3 as it was a
logical fit with the IoT theme. Questions 57-61 applied directly to this portion of strand 3, and accordingly
every one of them showed a statistically significant improvement over Invent Week. Figure 2 illustrates
the improvements within each of the strands as an aggregate.
Most importantly, we found a statistical significance in the improvement within the aggregated Strand
3. This was our target goal throughout the week. Additionally, we found a statistical significance in the
improvements of Strand 1 (Practices and Principles). The entire statistical report is in Table 3.
Table 3. The statistics behind each Leaving Certificate Outcome Strand over Invent Week.

Strand 1
Strand 2
Strand 3

Pre-Invent Week (M)Post-Invent Week (M)T valuedf

P value

3.6
3.2
3.5

4.1
3.7
4.3

-2.661-1.910-3.828

17
14
15

0.016
0.077
0.002
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Lastly, Figure 3 depicts the increases of the Strand 3 outcomes individually. This is a significant chartbecause it displays the central topic of the week and has major implications for project impact onlearning, and optimizations that teachers can make to target different skills. The dramatic improvements
and significance of the relevant Strand 3 topics indicate that a similar learning experience can provide
a massive learning opportunity for students in a given central topic.

Strand 3

5

Rating Scale

4,5
4
3,5
3
2,5
Q57

Q58
Pre-Invent Week

Q59
Q60
Post-Invent Week

Q61

Figure 3. Displays the increases in topics related strictly to embedded systems and Internet of Things.

Discussion
The pre and post questionnaires indicated growth in all surveyed areas. These include self-efficacy,
understanding of careers in computer science, 21st Century skills (team work, presenting etc.), inputs
and outputs, embedded systems, coding, debugging and more. The largest and most critical area ofgrowth was in embedded systems. The self-reported confidence levels of the students within these
topics, coupled with the robust prototypes that they created serve as sufficient evidence for theirknowledge, capability and learning. Furthermore, the differences from time one and two suggest a selfperceived growth in confidence to practice CS from their participation in Invent Week. This growth inpractical experience is a direct result of the “learning by doing” pedagogical design.
The results from the surveys suggest that the students received a broad learning experience from thehackathon-like delivery method. This is evidenced by the fact that there were only increases in scores
i.e. none of the averages decreased because of Invent Week. Beyond that, 16 of the questions returned
statistically significant increases. Importantly, all of the targeted questions for the embedded systems
ALT showed significant increases but, outside of those, 11 other questions also achieved significant
improvement. The programme managed to encourage a wide array of skill sets, including 21st Century
skills, even though those were not the primary objective of the learning experience. Those were rathera favourable consequence of the constructivist/constructionist pedagogical approach.

Conclusions
Limitations
The small participation numbers is a limitation for our study, however it is comparable to a common
classroom size. The high level of mentor involvement in the process makes the hackathon-like difficult
to scale up beyond the size we’ve already achieved. Perhaps a classroom could call upon external
technical help via remote mentoring with IT professionals.
The elevated prowess of the students also posed a limitation for us. The pre-survey indicates thateveryone involved had a decent-to-good understanding of all of the topics we surveyed before theprogramme commenced. Thus it is hard to track how Invent Week would assist a student with a lower
level of knowledge at the beginning. The students were hand-picked based on their expressed interest
and performance in prior Bridge21 activities, this may not be strictly comparable with a school setting.

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A second issue that arose from this hand-picking was the relative uniformity of the student skills. Not
only were they all high at the beginning, many of them gravitated around the same level. This may not
be representative of a post-primary school classroom either, considering that many different
backgrounds could be better represented without the selection process. This could lend to greater
diversity in other settings, however, as skills beyond computer science are helpful and constantly
developed through this 21st Century learning method.
Finally, we are operating under the assumption that high self-reporting on confidence translates into anelevated level of actual learning in a given area. The related literature supports this assumption, but
further research must be conducted.

Final remarks
There has been only a limited amount of work done measuring the effects of hackathons as pedagogicalapproaches (Byrne et al., 2017; Duhring, 2014). There is a need to further this field of research asteaching methods move to accommodate a more technology and future oriented focus. Furthermore,
Ireland’s introduction of Computer Science is an example of a global trend towards CS education for
pre-university students, which means that exploring a variety of approaches to teaching the topic will be
more important than ever. This paper addressed both of those problems by measuring the amount ofself-perceived learning that occurred because of the learning activity, while also putting thesemeasurements in terms of the new Irish Leaving Certificate learning outcomes.
The outcomes of the pre and post questionnaires indicate that the students grew academically due toInvent week. They apparently left the week more prepared for the Leaving Certificate than they werewhen they entered it. It is encouraging to note the measurable success of Invent week, because thismeans that there is at least one proven method for project facilitation relevant to Irish Computer Science
course. This model could be adapted to target learning outcomes at different levels and in different
jurisdictions.
It will be important to attempt a similar approach with other groups of students, more often, using
different curriculum outcomes and hopefully with larger groups to confirm that this approach is reliably
and globally applicable.
Other research that should follow as a result of this would include tracking each individual’s role within
the group and seeing how that impacted their specific areas of learning. Also, more methods of project
facilitation such as Agile (Kastl et al., 2016; Rico & Sayani, 2009) or SCRUM (Scharf & Koch, 2013)
should be considered and measured in order to discover new and effective pedagogical methods.
Finally, methods currently in development (such as the hackathon-like activity) should be compared tomore traditional approaches to evaluate the relative effectiveness.
In conclusion, allowing students to be immersed in a scaffolded, but ultimately open-ended hands on
project gave them a chance to develop their technical and domain knowledge along with theirinterpersonal and 21st Century skills.

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EduRobot Taxonomy and Papert’s Paradigm
Dave Catlin, dave@valiant-technology.com
Valiant Technology Ltd., UK

Martin Kandlhofer, mkandlho@ist.tugraz.at
Graz University of Technology. Austria

Stephanie Holmquist, sh2@usf.edu
University of South Florida, USA

Andrew Paul Csizmadia, A.P.Csizmadia@staff.newman.ac.uk
Newman College, UK

Julian M. Angel-Fernandez , Angel-Fernandez@acin.tuwien.ac.at
Technische Universität Wien, Austria

John-John Cabibihan, john.cabibihan@qu.edu.qa
Qatar University, Qatar

Abstract
Seymour Papert was the first person to suggest using robots in education. And for nearly 50 years his
constructionism principles guided their application in school. Over the last few years, a multitude of new
robots have become available. Do they all meet Papert's Paradigm? What of those that don't? In thispaper, we further develop the EduRobot Taxonomy which puts an ever-increasing number of education
robots into order. We’ll examine what they’ve got in common and how they differ: we’ll also see if they
all comply with Papert’s ideas and what does it mean if they don’t.

Keywords
education robots; EduRobot taxonomy; build bots; use bots; social robots

Introduction
In 1969 Seymour Papert set Mike Paterson, a visiting graduate student from England, the task of writinga specification for a Turtle robot (Paterson, 1969). Papert believed the robot enriched Logo, a computer
language he’d invented a few years earlier. He’d imagined the Turtle as a ‘transitional object’, a link
between students the computer and some powerful idea.
With the ‘birth’ of the Turtle in the early 1970s, Papert became the father of educational robots. In thefollowing years a few commercial Turtles became available and the odd non-Turtle education robot,
like, Hero found their way into schools. Piaget’s constructionist theories underpinned Logo and theTurtle. By 1993 Papert had gradually created constructionism, a variation on Piaget’s ideas wherestudents gain knowledge of the world by making ‘stuff’ for it. As part of this progression he’d looked
beyond Turtles and started to explore cybernetic machines by using Lego to build robots (Papert, The
Children's Machine, 1993). Eventually, this led to his collaboration with the Lego Company, theintelligent brick and Lego Mindstorms.
So Papert had created two types of education robots: those ‘out-of-the-box-ready’ to explore ideas andthose you learn through building them. Both these types of robot shifted the focus from teachers
teaching to students learning. Both captured the spirit of intellectual adventure: children learnt to think,
to examine ideas – not simply remember what teachers told them. In a review of education robots inspecial needs education, Catlin and Blamires (In Press) realised that Papert had created a Paradigm inthe sense of Kuhn’s Paradigm Shifts and scientific revolutions (Kuhn, 1996). Everything about
education robots since 1970 embraced what Papert called the ‘Spirit of Logo’ – effectively Papert’s
Paradigm.

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Catlin and Blamires reviewed work which reported negatively on Papert’s efforts. Unlike Popper’s
falsification theory of science (Popper, 2002), where a single conflicting result is enough to destroy ascientific proposition, Kuhn’s Paradigm supports a theory based on probability. More importantly, aKuhnian Paradigm is a theory, like Newton’s Laws or Einstein’s Relativity, which changes the way weview the world. Papert’s work on robotics and education did this. This paper finds new robots in
education which don’t comply with Papert’s ideas. However, this new perspective can coexist with
Papert’s propositions in some ways but clashes with them in others.
Until roughly about 2010, a few small companies and Lego supplied the education space with robots.
Since then a host of robots have appeared: for example, in January 2010 ten new robots appeared at
London’s BETT Show. Catlin and colleagues realised a schema grouping similar robots together would
help our understanding of their use in education. So they presented an provisional schema called
EduRobot Taxonomy version 1.03 at the Robotics in Education (RiE) Conference (Catlin, Kandlhofer,& Holmquist, 2018).
This paper has two objectives. The first is to develop the EduRobot classification further and the second
is to draw attention to the paradigm issues mentioned above. We’ll start by presenting our methodology
and then introduce EduRobot version 1.03. Then we’ll look at issues with this version and solutions
which lead to EduRobot version 2.01. We’ll conclude with a short discussion and conclusions on thework done so far and the next steps.

MethodEduRobot version 1.03 resulted from the expert experience of its authors whom between them haveworked with education robots for over 50 years. We chose a team from Europe and the USA to ensure
a broad viewpoint. We presented the paper and a poster at the RiE (Robotics in Education) Conference
held in Malta, April 2018. Delegates to the conference included about 40 experts from Europe, North
America, the Middle East and the Far East.
Following the presentation, half the attendees completed a questionnaire which assessed approval,tested EduRobot, elicited suggestions, and highlighted confusions. This paper incorporates theinformation from the survey and the direct input from a further three co-authors from Europe and the
Middle East. How easily we and the Malta attendees’ classified robots tested the effectiveness ofprovisional schema. For the Malta Conference, we classified 30 robots – enough to show an exampleof each class. The issues raised by conference delegates and a new analysis from the team questioned
some of the original decisions and enables us to present EduRobot version 2.01. This we’ve tested byclassifying a further 30 robots (including those classified by the Malta delegates).

EduRobot Taxonomy
What is an Educational Robot?
We define an education robot with a combination of three of the ten Educational Robotic Application(ERA) Principles: The ‘embodiment, Intelligence and Interactive principles’ (Catlin & Blamires, 2010).
We can summarise these by saying that an education robot is a physical robot, in the same space as
the student. It has an intelligence that can support learning tasks and students learn by interacting withit through suitable semiotic systems.
We haven’t included virtual robots since they do not offer the same experience as tangible robots in thesame space and time as the learner. Sylvia Weir, who’d started working with Papert’s Logo team during
the early days, pointed out the original developers didn’t see a difference between the physical andvirtual robots. But, she went on to say – the children did (Weir, 1987). We’re not saying that virtual
robots have no educational value, but that it’s different to physical robots and would need a different
classification approach.

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What is EduRobot’s Focus?
EduRobot focuses on the robot’s construction and not its applications or educational value. The ERA
Principles deals with these issues. We note you can use a certain class of robot in many ways: you canalso use different classes of robots to do tasks of similar educational nature and value. If you try andinclude these traits in the schema you’ll find it hard to distinguish one robot from another.

Why an Education robot Taxonomy?
Given EduRobot doesn’t say anything about the education value of robots, what is its purpose. We cite
three reasons for EduRobot:
1. It means you can apply research results from one robot to all members of the same class.
2. It gives teachers a way of reviewing and comparing the technicality of different robots available.
3. It provides a way for the web to organise information about education robots.

EduRobot Version 1.03
Figure 1 shows the basic structure of EduRobot.
Type: We identified two basic types of education robots. These align with Papert’s ideas as robots you
build (Build Bots) and robots you use (Use Bots).
Class: This refers to robots like Turtles, Robot Arms
and Robot Kits and so on.
Subclass: It is acceptable to subdivide some classes
into distinct subclasses.
Brand: A specific robot product from a supplier.

Figure 1 EduRobot Taxonomic Structure.
Characteristics: This is the novelty of EduRobot:
Figure 2 Examples of Build Bot and Use Bot
animal taxonomies continue with a tiered structure,
Classifications.
trying to do this with education robots becomescomplicated. Characteristics define a robot’s nature and key details that don’t change. If a designer
adds new features, it enriches the robot, but, usually, it doesn’t change its type, class or subclass. You
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can think of characteristics as a set of tags that list the robot’s important features. The classification
allows for a picture and 30-word technical description (See Figure 2).
One point that’s not so obvious: a robot can only have one classification. Discovering the platypus
caused scientists great consternation. It was a mammal with reptilian characteristics like laying eggs.
It took 85 years to agree a classification: it was an egg-laying mammal. The higher taxonomic rank
makes the decision. You can build a Turtle out of Lego Mindstorms, but this does not change its nature:it’s still a Build Bot. Similarly, you can build and transform a Roamer Turtle into a social robot, but it’s
still a Use Bot.

Figure 3 EduRobot Taxonomy version 1.03 Presented to the 2018 Robots in Education Conference in Malta.

Summary of RiE Questions
We received 13 Responses from the 40 delegates. Because several delegates attended from the same
institutions this represented about 50% response.
1. Out of the responses 23% strongly agreed with the approach, 38% agreed, 38% felt undecided
and 1% disagreed.
2. To the question, “Is two Types of robot enough?” 72% felt we had enough the rest did not
answer.
3. Did we need more Classes? 85% said no - one of these felt EduRobot was already toocomplicated for teachers. Another thought we might need more in the future.
4. Did we need more characteristics? 77% said no and 23% said yes.
Because of the small sample size, these results do no more than encourage us to continue. The
richness of the survey comes from the comments. They helped us understand the respondent’s
misunderstandings. For example, the person who disagreed with the approach recanted in conversation
when we clarified some of his concerns. Although people said they were happy with the Types, Classes
and Characteristics their comments inspired positive changes. We’ve referenced these in the next
section.

Issues with Version 1.03
Social Robots
Seymour Papert once asked the rhetorical question, “Will computers program children, or will children
program computers” (Blikstein, 2013) (Papert & Solomon, 1972). With robots that comply with Papert’s
Paradigm, students use the robot as a tool to express themselves and explore ideas. Students act andthe robot responds. With social robots, it’s the other way. Autonomous social robots respond to students
in pre-set ways: normally the learner cannot change the response. Social robot research records
children’s reactions to different stimuli. Designer’s use this data to make the robot recognise and153

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respond in apt social and cultural ways. We wondered whether we should classify Social Robots as a
“Type Bot”. The answer became clear when we tried to classify social robots as Build or Use Bots. The
RiE Poster for version 1.03 showed we could but, it felt forced. Once we gave Social Bots the status of‘Type’ it allowed us to create a more fitting set of characteristics – which focus on Human-Robot
Interactions (HRI).

Social Bot Ontology
In education, Social Bots are autonomous robots that interact and communicate with students using
HRI technologies. The interactions aim to embody accepted social and cultural norms (Li, Cabibihan,& Kee Tan, 2011).
Normally social robots can interact with their environment, each other and people, but in an educational
context, our main interest is in the student robot rapport. We can classify many of the robots as "social"because of the physical and behavioural ‘affordances’ they display. For example, when students switch
on Nao its eyes and ears light up, it stands upright and turns its head towards the user. Table 1 defines
the Social Bot Classes for EduRobot version 2.01.
Marvin Minsky described Telepresence as “instruments that will allow us to work remotely” (Minsky,1980) (Minsky, 2010). His definition included robots in the next room or the Mars Rover. While inprinciple explorer and bomb disposal robots meet this definition, in education we normally use the noun
Telepresence for students projecting themselves into remote social locations. We’ll reserve the classfor robots of this definition.
People have used these robots to allow children confined to hospitals to go to school in their place.
They control the robot to take part in the lesson. Classmates treat the robots as if it was their friend andthe socialisation has a positive effect on the patients’ health (Mills & Laughlin, 2018).
Table 1 Definition of Social Bot Classes
Humanoid
Human-LikeToys
Animal-LikeCreatures
TelepresenceSocial robots that look like mechanical people: Example Nao.
Social robots trying to resemble humans: example Kaspar.
Smart toys with social robot characteristics: example Furby
Social robots that resemble animals: example Paro
Social robots without a form template: example Tega
Robots that students can use to represent themselves in social circumstances: example
Pebbles.

Social Bot Characteristics
Table 2 lists the characteristics applicable to social robots. We consider this table an initial effort tocharacterise the Human-Robot Interaction (HRI) spectrum. The extensive literature highlights thecomplexity problem. For example, the studies of Fernando Alonso-Martín and colleagues classified
human touches into tap, pat, push, stroke, scratch and slap (Alonso-Martín, Gamboa-Montero, Castillo,
Castro-González, & Ángel Salichs, 2017). Their work focuses on the human touching the skin of therobot. This forms one part of ‘Recognition’ and one aspect of ‘Gestures’. If you try and define the detailof the characteristics you run into complexity issues which defeat the purpose of the EduRobot
enterprise. If necessary we can expand these definitions later after we’ve more experience classifyingsocial robots.
Table 2 Characteristics of Social Bots.
Recognition
Acknowledgement
MotionGestures
Emotions
Conversation

The robot detects the presence of a human through its senses.

The robot acknowledges the human presence.
The robot detects and responds to movement.
The robot notices and reacts to gestures.
The robot senses human emotions and reacts to them.
The robot and child communicate through sound.
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Programming Characteristics
Our review EduRobot version 1.03 decided we could improve the programming characteristic ontology.
We can program education robots in a variety of ways that meet Papert’s aim of “low floor and highceiling” (Papert, 1993) later refined to “low floor, high ceiling and wide walls” (Resnik & Silverman, 2005).
These provide students with easy entry points, but great exploration possibilities. Robots allow teachers
to create environments which reflect Bruner’s Spiral Curriculum (Bruner, 1960). Young children startwith tasks like Roamer’s Incy Wincy Spider where all they do is put symbols in the right order, but as
their experience and interest grows they can end up coding in professional programming languages.
Several robots provide rich educational environments by offering different ways for students to program
them. Table 3 lists the characteristics of programming languages used with education robots.

Malta Feedback
David Miller from the KISS Institute raised the issue of Mechanical computing in his historicalpresentation which featured the robot ‘Scarecrow’. We’ve changed Mechanical to Mechatronics whichembraces programming by arranging mechanical, electrical and electronic parts. This made us rethink
the 14-in-1 Solar Robot shown on the EduRobot version 1.03 Poster; we’d decided it failed to qualify as
an education robot because it didn’t meet the ERA Intelligence Principle. The Mechatronic characteristic
revises this decision because you ‘program’ it by rearranging the parts. You can’t do this with toy kit
robots, for example, Owi’s Solar Wild Boar Robot doesn’t qualify because you can only build it one way.

Robot Architecture
Reflective analysis showed the characteristic we called ‘Architecture’ in version 1.03 needed
improvement; the original effort was too detailed. EduRobot needs to find a balance between providing
detail and giving a clear understanding of the robot’s nature.
Table 3 Programming Characteristics for EduRobot version 2.01
Programming
Icon

Tile-based

Block-based
Text-based
Graphical

Mechatronics

Tangible
Teaching

This characteristic explains the different forms of software used to tell robots what to do andhow to behave.
Symbols represent robot actions like move-forward. They’re often used on keyboards mounted
on the robot allowing students to program the robot directly. Example software: RoamerWorld
Graphics and example robot: Roamer.
This approach shows instructions as tiles, each with an image to depicting and instruction.
Learners create programs by putting the tiles in order and in a horizontal line. Example software:
Edware and example robot: Edison.
‘Jigsaw’ like blocks fit together in ways that create programs and reduces the chances of codebugs. Example software: Scratch and Blockly and example robot: We Do Robot Kit.
Traditional text-based programming languages. Software examples: Java, Python. Example
robot: Cozmo
The program is a graphical representation showing actions in sequence for example flow
diagrams and Finite State Machines. Examples: Flowol Software (Flow Diagrams) with Vex IQ
robots, and Finite State Machines with TI-RSLK robots.
Students create programs by organising mechanical, electrical and electronic parts in different
arrangements. Early examples include Babbage’s Machines and Braitenberg robots. Example
robot: Scarecrow.
This involves students arranging objects in a physical environment. The robot gets instructions
from those objects. Example robot: Cubetto
The learner shows the robot how to do the task. The robot remembers the movement andreproduces it. Example robot: Little Robot Arm

Robot architecture manages complex tasks in different environments based on a software approach. It
comprises of Artificial Intelligence (AI) and software engineering. AI include problem abstraction,knowledge representation, decision making and behaviour execution. Software engineering includes
programming, interfaces, data organization and transfer. Based on three ingredients (sense, plan andact) various authors propose three architectures (Figure 4): reactive, deliberative and hybrid (Siciliano& Khatib) (Murphy, 2000).
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Figure 4 Robotic Architectures

Architecture defines a technical strategy used by the robot to perform its tasks. However, we can lookat the issue from an educational perspective. In EduRobot version 1.03 we address the issue: can you
call a machine a robot only if it has sensors? While insisting it must, may have merit in a technicaldebate, it’s not essential for an effective education tool. Robotics is a subset of control engineering andin that discipline, you draw a boundary around the system under consideration and study what happens
within its confines. If you draw the boundary to encompass machine and learner, you have a system
which involves Papert’s key ideas behind the Turtle robot. The child sets goals plans and fulfils the planby programming the robot. The child sees the mistakes and debugs the program. ‘Playing Turtle’ isthe second part of Papert’s thinking: the child works out what the robot needs to do by imaging
themselves to be the robot. We’ll call this a direct architecture.
Table 4 Characteristics: Robot Architectures
Architecture

The strategies the robot uses for AI tasks like problem abstraction, knowledge representation,
decision making and behaviour execution, and software engineering like programming, data
organisation and interfaces.

DirectA robot where the learner is responsible for the world model, goals and plans. Example robot:
Bee-Bot.

Reactive

A direct connection between the robot’s sensors and its actuators lacking an internal model of
the world. For example the subsumption architecture (Brooks, 1986).

Deliberative

The robot contains planning and reasoning parts with its memory containing a model of the world.
This model receives sensor data and decides what action to take and tells the action module toperform them. The model understands and accounts for the robot’s long-term and short-termgoals. Example robot Shakey (Nilsson, 1984).

HybridA combination of the reactive and the deliberative methods which can preserve different levelsof abstraction. A common hybrid architecture is the three-tier robot architecture (Bonasso, 1999).

What Else is New in EduRobot Version 2.01?
StatusMany of the presentations in Malta and similar conferences present robots created by the University for
their teaching: schools cannot buy these robots. We, therefore, propose a new characteristic calledstatus (Table 5) which will incorporate the historical characteristic of EduRobot version 1.03.
Table 5 Status Characteristic
StatusStatus explains whether schools can get the robot. The status of a robot will change overtime.

Historic

Education robots no longer available.

Academic

Education robots used for teaching within a University and specific to that institution.
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Commercial

Robots currently available to schools and teaching institutions.

Robot Versions
Over the years Lego issued a few updates to theirMindstorms product. As a rule, brand development doesn’tchange type, class or subclass – if it does change any ofthese it’s a new brand with its own classification.
We should always classify the latest brand but list theprevious versions as an extra to the 30 word description(Figure 6).

Changing Build Bot: Maker Bots – Printed
Parts
The Malta survey revealed two robots made using
machined and laser cut parts. Rather than inventing a new
subclass, we felt it better to broaden the ontology of the
Printed Parts subclass. The new definition is: Robots Figure 5 Lego Robot Classification EduRobot
Version 2.01
where students make the parts using various methods, forexample, 3D Printed Parts, machined and laser cut parts.

One Time Builds
You can only build some robots once, but it is clear building it is a technical experience aimed atdeveloping student skills (example Pololu 3pi). You can also buy some Use Bots built or in a kit form.
However, you get the impression the motive for the kit version is to reduce the sales price: the build is
too complicated for the age range of the students (example Oh-Bot). The classifier needs to judge fromavailable details.

Marine Robots
The 2018 European Conference on Educational Robotics (ECER)41 ran in parallel with RiE. It included
a Botball competition and challenges staged by the PRIA (Practical Robotics Institute Austria). This
includes a submarine challenge. At first, we thought this needed a new class of robot. However, theShark robot classifies as a Build Bot: Maker Bot – Made Parts. However, we can anticipate a User Bot
version one day when we’ll add a new User Bot Class.

41

An international scientific conference for students (aged 10 – 18 years old) to present their research.
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EduRobot Taxonomy Version 2.01

Figure 6 EduRobot version 2.01

Discussion and Conclusions
We feel EduRbot is developing satisfactorily. The response at RiE was positive and supportive andconfirmed the need. However, a few issues still seem tentative and the true test of the proposal will
come when people try and use it to classify new robots or find the classifications of robots they know.
Following the request of several people at the RiE Conference, we’ve set up a site for the EduRobot(www.about-educational-robots.com). This will include the latest brands. Once the site is live we plan
to contact education robot makers and ask them to classify their products. We expect to adjust
EduRobot during this phase.
Papert’s work has so successfully justified the role of robots in education, it’s a shock to find the social
robots do not live up to expectation. This discovery highlights the need for a more detailed study. Can
we find an underlying justification for this technology, or can we find a way of using the technology thathelps the robots comply with Papert? This is the subject of a future investigation.

References
Alonso-Martín, F., Gamboa-Montero, J. J., Castillo, J. C., Castro-González, A., & Ángel Salichs, M.
(2017, May 16). Detecting and Classifying Human Touches in a Social Robot Through Acoustic Sensing
and Machine Learning. (Xiaoning Jiang, & Chao Zhang, Eds.) Sensors. Retrieved May 11, 2018, fromhttp://www.mdpi.com/1424-8220/17/5/1138/htm
Blikstein, P. (2013). Seymour Papert’s Legacy: Thinking About Learning, and Learning About Thinking.
(Staford Graduate School of Education) Retrieved May 15, 2018, from Transformative Learning
Technologies Lab: goo.gl/Kw4NkY
Bonasso, P. (1999). Issues in providing adjustable autonomy in the 3T architecture. Proceedings of the
AAAI Spring Symposium on Agents with Adjustable Autonomy.
Brooks, R. (1986). A robust layered control system for a mobile robot. IEEE journal on robotics andautomation, 2(1), 14-23.
Bruner, J. (1960). The Process of Education. Cambrige, Ma: Harvard Univerity Press.
Catlin, D., & Blamires, M. (2010). The Principles of Educational Robotic Applications (ERA):A framework
for understanding and developing educational robots and their activities. Constructionism 2010. Paris:
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Catlin, D., & Blamires, M. (In Press). Designing Robots for Special Needs Education. In L. Daniela, &M. D. Lytras (Eds.), Technology, Knowledge and Learning Special Issue on Education Robotics for
Inclusive Education. Springer.

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Catlin, D., Kandlhofer, M., & Holmquist, S. (2018). EduRobot Taxonomy:: A Provisional Schema forClassifying Educational Robots. Robots in Education. Malta. Retrieved May 14, 2018, fromgoo.gl/2tdGTh
Kuhn, T. S. (1996). The Structure of Scientific Revolutions (3rd ed.). Chicago: University of Chicago
Press.
Li, H., Cabibihan, J., & Kee Tan, Y. (2011, November 9). Towards an Effective Design of Social Robots.
International Journal of Social Robots, pp. 333-335. Retrieved May 15, 2018, from goo.gl/dfn8ys
Mills, J., & Laughlin, E. (2018). In the beginning.... Retrieved May 15, 2018, from Telepresence
Robots.com: https://telepresencerobots.com/beginning
Minsky, M. (1980, June). Telepresence. Omni magazine, June. Omni Magazine.
Minsky, M. (2010, September). Telepresence: a manifesto.,. Spectrum.
Murphy, R. (2000). Introduction to AI robotics. (R. (. Murphy, Ed.) MIT Press.
Nilsson, N. J. (1984). Shakey the Robot. Retrieved May 15, 2018, from SRI International's Artificial
Intelligence Center: http://www.ai.sri.com/shakey/
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Analysis of Constructive and Cognitive Activities ofParticipants in Online Competitions in Computer
Science
Anton Chukhnov, septembreange@gmail.com
Saint Petersburg Electrotechnical University “LETI”, Russia

Sergei Pozdniakov, pozdnkov@gmail.com
Saint Petersburg Electrotechnical University “LETI”, Russia

Ilya Posov, iposov@gmail.com
Saint Petersburg Electrotechnical University “LETI”, Saint Petersburg State University, Russia

Athit Maytarattanakhon, seaay2499@gmail.com
Saint Petersburg Electrotechnical University “LETI”, Russia

Abstract
The paper discusses a certain type of competitions based on distance interaction of a participant withsimulation models of concepts from discrete mathematics and computer science. The “Construct, Test,
Explore” competition, developed by the authors, is chosen to be a representative of such competitions.
One of the features of this competition is that all its tasks are accompanied not only with simulation
models and tools to manipulate the model’s objects, but also with a hierarchical criteria system, thatdefines an objective function to be optimized while solving a task. The presence of such a criteria systemallows for treating a task’s subject as a set of several tasks of different complexity (2–4 tasks). Eachcriterion has means to asses partial solutions, that provide permanent feedback for a participant. The
transition of a participant from optimizing one criterion to optimizing the next one, means that he or she
has already fully understood an idea corresponding to a former criterion. The work is supported by the
Russian Foundation for Basic Research (Project No. 18-013-01130).

Keywords
olympiad; computer science; discrete mathematics; electronic manipulator; CS competition;mathematical thinking

Background and theory
One of the important issues in arranging for mental activity in online (distance) competitions is toautomate an analysis of mental actions, that are performed by participants while they solve tasks. Some
information about the characteristics of mental actions may be obtained by analyzing competitions
based on multiple choice questions (Yagunova, 2016). But this information is not enough to draw
conclusions about the degree of understanding, because the correct answer could be selected
accidentally, or by means of indirect reasoning that have nothing to do with an idea presented in a task.
Moreover, the very form of a task with a multiple choice question is very limiting. Authors think, thatmuch more information about mental processes may be gained by analyzing solutions of constructive
tasks. Constructive tasks are convenient because they may be automated in a straightforward way.
They may be stated inside an informational environment, built on computer tools and simulation models.
Computer tools and simulation models allow for analyzing constructive solutions of participants. These
solutions are very diverse and provide interesting information about how strongly have participants
formed understanding of concepts, on which the task is based.
Also, while a participant work with a tool, rich information about his or her actions may be collected.
However, the problem of actions logs analysis aimed to get mental acts is quite complicated and does
not have full solution by now (Gibson, etc., 2016). So we do not consider logs analysis in this work. At
the same time, the experience of working with imitation models has been gained (Honey, etc., 2010;160

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Potkonjaka, 2016), their important role in tasks solving has been demonstrated (Baker, etc., 2008). It
has been proved that the work with black-box models aids in better conceptualization of concepts: thework (Hosein, etc, 2008) demonstrates that “students using the black-box did better on the constructive
tasks because of their increased explorations”. This work also shows that “students with low maths
confidence resorted to using real-life explanations when answering tasks that were application related”.
For this reason, this paper investigates subjects that have clear real-life sense and allow for transition
to serious theoretical problems starting from understandable tasks that can be solved by participants
only by common sense.
The work (Mislevy, 2011) demonstrates that it is important for concepts interpretations chosen for tasks
statements to be obvious and natural. This approach corresponds to the idea of engineering design
developed in the project WISEngineering (Chiu, 2013).
The type of feedback is important. The article (Attali, 2015) explores several feedback types: no
feedback (NF), immediate knowledge of the correct response (KCR), multiple-try feedback withknowledge of the correct response (MTC), or multiple-try feedback with hints after an initial incorrect
response (MTH), and determines that the latter is the most efficient. That is why this paper attaches
great importance to the analysis of partial solutions and the automation of responding to them.
Not without interest is a study and development of such types of educational activities, that combine
learning of new scientific and technical ideas with an ability to assess results of learners’ mental activities
without specially set up additional assesments of obtained knowledge and skills. Such steps were taken
in works (Pozdniakov, 2012, 2013; Posov, 2013; Yagunova etc., 2016; Akimushkin etc., 2015). This
paper proceeds with a study of these means and abilities.

Methodology
We use a method of software supported subject tasks (Pozdniakov et al., 2013). By subject task wemean a task with some understandable real-world statement that does not need any specific knowledge
to understand it and to make at least first steps in the solution. By software supported we mean that atask is accompanied with a computer tool, that demonstrates the statement and allows for searching fora solution (Honey et al., 2010, Hosein et al., 2008). Thus, such tasks have to be constructive, and a tool
exposes their constructive nature.
The approach presented in this paper is connected with the study of possible feedback for partial
solutions of subject tasks. To provide a feedback, a system should have access to information about
students actions. Mental actions of a student are accompanied with actions performed with the tool, anda potential ability to analyze and to control the solution search activity of a student arises [8].
To use the information about a work of a student within a computer tool we propose a method of acriteria system [9]. These criteria assess partial solutions. The hierarchy of criteria is built in the waythat every progress of a student is somehow evaluated with a usually numerical value. It allows forcomparison of partial solution based on how close are these solutions to the full solution of the task.
Criteria of higher levels are intended to reveal harder aspects of a subject. Thus, subject tasks may beconsidered both as quite simple (over the lower level criteria) and as olympiad (over higher level criteria).
Subject tasks usually either implicitly or explicitly require to find the optimal solution.
Let us look into a technique of designing software supported subject tasks on the example of the graphtheory with the visual metaphor based on the idea of constellations. Such interpretation allows for
decreasing of an introductory part of a statement because participants must have already beenacquainted with the concept of constellations.
The idea of constellations comes from the need to orient oneself in the space quickly. This can beachieved using stars in the sky. One should split the set of bright points-stars into groups, each of whichis considered to be a single object. For that purpose people invented an idea of figures called
constellations. Each constellation is obtained by connecting several points with segments. After that,the laws of human perception make a human quickly restore segments which are absent on the sky. Heor she perceives a sky as a graph, consisting of several connectivity components. Thus, the task to161Constructionism 2018, Vilnius, Lithuania

introduce new constellations can be described in mathematical concepts as a construction of a nonconnected undirected graph.
If the first stage is to come up with a metaphor, the second stage is to highlight a goal, that may beeffectively supported by a computer tool. Such goal is to split stars into constellations. This task is naturalfrom the context point of view, and thus the explanation of the “split” concept is not needed (see fig. 1).

Figure 1. the user interface of the “constellations” task

To measure the progress of students in achieving of the goal one needs to define criteria, that will assess
the solution.
Criteria should not contradict the context of the problem, they should be natural inside this context, whileleading a student to master a new idea, a concept or an algorithm. These requirements are satisfied bya splitting of stars, firstly, into different constellations, secondly, into constellations consisting of a fixed
or upper-bounded number of stars. The requirement for the constellations to be different is describedon the graph theory as a requirement for connectivity components not to be isomorphic. Thus, theimportant mathematical concept has a natural interpretation inside the chosen metaphor (context). The
upper bound on the number of start is also natural, because a human can perceive a group of 5–7
objects as a unit, and he subconsciously splits the greater number of objects into subgroups with lessnumber of elements.

Formalized task statement and conditions of the experiment
The participants of a competition were presented with a task. It was accompanied with a tool that allowed
for building configurations of constellations and evaluated numerical values of criteria for eachconfiguration. Here is the problem statement:This problem asks you to define constellations on a starry sky, connecting stars with segments using
your mouse. Segments must not intersect. A constellation should contain at least 2 (4, 5 for other levels)
stars and not more than one cycle (a closed poly-line).
The more different constellations you build, the better. How to tell whether two constellations are equal?
The stars of equal constellations can be numbered in the way that if a pair of stars is connected in one
constellation, then in the other constellation stars with the same numbers are also connected (see fig.
2).

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Figure 2. Isomorphic constellation

If you do not succeed to create a new form of a constellation, then the more constellations you have(including same), the better is the result.
Two solutions with the same number of different constellations and the same total number of
constellations are compared over the total length of all the segments. A better solution has a smallersum.
The proposed subject is an example of subjects proposed annually in the “Construct, Test, Explore”
competition. Each year schoolchildren get access to three software supported subject tasks. Now they
are implemented as web applications, until two years ago they we implemented in Flash. So,
participants use their browser to work with tasks. Solutions, that are best for a participant according to
the criteria of the task, are saved automatically. Participants may also save any other partial solutions
they want, and they can return to saved solutions any time on any browser on any computer.
The work on tasks usually starts in a class and continues at home. The competition lasts for one week,
and the time to solve tasks is not bounded during this week.
All participants solutions have a log of actions made during the search for the solution. After thecompetition week, the best solutions of each participant are processed and compared between eachother according to the same criteria, that were used for a single participant to obtain his or her bestsolution. So, each participant gets a rank for each task. The rank is a number demonstrating how many
better solutions were found by other participants. The lower a rank is, the better is a solution. The
participants are split into three groups according to their age, statements for each task for higher ages
is more complicated. The ages split is: 0th level: 1–4 grades, 1st level: 5–8 grades, 2nd level: 9–11 grades.

Results and discussion
The qualitative analysis was done by viewing the best solutions of all participants in the order from thefirst rank to the last. Solutions sometimes contain mistakes that give evidence about not understanding
by a participant of some corresponding mathematical concept. The bounds between solutions with andwithout such mistakes were found. Typical and bounding solutions are demonstrated on figures 3 and4. This is the qualitative analysis of solutions for the “constellations” problem for the 1st level (5–8
grades). There were 666 solutions in total.
Fig. 3(f) is the last solution with the maximal number of non-isomorphic graphs.
Fig. 4(a) has 8 different graphs, and no isomorphic.
Fig. 4(b) has 6 different graphs, and no isomorphic.
Fig. 4(c) has 5 different graphs, and no isomorphic.
Fig. 4(d) has 5 different graphs, with some isomorphic.
Fig. 4(e) has 2 different graphs, with many isomorphic.
Fig. 4(f) has all graphs isomorphic.

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Constructionism 2018, Vilnius, Lithuania(a) 1st place(c) 3rd place(e) 100th place(b) 2nd place(d) 8th place(f) 227th place

Figure 3. Best solutions of the 1st level according to the 1st criterion: the number of non-isomorphic planarconnected components

Figures 3 and 4 present examples of solutions, that demonstrate different levels of diving into theessence of the problem. 227 participants of 666 (34%) managed to find the optimal number of
constellations, that is, they managed to build a graph from a maximal number of non-isomorphic planarcomponents, with at least 4 vertexes each (fig. 3a). What is the difference between the beginning of thelist and its end? The answer came after the analysis: the difference is in the degree of taking into accountthe criterion of graph minimality (the minimality of total edges length). This is especially obvious fromthe last 227th solution, that surely does not take into account the minimality at all. Note, that well known
greedy algorithms (for example, the Kruskal’s algorithm) are not applicable here because they do notconsider the requirement of non-isomorphism of components being built.
The qualitative analysis of solutions demonstrates that during the solution almost all participants (morethan 90%) gradually mastered the concept of isomorphism. Only the lowest rated solutions demonstrateparticipants that were trying to optimize the second criterion (not the main one) about the number ofgraph components. Note that best solutions do not use all the stars. There were no requirement to build
a graph on all vertexes, however only a small fraction of participants were looking for a solution on asubset of stars. The two best found solutions have this form.

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Constructionism 2018, Vilnius, Lithuania(a) 500th place(b) 575th place(c) 580th place(d) 600th place(e) 650th place(f) 658th place

Figure 4. Examples of solutions of the first level with non-optimal solutions according to the main criterion

Statistical analysis of solutions for the “Constellations” task
In the “Constellations” task the best solutions over the main criterion (the number of non-isomorphic
graphs) had also the best value of the second criterion (the total number of components) (fig. 5, 6). In
this case the good dispersion of results was achieved by the third criterion: the total length of planargraph edges.
At the same time one can see, that the second criterion played the important role for solutions with a
few non-isomorphic graphs (see fig. 7). If only the dispersion of results is important, then the first andthe third criteria are enough.

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Figure 5. The number of different (non-isomorphic) graphs in the solution of the “Constellations” task on the 1stlevel. The first decrease from 12 to 11 corresponds to 227 optimal results according to the first criterion.

Figure 6. A histogram of the number of different (non-isomorphic) graphs in the solutions of the “Constellations”
problem on the 1st level

Figure 7. The number of all graphs in the solutions of the “Constellations” problem of the 1st level, results areordered according to the number of non-isomorphic graphs (the main criterion). Splashes on the right of the plotshows differences by the second criterion for solutions that are neighbors by the first criterion.

However, the second criterion is important for the support of the individual work, because it is much
more visually obvious for a participant: the number of built graphs is the first thing obvious for a
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participant, and the total length of edges is hard to be estimated visually and thus it plays smaller rolein the feedback (fig. 8, 9).

Figure 8. Total lengths of graphs in solutions of the “Constellations” task. Results are sorted according to thenumber of non-isomorphic graphs and the number of all graphs. There are no horizontal areas o the graph, thatmeans that all solutions of participants are different according to the three criteria.

Figure 9. A histogram of deviations of total lengths of graphs from the best total length in the solutions of the“Constellations” task of the 1st level with the maximal number of non-isomorphic graphs (elements are groupedby 10).

Concluding remarks
The qualitative and statistical analysis of results of 6 subjects (the paper contains only one subject)leads to the following conclusion:
1. Practically all participants (99%) got partial solutions of the task, that proves that subjects areunderstandable for participants.
2. Participants solutions demonstrate than more than 90% of participants not only mastered theconcept of graph (vertex, edge) and learned to build graphs with different properties (planar,connected), but also formed an understanding of the isomorphism concept.
3. More than 30% participants managed to optimize a solution over the main criterion, they built agraph with the maximal number of non-isomorphic components. And the majority of them formed
an understanding of building minimal spanning trees.
4. The stated task contained elements of an open research problem. The two best found solutions(different) lay in an unobvious region of a solutions set, they do not use all the stars as graphvertexes.
5. The proposed approach to construct tasks and their automated support provides an ability for avast majority of participants to achieve success in solving proposed task and not to reject searchingfor solutions despite that problems in the formal statement are quite hard and belong to the classof olympiad problems.
6. The implementation of problems in the form of constructive-research subjects provides wide
abilities for building own solutions and individual routes of searching for these solutions. The
analysis of results demonstrates that this potential of problems really shows up in the works ofstudents. More of that, it is interesting that participants usually add their own implicit criteria to theirsolutions, that are not stated in the problem and that may be considered “estetic”, this shows that
it is possible to automatically support elements of creative activity by the proposed types ofproblems.
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7. The proposed approach supposes an ability to state and assess success in solving problems withthe optimal solution unknown to problems authors. The results of works sometimes really reveal
effects of “discoveries” by participants and the arise of original solutions, that do not lay in the areaof common tendencies of searching for the optimal solution. Thus, it confirms an ability to automate
a support of creative activity by the proposed means.
8. The proposed approach of organization of online competitions, the usage of several subordinate
criteria both for the feedback for the participants, and for the ranking of participants, allows for wide
dispersion of results and an ability to objectively compare results of a big number of participants(thousands of different results).
9. The fact that subjects are based on hard and even unsolved problems connected with important
ideas of mathematics and informatics, together with the huge amount of sensible solutions (morethan 90%), show that it is possible to use proposed automated subjects for popularization ofimportant and hard for understanding theoretical ideas, that do are not included in the school
curriculum.

References
Akimushkin V.A., Maytarttanakhon A., Pozdnyakov S.N. Olympiad in theoretical computer science anddiscrete mathematics in "Informatics in Schools. Curricula, Competences, and Competitions. 8thInternational Conference on Informatics in Schools: Situation, Evolution, and Perspectives", ISSEP
2015, Ljubljana, Slovenia, September 28 - October 1, 2015, Proceedings / Springer, LNCS 9378, 2015,
p.94-105.
Attali. Yigal. Effects of multiple-try feedback and question type during mathematics problem solving on
performance in similar problems Computers & Education, Volume 86 Issue C, August 2015, Pages 260267.
Baker EL, Dickieson J, Wulfeck W, O’Neil HF (editors): Assessment of Problem Solving Using
Simulations. Mahwah, NJ, Erlbaum, 2008.
Chiu, Jennifer L.; Malcolm, Peter T.; Hecht, Deborah; DeJaegher, Crystal J.; Pan, Edward A.; Bradley
Michael; Burghardt, M. David. WISEngineering: Supporting precollege engineering design andmathematical understanding: Computers & Education, Volume 67, September 2013, Pages 142-155.
Gibson, D. & de Freitas. Exploratory Analysis in Learning Analytics. Technology, Knowledge andLearning, April 2016, Volume 21, Issue 1, pp 5–19.
Honey, M. A., & Hilton, M. (Eds.). (2010). Learning science through computer games and simulations.
Washington, DC: National Academies Press.
Hosein, Anesa; Aczel, James; Clow, Doug and Richardson, John T. E. (2008). Mathematical thinking ofundergraduate students when using three types of software. In: The 11th International Congress on
Mathematics Education, 06-13 Jul 2008, Monterrey, Mexico.
Mislevy, R. (2011). Evidence-centered design for simulation-based assessment. Los Angeles, CA: The
National Center for Research on Evaluation, Standards, and Student Testing.Google Scholar
Posov Ilya, Pozdniakov Sergei. Implementation of Virtual Laboratories for a Sci- entific Distance GameCompetition for Schoolchildren / The 2013 International Conference on Advanced ICT (Information andCommunication Technology) for Education (ICAICTE2013), September 20-22, 2013, Hainan, China.
Potkonjaka, Veljko; Gardnerb, Michael; Callaghanb, Victor; Mattilac, Pasi; Guetld, Christian ; Petrović,
Vladimir M.; Jovanović. Kosta. Virtual laboratories for education in science, technology, andengineering: Computers & Education, Volume 95, April 2016, Pages 309-327
Pozdniakov S., Posov I, Akimushkin V., Maytarattanakon A. The bridge from science to school / 10th
IFIP World Conference on Computers in Education // WCCE 2013 Torun, 25 July 2013.
Pozdniakov, S, Posov, I, Pukhov, A, Tsvetkova I. Science Popularization by Organizing Training
Activities Within the Electronic Game Laboratories/International Journal of Digital Literacy and Digital
Competence (IJDLDC), Volume 3: 2 Issues (2012), p. 17-31.
Yagunova E., Pozdniakov S, Ryzhova N., Razumovskaia E., Korovkin N. Evaluation of Difficulty andComplexity of Tasks: Case Study of International On-line Competition "Beaver", International Journal ofEngineering Education Vol. 32, No. 3(A), pp. 1141–1150, 2016168

Constructionism 2018, Vilnius, Lithuania

Short Tasks – Big Ideas: Constructive Approach for
Learning and Teaching of Informatics Concepts inPrimary Education
Valentina Dagienė, valentina.dagiene@mii.vu.lt
Vilnius University Institute of Data Science and Digital Technologies, Lithuania

Gabrielė Stupurienė, gabriele.stupuriene@mii.vu.lt
Vilnius University Institute of Data Science and Digital Technologies, Lithuania

Abstract
Constructionism as a learning paradigm is based on a design, actions and constructing things or
solutions by using means via collaboration and the construction of knowledge (Papert & Harel, 1991).
The main idea is that pupils can learn by performing activities by making things, or even deeper, by
speaking about what they are performing (doing).
Our approach focus on informatics concept-based tasks (problems) as scaffolding activities to introduce
informatics concepts to primary schools. The tasks are short and can help pupils to construct theirknowledge and mental structures of informatics as a science discipline. In our long experience (morethan ten years) hundreds of short tasks on informatics concepts were created.
Resent years many countries, and Lithuania among them, have been developing informatics (computerscience, computing), or digital technologies, curriculum for primary school level. Usually the curriculum
is based on integration with other subjects, nevertheless the principles and concepts of informatics areneeded to be introduced. The key components and competence areas of informatics for primary school
education needs to be identified and described (Fig. 1).

Figure 1. Lithuania identifies the key components of informatics curriculum in 6 competence areas

Main educational goal is to provide the different teaching possibilities and environments supporting
various activities and to motivate pupils to discover new ideas. Constructionist teaching and learning is
one of the important approaches and takes place through conceptually open learning activities inindividual as well as group exploration by seeking common knowledge and understanding.

Keywords
Constructionism; informatics education; informatics concepts; short task; concept-based solving;
primary education.

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Introduction
Informatics (or computer science, or computing) education is emerging area starting from the first levelin primary schools. Informatics activities can be included in other subject but not only at the level ofusing digital technologies. Our ambition is to go beyond technologies and present informatics as ascientific discipline for children. We show that by introducing a methodology for solving informatics
concepts-based tasks.
There are many reasons for including informatics education at primary level. One of them is reducing
gender inequality in the information technology sphere. Upper school students already have a vision onwhat is “for girls” and what things are “for boys”. Informatics usually falls into “for boys only” category.
This problem might be partly avoided by introducing the course earlier (Fisher, Margolis, 2003).
The main goal of informatics at school is to teach how to think – to solve problems by using different
ways including a computer. These different ways can be expressed by computational thinking – a term
which become high-profile nowadays. Computational thinking involves many components starting fromformulation of task, data collection and analysis, data representation; logical reasoning, abstraction,
algorithm design, decomposition, parallelization, automation, pattern generalization, patternrecognition, simulation (Wing, 2011; Selby & Woollard, 2013; Weintrop et al., 2016; Google for
Education, 2018)
Thinking computationally draws on the concepts that are fundamental to computer science, and involves
systematically and efficiently processing information held in the tasks. Computational thinking involves
defining, understanding, and solving problems, reasoning at multiple levels of abstraction,
understanding and applying automation, and analysing the appropriateness of the abstractions made(Lee et. al., 2011).
When considering the transfer of knowledge, we need to understand what type of knowledge is beingtransferred. We need to discover conceptual and procedural knowledge. Conceptual knowledge
consists of a connection of networks (chains) and is rich in relationships. Procedural knowledge is close
to algorithmically thinking: consists of a series of steps on actions. Conceptual knowledge is related withdeep quality of knowledge, while procedural knowledge is related with superficial quality of knowledge(Star & Stylianides, 2013). Our approach is based on obtaining the conceptual knowledge with aim todevelop the procedural knowledge.
Two years ago the Ministry of Education and Science of the Republic of Lithuania organised a team ofresearchers, teachers, education experts and businessmen for developing a framework of informatics
as well as digital technologies curricula covering both primary and secondary education. In 2017 a pilot
project for introducing informatics to primary education started in ten schools. The aim of the project is
to create educational content for primary schools based on the developed curriculum framework andinnovative ideas. The framework of informatics curriculum is built on six core competence areas:
1. Problem solving: using computers, digital devices, and computer networks – principles offunctioning of computers, digital devices, and computer networks; performing calculations andexecuting programs; using digital tools and technologies to create knowledge and to innovate
processes and products; engaging individually and collectively in cognitive processing tounderstand and resolve conceptual problems and problem situations in digital environments.
2. Digital content: creating and editing digital content in different formats, to express oneself
through digital means; modifying, refining, improving and integrating information and content
into an existing body of knowledge to create new, original and relevant content and knowledge.
3. Algorithms and programming: problem solving by using computers and other digital devices– designing and programming algorithms; organizing, searching and sharing information;utilizing computer applications;
4. Information and data: understanding and analysis of problems – logical and abstract thinking;algorithmic thinking, algorithms and representation of information;

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5. Virtual communication: developing social competences especially in virtual environments;
project based learning; taking various roles in group projects.
6. Safety and protection: observing law and security principles and regulations – respecting
privacy of personal information, intellectual property, data security, netiquette, and social norms;
positive and negative impact of technology on culture, social life and security.

Related Works
Since 2010, Austria has a project “Informatik erLeben” (experiencing informatics) that aims at attracting
students for Informatics as a constructive, technical discipline (Bischof, Sabitzer, 2011). Pupils fromprimary school up to upper secondary school obtained lectures by university teachers spread over a
period of one and a half year. The lessons developed show pupils of all grades selected core-concepts
of Informatics/CS in a playful way and at an age-specific level. The prepared lessons cover the topics
adequate for primary school pupils. Topics are divided to core-concepts and into several modules thatcan be composed individually. For example, Coding (Morse Game; Creating a Code with Colours; Codetrees; Error Detection); Computer Networks (Chinese Whispers; Communication Rules; PostmanGame); Algorithms (Instructions how to get somewhere); Sorting (Binary Search-tree); Searching (Blind
Search; Searching in a linear Structure) etc.
Depending on the topic they act either as part of the computer, serving as data or as object being
manipulated by algorithms, or assuming some role of a program. Out of principle, computers were
specifically not used during the lessons. The pupils learned, based on activities, simulations, andanimations. Important didactical principals behind the concept are discovery learning and teamwork.
Based on the project reflection there are some useful findings:•
It is very important to start at an early age to broaden the pupils’ image of CS and to create
interest.
•

While some boys already have been interested in informatics before, all participating girls could
be influenced.

•

Pupils must have the possibility to attend exciting CS lessons during all grades.

•

Because primary school kids are very open and enthusiastic towards new topics and concepts,
it is necessary to bring more technical topics in all primary schools.

Duncan and Bell (2015), having established the six general areas covered by existing primary school
curricula, have analysed three key English-language computing curricula: the CSTA K-12 Computer
Science standards (2011), the English computing curriculum (2014), and the Australian Digital
Technologies curriculum (2013). They found some notable features:•

All three curricula introduce programming concepts from the first year (5 or 6 years old), using
only sequencing and turtle graphics, which are based on concrete physical motion that students
can relate to.

•

Selection (branching) is introduced from about 7 years old, and if iteration (repetition) is
introduced, it seems to be in the form of simple counted loops. More sophisticated iteration withconditions on the loops, and the introduction of textual (general purpose) languages, seems to
be expected around 11 or 12 years old.

•

Topics relating to safety and ethics are covered from the very first year, again gradually
increasing in sophistication from simple scenarios for young students to more serious issues ofidentity and privacy as students approach their adolescent years.

•

There is some difference in what is taught around “algorithms", which covers both the design ofsimple programs, as well as understanding algorithms for standard problems such as searchingand sorting. These standard problems serve as examples of clearly defined problems, but also
allow students to investigate their performance. The Australian curriculum starts earlier withstandard problems, but by 11 years old all three curricula include such algorithms. This will be
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another important area to evaluate in studies with students to determine if there is value instarting early with these concepts.
Six core learning areas have been announced in New Zealand curriculum: (1) algorithms, (2)programming, (3) data representation, (4) digital devices and infrastructure, (5) digital applications, and(6) humans and computers. The proposal to relate these areas to the principles of Computational
Thinking are made (Duncan, Bell, Atlas, 2017).
Webb et al. (2018) have discussed the evidence that young students, of 7–8 years old can start todevelop understanding of important informatics concepts. Students can learn through hands-on
experience and gradually begin to link theoretical concepts to their developing practical problem-solving
capabilities. Therefore, identifying trajectories in the development of these concepts and devising
effective pedagogical approaches which make use of the tools available are important current researchchallenges. Furthermore, in addition to developing informatics concepts to support the subject per se,
it is necessary to define the underlying knowledge base of informatics concepts and crucial skills needed
to support digital citizenship.
There are some suggestions about introducing informatics in primary education in Poland: informatics
activities need to be included in the same place where kids playing, so no need for a full equippedclassroom. Integration of informatics with other subjects during the whole week (1 hour lasts a week).
Of course, sometimes a teacher may take pupils to a computer lab. Teacher has access to pupils’ results
regardless of the place they work, in school or at home (home works). Flipped learning method issuggested to use (Sysło, 2017).
Many countries are integrating digital competencies in primary education and introducing basics ofinformatics by using various activities.

Learning and Teaching Informatics in Primary Schools throughSolving the Short Tasks based on Informatics Concepts
The predominant theories of learning are based on the premise that learning is an epistemologicalproblem involving individual psychological processes that lead to the acquisition of knowledge (Lave,2008). Thus, an individual constructivist view sees learners as active agents who construct knowledge
as their own internal model of ‘the world’ based on the result of interactions within it. Knowledge changes
from being something you acquire to be an ability to act within a community of practice (Fig. 2).

Figure 2. From acquiring knowledge individually to building community of practiceSchools should help pupils to make this transition by scaffolding their constructive work performed
individually and in groups. For example, teachers can set pupils work, which were to find solutions of
the given short tasks (presented as the set of several task cards). At first pupils should work individuallyand solve the given tasks. Then they join in groups of 3-4 pupils and discuss the tasks. Each groupshould discuss and agree on the presented solution. This is one of scenarios developed for teachers.
We know that scaffolding increases pupils’ ability of deeper understanding knowledge and promotes
the acquisition of computational thinking’ (Lee et. al., 2011). Thus, pupils’ skills and capacities are
increasing.

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Figure 3. Pupils solving individually (sometimes with help of a teacher) and by groups

Figure 4. Solving process of tasks based on informatics concepts

Children build their own knowledge structures as they are engaged in constructing things and then thesestructures are becoming a materialization of ideas and thoughts. Similarly, during the solving process
pupils read, think, explore, and analyse the task (text structure) and develop the solution path.
Our practice of pupils’ observation during the solving informatics concepts-based tasks showed that thisprocess can support pupils to generate and share meanings about the informatics concepts involved in
the tasks and promote the development of computational thinking and understanding of informatics.
Primary school teachers usually have too little informatics background. For them, a deconstructionist
process of tasks is very important (Boytchev, 2015; Dagiene, Futschek, Stupuriene, 2016). Primaryschool teachers can improve their informatics competence through analysing, solving, and explaining
the essence of the informatics concept-based tasks (Fig. 5).

Figure 5. Pupils’ and teacher’s attitude towards learning by solving tasks

Informatics concepts need to be brought to pupils in attractive way, what is done by establishing Bebras
challenge model (www.bebras.com) (Dagiene & Stupuriene, 2016). The Bebras challenge is aimed topromote pupils interest in informatics from the very beginning of school and lead them to develop
computational thinking abilities. Main idea is to involve pupils into informatics task solving activities andto use computational thinking and modern technologies more intensively and creatively. At the
beginning the Bebras challenge was aimed mainly at junior level, however nowadays more and morecountries focus on primary school level as well (Carteli et al., 2010; Dagiene et all, 2017; Izu et al.,2017).
However, we need well developed short tasks which are based on informatics concepts and aimed todevelop computational thinking. Since knowledge and understanding are both essential for educational
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progress, the informatics curriculum should contain main concepts, and provide a clear structure, alogical progression and open ways for various didactical approaches.
The tasks should be really short, answerable in a few minutes through a computerized interface, andrequiring deep-thinking skills in the informatics field. The tasks should be answered without priorknowledge in informatics, and they are clearly related to fundamental informatics concepts. To solve
those tasks, pupils are required to think in and about information, discrete structures, computation, dataprocessing, data visualization, and they should use algorithmic as well as programming concepts. Eachtask can both demonstrate an aspect of informatics and test the participant’s ability of understanding
informatics fundamentals.
International Bebras community has agreed to develop the informatics tasks according to the following
framework (Fig. 6-a). The framework consists of two parts: a task formulation (text, image, question)and metadata (solution, explanation why it belongs to informatics, etc.). Metadata is very important forteachers, also for others tasks developers.

Figure 6. Structure of informatics concept-based task: a) for a Bebras task, b) for a card

Informatics concept-based tasks on cards, or the informatics tasks cards have been originated from the
Bebras tasks and supported informatics without computer paradigm (CS Unplugged idea). The tasks
on cards are more compact (Fig. 6-b), their metadata is published separately (in additional cards or in
the internet).
The informatics concepts-based tasks are based on problem solving process. Problem solving is wellknown paradigm in education. Our didactical approach to introduce informatics concepts is based on
short tasks which involve informatics concepts carefully selected for primary level. Bebras community
has a long experience in creating informatics concept-based tasks (more than ten years’ practice, morethan 50 countries are involved). Solving short informatics tasks can be a transformative teaching
methodology and can support an intensive learning atmosphere. Incorporating reasoning anddiscussions, as well as other active learning strategies is an important didactical component for
educators. Many informatics concept-based tasks are developed during the annual Bebras tasks
development workshops.
Short informatics concept-based tasks solving is a powerful method that can support a pedagogical shift
in the classroom and foster pupils’ engagement and motivation to learn. Many publications deal withproblem solving methods. Problem solving of the short tasks can be considered as a systematic process174Constructionism 2018, Vilnius, Lithuania

involving pupils into deeper understanding of informatics concepts. The short task solving can be oneof the strategies that engage and motivates pupils for deeper learning and fosters the deeper thinking
skills.
There are many ways for selecting problems to be solved by pupils in the classroom. For primaryeducation two type of problem solving are usually declared: practical problems which take more timeand cover several topics (1), and everyday exercises. Exercises are very common in mathematics andlanguage (grammar) lessons (2). But we suggest the third type of problems - short tasks with double
folded aim: to cover informatics concepts and to be solvable in few minutes.

A Pilot Study in Primary Schools: Solving the Small Tasks
containing Big Ideas of Informatics
In 2017, the project introducing informatics in primary schools (“Informatika pradiniame ugdyme“42) was
initiated by the Ministry of Education and Science of the Republic of Lithuania together with the
Education Development Centre. The aim of the project is to create informatics educational content inprimary education and test it in ten schools before summer 2018. Ten schools were selected fromdifferent locations in Lithuania (https://informatika.ugdome.lt/en/teams/). The school teachers are asked
to integrate informatics education in their lessons (grades 3 and 4): develop and testing teaching andlearning material, sharing their best practices, giving suggestions on informatics educational content
and curriculum in primary education.
Various activities and resources have been developed during a year working with these schools.
Schools exchange their lessons plans and scenarios using different educational means andtechnologies, for example, Scratch Juniors, Bee Bots, CS Unplugged activities, Scottie Go, etc. Having
a huge experience in informatics gamification and contests (namely, the Bebras challenge) wesuggested a set of short tasks based on attractive stories and containing informatics concepts. For
supporting pupils’ engagement in learning informatics concepts, we developed a set of tasks and design
them as attractive story based task solving card set. Each school get 10-15 set of cards and didactical
recommendations.
In January 2018, we visited 5 schools and made observation of 3 and 4 grades pupils’ work on solving
short informatics concept-based tasks presented on cards. Some results are described below.

Participants
The study was divided into several 45 min sessions and took place in 5 public primary schools as aschool activity – integrated into various school lessons. The participants were 87 pupils, mixed boys
and girls, from the third and fourth grades, aged 10-11 years old. They worked collaboratively in small
groups of 2-3 pupils in the school’s by using task cards. When discussing of solving process someschools used a projector to show the task. All of the pupils had little previous experience with informatics
concepts.

The Cards – Short Tasks
A set of cards with 54 informatics concepts-based tasks was developed (Fig. 7-a).
Also an additional card contains a list of the informatics concepts with link to the tasks (card numbers)
and rules example how to use the tasks. During a pilot study we mostly pay attention to solving tasks
which based on the following informatics concepts: sorting and grouping, information analyses andsearch, understanding of an algorithm, program and automation, pattern recognition, selection,
repetition, logic, and coordinates.

42

https://informatika.ugdome.lt/en/about-project/175

Constructionism 2018, Vilnius, Lithuania

Figure 7. A tasks card box (a) and some cards with tasks (b)Data collection and analysis
During the study, we collected qualitative data: photos, pupil’s reactions, answering questions andreasoning of the solving process. We observed both pupils solving process, and presentation of solution.
Also we focus on informatics concepts in tasks: are they recognisable by pupils as well as teachers.

Findings
At the end of the study, we got very broad view of the short tasks and their suitability to pupils for learning
informatics. The tasks, which pupils have solved during our observations, are briefly presented in Table
1 with a short description and the comments they had.
Table 1: Some findings when observing pupils work by solving informatics concept-based tasks on cards
Informatics concept
Sorting, grouping

Description / Activities- sorting buttons, toys,
searching for features- groupings the given items(mentally and physically)

Information analyses
and search- observing images, texts,
extracting details,- dealing with text andnumbers, searching for them

Algorithm- following precisely rules anddescriptions,- discovering steps andimplementing them.

Program, automation- implementation of algorithms
using commands,- seeking to determine
repetitive parts or components

Pattern recognition- dealing with everyday items(decoration, neckless) andsearching for structures,- determining and finding
patterns,- matching patterns.

Selection- choosing one item from twounder certain condition,- dealing with condition’

Discovery
About haft of the third grade pupils can easysort items mentally by recognizing different
features. 1/3 of pupils have difficulties inmental sorting and need to manipulate withphysical items.
Pupils are familiar with such type of tasks inlanguage or mathematics lessons, they are
comfortable and solve them without
questioning. About 1/5 of pupils lack ofconcentration.
Algorithm tasks are very different, from simple
ones to quite complex. Then it is not easy topresent summary. More than half of pupils
understand rules and can follow or implement
them. About 1/5 of pupils struggle withattention to follow strictly an algorithm
description (rules).
Many pupils are familiar with table games andare able to follow rules. They can easy
perform set of commands, sequences.
However, automation needs more work andmore tasks to be solved.
Task solution process is similar to sorting.
About haft of third grade pupils can easy
recognise simple patterns. 1/3 of pupils havedifficulties in reasoning and need tomanipulate with physical items. Complex
patterns take more time and few pupils inclassroom can properly deal with such tasks.
Half of pupils can deal with making decision
for selecting items under conditions. Almost
all pupils can follow the command’s IF-THEN
is implementation. Some pupils have176

Constructionism 2018, Vilnius, Lithuania- understanding statement IFTHEN-ELSE
Repetition

Logic

Coordinates- recognizing repetitive parts,- following repetitive rules andcommands,- applying repetition to
everyday life processes.
- reasoning by logic rules,- excluding alternative,- applying logical operations:AND, OR.

- understanding
representation,- two dimensional way of
thinking.

problems with understanding ELSE. Physicalgames and activities would help pupils tounderstand the selection deeper.
Pupils like to follow repetitive (iterative) rules.
Some pupils due to lack concentration caneasy loose track in repetition especially by
doing mentally. Pupils enjoy playing repetitive
games, mentally and physically.
Many pupils solve logical puzzles frommagazines, they are familiar with several
types of logical games. Mathematical oriented
pupils (about 1/4) like logic tasks and solve
them very fast. Other pupils need time andsupport.
Pupils know usually tables however they do
not pay attention to indicate a cell location.
Coordinates introduce to two-dimensional
way of thinking and it takes time forunderstanding. Many tasks should be solved.

In everyday life pupils apply algorithmic concepts to describe different activities and especially game
rules. The most common are the sequences of do-this commands, conditional statements (if ... then …
else), logical expressions, and variables in the form of task attributes. Everybody agrees that theseconcepts are very important for many informatics areas, especially in artificial intelligence, robotics aswell as blockchains.
For this paper, following findings from the study are relevant:•Informatics integration model is used in primary school’s education: usually schools dedicate
from 15 to 45 min. per week in each grades 3 and 4. The short informatics concepts-based tasks
on cards were used as an introductory resource to various topics, mainly integrated withmathematics: data analyses, pattern recognition, repetition, commands, etc.

•

We were pleased to learn that all teachers use of the short tasks cards in classes as means forteaching and learning informatics basics.

•

The study proved what we had worried about – there are several misconceptions about the
informatics concepts. Primary school teachers do not have informatics background and needcarefully prepared trainings on informatics topics.

•

In general, the reactions of pupils to the short tasks are highly positive. Both teachers and pupils
are enjoying by solving the tasks and discussing various topics connected to informatics
concepts.

•

The short tasks cards inspired the teachers themselves to learn about informatics concepts, tocreate similar tasks or exercises, and to extend some short tasks by physical tools.

Conclusions
Informatics education is important area of XXI century. Many research studies are aimed atcomputational thinking as one of the most discussed part of informatics education. In this paper, we
explored the informatics concepts through the process of solving short tasks. We presented a small pilot
study in which pupils were engaged in solving informatics concept-based tasks on cards. The task
solving activities were developed around a set of cards. The results of the data analysis revealed somesignificant outcomes regarding task solving. First of all, it seems that short and story-based tasks
engage pupils in solving and thinking process. Not only pupils but and teachers are interested in thisprocess, because a lot of primary school teachers never study informatics as a science discipline.
Moreover, the analysis showed that the short task solving approach can contribute to guidance for thesolving process. This study showed that the solving short tasks has significant potentials as an
177Constructionism 2018, Vilnius, Lithuania

educational approach for fostering computational thinking skills. However, more research needs to bedone on this field.

Acknowledgments
The authors gratefully acknowledge the support of the Nordic Research Council through the NordPLUS
programme of transverse actions, in particular through the funding of the two year project “Culturally
Diverse Approaches to Learning Mathematics and Computational Thinking” with project code NPHZ2018/10063. We also acknowledge the participated Lithuanian schools. The authors wish to thankNicklas Anttu for proofreading the paper and making suggestions.

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Constructionism 2018, Vilnius, Lithuania

Design Science Research for Computational
Thinking in Constructionist Education: A
Pragmatistic Perspective
Vladimiras Dolgopolovas, vladimiras.dolgopolovas@mii.vu.lt
Vilnius University, Institute of Data Science and Digital Technologies, Lithuania

Valentina Dagienė, valentina.dagiene@mii.vu.lt
Vilnius University, Institute of Data Science and Digital Technologies, LithuaniaEglė Jasutė, egle.jasute@mii.vu.lt
Vilnius University, Institute of Data Science and Digital Technologies, Lithuania

Tatjana Jevsikova, tatjana.jevsikova@mii.vu.lt
Vilnius University, Institute of Data Science and Digital Technologies, Lithuania

Abstract
Constructionist educational environments have provided solutions for effective learning process. What
are the criteria of the effectiveness of using such environments? The question is even more difficult if
we are seeking computer enhanced constructionist solutions. One more problem is how to train thecomputational thinking (CT) skills and especially to develop their assessment. If we consider the levelof understanding of the relevant scientific knowledge to be transferred to the learner as the effectivenesscriteria, like done in such traditionally positivistic fields like physics or mathematics, how it corresponds
to the computational thinking skills and abilities? This is an even more difficult question to answer in thecase of multi-paradigmatic fields like engineering or informatics. The nature of knowledge in these
disciplines is non-positivistic in general, such research areas usually include not only technical but alsosocial domains and the research methods in these fields are mainly qualitative.
The article makes an attempt to start a discussion on the above presented problems. This researchaims at a detailed analysis and study of the possibility to arrange a constructionist platform and therelevant software enhanced educational environment in an effective way as it could be seen from thepoint of view of acquisition of CT skills. The possible solution is based on Design Science Research(DSR) methodology. The DSR methodology is developed under the pragmatistic research paradigm
and could provide a framework for unification of educational solutions, especially in such educational
fields like informatics and Science, Technology, Engineering, and Mathematics (STEM).
Research questions as related to software enhanced constructionist educational platforms areformulated as follows: (RQ1) What are the relations between inquiry-based learning (IBL) and CT? How
can IBL help to develop CT? (RQ2) What are the advantages and possible disadvantages of inductivedeductive reasoning in connection with IBL? What is the role of abductive reasoning as related to IBL
and CT? (RQ3) How to effectively (as related to CT) incorporate DSR into IBL? The article focuses on
cognitive and epistemological aspects of the presented approach and analyses the possible connectionof DSR to the inquiry-based educational process. Another important aspect is the possible approach to
IBL in the paradigmatically non-positivistic areas of scientific research. Pragmatistic aspects of DSR,
based on the implementation of abductive and circumscriptive cognitive features, allow implementationof an inquiry-based educational process that focuses on CT in various inter-disciplinary areas like, forexample, informatics.

Keywords
Computational thinking, constructionist education using technology, design science research, inquirybased education, pragmatism.

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Constructionism 2018, Vilnius, Lithuania

Introduction
Modern educational environments are usually based on technology. Appropriate software and hardware
platforms are an integral part of modern teaching and learning. There are two main directions in thedesign and application of educational software: (1) the development of educational software, including
software platform, and (2) development of software-based learning objects. Examples of educational
software may include various educational software platforms, such as virtual laboratories (Scheckler,
2003) or serious games (Ritterfeld et al., 2009). Software-based learning objects promote theeducational use of software in a form of programming code and can be used in engineering andcomputing educational environments, for example, in robotics (Štuikys, 2015), introductory stochastics(Dolgopolovas et al., 2014), or programming education (Dolgopolovas et al., 2015). Computers affectand at the same time provide requirements for the skills that students must acquire. One of these skills
is computational thinking. These skills are one of the most important competencies in the 21st century(Kurilovas & Dagiene, 2016).
In this respect, the main questions to be studied are: What is the "proper" list of skills, and if it is alreadyprovided, is it still relevant for current business and society requirements? How to organize learning
process that allows us to develop or improve the student's computing skills? How to integrate computers
it the way that improves the learning process or, conversely, the use of computers will require additional
efforts and skills of the student, and this will make the process of achieving educational goals even moredifficult and less effective? How to organize a "personally significant" environment, taking into accountaspects of social communication that naturally arise in the classroom and are promoted by modernmeans of telecommunications? Another type of questions is related to evaluation. How to evaluate
progress in acquiring skills and assessing cognition skills, such as thinking skills? What is therelationship between mental abilities and cognitive abilities and how this to be assessed?
Another group of questions focuses on the possible relationship between formal requirements for the
results of the educational process, such as a clearly formulated list of professional skills and knowledge,
or rather abstract requirements for mental skills that need to be improved. How to organize the learning
process, which will correspond to the student's acquisition of both practical knowledge and "proper"
thinking skills? How, in this aspect, is possible to teach different scientific and technical disciplines thatprobably relate to even different research paradigms? We will try to discuss at least some of theseissues and tasks with an emphasis on philosophical, epistemic and cognitive aspects related toconstructionist education in general and constructivist aspects of engineering education in particular.
The history of the concept of "Computational Thinking Skills" originates from the definition given andfurther developed by Jeannette M Wing: “Computational thinking is taking an approach to solving
problems, designing systems and understanding human behavior that draws on concepts fundamental
to computing” (Wing, 2008). Computational thinking skills incorporate analytical thinking, engineering
thinking, and scientific thinking thus they could be positioned as a kind of universal skills for the modern
student, and this is especially true for engineering and STEM education. Wing wrote: “Computational
thinking is a kind of analytical thinking. It shares with mathematical thinking in the general ways in which
we might approach solving a problem. It shares with engineering thinking in the general ways in which
we might approach designing and evaluating a large, complex system that operates within theconstraints of the real world. It shares with scientific thinking in the general ways in which we might
approach understanding computability, intelligence, the mind and human behavior” (Wing, 2008). How
to arrange a proper educational environment which enables computational thinking skills to besystematically developed? There is no doubt that an integrated approach is needed. We need to provide
solutions for the navigation and motivation of students, including different educational environments forschoolchildren. (Dagienė & Sentance, 2016). A more difficult problem is how to evaluate the acquisitionof such skills (Dolgopolovas et al., 2016)? It seems that the software enhanced environments could
provide proper solutions. The logic is straightforward: computers and skills of using computer improve
the skills of computational thinking, because computational thinking “…draws on concepts fundamental
to computing” (Wing, 2008). However, this is not that simple. There are many contradictory examples
provided in literature (Coughlan, 2015; Richtel, 2011). In many educational cases, computers andsoftware enhanced environment do not work as expected. Instead of improving the abilities and skills
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Constructionism 2018, Vilnius, Lithuaniaof students, the skills and abilities of students are degrading, including the skills associated withcomputational thinking. In order to solve this problem, we do not recommend avoiding the use ofcomputers in educational environments. Instead, we promote the proper organization of the "computer
friendly" educational process. Such an educational process must be based on a properly designed
constructionist environment. Such a "proper" environment should include the appropriate "cognitive
interface", provided to the student. This cognitive interface will serve as an intermediate link between
the extended educational environment, which includes a computer with the appropriate programming
interface, on the one hand, and the cognitive processes and epistemological actions of students on theother, and all this allows to correctly direct the "flow" of the constructionist learning process.
The article examines the modern and computer-based educational environment, including an analysisof the relevant types of educational software and its application. Also detailed analyses on therequirements and a specification of the previously described cognitive interface are provided. Although
the main emphasis is on cognitive and physiological aspects, we present a description of some practical
tools for computational thinking enabling modern constructionist educational environment. Since westudy the computer enhanced educational environment, the presented analytical material anddeveloped solutions are aimed at education with computers. But in connection with the generalprinciples of constructionist education focused on computational thinking, the proposed solutions can in
any case be generalized, which makes it possible to create a computer-free environment andcorresponding educational solutions. The universalizing paradigm here is pragmatism, viewed as aphilosophical assumption. By designing and creating a pragmatic educational environment, one canfind a common way of organizing computational thinking enabling constructionist educational solutions.
We will discuss this pragmatistic approach in the following sections.

Inquiry-based education and computational thinking skills inconstructionist settings
In this section we discuss the educational aspects of a scientific inquiry. Then we create a link tocomputational thinking skills, which are acquired by students during the learning process. First, we needto answer the following question: what is the scientific or engineering inquiry and why is it important for
all levels of education? There is a strong opinion (Council, 2000b; Jadrich, 2011) that students should
not only learn science and scientific methods, but also should have the opportunity to “... do science.
This vision for science teaching stems directly from the educational imperative to develop scientificallyliterate students” (Jadrich, 2011). Students can become scientifically literate if they participate inpractical scientific activities. Moreover, this is true for students at all levels of educational programs inschools, as well as in universities. The practical implementation of educational technology based on
scientific inquiry is a complex and challenging task (Flick & Lederman, 2004; Jadrich, 2011). Learningof scientific content corresponds to the highest levels Bloom’s taxonomy. “Consequently, learning tothink and act like a scientist is much more difficult to do than just learning about scientific content”(Jadrich, 2011). How could scientific inquiry be defined?
First, what constitutes scientific inquiry? Several approaches could be presented: scientific inquiry (1)begins with a scientific question; (2) is a hands-on activity; (3) is a set of specific methods and practices
used by scientists; (4) is a set of reasoning strategies or skills needed while driving a scientific process.
The main common feature of the presented approaches to the definition of scientific inquiry is that allthese definitions are process oriented, as they attempt to define scientific inquiry, describing theactivities of scientists. Another solution is to define scientific inquiry using a result-oriented approach(Jadrich, 2011). What is the primary goal of scientific activity? We share the opinion (Jadrich, 2011;
Nersessian, 2010; Windschitl et al., 2008) that the primary goal of science are scientific models and theaim of any scientific work is to develop, test, and modify the scientific model of the subject of study.
Generally, we could name the process of development, testing, evaluating and modification of a model
as simulation. The reason for this is the following. In any case, such operations with the model shouldtake place within time, therefore it can be described as a simulative modelling process or simply asimulation. Why do we focus on this? The reason will be clear after a closer look at the nature of thesimulation. Scientific activity in the design (development, testing, evaluation and modification) of
simulations as artefacts is closely related to the cognitive activity of constructing mental simulations and182Constructionism 2018, Vilnius, Lithuania

simulative reasoning (Nersessian, 2010). Summing up, scientific inquiry can be defined as an activity inthe design of scientific simulations, and the goal of scientific work is the design of model-based scientificsimulations.
Why are simulations so important? The process of designing simulations provides a part of the cognitive
interface enabling the leaner’s relevant cognitive activities of constructing and grounding of mental
models. We will discuss this in more detail. The definition of a model-based simulation is based on thefollowing meanings (Landriscina, 2013): (1) The meaning of a system. A system is a collection ofdifferent elements whose combination yields results that are unobtainable by the elements alone.
Therefore, the system is more than a sum of its parts; (2) The definition of a model. A model is a
simplified representation of a real or imagined system; (3) A simulation could be defined based on (1)and (2): a simulation is an interactive representation of the system to be studied based on a model ofthe system. This definition has a wider meaning than the traditional view of simulations as a dynamic
set of interactive representations. Basically model-based simulations are associated with cognitive
activity of students and with the process of constructing appropriate mental models (Landriscina, 2013).
This approach to simulations also improves learning related cognitive processes, facilitates
modification, construction or replacement of appropriate cognitive structures (Mayer, 2009). These
processes include enhancement of “… cognitive processes that are crucial to learning, such as:
selecting key information; organizing this information into a cognitive structure; integrating this new
information into previous knowledge; accessing and creating appropriate analogies and metaphors;
generating inferences; reorganizing cognitive structures” (Jadrich, 2011). One should mention analternative – a cybernetic approach based on engineering traditions. This approach has roots in thetechnique of representing any system in the form of a "black box" with a certain behaviour. Typically,
the aim of simulation is to observe the dynamic behaviour of the model of a real system. Thus, a person,
especially with engineering experience, could view modelling as something superfluous, complex and
not essential, especially for educational needs. The model can be considered more important than its
simulation, and one can confine himself to the learning task only with the use of modelling. At the sametime, educational simulations are usually seen as an example of the use of information andcommunication technology (ICT) tools, so the effectiveness of such a "traditionally understandable"
educational process based on simulations can be questionable. This article focuses on simulation
making rather than simulation using activities, although the role of simulation as an ICT tool is notcompletely rejected.
The constructionist approach focuses on the creation of physical or mental "things" (artefacts) during
instruction and student interaction with these artefacts in order to facilitate knowledge (Papert, 1980).
Focusing on simulations making, simulating using activities can be an alternative in some cases
depending on the available learning environment. Implementing the engineering point of view, modellingand simulations can be defined more generally as follows (Raczynski, 2014): modelling as a link
between real systems and models and simulation as the relationship between models and computers.
In fact, the simulation is based on the model. From the engineering and pragmatistic positions, themodel can be defined as: “A model is a description of some system intended to predict what happens if
certain actions are taken” (Bratley et al., 2011) and extended in (Zeigler, 1975). First, you need to specify
a set of model components. Each component is described by a set of input, output, and state variables.
Then the experimental frame is defined as the set of all descriptive variables. Depending on the chosenlevel of simplification, various experimental frames can be determined (Zeigler, 2014).
Another important aspect is the aspect of simulative scientific reasoning. A model-based scientificreasoning based on simulations of mental models was developed (Nersessian, 2010). As a rule, themental model corresponds to the conceptual model, which is built on the first stage implementing modelbased simulation activities. A properly designed learning process should implement a unique mapping
between model-based cognitive simulations and computer simulations based on models that must be
designed during the educational activity. This mapping can be provided by the teacher also in the formof co-mediated teaching. Presented ideas of intermediate conceptual models and simulation making
educational activities provide a clear bridge to the environment aimed at acquiring of computational
thinking skills. For example, there is clear evidence of conceptual support of acquisition ofcomputational thinking skills practically applied in the contest-based educational environment (Dagiene
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Constructionism 2018, Vilnius, Lithuania& Stupuriene, 2016). As will be discussed in the following sections, an approach that focuses ondeveloping learner's conceptual models as intermediaries for his or her cognitive grounding will provide
a solution to the computational thinking skills to be acquired. In this aspect, the set of computational
thinking skills can serve as a kind of criterion for evaluating the effectiveness of the educational process,with an emphasis on epistemological and cognitive educational aspects. Such an assessment is aimed
to root the process of justification that takes place in the process of the student's abductive reasoning.
We will discuss this in more detail in the following sections.

Inductive-deductive reasoning scheme and educational aspects
Computational pedagogy is an example of one possible approach to arrange the educational
environment in the constructionist manner and is especially relevant for the introductory level education.
The approach is based on didactic schemes, general computational thinking skills and practical abilities
to conduct simulations. Traditionally, a deductive approach is considered as the most applicable forteaching scientific topics. Such an approach is considered as demotivating for students, especially for
topics where sufficient theoretical background and a large amount of pre-knowledge are required (Yasar& Maliekal, 2014; Prince & Felder, 2007). On the contrary, the inductive approach is promoted as suchan alternative, which can improve the positive attitude of students towards the learning of science(Council, 2000a). Inquiry-based learning, problem-based learning, and project-based learning, all relate
to forms of inductive instruction (Prince & Felder, 2006). To overcome the described problems of thedeductive approach, modelling and simulation-based computational pedagogy is proposed. Suchpedagogy allows “cycle back and forth between the inductive and deductive approaches to learning”(Yasar & Maliekal, 2014) with the assistance of modelling and simulation tools.
The theoretical foundations of computational pedagogy are based on the concept of cognitive retrieval,presented by P. C. Brown, H. L. Roediger III, and M. A. McDaniel (Brown et al., 2014). This so-calledinterleaved retrieval practice creates a cognitive basis for the interdisciplinary computational
pedagogical content knowledge. “Interleaving retrieval practices by weaving together multi-disciplinaryfeatures around a common topic (i.e., interdisciplinary education) have great advantages for gaining
deep and lasting knowledge” (Yaşar, 2013). The process of model-based reasoning is presented as an
inductive/deductive cycle of modelling and retrieval of models. This process “is consistent with the dual
deductive and inductive process of computational modelling and simulation” (Yaşar, 2016). Using
modelling and simulation, the learner can receive feedback from the modelling and simulation
environment, thereby promoting his or her construction of knowledge.
The presented approach consists of two main principles: (1) the principle of designing educational
environment based on using modelling and simulations and (2) the idea of scientific reductionism. There
are several important aspects for discussion. First, and this has already been mentioned in the previous
section, from the educational perspectives, there is a great difference between simulation making andsimulation using activities. Moreover, there are many doubts about the effectiveness of simulation-using
tools aimed at increasing the learner's motivation and his conceptual understanding of scientific topics(Council, 2011). Usually, the practical implementation of simulation using predefined software can beconsidered as a sample of a pre-designed cognitive artefact.
Application of the artefact in the educational environment can be studied from different points of view:
the system view and the personal view. The effectiveness of educational software is a key motivating
factor for educators in favour of including such solutions in the educational process. However, from thelearner’s point of view, such a cognitive artefact is positioned as another tool with a personal view of its
effectiveness (Fig. 1 and Fig. 2 are adapted from Norman, 1991, p. 3). D. C. Norman provides thefollowing illustrative example: consider a to-do list (1991, p. 3). Such a checklist, for example, developed
for aircraft pilots, enhances the cognitive abilities of pilots and improves their memory. Therefore, fromthe point of view of the system, it is a memory enhancer. From the point of view of the pilot or from apersonal view, using the list is just another task requiring different type of activity. Without a check list,a person should remember all the to-do tasks. To use the list, you must perform the following task: (1)building a list (in the described case is done beforehand by the third person); (2) do not forget to readthe list; (3) reading and interpreting the items of the list items.
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To sum up, if from the point of view of the system (read the "teacher") the cognitive abilities are
improved, from the personal (student's) view the person participates only in (2) and (3) activities,
because instead of trying to memorize the elements of the list, the person now should remember only
to consult this list. This type of memory degradation is clearly manifested in the daily practice of
education. Perhaps this is one of the reasons for the current popular movement towards educational
technology without a computer (Richtel, 2011). With regard to the use of educational software as anadvanced educational tool for constructionists educational platforms, such educational platforms,
viewed as cognitive artefacts from the user's point of view, can be very harmful and demotivating.
Obviously, during the process of using software there is some shift in tasks and cognitive processes,
therefore educational software platforms must be carefully designed and tested. Another obstacle is the
problem of bricolage (Papert & Harel, 1991). By focusing on using the provided educational platforms,
the learner will improve his / her skills in using the software, rather than constructing knowledge for thelearning topic. The bricolage leads to the “endless debugging of the ’try-it-and-see-what-happens’
variety” (Ben-Ari, 2001). As can be seen, the described aspects are interrelated. Next, the relevanceof the use of the ideas of scientific reductionism for the theoretical grounding. To argue this, we firstprovide a description of inquiry-based educational process: “… Inquiry also refers to the activities ofstudents in which they develop knowledge and understanding of scientific ideas, as well as anunderstanding of how scientists study the natural world” (Colburn, 2000). As can be seen from the above
description, it would at least be questionable to reduce scientific methods to positivistic and basicallyquantitative, as scientific reductionism does. Other forms of research and the paradigm of research arealso important. This is especially true for interdisciplinary engineering areas, such as, for example,
research of information systems, where interpretivist and pragmatist paradigms and methods ofqualitative research are of paramount importance (Goldkuhl, 2012; Walsham, 2006). As for cognitiveand epistemological aspects, to reduce the scheme of reasoning only to inductive-deductive reasoning
is doubtful. This can lead to the disabling of the proper grounding process for the student's cognitivemodels, and we will discuss this aspect in the next section.

Fig. 1. System view of a cognitive artefact. Adapted from (Norman, 1991, p. 3)

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Fig. 2. Personal view of a cognitive artefact. Adapted from (Norman, 1991, p. 3)

Pragmatistic approach as the key to inquiry-based education
The psychological aspects of situated cognition give recommendations on how the learning environment
should be designed and constructed. Obviously, there is a clear distinction “between naturalenvironments which afford the learning of ‘percepts’ in everyday life, and unnatural environments”(Laurillard, 2002). Viewing from pragmatic positions, modelling and simulation is considered as a toolfor grounding a student to such an artificial environment in terms of grounded cognition (Barsalou, 2008,2010). Using computer simulation in a constructivist way could be considered as a specific type ofgrounding through situated simulations (Barsalou, 2008; Pezzulo et al., 2013). How can this grounding
be practically achieved? Simulation-based learning can be described from the standpoint of the
progression of mental models (Landriscina, 2013): “... beginning with a student’s initial model of an
examined system and developing into a target conceptual model-presumably the same one underlying
the simulation’s computational model. Moreover, to arrive at the target model, students must firstdevelop their own intermediate conceptual models, which are mental models expressed as cognitive
artifacts”. The presented approach focuses on the conceptual model, as an intermediate for grounding.
How can such conceptual models be developed? Following is noteworthy. Conceptual models are
based on mental models, so first one has to ask the next relevant question: how does the mental model
develop by the human brain? The approach developed by P. Thagard considers “mental models as
representations consisting of patterns of activation in populations of neurons” (Thagard, 2010). This
cognitive model-based approach overcomes the limitations of sentential models of theoretical abduction(Magnani, 1999; Magnani, Casadio & Magnani, 2016) and expand C. S. Peirce ideas of how mental
models can “contribute explanatory reasoning” (Peirce, 1992) going beyond verbal information andincluding “visual, olfactory, tactile, auditory, gustatory, and even kinesthetic representations” (Magnani,
1999). Considering mental representations as patterns of firing in neural populations, the process ofconstructing of mental models could be presented as a chain of patterns developing by the process of
causal correlations (Magnani, 1999). Such an approach could be used to provide explanations of how
abduction could generate new ideas. The neural model of abduction presented by P. Thagard and T.
Stewart (2011) implements a fully multimodal convolutional model of “creative conceptual combination”
describing “many kinds of creativity and innovation, including scientific discovery, technologicalinvention, social innovation, and artistic imagination” (Thagard, 2010). The human brain is adapted to
powerful learning mechanisms: “One of these learning mechanisms is an abductive inference, whichleads people to respond to surprising observations with a search for hypotheses that can explain them.
Like all cognitive processes, this search must be constrained by contextual factors such as triggering
conditions that cut down the number of new conceptual combinations that are performed” (Thagard,2010). It is obvious that such triggering conditions use circumscription (McCarthy, 1981) in the form ofa previous experience to eliminate inapplicable transactions.
How could the abductive reasoning, viewed from pragmatistic positions, practically improve thesoftware-based constructionist educational environment towards acquisition and improving ofcomputational thinking skills? First, we should discuss the pragmatistic meaning of abduction (Peirce,1992). What are the differences between abduction and the well-known hypothetical-deductive method(Lawson, 2015; Magnani, 2009)? The essence of abduction or abductive reasoning, as can be seenfrom the pragmatistic perspective, is to provide a way to generate a "clear" hypothesis. How to describe186

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the definition of "clarity"? First, according to Pierce, the clear idea is that it is possible to experience inpractice. As he reminds us by the example of the concept of hardness “there is absolutely no difference
between a hard thing and a soft thing so long as they are not brought to the test” (Magnani, 2009;
Peirce, 1992). Regarding the research issue, the learner can test the hypothesis by implementing thesimulation and modelling process. Further, C. S. Pierce's idea of "pragmatistic" truth as a result of inquiry(Peirce, 1992). In this respect, abduction can be considered “as inference to the best explanation, that
also evaluates hypotheses by induction.” (Magnani, 2009). The criteria for choosing the "best"
hypothesis among the possible ones are presented in the form of a list of requirements for computational
thinking skills that must be acquired and is provided during the co-mediated teaching process. The
description of computational thinking (Barr at al., 2011) could be positioned as a pragmatistic criterion
for evaluating the effectiveness of the process of abductive reasoning.

Enhancing educational process by circumscription andabductive reasoning
To formalize the presented approach, a model of modelling (Justi & Gilbert, 2002), which describes how
students produce their models as mental and as expressed ones, will be used. The presented approachconsiders modelling as “non-linear creative process comprised of multiple and complex stages mainly
concerning with acquiring information about the entity that is being modelled (from empiricalobservations and/or from previous knowledge); producing a mental model of it; expressing that modelin an adequate mode of representation, testing it (through mental and empirical experimentation) andevaluating its scope and limitations” (Justi, 2009). From the point of view of cognitive reasoning, thepresented "model of modelling" could be generalized as follows. First, the propositional phase is needed
to generalize existing information by inductive reasoning. Further, the process of producing mental
models based on existing information requires a kind of hypothetical model-based reasoning that should
be involved, therefore, as previously described, a kind of abduction (or grounded abduction in thisparticular case) is required to be implemented in such a case. The following steps are based on such aclassical method of reasoning as a deduction, requiring some form of conceptualization through anempirical design process. Finally, all these processes are based on existing knowledge and skills, which
provide some sort of limitations in the form of circumscriptive reasoning.
This practical model, related to modelling and simulation in education, is generalized by Franco
Landriscina (Landriscina, 2013). The processes of abduction and circumscription are extremely
important, since they provide a plane dividing simulation using educational methods from simulation
making educational methods. At the same time, the presented approach provides a clear picture of thegeneral practice of eliminating abduction and circumscription from the educational process. This is akind of common practice based on complete reliance on software tools, like a magic wand. Thus, you
can completely defocus your educational goals, focusing not on teaching inquiry and scientificreasoning, but simply training you additional skills in using software tools. Obviously, the practicalimplementation of the described approach is not a trivial task. This requires as additional efforts for preand post-training of teachers, as well as to develop additional educational programs. Moreover, the lackof practical examples for such activities stimulates a strong movement for education without computers,
like presented by (Bell et al., 2009), that is, behind-the-scenes movement for the introduction of
abduction and circumscription in pedagogical practice. Another reason for such popular movement oflearning without computer can be a strong influence of instructional techniques, a sort of Instructionism
vs Constructionism as it was defined by S. Papert and I. Harel (1991).

Design Science Research cognitive aspects
Previously described cognitive and epistemological aspects require additional efforts to improve thesoftware driven educational environment, which allows to develop inquiry-based constructionist
approaches to computational thinking skills. Correct application of educational software tools allows us
to organize the learning context in a constructionist manner, that is, to develop a context that is
personally significant for the learner. In this regard, the following important remark should be made. We
are studying a complex environment that includes both technical and social factors. Technical factors,
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in addition to hardware, also include software products in the form of educational software or software
learning objects. This environment can be characterized as socio-technical. When studying, developing
or implementing such educational environment, it is necessary to take into account not only the technicalaspects of the system, but also social aspects and interactions. Such environment: has a type of asocio-technical system; includes participants (teachers, students, educational authorities, community,
other stakeholders), as well as educational technology, instructional design methods, educational tools;
educational tools are (mostly) artefacts, including cognitive artefacts in the form of educational softwareor software-based learning objects (software as a learning object). We could continue to analyse thepreviously described aspects from an epistemological standpoint. In general, science can be divided
into formal science, such as logic or classical mathematics and factual science, which describes,
explains and predicts phenomena and is tested when it gives empirical data. Factual science is divided
into natural and social sciences. Natural sciences are interested in objects or phenomena, and the main
research activity is to analyse their nature and the reasons for their occurrences (Dresch et al., 2014).
Social science describes and reflects the society and individuals. Research conducted in social scienceis usually question-based and it is focused on the researchers’ view on the problem in study, therefore
it is subjective in its nature (Dresch et al., 2014; Romme, 2003). Social science could focus on
descriptions with attention to a quantitative approach. Another focus, for example in management
science, is on solutions to given problems or on artefacts creation (Dresch et al., 2014). The concept of
Design Science as science of artificial was first introduced by H. A. Simon (Simon, 1996). The Design
Science is focusing on practical solutions and artefacts. The motivational cause of any research canrange from research focused on solving theoretical problem and without any or less concern withpractical applications, or applied research focused on practical solutions (Saunders et al., 2009).
Generally, design means creation (or invention) of some new artefacts and its implementation into thearea of application. This could be done under existing or non-existing (innovative design) theoreticalbackgrounds (Vaishnavi & Kuechler, 2004). If Design Research focuses on the question of how todesign artefacts, Design Science Research (DSR) focuses on the problem of using design as a researchmethod (Vaishnavi & Kuechler, 2004). Therefore, DSR could be positioned as a well-formalizedteaching technique, which implements learning through building an educational paradigm and inquirybased educational methods. Considering inquiry-based educational process, DSR could provide a setof formalizations for implementing in the practical process of instruction. The main focus of theapplication of DSR methodology in education is “to teach research” (Vaishnavi & Kuechler, 2004). To
go further, the artefact is the highlight of our interest, which gives us a target for the design process.
Therefore, the design can be described as relating to the artefact, its internal structure and the “crafting”
process of the outer environment (Vaishnavi & Kuechler, 2004). The next question should be answered:can design be research? The answer is affirmative, if the learning process is built as an artefactoriented, enabling research through the process of designing an artefact. A model for DSR process
focuses on the contribution of new knowledge to be produced. Fig. 3 (adapted from Vaishnavi &Kuechler, 2004) presents the cognitive aspects of the DSR process.

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Fig. 3. The cognitive aspects of the DSR process. Adapted from (Vaishnavi & Kuechler, 2004)

Towards computational thinking by using Design Science
Research approach
The DSR methodology provides a way for the formalization of an inquiry-cantered approach based on
the development of model-based scientific simulations. This methodology provides a set of analyticaltechniques based on circumscription and abduction reasoning. The formal structure of research basedon DSR, very clearly corresponds to educational objectives. This correspondence can provide a set offormalisms for practical implementations of DSR as a set of educational tools. The purpose of DSR as
an educational tool is twofold: (1) First, formalize the process of design of model-based simulations as
cognitive artefacts. For this educational purpose, the set of DSR techniques is truncated and adapted
for the educational needs. The learner is immersed in a kind of “quasi” scientific research environment,satisfying inquiry by designing a set of simulations based on provided models of one or another type.
Therefore, a practical algorithm could be provided for such a case. The most important remark is thefollowing: the learner should get a clear idea of the meaning of "rigorness" (with reference to thiseducational scientific research environment and possible future "real" scientific research). In thiseducational case, the word rigor strict adherence to the steps of the algorithm and the instructions of
the teacher; (2) Next, to introduce DSR as a practical design tool for the future scientific activities of thestudents. For this purpose, an example of one of the DSR formalisms can be introduced to the students.
Such an introduction will provide a clear understanding of the method and its possible future (research)and present (educational tool) applications.
Educational process could be arranged as follows. The research question is provided by an educator in
the form of a project or a problem to be studied in detail (Bell, 2010; Mills & Treagust, 2003; Solomon,
2003). The learner should start with developing some definitions and generalizations and specify how
the system to be modelled should be defined (from reality to a system, identification of the problem).
This identification should be provided in the form of definite and clear answers to the presented
educational research questions (Dresch et al., 2014). Then, a formal review-based on the previous
study should be provided and approved by an educator. The fourth step is to provide the generalization(factorization) of the previous studies in the next form: what class of similar problems could be named?
The final step (implementation) is to propose a practically implementable solution for the problem: thelearner starts from proposition for the simulation solution (from system to model, the representation
process): how to implement a simulation solution for the previously formulated problem? After, thedesign process for a specific cognitive artefact for the presented problem has to be solved: what are
possible models of the system? Then, the implementation phase follows (from model to simulation, the189Constructionism 2018, Vilnius, Lithuania

exploration process): what is the practical solution for simulation? Evaluation phase follows the next. In
this phase, the learner runs the simulation on the computer and evaluates the results. After, theclarification of the problem could be done and the steps are repeated if needed. Finally, the learnersummarizes the problem and provides a kind of generalizations in the form of the final report. All thesesteps provide a formal basis for inquiry-based research in the form of developing of model-based
scientific simulations.
The presented approach provides a practical educational methodology for the constructionist
inquiry-based learning. Such well-developed approaches as a model-based approach to instruction and
DSR are implemented in the form of inquiry-cantered pedagogy. The model-based approach provides
a solid foundation for teaching via development of model-based scientific simulations, enhancing
scientific inquiry and project-based constructionist teaching methods. Design Science Research
provides clear formalizations in the form of universal educational techniques. Using these
considerations, students can act within an interdisciplinary group as quasi-researchers, generating
hypotheses, designing simulations, and evaluating results using DSR techniques. Teachers should
provide a quasi-research environment by predesigned multifaceted models, educational instructions,and seamless theoretical backgrounds.

Conclusions
The provided discussion analyses the pragmatistic aspects of the constructionist inquiry-based
educational environment. The focus is on the cognitive and epistemological features of the learning
process, including descriptions and studies of applications of such important meanings as
circumscription and abductive reasoning. Abductive reasoning is considered both from the point of viewof the formation of hypotheses, and from the point of view of evaluating hypotheses (Magnani, 2009).
This provides foundations for a model of grounded cognition and allows and inquiry-based educational
activities to be organized in a “personally significant” way. At the same time, the formalities of the DSR
provide a framework for the teacher to design the inquiry-based educational environment that is aimed
at acquiring of computational thinking skills while developing new knowledge. The cognitive aspects of
DSR clearly correspond to previously described epistemological considerations, and at the same time
this approach can be viewed as a kind of universal approach that could be used for various educational
topics and in various educational environments, including computer-based STEM or engineering
education. The list of computational thinking skills provides criteria for assessing of the effectiveness ofthe process of the student's abductive reasoning. The methodological paradigm for this approach isclearly not positivistic, and, as discussed in the article, the pragmatistic nature of the presented
approach provides a way of practical implementation of the study results.

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Computational Thinking in Teacher Professional
Development Programs
Xiaoxue Du, xd2164@tc.columbia.edu
Columbia University, New York, NY, USA

Kay Chioma Igwe, kci2104@columbia.edu
Columbia University, New York, NY, USA

Abstract
The integration of computational thinking, CT, through technology, into curriculum strongly depend onteacher openness to compliance, of which is strongly correlated to awareness and technological skill
level. Here, technological skill refers to ability to manipulate hardware, such as using problem solving
to build a simple car robot, and/or software creation and implementation. In this pilot study, we create aproject-based technology based workshop for teachers that introduce principles of computational
thinking and analyze their abilities and thoughts about adding these learning tools into their curriculum.
Six pre and in-service teachers in New York City were recruited to participate in this forty five minuteworkshop at separate times. The results suggest that technologically driven project-based learning
design principles have the potential to increase participants’ confidence and as a consequence motivatethem to consider applying computational thinking related pedagogical practices in their classroom
especially in subjects relating to math and science. Although CT pedagogical practice is rather technicaland new to the majority of the participants, technology driven workshops may be necessary to increaseteacher participation in CT classroom CT practices. This paper outlines the process by which
computational thinking, through technology and problem solving, can be embedded in a professional
training program for teachers. The results suggest that teachers need to have an understanding oftechnology and ways to implement computational thinking in order for it to be introduced in curriculum.

Keywords
computational thinking; project-based learning; teacher professional development; pedagogicalpractice; curriculum; workshop; program
In a global atmosphere where digital technology plays an important role, it is critical for individuals tohave the education, knowledge, and skills to recognize their rights and responsibilities in thisinterconnected digital world (Standard 1, Empowered Learner, ISTE), and to develop strategies forunderstanding and solving problems (Standard 5, Computational Thinker, 2014 ISTE). Czerkawski and
Lyman (2015) argues the knowledge that individuals have that corresponds to 21st century skills has
to go beyond knowledge acquisition; i.e., individuals are expected to understand the basics of computer
systems and practices. Students need to develop skills in critical thinking, communication, creativity andcollaboration. In the 1970s, numerous computer scientists researched the field of Computational
Thinking (CT) and defined it as “procedure thinking” (Papert, 1981). Several findings suggested that key
CT constructs, including algorithms, abstraction and automation, can move students from merely being“technology-literate” to using “computational tools” to solve problems (Yadav, Hong, & Stephenson,2016).
The question is how educators prepare for students to develop relevant concepts and skills in order tocope with the technological changes of the 21 century. This educational goal can be achieved by
preparing teachers by increasing their technological and computational problem solving literacy. Thus,
these educators will be more equipped to teach students certain CT principles such as how computers
make decisions based on human being behaviors in order to solve the real-life problem.
The CS4All policy initiative makes a concerted effort to increase the level of computer science educationand provide equitable access to the students in the New York City. However, building educatorknowledge and capacity in CT is a complex issue that involves legislative, administrative, political and
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educational challenges. Specifically, there are two major educational challenges, which are i)understanding the body of knowledge teachers need in order to be able to embed the CT framework
into the regular class practices; ii) how to introduce the concepts and skills of CT to build teacherscompetency considering the common core standards in the Next Generation Science Standards(NGSS), Math and English language learning (ELA).
This paper begins with the discussion of challenges and changes that schools are facing in the digitalage, and the rationale to introduce CT focused professional development to pre-service and in-service
teachers. Next, this paper introduces a hands-on project project based professional development
program to increase educator CT related knowledge and skills. This paper concludes with a discussionof the study design, the challenges of incorporating CT pedagogical practices into the classroom, andthe potential for a larger scale research study that looks into more ways of designing educator programsfor increased probability of computational thinking classroom integration.

Literature review
Computational Thinking
The importance of the integration of computational thinking into curriculum is based on the idea thatknowledge and skills derived from the application of computer science principles can be beneficial to all
the learners, especially in areas where problem solving is imperative (Weintrop, Holbert, Horn &Wilensky, 2016). Computational thinking is necessary for building learner skills in areas such asalgorithmic and critical thinking, of which both increase learner ability to tackle tough decisions. Thisskill set can be applied to all subject areas such as music, english, and are not limited to science,technology, engineering, and mathematics, STEM, type classes. The increased accessibility of thecomputer facilities and the development of the national education technology plan has further validated
the need for the application of CT in the K-12 education.
Central to the skill of CT is “the ability to encode ideas into a form that can be interpreted and executedby a computational device ” (Weintrop et al., 2016). Recently, Wing (2006) suggested that CT involves
solving problems, designing systems and understanding human behaviors by drawing on the concepts
fundamental to computer science.
The prospective limitations including a definition of computational thinking and computer science has
been discussed in the research fields and has not reached consensus. Research on CT and its
relevance to students’ problem-solving skills was pioneered by Seymour Papert. He explained, in
Mindstorm, that CT was closely related to “procedural thinking”, including developing, representing,testing and debugging procedures. When people design a program, it is important for them to use
procedural thinking and plan their programs in terms of a sequence of what happens next, before, or
until another action (Pea & Kurland, 1984). Programming is a valuable educational tool that provides
the cognitive artifacts necessary for the human mind to build a representation of the world with which itinteracts (Papert, 1981).
Numerous scholars argue that computational thinking refers to a cross-disciplinary set of mental skills
that entail the basic elements of problem representation, abstraction, decomposition, simulation,verification and prediction. Hu (2011) argues that CT is a hybrid paradigm that accommodates different
thinking modes with similar practices. Denning (2017) argues how that CT is a part of mental skills that
can automate. It is not clear whether CT should be categorized under computer science, or is a subject
that is able to connect the abstract knowledge representation in the mind, such as “connecting explicit
knowledge into a concrete form” to student (NRC, 2011). Research suggests that professional
development training workshops on the use of CT can help learners develop better understanding ofCT and adopt relevant skills to participate in the digital world (Denning, 2017).

Systematic Changes and CT Classroom Practices
A systematic change that educators are facing to is how to support teachers in developing meaningful
approaches to implementing CT practices in the K-12 classrooms. First, a few teacher education
programs focus on training pre-service teachers to incorporate CT into K-12 classrooms (Yadav,
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Gretter, Good, & McLean, 2017). The challenge has multiple aspects that include; how CT benefits
students’ understanding of fundamental disciplinary concepts and cross-cutting concepts found in the
Common Core State Standards and the Next Generation Science Standards (Stephenson et al., 2016);
how to identify promising pathways and pedagogical strategies that help preservice and in-service
teachers infusing CT in their curricula; and how to embed CT in the disciplinary knowledge of eachsubject area in K-12 teaching to avoid the situation by simply “adding it on” to what they are alreadydoing—not substantively changing what they do (Cuban, 2001; Means, Roschelle, Penuel, Sabelli, &Haertel, 2004; Sandholtz, Ringstaff, & Dwyer, 1997).
Secondly, assessments of CT remain underdeveloped and under-researched (Yadav et al., 2017) andthis issue has been called out as a key computer science education research imperative (Cooper,
Grover, Guzdial, & Simon, 2014). The few research efforts that have specifically targeted tackling theissue of CT assessment—especially in the context of activities involving programming—suggest thatassessing the learning of computational concepts and constructs in popular programming environments
is a challenge (e.g., Fields, Searle, Kafai, & Min, 2012; Koh, Nickerson, Basawapatna, & Repenning,
2014; Meerbaum-Salant, Armoni, & Ben-Ari, 2010; Werner, Denner, Campe, & Kawamoto, 2012;
Werner, Denner, & Campe, 2015).
Thirdly, a few efforts have looked at the issue of transferring computational thinking skills beyond thecommon core curriculum. Transferring of learning is an aspect of assessment that deserves attention
since computational experiences at various levels of K-12 aim to serve as bridges to futurecomputational work. New approaches to transfer such as Preparation for Future Learning (PFL;
Bransford & Schwartz, 1999; Schwartz, Bransford, & Sears, 2005) have shown promise in the context
of science and mathematics learning at the secondary level (Zakaria, Chin & Daud, 2010; Dede, 2010;
Schwartz & Martin, 2004).
Integrating CT into K-12 system poses systematic changes in schools. The personnel andorganizational changes are complex for any groups, but perhaps particularly challenging for educators
because of the complexity of the schooling process and the number of forces competing for the timeand attention of teachers and leaders (Meier, 2015). As Fullen mentioned (2007), educational change
depends on what teachers do and think - it is as simple and as complex as that (p.129). One key to help
teachers and leaders understand the required changes of CT envision in the K-12 classroom is to build
the active professional learning communities. An effective teacher professional development workshop
needs to be developed for teachers to make ongoing improvements (Fullen, 2007). There is a need forthe ongoing research work to foster the professional training communities, support teachers to model
applicable steps to develop CT skills, and apply the cross-cutting concepts through the process ofprototype building, creating and designing the prototype. The backward design process positions
teachers as designers and helps them move from covering content to uncovering the big ideas (Meier& Richards, 2016, Wiggins & McTighe, 2005) in face of curriculum planning.

Project-based Learning (PBL) and Backward Design Principles
To gauge interest in the field of computational thinking, numerous studies have been accomplished to
introduced educators to the field of computational thinking. Constructionism as a paradigm for teaching
and learning provides the theoretical framework of project-based learning and backward design models.
As a form of situated learning (Lave & Wenger, 1991), PBL is based on the constructivist finding thatstudents gain a deeper understanding of material when they actively construct their understanding by
working with and using ideas in real world contexts.
Project-based learning (PBL) starts with a driving question in a real-world situation that learners findmeaningful and important (Krajcik, 2015; Nahum, Mamlok-Naaman, Hofstein & Krajcik, 2007). First, thedriving question in PBL project design helps the participant focus on the main goal. Second, while
engaging in the practice of PBL, participants are introduced to learning goals and technologies that
support the inquiry process which is normally non-existent in the regular in-class projects: a process
also known as scaffolding learning. Third, learners who develop these new skills can explore the driving
question by participating in scientific practices in collaborative activities that are central to expert
performance in the discipline. Fourth, participants can create a set of tangible products that address the195Constructionism 2018, Vilnius, Lithuania

problems that become shared artifacts, a publicly accessible external representation of the class’s
learning (Lave & Wenger, 1991).
The pedagogical framework Understanding by Design (Wiggins & McTighe, 2005) as a backward design
approach has played a crucial role in the educator professional development. UbD focuses on guiding
educators through the process of designing curriculum units, performance assessments and classroom
instructions, that integrate computational thinking. Research has shown that the project-based learning
principle can maximize the chances that students be exposed to big ideas specified in the learning goal
(Darling-Hammond, 2008; Larmer & Mergendoller. 2015). Also, engaging teachers in the designcentered approach and the backward design approach (Meier, 2005; Wiggins & McTighe, 2005) can
deepen the content knowledge and pedagogical practices in CT .
Project-based learning hands-on projects can be used to model the integration of CT practices intoclassroom settings, of which the applicability of CT concepts in real world situations, as well as theapplication of tools and techniques from computing to understand natural, social and artificial systems
and processes, is realized. The project-based learning design principle can be a powerful tool forallowing participants to transform computational thinking principles into real world problem solving skills.

Research questions
Research Question I: How can a technologically based CT program help educators increase theirconfidence and motivation for integrating CT concepts into curricula;
Research Question II: Is the project-based learning, hands-on project framework an effective model for
integrating CT concepts into pedagogical practices.

Study design
This plot study focused on introducing participants to the applicabilitablity of computational thinking
pedagogical practices into the regular classroom practices, and to create the understanding of how
computers can function in different ways to solve real-world problems. First, the principal investigator(e.g., facilitator) conducted a pre-interview questionnaire to get information about participants’ attitudes
towards computational thinking and prior experience in the field of computer science and hardware
design. Some of the questions included were, “What is computational thinking?”, “What terms come tomind when you think of computational thinking?”, “Do you think it is realistic to integrate computational
thinking practices into regular classroom teaching environments?”.
Next, the participant completed a short survey to indicate the applicability of computational thinking inthe subject areas of English Language Arts, Math and Science by way of crosscutting concepts. Eachquestion is rated on a scale from 1 to 5, (1 = the least applicable, 5 = the most applicable). The big ideas
in ELA are key ideas and details, craft and structure, and integration of knowledge and ideas. Some of
the key ideas in mathematics and science under the cross-cutting paradigm are; geometry andrelationships, patterns and relationships, cause and effect, energy and matter, etc.
Next, the principal investigator walked participants through a hands-on robotic car assembly workshop
while discussing concepts such as speed, velocity, acceleration that could be reinforced through therobot assembly. The principal investigator told the participants to imagine they were the designers andto develop an autonomous driving car of the future with the materials in front of them. The participants
used the material provided to assemble the car without fixed instructions. The material consisted of two
car wheels, two motors, two line follower sensors, one raspberry pi hardware, multiple female and malewires, and screws.
Lastly, the principal investigator guided participants in writing a computer program that simulated thecar driving process in order to model how the computer processes signals. Participants used the Python
turtle graphics programming environment to simulate the moving forward and backward procedures.
The purpose of the variables and functions as well as how the computer execute the specific commands
were to be explained.
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To engage participants in scientific, engineering and mathematics practices, the workshop facilitator
encouraged participants to develop a collaborative learning atmosphere, and to ask questions. Someof the questions asked included the mechanism of the self-driving cars, why sensor matters in theautonomous driving, and how to use Raspberry Pi could control the car’s navigation. The inquiry andguiding process allowed participants to use their knowledge to explore emerging technologies.

Pre-Data Collection
In designing the program, the principal investigator surveyed 50 participants to collect the preference of
the introductory projects to learn computer science related concepts at New York City. Eighty percent
of participants indicated that they did not have any previous hardware design experience ( i.e., using
Arduino or other types of microcontrollers). Only 38% participants showed they do not have previous
programming experience. When asked about participants’ levels of interests in the following project (0= the least interested, 4 = the most interested), a majority of the participants selected the Line Following
Robot project.

Figure 2. The level of interests in the project Line Following Robot . The vertical axis is the amount ofparticipants, and horizontal axis is the level of interests (0 = the least interested, 4 = the most interested)

Methods
ParticipantsThis research study used the mixed methods research methodology to collect qualitative andquantitative data from six participants, (three males and three females), who represent a group of preand in service teachers in New York City. The workshop is approximately 45 minutes, and is videotaped
to capture the entire process. There were no restrictions relating to teaching discipline of theparticipants. Participation in the study was completely voluntary. Convenience sampling was used torecruit participants who are affiliated with educational institutions of this research.

Procedures
The study followed a four-stage, iterative protocol. Step 1, participants discussed their understanding
about computational thinking concepts and skills. Step 2, the participants were given the brief survey torate the applicability of computational thinking in different areas. Step 3, the participants were asked toassemble a robotic car, without formal instructions, while discussing the relevant big idea concepts that
could be applied to hands-on classroom exercises. Step 4, the participants were asked to use Turtle tosimulate the car driving process. This interactive interview-play design allowed us to understand how
the participant made sense of the hands-on project and the correlation of CT concepts. Along withrecording the interview, we collected and analyzed the strategies that participants used in assembling
the car and their programming command structure to support findings from our computational analysis.
197Constructionism 2018, Vilnius, Lithuania

Results
At the beginning of the workshop, participants believed that computational thinking was more applicablein science and math subject areas. The following table shows the average number that participants
marked for the applicability of CT in the big ideas learning areas.
English Language Arts

Math

Cross Cutting Concepts

Big ideas

Applicability

Big ideas

Applicability

Big ideas

Applicability

Integration ofknowledge and
ideas

2.3

Ratios &Proportional

2.6Interdependence of
Science, Engineering,and Technology

4.3

Craft and structure

3

Relationship

1.3

Cause and Effect3

Key Ideas andDetails2

The Number
System2.3

Scale, Proportion, andQuantity

3.6

Expressions &Equations

2.3

Systems and System
Models4

Geometry

3.7

Energy and Matter

2.3

Statistics &Probability

3.4Structure andFunctions

4

Patterns

3.3

Figure 3. The participants rated from 1 to 5 in each category (1= the least applicable, 5= the most applicable) tointegrate computational thinking into the listed content areas.

Computational thinking is a fairly new term to the participants, some of the definitions included“ …...decisions through mathematical computation……”, and some of the participants believed thatcomputational thinking was similar to the systematic thinking. It is similar to programming and is very
technical as well as procedural. Also, participants expressed “ logic”, “ deduction” and “calculation” arethe terms related to the concept of computational thinking.
After the car assembly exercise, a majority of the participants indicated their willingness and interest in
integrating CT relevant pedagogical practices into their curricula. Below are some of the responses fromthe participants.
Level 1

Level 2

Level 3

Level 4

Have no confidence on CT
integration;
Reject CT curricula without
rational reasons

Have less confidence on
CT integration;
Reject CT curricula withrational reasons

Have confidence on CT
integration;
Accept CT curricula without
rational reasons

Highly confidence on CT
integration;
Accept CT curricula withrational reasons198

Constructionism 2018, Vilnius, Lithuania

The teachers reject and did
not accept to integrate CT
into the classroom teaching.

The teachers reject withrational reasons that theycannotimplementCT
curricula in the classroom.

The teachers accept the CT
curricula and would like toimplement CT curricula in theclassroom.

The teachers display aminimal understanding of
how computational thinking
can be applicable into theclassroom setting - and little
knowledge of their varied
approach to integrate CT
knowledge,
skillsintoclassroom practice.

The
teachers
display
generally
accurateknowledgeofhowcomputational thinking canbe applicable into theclassroom setting, yet not
to apply the knowledge into
the classroom teaching
and learning,

The teachers understand thenature of the CT, andpurposefully to think about the
how to design CT curricula
into the classroom setting.
However, the CT curricula is
not authentic and fit to theschool learning environment.

The teachers do not feel
motivated
about
using
project-basedlearning
methods in the classroom
teaching.

The teachers give rational
reasons why they do not
feel motivated about using
project-basedlearning
methods in the classroom
teaching.

The teachers show thewillingness to integrate CT
into the classroom currently orin the future without theconcrete plan and rational
reasons.

“ I see the values tointegrate CT curricula into
my class, however, I
cannot accommodate mydaily
teaching
realityconsidering my school
environment.”“ I see the values to integrate
CT curricula into my class,and I would like to give a try!
Although I am not sure how toachieve my goal.”“ There is no use to integrate
CT in my classroom…”“ There is no applicability tointegrate CT curricula into
my daily classroom practiceor
student
teaching
work……”“Computational
thinking
concepts are confusing andthe hands-on practice istedious….
“ I did not see theimplementable potentials oftheproject
intomyclassroom……”“ I see the applicability tointegrate CT curricula intomydailyclassroompractice, however, I have toaccommodate
otherinitiatives to our leadershipteam at school.”“ I understand the conceptsof CT and values of projectbased learning, however,my teaching styles did not
fit with the hands-on
workshopdeliverable
methods.”“ There is a great potential tointegrate CT curricula into mydaily classroom teaching,
however, I feel CT is about
science and math, and hasless focus on writing andreading skills.”“I see the values of projectbased learning into myclassroom. I am not sure how
to design the project-basedlearning
unit
with
the
emphasized concepts ofcomputational thinking.”

The teachers accept the CT
curriculaandactivelybrainstorm
ideas
to
implement CT curricula in theclassroom,
schoolsor
districts.
The teachers not only
understand the nature of CT,
andalsosystematicallyacquire knowledge fromseveral sources about how todesign project based learningmodel to put CT curricula into
practices.

The teachers are highly
motivatedto brainstorm
ideas to integrate CT
curricula into the classroom
with rational reasons andconcrete plans.
“There is a great potential tointegrate CT curricula into theclassroom, and I am ready todesign the curricula with mycolleagues in our makerspace.”“I fully appreciate theworkshop study that allows
me to think about how tointegrate CT curricula into myclass. CT is more about thegeneral skills about patternrecognition, problem solving
and critical thinking. I would
like to collaborate with mycolleagues in the school todesign the project-basedlearning focused unit.”“ I am excited to implement
project-basedlearning
hands-on workshop into myclassroom. Also, I would liketo modify the workshop, and
think
about
how
computational
thinking
concepts can connect to myown content teaching areas”.

Figure 4. Rubrics to evaluate participants acceptances of the research study and CT curriculum implementation
based on project-based learning model.

After this hands-on exercise, the participants showed more confidence and willingness to use CT
concepts and hardware material in their educational environments. Some of the participants expressed
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Constructionism 2018, Vilnius, Lithuania

their willingness to utilize the modeled examples in the hands-on projects and to integrate into thekindergarten teaching practices. One participant explained the application to integrate CT into theirlanguage learning programs.
Pre-interview

Hands-on Workshop

Post-Interview

No.1

Level 2

Level 3

Level 4

No.2

Level 2

Level 2

Level 3

No.3

Level 1

Level 2

Level 3

No.4

Level 2

Level 3

Level 3

No.5

Level 3

Level 4

Level 4

No.6

Level 1

Level 3

Level 3Figure 5. Workshop participation result analysis

By the end of the workshop, participants changed the previous beliefs on computational thinking andrealized that it does not have to be strongly correlated with high technical skill. It is a way of teachingthat incorporates algorithmic, or procedural thinking, critical thinking and hands-on experiences thatfacilitate learning retention and better prepare students for the real world experiences.

Discussion and implication
Although computational thinking pedagogical practice are a bit more technical and new to a majority ofthe participants, technology driven programs may be necessary to increase teacher participation in CT
practices. More research work needs to be accomplished to design a more robust hands-on project toguide educators towards that goal. To ensure participants were not intimidated by the proposed projects,
the facilitator told the participant that complete assembly of the robotic car was not required. The
principal investigator also gauged the stress level of participants when they believed they were to build
the robot to completion. A workshop design the includes how to guide participants in building the robotic
car to completion that minimizes stress level, needs to be explore. Some variables that need to betested are the difficulty level of assembly with and without formal instruction and whether that plays into
stress levels. Also, whether stress is necessary for completing the task.
Some of the critiques from participants are the car assembling project tend to be mechanical with lessflexibility and does not foster participant creativity. A future hands-on project can allow participants to
use their own imagination and create technology prototypes of their own choosing. Ideas about
computational thinking stemmed from participant level of experience with technological ideas andconcepts. Although user technology experience data was not collected at the beginning of the workshop,
their experience level could be implied based on their attitudes towards their computational integration
during the workshop. Without the prior experience and expertise in computer science, participants can
not realize the direct connections between the computational thinking and content areas in ELA, scienceand Math. In the designed study, there should be the specific sections to offer some examples of the
big area to the participants to familiarize their understanding at the beginning of the workshop.
More research needs to be done to capture ways of introducing technical concepts to educators in orderto change perspectives adding computational thinking principles in K-12 education. Helping educators
gain the experience and knowledge to integrate CT into the regular classroom settings is an ongoingprocess and requires an excitement and acknowledgement of necessity. Research supports the needto facilitate this change-in-agency work in the individual classroom of each teacher and the individual
ethos of each school (Bull, Spector, Persichitte, Meier, 2016). This pilot study needs to be scaled toinclude a larger cohort of pre and in service educators.

200Constructionism 2018, Vilnius, Lithuania

To conclude, this study showed that teachers need to have an understanding of technology and ways
to implement computational thinking in order for it to be introduced in curricula. Also, the technologicallydriven project-based learning design principles have the potential to increase participants’ confidence
and in consequence motivates them to consider applying computational thinking related pedagogicalpractices in their classroom.

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What is Constructionism? Views from a Thai
Perspective
Deborah A. Fields, deborah.fields@usu.edu
Utah State University, USA

Paulo Blikstein, paulob@stanford.edu
Stanford University, USA

Abstract
This paper investigates the interpretation and implementation of constructionist principles in Thailand.
Interviewing key founders of the movement as well as teachers, village leaders, business people, andothers vested for many years in the constructionism movement in Thailand, we ask what
constructionism means within the diverse Thai community where it has been applied.

Keywords
Constructionism; developing countries; educational reform; Thai perspective

Introduction
In a 1991 paper, Seymour Papert faced the difficult challenge of defining constructionism, an
educational philosophy that prided itself in not telling people what things are—after all, he was aconstructivist who advocated that knowledge should be constructed and not simply transmitted. At thesame time, pressure was mounting on Papert and his collaborators to offer a better definition of
constructionism, which, alongside the explosive growth of the Logo language, was become increasingly
popular and influential. It was one thing to define constructionism within the confines of Seymour’s
weekly seminars at the Massachusetts Institute of Technology (the “Loud Thinking” meetings), but
defining it for a broader audience, in an academic article, was an entirely different business. Papert’s
response was that,“If one eschews pipeline models of transmitting knowledge in talking among ourselves as wellas in theorizing about classrooms, then one must expect that I will not be able to tell you my ideaof constructionism. Doing so is bound to trivialize it. Instead, I must confine myself to engage
you in experiences (including verbal ones) liable to encourage your own personal constructionof something in some sense like it.” (p. 1).
If Papert succeeded in steering away from contradicting his own principles by offering a set-in-stonedefinition of constructionism, intentionally or not he opened up a wide and complex set of possibilities
for implementing and thinking about constructionism. In traditional academic circles, whenever a newtheory or methodology is created, the proponents spend the rest of their lives making sure that theirideas are not “distorted” or wrongfully appropriated. By allowing his readers to “personally construct”
their own definitions, Papert was doing the opposite. It sounds like a good idea in principle, but what
happens when people outside of Papert’s close circle of students and colleagues try to implement
constructionism? What happens when people have indeed the license to appropriate it in very personaland culturally-aware ways?
This paper investigates one such case: the interpretation and implementation of constructionist
principles in Thailand. In 1996 a group of MIT graduates from Thailand started a foundation to support
what they hoped would be a transformative initiative to reshape education in their country. They brought
Papert and later many other researchers from MIT, including his then student Cavallo, to visit Thailand
and begin a project to introduce constructionism to the country, starting first with teachers then reachingout through non-formal education to people in rural communities (e.g., Cavallo, 2000). After a few years,
the collaboration with MIT phased out and the movement was left largely to its own devices, withrelatively little contact with U.S. institutions or researchers. The foundation, led by a few visionary figures203

Constructionism 2018, Vilnius, Lithuania

and several educators and leaders, with some support from the ministry of education and various
corporations, continued to figure out by on their own how to implement constructionism in Thailand.
Intriguingly the Thai leadership and people who joined the constructionist community applied thephilosophy not just in K-12 education but also in industry (from chemical companies to banks), remotefarming villages, technical colleges, and non-formal education. Interviewing key founders of the
movement as well as teachers, village leaders, business people, and others vested for many years in
the constructionism movement in Thailand, we ask what constructionism means in Thailand within thediverse community in which it has been applied.

Background
In order to understand how constructionism has been framed and practiced in Thailand, we must situate
those meanings against the background of how constructionism has been defined and applied in otherareas, namely the developed countries in the West (in the context of this paper, we will refer to the Westas mostly North America and Europe, from which most of the constructionist research originates, but
we are aware that there are active communities in Latin Brazil, Mexico, Senegal, Australia, and manyother countries.) Because of Papert’s intention to not overly define constructionism and to convey what
it is through stories and interventions, it can be difficult to pin down what it is and how it has been
applied. Yet given the substantial literature on the topic, we certainly can frame some key foci of the
movement. Perhaps the most common definition of constructionism comes from succinct statements,
such as that the following one by Papert himself (Papert & Harel, 1991):“Constructionism--the N word as opposed to the V word--shares constructivism's connotation oflearning as "building knowledge structures" irrespective of the circumstances of the learning. It
then adds the idea that this happens especially felicitously in a context where the learner isconsciously engaged in constructing a public entity, whether it's a sand castle on the beach ora theory of the universe” (p. 3).
Later, Ackermann (2001) tried to clarify the definition in broader terms, saying that Papert’s
constructionism “focuses more on the art of learning, or 'learning to learn'.” She further emphasized
Papert’s interest in learners engaging in conversation with their and others’ artifacts and “how theseconversations boost self-directed learning, and ultimately facilitate the construction of new knowledge”(p. 1).
These two sets of statements embody several themes in constructionist literature, including the roles ofcreating things/artifacts, engaging with others about these public artifacts, learning in a self-directed
way, and developing new knowledge. Ostensibly constructionism could be applied to any human being
in any context creating anything of relevance to them and others. Yet within these very general themes
the application of constructionism has tended to be far narrower. For instance, it has not often beenused to study the construction of knowledge related to sand castles or cosmological theories. At the riskof grossly overgeneralizing a rich movement in just a few paragraphs, below we suggest four
overarching ways to understand constructionism in the West. We consider the foci of people,
institutions, activity, and mechanisms of building constructionism. Our goal is not to oversimplify aphilosophy that has been explored in multiple books, but to situate the simultaneously familiar andunique 20-year implementation of constructionism in Thailand.

People Focus
Given its roots in developmental psychology, published literature of constructionism has focused almost
exclusively on children. In the early years of constructionist research, in the 1980s and 1990s there waspractically no work on adult education, higher education, or workforce development. Thoughconstructionists worked considerably with teachers, they were seen as a vehicle to ultimately reach thechildren. There was a strong child-centered focus both in terms of research and implementation, butalso in terms of the philosophy of the movement. On occasion, Papert would state that technology wasgiving children the opportunity to “learn on their own” or make some of the teaching redundant (for204Constructionism 2018, Vilnius, Lithuania

example, in a video-debate between Papert and Freire recorded in the 1990s43). In contrast, in Thailand
the constructionist movement has been broadly applied to people of a wide age span: children, youth,
young adults, adults, the elderly.

Institutional Focus
Deriving from the focus on children, educational institutions have been the main focus of constructionism
in the West. In 1985, MIT started the well-known project in the Hennigan School in Boston which
implemented Logo across the curricula (Tabor, 1990), and in the 1980s and early 1990s, schools intens of countries started to incorporate Logo in their curricula. Much of the work was bottom-up withteachers bringing it on their own to schools, but given Papert’s and MIT’s centrality and international
reputation, they were approached by many big school systems and ministries of education to implement
constructionism in entire countries or cities (e.g., Costa Rica, Thailand, Senegal, etc.). Eventually, withthe “Logo crisis,” of the late 1990s, there was a bigger focus on out of school implementations:
extracurricular time, summer programs, and after-school workshops. Over the past two decades,
especially with the emergence of games-for-learning and FabLabs, libraries, community spaces, andmuseums have also begun to play a strong role in constructionist education for children. Still, all of theseare generally educational institutions. In the Thailand constructionist movement, there has been a triplefocus on schools, businesses, and rural villages, laying the groundwork for some potentially unusualapplications of constructionism.

Activity Focus
The initial Logo implementations focused considerably on project-based learning—students werewriting computer programs based on personal interest. Papert defined the terms “project” and “problem”
in very particular way: “projects are primary, problems come up in the course of projects” (Papert, 1996).
“Projects” were larger-scale personal endeavors, and within them students would find “problems” tosolve. This was a specific reaction to the “problem” based approach in traditional math instruction, in
which students are given different math problems to solve without a connection to a broader project.
The definition of these terms is relevant in our context because “problem” and “project” are used in
Thailand in slightly different ways. This focus on making things has continued in recent years, visible in
the number of titles in Constructionism conference proceedings that focus on learning programming,
making games, creating animations, developing system models, and so forth.

Mechanisms of Dissemination
It is impossible to deny the role that digital tools have played in disseminating constructionism
throughout the world. Since Papert introduced turtle geometry and Microworlds, there has been an
almost inevitable focus on designing tools that children can use as much as designing the learning
environments those tools are used in. Indeed there are many constructionist tools and “constructionkits” since the early days, including Lego/Logo, NetLogo, StarLogo, Scratch, electronic textiles,
FabLabs, and digital games. Even though many of those tools were not originally designed as standalone education materials, they have been undeniably a primary means of disseminating
constructionism to educational settings. Programs, workshops, and curricula are built around the use of
a tool, albeit with reflection, social expression, and sharing with a broader audience as familiar attributesof these learning environments. These are some of the primary (though certainly not the only) ways that
constructionism has been disseminated, along with some academic artifacts such as books,
publications, and conferences.

Methods
Context and ParticipantsThis is part of a larger study to investigate the historical development of the constructionist movementin Thailand. Constructionism was formally introduced in Thailand in 1997 and has continued there
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Constructionism 2018, Vilnius, Lithuania

through the present time. What started as a small movement with a foundation established in 1996,
some government and business sponsors, and initial training and workshops led by Seymour Papert
and David Cavallo has grown substantially. It would take a full article or perhaps a book to wholly
describe the breadth and history of that movement, but here we seek to provide a little context to provide
a backdrop within which to situate what constructionism means in Thailand. The Thai community has
three overlapping domains of implementing constructionism: education (K-16 schools and non-formal
education), business (chemical, agricultural, tech-driven, and financial institutions), and rural villages. It
has spread through both word of mouth (ground-up) and by top-down support from business, village,
and school leaders.
Implementing constructionism has involved many types of activities for the different individuals andcommunities involved. Many teachers have sought to apply constructionism in their classrooms, often
expanding to a school-wide approach if school leadership has been supportive. A number of large
corporations in Thailand have used workshops to train employees and subsequently supported theseworkers in identifying and working through problems in manufacturing, human resources, or community
outreach. Villages have identified problems such as water management, sustainable agriculture,
managing finances, improving family education, and so forth as areas to work with constructionism.
Some of these areas are described in more detail by the Thai community itself (e.g., Israsena et al.,2014). None of these areas are without contestation. When a principal at a school or the head of abusiness changes often constructionist efforts have been thoroughly disrupted or ended altogether. As
we discuss below, it can take months, even years, for people to accept a constructionist mindset. Yet
the community has persevered, largely isolated from out-of-country resources, for more than two
decades, raising the opportunity to study what constructionism means in Thailand and how it hasdeveloped there.
Data and Participants
During a two-week visit to Thailand in December 2017 one of the co-authors (Fields), with a translator
as needed, conducted 1-2-hour semi-structured interviews with 22 participants nominated by leaders of
the community with the goal of sampling from people long- and/or heavily-involved in one or more ofthree main domains of constructionism in Thailand: education (9 participants), business (8), and villages(5). An interview protocol and a list of interviewees was developed by the authors with collaborationfrom some other researchers familiar with the history of Constructionism in Thailand. With a few
exceptions, most participants had more than 10 years of experience working with constructionism,
including some who were involved from the very early years of the project. A few people were relatively
new to constructionism but were highly involved in growing the constructionism project over the past 23 years. Geography and time limited the number of people who could be interviewed during this first
visit of the larger study. Interviews focused on participants’ histories with constructionism, key
characteristics they thought important to constructionism, and positive and negative examples of how
they had seen constructionism in practice. All interviews were transcribed in the main language theinterviewee used—English or Thai depending in participant preference. Thai interviews were thentranslated into English.

Analysis and Limitations
Multiple rounds of grounded, comparative analysis (see Charmaz, 2002) were conducted on theinterviews with the focus of identifying key attributes of constructionism in Thailand. We initiallydeveloped 16 codes grounded in the participants’ descriptions that were applied to the entirety of thedata. In a second round we compared, condensed, and reorganized the codes to better reflect thedominant themes emerging from the analysis. We identified four overarching themes spoken of byalmost everyone interviewed, with several subthemes that provided further clarification. We do not claimthat any of these themes are universally shared by all members of the constructionist community in
Thailand. Findings in this paper emerged from discussion with a particular subset of very experienced
leaders in the community and are likely not reflective of the thousands of individuals who haveexperienced and applied constructionism overall. Further, we as authors recognize that we are foreign
to the Thai context and though we have been involved in varying levels with the constructionism
community in Thailand for several years (primarily during 1-2 week visits, often as workshop leaders or
206Constructionism 2018, Vilnius, Lithuania

observers), it has been largely as outsiders. Member checking with two leaders of the constructionist
community at multiple stages of analysis and writing has helped us check our understanding againstthat of participants.

Findings
Given that the Thailand community applies constructionism across a wide range of contexts, from K12 schools to non-formal education, and from rural villages to large industrial companies, it should not
be surprising that there was a wide range in what people valued and prioritized in their explanations
and descriptions of constructionism. Yet there were several core themes that emerged across people’s
accounts. Intriguingly, what might be considered one of the core aspects of constructionism in the West,the idea that learning happens when creating something was mentioned by only a few people, namely
those with fluent English who had an advanced degree from a U.S. university, and had read Papert’s
original works. So if the idea of “learning by constructing and sharing objects” is not the core of Thai
constructionism, what is?

Control over One’s Own Learning
One thing that nearly all participants spoke about in regard to constructionism was that learners werein control of their own learning and that this was a transformative attitude compared to traditional Thai
culture. Boonkong44, a businessperson who had spent extensive time studying and observing
constructionism in Thailand, described this as a powerful transformation in one village that took up
constructionism across the past 18 years: “[M]ost of the villagers in Chuenchit, they control their lives.
Before their life was controlled by someone else. Now they control their life. They say that everyday
they wake up, they feel happy, not because they’re rich, because they can control their life” (p. 4).
Participants reported that without constructionism, when people in Thailand encounter a problem, theyeither wait to be told what to do or report a problem to higher authorities rather than trying to figure out
what to do oneself, whether in a business or a village. Similarly, in schools, children are told what to do
and are in continual search for what the “right” answer is, something that many participants said wasstill a tendency in their adulthood. In contrast, taking control over one’s own learning meant having thepower to deal with issues that came up in one’s life and actively doing something about it.
Implied in the idea of being in control of one’s own learning was the notion that learning should beinterest-driven, which came with both a sense of freedom and responsibility. In other words, people
should get to choose what to do, whether in class, at work, or in community life. For instance, Saijai, aleader in her village, described that constructionism is “the learning process, that the learner is thecenter of the learning” (p. 3). Or as Punya, a long-time teacher, explained, students should have thefreedom to “do whatever they want to complete [their] project[s]” (p. 3). Learning should be driven bylearners, not by others in authority. At the same time, there was an emphasis on responsibility, on doing
things oneself rather than having others do a project or solve a problem for them, though support wasalways available in the form of mentorship. As an example, Saijai described how she and othermembers in her village helped others begin to apply constructionism: “We didn’t do a project for them,
they have to do it by themselves with our support” (p. 5). Thus applying constructionism meant taking
responsibility and ownership over one’s life, one’s problems, one’s projects.
This belief that people should have ownership of their own learning had empowering implications for
how people thought of themselves and others. As Pinit, a business leader and constructionist facilitator,
spelled out, “I’ve changed from a person who lacks confidence, afraid to try new things, to have more
confidence. I now know the beauty of learning,” (p. 2). Further, just as participants like Pinit took more
empowered views of themselves, they took similar views of the students they worked with, whether
those students were children in schools, family members, or colleagues in companies, or fellow
members of a village. Many teachers described the transformation they experienced as they witnessed
constructionism in practice with their students. Students became more engaged, happier, spoke up
more, presented with confidence, and demonstrated changed mindsets even years later. Thus not only44

All names are pseudoymns. Quotes are cited by page number of the interview.

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did participants describe greater confidence in themselves, they expressed similarly strong confidence
in the capabilities of the students or learners that they worked with.
Notably participants described learning this attribute of Thai constructionism in practice, not by adefinition read in a book or heard in a lecture. Most participants first encountered constructionism
through a one-week workshop on Microworlds Logo. Designed to put learners in an unfamiliar situation,
participants often felt very uncomfortable at the beginning because they were not told exactly what to
make or how to make it. Yet through the weeklong experience they learned how to learn and grasped
that learning could be driven by their interests and learned through practice not through lecturing or
being told what the right answer was. Not only this, but as participants went on to apply constructionism
in their own jobs and families (many expressed applying this with family members), the support theyreceived further emphasized the interest-driven and learner-centered characteristics of constructionist
learning. For instance, one teacher, Samorn, explained that when she and her fellow teachers tried tofigure out how to implement constructionism at their own school, their director “didn’t try to control us‘how’ to do it, we managed ourselves,” figuring out how to design constructionist learning environmentsin their classrooms and grade levels (p. 2). This was emphasized again and again in participants’interviews; they were never told how to implement constructionism, something that was occasionally
frustrating but overall fulfilling. Instead, it was up to them to try it out, learn through a process of trial,
error, observation, and trying again.
Real-World-Problem-Focused and Process-Driven
In the Thailand constructionist community, solving real-world problems and the processes of solving
them are at the center of doing constructionism45. Whereas in the West the types of projects that are
most common are related to personal interests of the individual child, in Thailand the context ofconstructionism involved working on a real-world problem, usually one of relevance to a local
community. In businesses, employees need to be able to identify what a problem is when it arises andfind a solution to solve the problem themselves. In villages, “Rural people need deep understanding on
their own problem[s],” finding the root cause of an issue, collecting data and community knowledge
about that, and solving the bigger, underlying issue (Worawech, p. 17). Finally in schools, students canidentify problems in their class or in their community and try to work on a solution to that. In all of this,people work toward bettering their local community, be it a school, neighborhood, village, business, orThailand itself. In other words, the interest-driven emphasis of constructionism in Thailand is based onbroader collective interests rather than personal intellectual inclinations or hobbies.
Solving these problems involves applying a process. Almost everyone interviewed explicitly mentioned
the word “process” in discussing constructionism, and those who did not mention the actual word
described a type of process or set of steps that they used to solve problems in a constructionist manner.
There was variation to the processes described, but in general it involved identifying a problem, doing
something about it, reflecting on the result, and adapting to continue to fix the problem. One seniorfacilitator of constructionism, Boonmee, summarized one process as follows: “Think, make, reflect are
the core principles… Then rethink, remake, and reflect.” (p. 2). Perhaps one reason why process is so
important in Thai constructionism is that it is a set of abstracted principles that can be applied to a wide
variety of situations. As village leader Pana Pong explained, “In the past, everything is scattered andunorganized, not systematic, but when we learn things we can get into clear processes” (pp. 1-2). Like
Pana Pong, many participants emphasized that the process of doing constructionism gave them power
to deal with problems, work through mistakes, and persevere when things did not work out the first time.
Anurak explained that the idea of a process may work well with common cultural ways of thinking in
Thailand: “If you look at all the different interpretations of the learning process in Thailand, all of it is acycle. They might have five steps, six steps, seven steps, eight steps, but it’s all a cycle” (pp. 4-5).
Internalizing a process of constructionism allowed people to use it again and again.
Although people had slightly different expressions of what a process of constructionism involved,
several overarching themes emerged across the interviews. First, the process involved community.
Note that many constructionists would consider the term “problems” as an equivalent to “textbooks problems,” so here we used “real-world
problems” to differentiate.
45

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Identifying, understanding, and working through a problem was best done with others. Manit described
this insight as transformative. Instead of facing a problem by himself and when often he could not finda way to solve it, “by sharing information with the other people, other workers, there are many solutions
to solve the problems” (p. 3). Many like Manit shared that constructionism brought people together,whether in a classroom, village, or workplace. Teachers intentionally supported peer pedagogy, villages
thought about issues together in ways that built community, and workplaces experienced teamwork.
Pinit shared that constructionism “made us understand or empathize with others’ perspectives andideas” (p. 4). He explained that before technicians and engineers did not share their expertise in working
through issues, but that constructionism helped them to listen to each other, share ideas, and work with“cross-disciplinary knowledge.” It would be all too easy to stereotype a community focus as relating tobroad ideas about collectivist versus individualist cultures. But that idea does not explain why groups
across the spectrum of Thai culture had previously failed to collaborate on solving problems before they
were introduced to constructionism. Constructionism may have opened up deeper, more collaborative
communities than were able to exist before.
About half of the participants mentioned managing emotions, often through meditation, in theirdescriptions of a constructionist process. They spoke about this as a means to concentrate, stay
focused, understand oneself, and be receptive to constructive criticism. As Boonmee explained, “If you
can’t control your mindfulness, you will get angry and you can’t think thoroughly about the cause of the
problem you made” (p. 2). He shared that this was very personal to himself. Before constructionism hehad a very short temper and was especially unreceptive when people criticized his teaching. But after
learning to apply constructionism he was calmer and think about the other person’s perspective when
they gave criticism. For many, managing their emotions, especially anger, allowed them to listen deeply
to others and also helped them to be more humble. This all helped them to broaden their perspectives
on the problem or project they were working on and to get help from others—to be more open to theircommunity in productive ways.
Finally, about half of the participants also cited reflection as a core attribute of the constructionist
process. This goes back to the “think, make reflect… rethink, remake, reflect” process that Boonmee
described. It was important to take time to think about what one had done, evaluating what went welland what to change. Pimchan highlighted the importance of reflection in the changes her village
experienced once they began to apply constructionism:“Because normally, in the normal village, when they have activities or the things that they
have to do, after those activities end, then it is finished. But our village has applied
constructionism to the working process. And when we finish one activity, we have areflection time asking whether or not this one is good? This one is bad? Are we going tocontinue doing this?” (pp. 6-7).
Like Pimchan, for those participants who mentioned it, reflection was tremendously powerful to theirlearning experiences. It gave them the ability to improve, to persevere through initial failures orimperfections, and to “crystallize” their knowledge. Those who were teachers saw a further value toreflection in that it enabled them to see their students’ thinking. Samorn, a retired teacher of 3rd graders
explained that when she changed to constructionism, her students can “express into words so I knowthat they can think!” (p. 3). Reflection allowed students to externalize their thoughts so that they could
be shared and seen by others.
Many of the characteristics shared here should be familiar to constructionist audiences, though some
may be a surprise. It is not a far stretch to shift from a project focus (in the classical sense used in theWest of personally-meaningful endeavors) to a problem focus (i.e., community problems in the realworld,) especially since the types of problems solved in the Thai community often involve projects to fix
them (e.g., water management projects, industrial design machines, agricultural plans). Further, interms of an emphasis on process, the ideas that Thai leaders shared here are not far from processes
such as “design thinking” that involve idea-generation, creation, testing, and evaluation common to
many implementations of constructionism. Although reflection is far from a universal characteristic ofconstructionist literature, it has had an explicit part of some early constructionist work, such as thedesign notebooks that children in Harel’s software and game design environments used to keep track209

Constructionism 2018, Vilnius, Lithuaniaof changes they made in their projects every day (Harel & Papert, 1990). In terms of community, Papert
himself suggested some “social criteria” for the learning environments he promoted, drawing on the verysocial Samba School to compare it to the traditional “lonely, impersonal” mathematics environments.
Managing emotions and using meditation may seem the most unusual characteristics of constructionism
to western mindsets, but if we note that this is to facilitate focus and concentration in problem solving
and to be more open to criticism on one’s work, then it may be something we can learn from.

Contested/Conflicting Roles of Digital Technology
While digital technology has played a key role in many, if not most, reports of constructionism in thewest, it has a contested role in constructionism in Thailand. On the one hand, nearly all participants
were first introduced to constructionism through a technology-centric, weeklong workshop on
Microworlds, Gogo Boards, or photojournalism. This workshop structure was inherited from the initial
work by Papert and Cavallo when they introduced constructionism in Thailand (see Cavallo, 2000).
Since then local leaders started to create their own version of a trio of weeklong Microworlds,
photojournalism, and Gogo Board workshops. The emphasis in these workshops was creating asituation where attendees could “learn how to learn” in a way they were unused to. These tech-driven
workshops allowed participants to learn teamwork, meditation, reflection, the constructionist process.
As the director of a technical school explained, “Tools are the first step—used to encourage learners sothey can understand and see the process of learning” (p. 1). Then learners could figure out how to apply
constructionism in their own locales. This training was common for people in all contexts - industry,villages, and education.
A few people continued to view technology as core to their implementation of constructionism andsometimes as essential to constructionism more broadly. One teacher who first experienced a
Microworlds workshop 18 years ago immediately implemented it in her grade 6 classroom and continued
to use technology as her primary way to share constructionism with her students, shifting to Scratch
and Crickets (an early platform for robotics) as she learned of newer, more contemporary tools thatworked with the computing systems available. A former teacher and now school leader, Nisa, prioritized
introducing various digital technology in the school where she has worked for many years: starting with
Microworlds and then shifting to Scratch, Gogo boards, and now FabLabs as ways to engage children.
She spoke of a desire to emphasize the computational side of constructionism more in Thailand andher school. Another participant, Anurak, is a computer engineer and focuses most of his constructionist
work on “building things to build other things with,” designing new technologies that are affordable foreveryday Thai people to design and think with. However, outside of a few members like these, mostparticipants did not necessarily focus on digital technology in how they put constructionism to practice.
Some participants, especially those in areas of industry and rural outreach, expressed concern that“digital technology is not essential for doing constructionism” (Worawech, p. 10) and that it could actually
be a “block” to people in industry and rural areas if they became too focused on a particular technology(Boonkong, p. 7). After all, most people in workplaces do not use Microworlds or Gogo boards as
professional tools. These participants hinted at a misunderstanding by workshop participants that
constructionism was only relevant in the context of digital tools. Boonkong, an engineer at a technicalfirm sought deeper understanding of the key elements and outcomes of the now-traditional introductory
constructionism workshops: “[W]hen we know those key elements, there’s no need of Microworlds,
photojournalism, or Gogo Boards anymore. We can all create our own tools,” (p. 8). Similarly, one ofthe primary leaders of the introductory workshops thought that the community should not be stuck on
particular technologies. Instead it might be better to shift to broader “project-based or work-placelearning” (Boonmee, p. 5) so that participants could see the potential for using constructionism in theirareas of employment or everyday life. The range of opinions and concerns about the role andimportance of digital technology in constructionism raises it as an issue in the broader field worldwide.
Constructionism was introduced in the context of digital tools, but Papert clearly drew insight from andsaw applications in other areas, such as Samba Schools (Papert, 1980) and in sand castles (Papert,
1991). Yet in Thailand there is concern about over-associating constructionist with a particular tool. It
raises the question of what is the role of digital technology in constructionism, historically and incontemporary times?
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Constructionism as a Life-Altering and Lifelong Pursuit
One very striking element that participants spoke of was what they saw as the lifelong, transformative
aspect of constructionism. Learning to think and act in a constructionist way involved taking up a newmindset that affected all aspects of life, both present and future. Teachers saw helping students (fromelementary to vocational schools) to learn constructionism as valuable not just for particular academicbased thinking skills (e.g., computing) but as something that they would use for a lifetime. “The mostimportant key of constructionism is to develop people with lifelong learning skills,” (Mana, p. 3). Taking
on a constructionist mindset meant being able to learn and continue to improve for one’s entire life,
regardless of problems one might need to face. Teachers reported that students came back years after
their education was over to say how much they had learned from the experiences in the constructionistbased classes. That they had the ability to make decisions, to learn how to learn, to take an active
approach to their own learning. Participants regarded training people in a constructionist manner assomething that would enable them to improve their lives and contribute to their communities.
Participants saw a changed constructionist mindset and thus a trajectory of lifelong learning as key totransforming their local communities, workplaces, Thailand, and even the world and society as a whole.
This is nowhere more evident than in the ways that people took constructionism from the contexts inwhich they first used it and applied it to broader areas, often in their volunteer time or in their retirement.
Having received so much themselves from particular mentors over the years, many expressed thedesire to give back, to help people have “a happy learning experience and hope they can do more forour nation in the future” (Pinit, p. 4). A transformed mindset ready to tackle problems through athoughtful, iterative and reflective process within a shared community was important for the individual,
community, society, Thailand, and the world.
At the same time participants spoke clearly about how difficult it was to help people develop such achanged mindset. “Constructionism is like growing a tree. It takes time,” (Mana, p. 4). Working for overa decade with students in a vocational school program, Mana explained that it took six months to two
years for students to understand and adopt constructionism as a mindset with changed habits, and eventhen 10% of the students still did not change. It often took longer for some teachers at the school, butwhen they saw the evidence in the form of how students thought and acted (especially through a trial
program where some students were taught in a constructionist program and others were not), they werewilling to shift. Complicating this was that often results of constructionist projects were slow, especially
if they dealt with challenging problems in a community. Anurak said that some projects in elementary
schools he worked with took 1-3 years to finish. Pana Pong, familiar with nearly 18 years of educational,
agricultural and financial changes in his village, spoke of a 4-5-year timespan to see the results ofconstructionism. Yet everyone thought the investment worthwhile. Constructionism took a “lifetime” oflearning but was a “process that can pass on to different generations, young or elders or even kids”(Pana Pong, p. 4). In the Thailand constructionist community, this was about transforming their society
over the course of individual lives and across generations within society.

Conclusion and Discussion
Papert taught the philosophy of constructionism in a way that prioritized people learning it in aconstructionist manner themselves, through stories and personal experiences. One could argue that
the Thailand constructionist community’s 20-year process of learning and growth well illustrates theaffordances of this approach as they built their movement largely in isolation from the broader worldwide
community. In doing so they extended constructionism to a number of settings that are not often
considered in the literature of the broader constructionism community, namely businesses, non-formal
education, and rural villages. The values and practices that they ascribe to constructionism show both
cultural integration and transformation. They brought culturally relevant practices validating process,emotions, and community to their implementations in ways that, at least for them, transform how they
think, live, and relate. The way they conceptualize constructionism also raises a number of importantquestions for us to consider as we explore what this means for the broader constructionist community.
One question this study raises is what the starting point for constructionism can be. There are many
examples in constructionist literature of the starting point for an educational intervention being a
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particular digital technology (or a set of technologies, i.e., in a makerspace), albeit one with openness
for learners to design something of personal relevance to them. However, in Thailand the starting point
is more often a problem of relevance to a local community, which might lead people to a range ofdifferent solutions which may or may not involve particular tools.
A related question is what the role of technology should be in constructionism. Despite Papert’s
insistence that constructionism can involve making sand castles or cosmological theories, using
technology to do things that can't be done (at all or as effectively) with paper and pencil is one of thecentral foci of western constructionism (for a discussion see Kafai, 2006). One of the motivators for thismay be a prioritization to introduce computational literacy, but it may be helpful to tease apart the moredomain-centric drive to support computation and more broadly applicable philosophy of constructionism.
Perhaps it is time to revisit epistemological considerations (e.g., syntonicity, epistemological pluralism,
powerful ideas) or pedagogical strategies (e.g., audience collaboration, reflection) that have received
far less attention in constructionist literature.
Constructionists have always had a strong identification with the work of Paulo Freire and other scholars
in critical pedagogy, due to their agreement on issues of student-centeredness, real life relevance, andstudent empowerment. However, Freirean scholars had a much stronger stance on issues of power
within the classroom (“dialogical education”) and on the broader role of education in society (e.g.,
education as a way towards personal “emancipation”). In many school systems and countries we might
anticipate that even though policy makers are enthusiastic about to constructionist principles, they might
not have anticipated that, if fully implemented, those principles will upend some of the canons of
traditional educational policy: being able to predict, test, and track students, the ability create
environments that are predictable and stable, the respect for authority in classrooms or schools, andthe lowering of cost. Some interviewees raised some of those questions, and we believe that, if
constructionism keeps expanding in Thailand, at some point these larger societal issues will come tothe fore of the discussion.

Acknowledgments
This work was supported by the Suksapattana Foundation and the Lemann Center for Entrepreneurshipand Educational Innovation in Brazil. Any opinions, findings, and conclusions or recommendations
expressed in this paper are those of the authors and do not necessarily reflect the views of theSuksapattana Foundation, Utah State University, or Stanford University. Special thanks to Nalin
Tutiyaphuengprasert and Arnan Sipitakiat for substantive constructive feedback and earlier reviews ofthis paper.

References
Ackermann, E. (2001). Piaget’s constructivism, Papert’s constructionism: What’s the difference. Futureof Learning Group Publication, 5(3).
Cavallo, D. (2000). Emergent design and learning environments: Building on indigenous knowledge.
IBM Systems Journal, 39(3.4), 768-781.
Charmaz, K. (2002). Stories and silences: Disclosures and self in chronic illness. Qualitative Inquiry,8(3), 302-328.
Harel, I., & Papert, S (1990). Software design as a learning environment. Interactive Learning
Environments, 1(1), 1-32.
Harel, I., & Papert, S. (1991). Constructionism. Norwood, NJ: Ablex Publishing Corporation.
Israsena, P., Wongviriyawong, C., Sipitakiat, A., Tutiyaphuengprasert, N., Tantikul, T., Limpiti, N.,
Rattanathavorn, I. & Cheamsawat, S. (2014). Constructionism in Thailand and its transformative effecton the lifelong learning process. In Proceedings of Constructionism, Vienna, Austria. Retrieved online
at http://constructionism2014.ifs.tuwien.ac.at/papers/2.1_2-8590.pdf on March 30, 2018.

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Kafai, Y. B. (2006). Constructionism. In R. K. Sawyer (Ed.), The Cambridge Handbook of the Learning
Sciences (pp. 35–46). New York: Cambridge University Press.
Papert, S. (2000). What's the big idea? Toward a pedagogy of idea power. IBM Systems Journal,39(3.4), 720-729.
Papert, S. (1996) An Exploration in the Space of Mathematics Educations. International Journal ofComputers for Mathematical Learning, Vol. 1(1), pp. 95-123.
Papert, S. (1991). Situating constructionism. In S. Papert & I. Harel (Eds.), Constructionism (pp. 1–11).
Norwood, NJ: Ablex
Tabor, M. B. (1990). A Lab to Re-invent Communication. The New York Times. Retrieved online athttps://www.nytimes.com/1990/12/26/news/a-lab-to-re-invent-communication.html March 30, 2018.

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Personal Learning Journeys: Reflective Portfolios
as “Objects-to-Learn-With” in an E-textiles HighSchool Class
Deborah A. Fields, deborah.fields@usu.edu
Utah State University, Logan, UT, USA

Mia S. Shaw, mshaw12@gse.upenn.edu
University of Pennsylvania, Philadelphia, PA, USA

Yasmin B. Kafai, kafai@upenn.edu
University of Pennsylvania, Philadelphia, PA, USA

Abstract
Much attention in constructionism has focused on the design of learning tools and support for students
building artifacts. Far less attention has been placed on reflection and reflective artifacts that let students
consider their own learning. the design of their electronic textile projects during an eight-week curricular
unit in an Exploring Computer Science class, an introductory computing course for high school(secondary) students in the United States. We examine the portfolios as sites of student self-authorship:
places where students showed agency in positioning themselves in relation to how they made their etextile projects and to computer science more generally. In the discussion we consider the implicationsof reflective portfolios as “objects-to-learn-with” for educational implementation and constructionist
pedagogy.

Keywords
Electronic textiles; computer science education; identity; portfolios; objects-to-think-with

Introduction“Studying one’s own learning process...can be a powerful method of enhancing learning.”(Papert, 2001, p. 85)
A lot of attention in constructionism has centered on how how learners can construct knowledge throughthe design and interaction with artifacts ranging from Logo programs, video games, robots, andanimations to 3D-printed and laser-cut objects (Blikstein, 2013; Harel, 1990; Kafai, 1995). To support
young designers in constructing objects on and off the screen, hundreds of tools have been designed,
including programming environments like Logo, Scratch, Squeak, Blockly, Snap!, and Alice in addition
to construction kits such as Lego Mindstorms, LilyPad Arduino, MaKey MaKey, Chibitronics, and many
more, working with materials such as Lego bricks, textiles and paper. A major emphasis has been on
what kids can make and what they learn by making through “objects-to-think-with” that engage themwith powerful ideas (Papert, 1980) and through “objects-to-share-with” that foster interactions withothers (Kafai & Burke, 2014). An emphasis in this work is highlighting children’s capabilities to makeand create when they are provided with powerful tools such as computers. In these situations, children
and youth can make interest-driven, student-centered projects that are highly engaging and canpromote rich learning through the process of creating these objects.
Far less attention has been placed on how learners can make their own learning an equally importantfocus as Papert (2001) suggested (see quote above) or formally reflect on their own learning with what
we would like to call “objects-to-learn-with.” Creating artifacts and reflecting on both the process ofmaking them and the products themselves engages students not only with disciplinary learning, but justas importantly supports students’ authorship of their own identities or senses of self within a particulardiscipline, i.e., a “disciplinary identity” (Van Horne & Bell, 2017). Being able to author one’s own
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disciplinary identity may be especially important in a field such as computer science (CS), a field that iswell-documented as historically exclusive and in which many students often struggle to develop a
positive sense of self or sense of belonging (Cheryan, Plaut, Handon & Hudson, 2013; Yardi &Bruckman, 2007). To help students articulate a clear sense of self or self-narrative in relation to a
discipline presents a critical dimension of the learning process (Carlone, 2017) and would suggest
involving learners in documenting their own development in design notebooks or portfolios that could
capture their reflections on their learning processes and outcomes. Such reflective artifacts could
engage students in making their own learning an object-to-learn-with—just as much as the actual designof games, robots or software already does—and uncover how they see themselves establishing (or not)personal connections with academic disciplines.
In this paper we share the design and analysis of learner-generated reflective portfolios that
accompanied the development of a series of electronic textile (hereafter e-textile) projects that highschool students completed during an eight-week curricular unit in an introductory Exploring Computer
Science (ECS) class (Goode, Chapman & Margolis, 2012). E-textiles are hybrid designs, consciously
combining traditionally “masculine” activities such as engineering and computing with traditionally“feminine” activities such as crafting and sewing (Buechley, Peppler, Eisenberg & Kafai, 2013).
Portfolios have been extensively used as a means of assessment, for instance, in studying how students
reflect on their academic learning by documenting computational thinking concepts and practices
learned (Chang et al., 2015; Lui, Jayathirtha, Fields, Shaw, & Kafai, 2018). However, here we wanted
to focus on another equally important aspect of constructionist learning—that of personal expression
and connection to the academic discipline—which could uncover critical aspects of students’ identities
as learners in the field of CS. This led us to consider the portfolios as sites of student self-authorship,
places where students could show agency in positioning themselves in relation to how they made theire-textile projects and to CS more generally. In this paper we ask, how do students use reflective
portfolios to express their own voices and self-authorship in relation to computer science? In thediscussion we consider the implications of reflective portfolios as “objects-to-learn-with” inconstructionist learning environments where students can personally express themselves as well as theramifications for educational implementations and constructionist pedagogy more generally.

Background
Early constructionist activities such as the instructional software design (Harel, 1990) or game design
projects (Kafai, 1995) included reflection in the form of design notebooks. For instance, when children
designed Logo software and games, they also wrote out daily reflections on the “problems and changes
they made each day” (Harel & Papert, 1991, p. 6). These journals or design notebooks encouraged
students to reflect on what they were learning and supported self-awareness of the process of creating
as students noted how their projects changed as they moved through the process of making them. Whilenotebook entries captured changes in students’ thinking and designs, such reflections tend to be underanalyzed in comparison with the actual programmed artifacts (e.g., games, software) that were createdby learners (Reynolds & Caperton, 2011). A further limitation is that even the learners considered thenotebooks transitional or process artifacts, as they ultimately did not become part of students’ final
artifacts that were shared with a wider audience.
In contrast, portfolios have begun to emerge as potential culminating artifacts in some classrooms to
showcase students’ work, accompanying the actual project(s) that students have made. For instance,
portfolios featuring the best projects or series of projects students have made, as in a professional art
portfolio, highlight students’ accumulated competency and skill (Býrgýn & Baký, 2007). Portfolios canalso be process-based, showing students’ progress through a series of projects or even within a project(Chang, et al., 2015). Here students can narrate how they have grown throughout the creation of asingle project or across multiple projects, allowing students to author their own pathways of learning
through reflection. While portfolios are more common in arts education, more recently they haveappeared in CS education. For example, in the Advanced Placement Computer Science Principles
class, students submit both a project and a portfolio that explicates the intention behind their projects
as well as documents how they were made (College Board, 2017). Thus the portfolio supplements the
project itself and can be used as a type of learning assessment for academic content.
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In addition to serving as a learning assessment or a demonstration of learning progress, the type ofnarration that portfolios encourage holds equal potential to be a key resource in supporting students’authorship of “disciplinary identity” (Van Horne & Bell, 2017). Identity is a broad concept that, from asociocultural perspective, includes how people act in particular situations (practice-based identities),
how people think about themselves (self-narratives), and how other people perceive someone (others’narratives) (Fields, 2010). Though creating personalized artifacts linked to core disciplinary content can
support students’ identities in practice-based ways (Van Horne & Bell, 2017), the design process alone
may not necessarily help students articulate a clear sense of self (i.e., self-narrative) in relation to a
discipline. In this paper we are primarily concerned with the aspect of identity that relates to students’
self-narrative—how they think about themselves (Fields, 2010). In discussing identity development,
Nasir and Cooks (2009) argue for the importance of “ideational resources” in developing andestablishing one’s identity within a particular learning space. Ideational resources are the “ideas about
oneself and one’s relationship to and place in the practice and the world, as well as ideas about what isvalued and what is good” (p. 44). Ideational resources might include specific lessons or sayings about
how to manage one’s emotions in a challenging scenario or how to name oneself in relation to others,
for instance as a core participant in a community (i.e., a “hurdler” or a “jumper” in track-and-field or a
“problem solver” in a computer science class). Having a sense of self as a type of computer scientist or
as having characteristics of capable computer scientists can situate students on an inbound trajectoryof participation in a discipline.
In turning to portfolios for capturing students’ identities as learners, we suggest that they can become
an ideational resource in helping students express who they are in relation to an academic discipline or
field, namely computer science. By combining the process features of the early design notebooks ofconstructionist projects (Harel, 1990) with the product features of portfolios found more in arts education,
we can turn reflective portfolios into meta-artifacts, or “objects-to-think-with”, in which students express
what, why, and how they have made a computational artifact and thus begin to narrate, in their own
personalized ways, who they are within the field of CS. Below we describe the type of portfolio thatstudents made in the e-textile curricular unit, a portfolio that was both project-based (featuring a seriesof artifacts students made) and process-based (highlighting challenges and revisions made in theprocess of making the projects). Then we explore the ways that students expressed and authoredthemselves through the personalized portfolios that they created, ending with a discussion about the
implications for educational implementation and constructionist pedagogy.

Methods
Curriculum & Portfolio
Over the past two years we have co-developed an e-textiles unit for the Exploring Computer Sciencecurriculum, a year-long course providing an introduction to computing with equity-focused and inquirybased teaching (Goode, et al., 2012). The e-textiles unit took place over eight weeks and consisted ofa series of four projects: 1) a paper-card using a simple circuit, 2) a wristband with three LEDs in parallel,3) a classroom-wide mural project where pairs of students created portions that each incorporated two
switches to computationally create four lighting patterns, and 4) a “human sensor” project that used twoaluminum foil conductive patches that when squeezed generated a range of data to be used asconditions for lighting effects. Each project allowed increasing flexibility in design and personalization,and the human sensor projects reflected this in the diversity of students’ projects: stuffed animals, paper
cranes, wearable shirts or hoodies, handbags, and gifts for family members.
In the second year of implementation, with the help of two teachers, we revised the unit, adding reflective
portfolios as a capstone to accompany the final project of the unit. The portfolio that students created
was co-developed with Ben, the teacher of the ECS class that is the subject of this paper. The portfolio
was both project- and process-based, showing the series of projects students made as well as
reflections about their processes of making them. The portfolio consisted of a set of at least four Google
Slides for each project, with students adding on slides until the portfolio contained reflections on all four
projects. For each project, the requirements included: 1) an initial drawing of the project and a reflection
on changes made to the project, 2) at least one challenge they faced and an explanation of how they216

Constructionism 2018, Vilnius, Lithuania

dealt with it (Figures 1 and 3), 3) how they had “grown as a computer scientist” accompanied by at least
one picture or video of their work in progress (Figure 2), and 4) a picture/video of the final project withan explanation of what it did plus a reflection about what they learned and how the project fits into theiridentities as computer scientists. The portfolio ended with one final reflection on students’ learning
across the entire unit. Figures 1, 2, and 3 provide examples of how students chose to illustrate thedifferent parts of the portfolio assignment, as well as how students personalized their portfolios. All but
one student personalized their portfolios by using specialized fonts and backgrounds, adding title pages
for each project, creating digital representations of their projects, and incorporating selfies or images
that exhibited their own styles and interests.

Figure 1. Ashley’s portfolio page on challenges in the human sensor project.

Figure 2. Alejandra’s portfolio page on growing as a computer scientist after the wristband project.

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Figure 3. Louis’s portfolio page on challenges in the human sensor project.

Ben piloted the e-textiles unit in Spring 2017 in his ECS class with 35 students (13 girls and 22 boys),with 32 consenting to participate in the research project. Most students were in 9th grade (14-15 yearsold); one student was in 12th grade (17 years old). At Ben’s school, 54% of students were identified associoeconomically disadvantaged as defined by the State of California and included the following
demographics: 4% African American, 18% Asian, 10% Filipino, 40% Hispanic or Latino, 25% White, 1%
as two or more races, and 2% race not reported). The school had a three-year trajectory of elective (i.e.,
optional) computer science courses, with ECS being the introductory course.

Data and Analysis
The data for this project included the digital portfolios from all 32 consenting students in Ben’s class,and we sought to develop a framework to analyze how students utilized the portfolio assignment toauthor themselves in relation to CS. We conducted multiple rounds of grounded, comparative analysis(Charmaz, 2002). We initially developed 11 codes stemming from the identity statements students
generated as answers to the question “How have I grown as a computer scientist?” However, we foundthese codes limiting, as we noticed that students related to and framed CS in more subtle ways outside
the identity statements. In a second review of the portfolios, we expanded our analysis to the entire
portfolio, developing several new categories in addition to the original ones. We created a dictionary ofcodes with examples for clarity, then two researchers independently coded a portion of the portfolios
until they reached uniform agreement. They then proceeded to separately code the remaining portfolios,
soliciting second opinions for borderline areas as needed.
In further analysis we compared and contrasted the categories of codes, looking at areas ofcommonality and areas of exception. Finally, we analyzed student narrative statements that did not fit
any of our six categories to consider what they revealed about the portfolios, reflection, and student
narratives about CS. Together these stages of analysis helped unveil how the portfolios afforded
students the space to articulate how they identify with CS. In the end we framed our research in the six
broad categories below:
Self as a Computer Science (CS) Person●

Computational: Students author themselves as problem-solvers and relate this to doing
computer science or being a type of computer scientist.
● Personal: Students explicitly link growth as a computer scientist with specific skills they learned,
such as coding or making circuits.
Self and CS as Personal218

Constructionism 2018, Vilnius, Lithuania●

Socioemotional: Students describe how doing the project or the project itself demonstrates
personal characteristics like dedication, perseverance, patience, getting out of one’s comfort
zone, making mistakes, or collaborating with others.
● Relational: Students express a relationship with a friend, family member, or teacher that either
provided feedback on the e-textile project, involved collaboration, or made a project intended forsomeone else.
Self in Relation to the Larger Field of CS●●

Aspirational: Students discuss themselves in a future tense in relation to computer science inthe context of applications outside the classroom such as future jobs or in other projects.
Projectional: Students describe new realizations of what computer science is or what it can
include.

Findings
Throughout our analysis of Ben’s students’ portfolios, we discovered that in addition to serving as a tool
to assess student learning of computer science content (Lui et al., 2018), the portfolios afforded students
the space to author themselves in relation to CS. In constructing their portfolios, students not only
articulated who they were in relation to CS but identified the various resources, skills, and personalqualities that helped them construct their artifacts. In addition, the portfolios allowed students to author
new and expanded understandings of CS as a field. They also allowed students to narrate who they
could be in the future in relation to CS, articulating what lessons they learned that could help themsucceed in CS beyond this unit.

Identifying as Types of Computer Scientists
Answering the question “How have I grown as a computer scientist?” in their portfolios afforded students
the opportunity to author themselves explicitly as computer scientists. Many students did so byarticulating specific functional skills they exhibited throughout the unit that they believed aligned withbeing a computer scientist. In total, 20 students said that they had grown as a computer scientist throughdeveloping specific skills: 14 of these students listed specific coding skills, 12 cited circuitry skills, and7 mentioned crafting skills (some students cited multiple skills across their portfolios). For instance,
Leon claimed that the project helped him become a better computer scientist by his ability to code anddesign light functions. Other students linked being a computer scientist with things such as making
LEDs light up, learning how to program a microcontroller, and even how to create a fabric that contained
LEDs. The frequency of students linking being a computer scientist with particular skills is striking andshows the association they saw between being a computer scientist and the things that they learned inclass while creating projects—they saw a direct link between what they were doing and who they were
as computer scientists.
This direct link between doing and being is also visible in the ways that students associated being acomputer scientist with problem solving. In all, 17 out of 32 students authored themselves as problem
solvers specifically in relation to computer science. Jarvis expressed this the most obviously by writingthat the project helped him as a computer scientist because in his words, a lot of computer science isproblem solving. In this, Jarvis made a direct statement that computer science is problem solving, andsince he did some problem solving in his work, he therefore was “helped” as a computer scientist in themaking. Other students explained this connection between being a computer scientist and problem
solving. For example, Jeevan “realized his mistakes so he would not make the same mistakes the nexttime, Anita shared that the project experience allowed her to solve problems on her own and work until
she figured out a solution (Anita), and Ana expressed that her final project illustrates that her identity of
being a computer scientist is she understands what to do but messes up a lot. This connection between
problem solving and identity is intriguing because problem solving is a more general skill or way of
thinking than particular forms of coding, crafting, or making circuits discussed above. Students’highlighting how to problem solve or work through challenges reflects broader, intentional teachingpractices in the classroom community valuing mistakes and failure as legitimate means of learning(Fields, Kafai, Nakajima, Goode & Margolis, in press). It also reflects the values behind the requirements219

Constructionism 2018, Vilnius, Lithuaniaof the design notebook and the portfolio, showing that students respected challenges and the processes
of working through them.
In positioning themselves as having new skills in computer science as well as abilities in problem
solving, these high school students challenged the common notion of not being “smart enough” toparticipate in computer science (Yardi & Bruckman, 2007). In other words, as opposed to being told
what computer scientists do or what kinds of people they are (which can act as a barrier for participationin computer science), the students personally authored what they did throughout the e-textiles unit andhow that confirmed their identities as computer scientists.

Relating to Computer Science as Personal and Social
Intriguingly, beyond listing particular skills and practices, 12 students also linked demonstrating
socioemotional characteristics to being computer scientists. Consider Ashley’s explanation about being
creative and taking risks, in which she shared that the project fits into her identity of a computer scientist
because it allows her to go out of her comfort zone and create something new. She further explains thatcreating doesn’t only mean putting everything together, but imagining, discussing, evaluating, andunderstanding. Ashley explored an expanding view of creativity with her willingness to step out of her
comfort zone as important personal attributes she associated with being a computer scientist. Other
students expressed how they felt “more capable” (Heidi), how their project work improved their“patience” (Anita) or how the process of making e-textiles demonstrated “hard work” that developed
through “constant practice” (Adeep). For these students, computer science was more than just learning
specific skills or coding; they linked CS to involving personal attributes that they valued.
In addition, students particularly challenged the stereotypical perception of computer science being
antisocial (Yardi & Bruckman, 2007). In total, 18 students acknowledged relationships as serving a rolein the construction of their artifacts, whether it was by working with a partner on the mural project,gaining help from their peers or the teacher, or eliciting feedback from peers or family members. Heidi,
for example, came up with her project idea by eliciting feedback from both her mother and her peers on
whether to design a bag or pants and a corgi or flowers. In other words, Heidi valued people’s opinions
in her design process, truly conceptualizing her e-textile project as an “object-to-share-with” (Kafai &Burke, 2014). Four other students specifically designed their e-textiles artifacts as gifts for family
members, integrating relationships in the purpose of their designs. For example, both Jeremy and Ana
designed their electronic cards for their mothers, while Ashley and Sara designed their human sensor
projects (a ladybug and a wolf, respectively) for their little sisters. Designing their projects for family
members and leveraging those relationships created additional meaning and relevance for students inconsidering the role computer science plays in their lives.
What is interesting about students noting how relationships played a role in their projects rests on thefact that most of their projects (aside from the mural project) and their portfolios were individualassignments. However, in reflecting on their learning through their portfolios, over half of the students
recognized how others played a role in their being able to construct their projects, demonstrating how
without explicit prompting they were able to author an expanded understanding of computing as a social
field. By acknowledging the social aspects of computer science, students who normally view the field
as antisocial can develop an increased interest and sense of belonging, such as Alejandra who
expressed that she got to work with someone new, which made her want to explore more in the field.

Situating Selves with Computer Science as a Field
The portfolios also afforded students the space to author new and expanded understandings ofcomputer science as a field. In analyzing all of the portfolios, we found that 14 students described new
realizations of what computer science is outside the classroom. For example, Kevin learned that coding
can be found in everything when he came to realize that lights required a software program to turn themon and off. Other students shared discovering new ways that computer science is done. For instance,
as opposed to computer science being a highly regimented field with limited ways to participate, Adeep
reflected on how the project helped him grow as a computer scientist by showing him that computerscience does not have a handbook. It should be noted that students were not required to reflect oncomputer science as a field. However, in reflecting on their identities as computer scientists, students
220Constructionism 2018, Vilnius, Lithuania

positioned themselves in the field by broadening its criteria in ways that validated their participation. In
addition, they reflected on how what they did in completing their projects related to how computerscience is done in the real world, which speaks to how they were able to create meaning and relevancefrom their projects.
Other students, 12 in all, authored themselves as participating in e-textiles projects in the future,including Shona who expressed interest in doing both of the projects again due to the wonderfulexperiences she had In addition to wanting to make e-textiles in the future, students like Leon andAshley identified specific lessons they learned from the projects that they would apply for future etextiles projects, such as listening more carefully in order to make fewer mistakes (Leon) or creating an
initial plan before designing (Ashley). By identifying these lessons, there were given the opportunity toreflect on how they could improve upon their initial designs and experiences, increasing their chancesof success. Other students like Jesse expressed interest in exploring computer science past the etextiles class, particularly in college. Interestingly enough, one student, Mario, explicitly stated that hedid not want to be a computer scientist in the future but acknowledged that it could help him get a job,
become a skill, or help his family with computer problems. Even though Mario had expressed in his
portfolio that he did not want to be a computer scientist, he still named meaningful ways he could apply
what he learned in the e-textiles unit to his future.
It is fascinating how students used the space of their portfolios to author these future selves in relation
to computer science. This reflects how portfolios can serve as ideational resources that allow students
to construct identities of themselves participating in computer science in the future in ways that are
personally relevant. Doing so affords students the opportunity to develop positive self-narratives thatsituate them on an inbound trajectory of participation in CS.

Discussion
When Papert (2001) wrote about valuing the study of one own’s learning, he was reviewing his own
journey—the various experiences, activities, and observations of learning—that contributed to his
conceptualizing learning as a multi-facetted process of connecting with the world. Students engaged invery much the same approach in reviewing their learning as they created their portfolios in addition toand in review of their e-textile artifacts; both served as powerful ways to reflect on their own learning
process and identities as e-textile designers. Not only did students apply computer science in real-worldcontexts through constructing their e-textiles artifacts but constructing their portfolios provided them withthe opportunity to author new understandings of computer science in ways that were meaningful to themas well as increased their interest in the field. In other words, as opposed to holding negative perceptionsof computer science, students were able to reframe their perceptions of computer science in morenuanced ways as a more relevant, sociable, and engaging field.

From “Objects-to-Think-With” to “Objects-to-Learn-With”
We started out with the observation that constructionist theory and practice have emphasized theconstruction of artifacts as “objects-to-think-with”, on and off the screen, as a primary vehicle forlearners’ knowledge reformulation and personal expression. Adding to this is the importance inconstructionism of creating objects in a supportive social environment. In creating shareable artifacts,
learners’ knowledge construction becomes not only an individual but also as a social process. One
could argue that by designing for an audience, learners have an opportunity to share the understanding
they have gained from making something and in the process revisit their learning. We have called thisdimension “objects-to-share-with” (Kafai & Burke, 2014). For instance, in Harel’s (1990) instructional
software design project learners developed software to teach others. However, in this instance the pointof reflection was always another person—customizing software for the user—not the designer. The
reflective portfolios studied in this paper move the examination of learning back to the learner him or
herself. While “objects-to-think-with” have student-created artifacts as the focal point, reflective
portfolios become “objects-to-learn-with”, places where students can trace their path of learning, record
what they have discovered, and situate themselves as learners within a larger disciplinary community.

Identifying with Process and Mistakes
221Constructionism 2018, Vilnius, Lithuania

The portfolios also served as ideational resources (Nasir & Cooks, 2009) that supported students’authorship of who they were (i.e., students’ expressions of identity) in relation to computer science. Most
students in Ben’s class clearly articulated themselves in relation to computer science, whether
expressing that by describing the skills they learned, the types of problem-solving and persevering
people they had become, or the friends, family and teachers who were part of their learning experiences.
It is further evident from students’ reflections that many felt able to clarify the type of computer scientist
they were: as someone who “messes up”, fixes things, thinks out of the box, works collaboratively withothers, or “solves problems on my own.” Here students were not limited to culturally dominantstereotypes about the types of people who do computer science (Yardi & Bruckman, 2007). Instead
they linked their personal interests and experiences in authoring new ways of participating in the fieldof CS.
There are interesting parallels and links between the reflective portfolios students wrote and the etextiles artifacts they made. Both are interest-driven and student-centered; students molded projects inrelation to their own personal interests and shaped their portfolios in similar ways. Both had constraints
that helped shape what students made. In the e-textiles projects, students were constrained by using
certain tools or creating a number of lighting patterns; in the portfolios students had to fill in specificsections with writing and evidence. Both allowed for personal aesthetics. E-textiles projects varied
based on students’ interests and showed visible links to family, schools, or popular culture; e-textiles
portfolios differed in fonts, backgrounds, digital images, and discourses of writing. In this we argue that
portfolios like the ones students created in Ben’s class are a type of secondary artifact, one that canaccompany the type of personalized object usually focused on in constructionist-based learning
environments. Yet while the primary artifact (i.e., an e-textiles or another project) only shows the end
product, portfolios complement this by sharing the stories behind the e-textiles artifacts’ creation:
troubles encountered, mistakes made, revisions that took place, and people who influenced the design.
While the primary artifact hints at students’ identities in the skills exhibited or interests made visible,
portfolios allow students to explain in explicit ways what type of people they are and how they relate to
the broader field in which they are participating.

Constructing Spaces for Learning Journeys
Just as we design tools and communities for supporting students’ constructions we need to attend to
the design of tools for reflection. Some key affordances of the portfolios in this study could be applied
to portfolios or similarly reflective artifacts in other contexts. One requirement of the portfolio in this study
was to write about challenges faced and changes made in the projects. This constraint may have hada direct influence on students’ prioritizing qualities such as problem solving, troubleshooting,persevering, and having patience. Another helpful requirement was specifically designed by the teacher:
explaining “how you have grown as a computer scientist.” Ben created this requirement to explicitly
support students’ identity development, and his success is evident in the frequency with which students
associated certain skills or personal qualities with being a computer scientist or doing computer science.
In future research we are investigating variations of these design features as well as other formats andcontexts to understand how to facilitate students’ reflections.
For instance, in our case, the portfolios were created in Google Slides format, very much resembling
PowerPoint presentations. Yet there is an implicit contradiction in using a presentation format that isoften introduced as visual medium with as few words as possible for a reflection journal that asked
students to elaborate in writing about their creative process. While the visual component is invited inpresentation format, the textual component is not. In the next iteration of the portfolio assignment which
is being implemented in more than a dozen schools, teachers are choosing different formats for theirclasses, including blogs, websites, documents, and, of course, slides (though those could be oriented
as a landscape or portrait format in ways that make them more or less like a traditional document). We
plan to analyze to what degree different formats support students’ creativity and self-expression in theirreflections. Further, we are maintaining and adding to core constraints for the portfolios, while allowing
teachers to have the flexibility to add to them. For instance, the new portfolio assignment has doubled
the emphasis on challenges and revisions (each student must share at least two instances of facing achallenge or revision) but has no requirement to express how students have grown as computer
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scientists. We will study to what degree students continue to author themselves explicitly in relation tocomputer science with and without that obvious prompt.
In this study, we focused on understanding how the construction of a personal and shareable artifact
itself with the accompanying construction of a reflective portfolio could support students’ learningjourneys. Portfolios as meta-artifacts or objects-to-think-with deserve their own place in thinking about
the philosophy and pedagogy of constructionism and how the combined artifacts can contribute tostudents’ growing self-narratives in a broader disciplinary way.

Acknowledgements
This work was supported by a grant #1509245 from the National Science Foundation to Yasmin Kafai,
Jane Margolis, and Joanna Goode. Any opinions, findings, and conclusions or recommendations
expressed in this paper are those of the authors and do not necessarily reflect the views of the National
Science Foundation, theUniversity of Pennsylvania, or Utah State University. Special thanks to
Tomoko Nakajima, Debora Lui, Justice Walker, and Gayithri Jayathirtha for their help with datacollection, portfolio design, and constructive feedback.

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Constructionism and De-Constructionism as
Complementary Pedagogies
Jean M. Griffin, jean.griffin@temple.edu
Temple University, USA

Abstract
Constructionism is advantageous for learners of all ages but underutilized in “serious” formal education
settings. It can be challenging for teachers to allocate enough time for students to construct their own
designs if the students are expected to master numerous concepts and skills. Also, some teachers lackexperience with evaluating creative constructions. Students often want to abandon their designs when
they encounter obstacles or bugs. Constructionism might be more widely adopted if teachers knew how
to pair it with a pedagogy that ensures coverage of key concepts, provides effective practice with coreskills, and helps students gain confidence with troubleshooting. This paper presents such a pedagogy,
de-constructionism, a learning-by-taking-apart approach grounded in reverse engineering and practicetheory. An experiment is described where students learning to program solve practice problems
designed with a de-constructionist approach. Students’ attitudes about the problems are reported.
Suggestions for balancing constructionist activities with de-constructionist ones are discussed.

Figure 1. Model for De-Construction

Keywords
Constructionism; De-Constructionism, code comprehension, bugs

Introduction
Constructionism is a pedagogy and learning theory conceived by Seymour Papert that encourages
learners to create personally meaningful computational artifacts and share them with others (Papert,
1980, 1987). Constructionism enjoyed a wave of popularity in the late 1900s when numerous education
initiatives engaged young children with the Logo programming language to explore computing, math,225

Constructionism 2018, Vilnius, Lithuania

animation, storytelling, and other topics. By the early 2000s Logo’s popularity had waned but new
constructionist technologies emerged that attracted an even larger number of participants from a wider
age range. Examples include Scratch, MIT App Inventor, Lilypad Arduino, and littleBits.
There are a few well-known college-level courses that use constructionist technologies and approaches
at the beginning of a “serious” programming course (Malan & Leitner, 2007) or throughout an
introductory computer science (CS) course that is also suitable for high school (Guzdial, 2003; “Mobile
CSP,” n.d., “The Beauty and Joy of Computing,” n.d.). Despite such examples, constructionism is often
perceived as a pre-college “just for fun” pedagogy. Although there is a constructionist component to the
new American advanced placement (AP) CS Principles course, which requires student to design andcreate a computational artifact, teachers are typically not informed that constructionism is a pedagogy
relevant to the course (“AP Computer Science Principles,” n.d.; Kick & Trees, 2015).
It would be helpful if teachers of such courses, and teachers of traditional CS courses with a problemsolving focus, knew about constructionism – its history, guiding principles, and varied techniques (fordesigning, managing, and evaluating constructionist projects) – because students of all ages are
motivated by making their own computational creations. For teachers who already take a constructionist
approach, it would be helpful to know how to counter-balance its focus on design with a pedagogy that
focuses on analysis and skill building. The Background section of this paper outlines the theoreticalframework for such a pedagogy, de-constructionism. The Method and Results sections describe an
experiment with a university Python programming course where the lab exercises were designed witha de-constructionist approach. The Discussion section considers the experiment within aconstructionist/de-constructionist dialectic. It discusses future work, suggests ways to incorporate
student collaboration, and invites others to apply de-constructionism to topics other than programming.

Background
The Background section discusses guiding principles for de-constructionism: learning from taking apartwell-built examples, learning from taking apart well-built examples with intentional errors, and learning
through effective practice. Note that de-constructionism is used here to mean a pedagogy; this is distinct
from uses of the term deconstruction relative to literary analysis, social theory, architecture, or as a
general decomposition strategy, e.g. (Boytchev, 2015; Self, 1997).

Learning From Taking Apart Well-Built Examples
How do people become mechanical engineers? Car mechanics? Fashion designers? In disciplines thatinvolve physical objects, there is a long tradition of learning-by-taking-apart. Future mechanics andmechanical engineers are often drawn to take apart and fix cars, appliances, gadgets, and toys. Future
fashion designers often take apart and then copy others’ garment designs while developing their own
unique style. While constructionism encourages this practice to some extent through remixing, a
remixing experience (which involves changing an existing artifact) is often serendipitous. Students may
choose to remix only an object’s surface features, and either avoid or be unaware of its more complex
features.
This section reviews three learning-by-taking-apart pedagogies. Two are used for hands-on engineering
education: mechanical dissection and Tod Phod Jod. The third, worked examples, has been researched
extensively for mathematics education and to some extent for computing education. Also discussed area few additional learning-by-taking-apart techniques used in computing education. These approaches
are compared and contrasted to find best practices applicable to a general pedagogy of de-construction.

Mechanical Dissection
In job training and vocational education settings, it is common for apprentices to disassemble, analyze,
and assemble (DAA) vehicles, appliances, and other machines. This is useful for developing not only
mechanics, but mechanical engineers and designers (Seabrook, 2010; Wu, 2008). For
college/university mechanical engineering departments, this presents challenges in terms ofworkspaces, staffing, and storage. Thus, many offer introductory courses that are theoretical. An
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exception is a Stanford University course that implements a hands-on DAA approach called mechanicaldissection (Sheppard, 1992).
Mechanical dissection is similar to the medical school practice of dissecting cadavers, but instead
students disassemble (and re-assemble) mechanical things such as bicycles, fishing reels, and drills.
Later courses engage students with design activities. Guiding questions for the mechanical dissection
course are: “How did others solve a particular problem?” and “Why does the solution work?” An
important goal is for students to become familiar with the terminology of mechanical engineering. Thisis accomplished by reading instructions, conversing in a structured team setting, answering questions,and writing reflections. Students are guided to study product diagrams, and to notice labels andcategories of parts and systems. Students also explain what they learned, and generate their own labels
and categories in written reports and reflections. Names, labels, and categories all serve as
metacognitive cues (Bransford, Brown, Cocking, Donovan, & Pellegrino, 2000). Researchers who
observed participants of a Toy Dissection module report “We observed most groups generating a causal
chain, in which they traced how a sequence of events propagates from one part of the device to thenext” (Roschelle & Linde, 1996).
Some mechanical dissection activities have students analyze multiple items from the same productfamily, e.g. coffee machines made by different companies. Students compare them and evaluate design
tradeoffs. Comparing and generalizing helps students learn design templates, develop design
strategies, and think abstractly. Sheppard says: “The reality is that very little design is actually new
design. Very good designers have this catalog in their brains of stuff – of mechanisms, of devices, ofmachine elements” (Wu, 2008, p. 59). Over thirty institutions have implemented mechanical dissection(Agogino, Sheppard, & Oladipupo, 1992; Regan & Sheppard, 1996; Roschelle & Linde, 1996; Wood &Agogino, 1996). It has been adapted to teach about products that aren’t solely mechanical; this is called
product dissection.

Tod Phod Jod
An innovative approach to hands-on engineering education in India is Tod Phod Jod. It is similar tomechanical dissection in many respects but offered to children through a series of out-of-school
workshops, each several hours long. Tod Phod Jod roughly translates to “break and make.” In the firstphase, middle school aged boys and girls deconstruct and reconstruct objects such as ceilingfans, clocks, and irons. Later they take apart more complicated things and fashion new things (Jods).
Like mechanical dissection, Tod Phod Jod encourages students to work hands-on with one or moreteammates, and ask questions such as: “ How does it work?,” “What’s inside?,” “Who made this?,”“Why?,” “How?” Also similar is the anticipation that discoveries participants make during Tod Phod(deconstruction) will come in handy later on for Jod (design). Yet another similarity is the goal of learning
relevant terminology, by “deconstructing the scientific jargon that is usually learned by rote” (Vishnoi,2012). Tod Phod Jod builds on the lower levels of Bloom’s taxonomy (remembering and understanding)to engage youth with higher levels – applying, analyzing, evaluating, and creating (“Tod Fod Jod: To
Encourage Students to Discover, Experiment, Innovate,” 2012). Unlike mechanical dissection,
participants of Tod Phod Jod do not fill out detailed worksheets and reports. Instead the emphasis is on
having a fun and motivating experience. India’s national initiative to promote youth interest in STEM
education recommends Tod Phod Jod for hands-on, activity-based learning (“Rashtriya Avishkar
Abhiyan,” n.d.).
Both mechanical dissection and Tod Phod Jod promote learning through reverse engineering, given
this definition: “Reverse engineering is the process of analyzing a subject system to identify the system’s
components and their interrelationships and create representations of the system in another form or ata higher level of abstraction” (Chikofsky & Cross, 1990, p. 15). Both help students develop mental
abstract representations, which can later be draw upon for design. There are other approaches forlearning by taking apart physical things, e.g. un-crafting (Murer, Fuchsberger, & Tscheligi, 2017).

Worked Examples
Another approach to learning from analyzing well-made examples is the use of worked examples. John
Sweller introduced this pedagogy, where students learn from studying problems that are completely
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worked out as an expert would solve them (Sweller, 1988). Contrary to the prevailing belief that students
should spend a lot of time solving problems, Sweller’s research showed that learning can be more
efficient if students study worked examples while gradually learning to solve problems on their own.
This is known as the worked example effect (Sweller, 2006). According to cognitive load theory, which
Sweller developed as the theoretical foundation for this research, humans can learn and store many
schemas (patterns of knowledge) in long-term memory, but have a quite limited amount of working
memory that can make sense of new information. Learning can be efficient if the cognitive load is
managed and the learner doesn’t experience cognitive overload due to excessive or confusing
information. Scaffolding (instructional support) is needed to manage cognitive load. Worked examples
can provide such support (Sweller, 1988; Sweller & Cooper, 1985).
Mathematics education researchers have conducted numerous large-scale experiments with worked
examples to measure students’ learning gains. After decades of research, experts recommend thatstudents spend roughly the same amount of time studying worked examples as they do solving
problems on their own (Booth, McGinn, Young, & Barbieri, 2015). It is important to consider priorknowledge; students with high prior knowledge may lose expertise if required to study too many
rudimentary examples (Kalyuga, 2007). Empirical research attests to the benefits of several techniques
used in conjunction with worked examples. These include self explanations, where students explain in
their own words or choose an explanation (Chi, De Leeuw, Chiu, & Lavancher, 1994), comparisons(Rittle-Johnson & Star, 2007), sub-goal labels (Catrambone, 1998), and feedback (Conati & VanLehn,
2000). These same techniques show promise with worked examples for computing education. There isrecent empirical research on students explaining programs and algorithms (Margulieux, Morrison,
Guzdial, & Catrambone, 2016; Sudol-DeLyser, Stehlik, & Carver, 2012), comparing algorithms(Patitsas, Craig, & Easterbrook, 2013), interacting with sub-goal labels (Margulieux, Catrambone, &Guzdial, 2016; Morrison, Margulieux, & Guzdial, 2015), and getting feedback via intelligent tutoring
systems (Di Eugenio et al., 2015; Harsley & Morgan, 2015; Sudol-DeLyser et al., 2012) and electronic
books (Ericson, Guzdial, & Morrison, 2015).

Best Practices from Mechanical Dissection, Tod Phod Jod, Worked Examples, CS Education
Mechanical dissection, Tod Phod Jod, and worked examples all guide students to deconstruct wellconstructed examples. Students explain how the examples work, identify parts, learn terminology, andcompare items. Participants of mechanical dissection and Tod Phod Jod get hands-on feedback, whilestudents that interact with worked examples can get computerized feedback. They all help students
learn exemplary design patterns and problem solving strategies. This prepares students both
intellectually and psychologically to master design challenges and problem solving challenges.
A few additional learning-by-taking-apart techniques that are used for teaching programming arenoteworthy. These emphasize code comprehension, with the philosophy that it is important that students
understand code before, or as, they learn to write code on their own. This stands in contrast to thetypical emphasis on code writing. There is evidence that students who are unable to understand code
samples are unable to write similar ones accurately (Lister et al., 2004; Lopez, Whalley, Robbins, &Lister, 2008). Some researchers investigate the cognitive processes involved with code comprehension(Schulte, Clear, Taherkhani, Busjahn, & Paterson, 2010), while others experiment with curricula
designed to promote code comprehension. A variety of curricular approaches engage students withanalyzing and comparing well-written code samples (Astrachan & Reed, 1995; Deimel & Moffat, 1982;
Kimura, 1979; Linn & Clancy, 1992). Some emphasize the importance of tracing code (Cutts, n.d.; Lopezet al., 2008) and understanding the invisible/notional machine of a programming language with the helpof program visualization aids (du Boulay, O’Shea, & Monk, 1981; Sorva, 2013). One popular technique
is to ask students to predict what code does. Another is to use completion problems; here students are
given well-structured code and asked to complete missing sections of it (Deimel & Moffat, 1982; Van
Merriënboer & Paas, 1990). Completion problems help bridge the gap between comprehending code
and writing code. Thus, predicting and completing are useful activities for de-construction in addition to
explaining, comparing, and labeling.

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Learning From Intentional Errors and Bugs
Given that budding bicycle mechanics and fashion designers typically gain mastery by fixing andmending broken things, it follows that an educator could systematically present a collection of broken
items to students to provide knowledge of common problems, and practice with fixing them. Neither
mechanical dissection nor Tod Phod Jod take this approach, perhaps due to the logistical challenges
of doing this with physical objects. This approach is used to some extent in math and CS education,
through intentional errors and bugs. Here, the instructional designer intentionally places a carefully
designed error or bug in an otherwise well-built example to highlight a key concept, common error, orcommon misconception.
Although people can learn from their own mistakes (Duncker, 1945), this is often serendipitous. Could
learning be more comprehensive and efficient if instruction included intentional errors? This question
sparks controversy. Behavioral psychologists view this approach as undesirable because it may
introduce or reinforce misconceptions. Several math education researchers who use intentional errors
view the behaviorist philosophy as normative, and characterize their own use of them as controversial(Isotani et al., 2011; Tsamir & Tirosh, 2003; Tsovaltzi et al., 2010). In contrast, several theories support
the idea that intentional errors can promote learning. These include the theories of cognitive dissonance(Festinger, 1957, 1962), negative knowledge (Kaess & Zeaman, 1960; Oser, Näpflin, Hofer, & Aerni,2012), impasse-driven learning (VanLehn, 1988), learning from errors (Ohlsson, 1996), and overlapping
waves (Siegler, 2002). Learning from intentional bugs may ease the pain of debugging one’s own
programs. Researchers of intentional errors hypothesize that they may ultimately reduce frustration andincrease feelings of competency.

Learning Through Effective Practice
Most of us are familiar with the proverb practice makes perfect, but what does education research haveto say about practice? The testing effect is a well-known phenomenon. Researchers have found thatfrequent quizzes provide retrieval practice that helps students learn, retain, and transfer knowledge.
This is most successful if tests are distributed/spaced over time, if they engage learners in effortful recall
rather than rote memorization, and if feedback is given (P. C. Brown, Roediger III, & McDaniel, 2014;
Dunlosky, Rawson, Marsh, Nathan, & Willingham, 2013; Roediger & Butler, 2011).
Some education researchers view practice from a metacognitive perspective. Palincsar and Brown
introduced the reciprocal teaching pedagogy, which guides teachers to explicitly teach metacognitive
strategies and encourage students to practice these strategies (Palincsar & Brown, 1984). Singley andAnderson researched the role of practice in developing the ability to transfer knowledge from one
context to another (Singley & Anderson, 1989). K. Anders Ericsson and colleagues introduced the ideaof deliberate practice in response to longstanding beliefs that expertise is innate and that experts don’t
have to practice much. Considering domains such as chess, music, sports, and writing, they found thata lot of time spent on practice does not necessarily lead to progress. In order for practice to be effective
it must have certain qualities: deliberate practice is focused, effortful, and individualized by a teacher orcoach to address weaknesses and build strengths. Although deliberate practice is usually not enjoyable,
people do it to master a domain (Ericsson & Charness, 1994).
Many computing teachers engage their students with blocked practice. For example, topic A is
introduced and students practice topic A. Then topic B is introduced and students practice topic B, andso on. Education psychology researchers have found that distributing practice over time and interleaving
topics is superior to blocked practice. It is also helpful to scaffold instruction from concrete to abstract,balance examples with problem solving, provide feedback, and consider students’ prior knowledge(Dunlosky et al., 2013; Koedinger, Booth, & Klahr, 2013). Practice correlates positively with developing
programming skills (Douce, Livingstone, & Orwell, 2005; Macnamara, Hambrick, & Oswald, 2014;
Ventura Jr., 2005). The Leeds group recommends that teachers frequently give students practiceproblems to develop automaticity with basic programming skills (Lister et al., 2004). Kranch studied
novice programmers and argues that they need plenty of practice with fundamentals (Kranch, 2011).
Unfortunately, many beginning students say they lack the time and motivation to practice (Kinnunen &Malmi, 2006).
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Research Questions•

How can a de-constructionist approach be realized in a programming class?

•

What are students’ attitudes about this approach?

MethodI conducted a design based research study. Design based research involves iterative experimentation
in authentic settings such as classrooms; it typically involves a curricular intervention and cooperation
with teachers (A. L. Brown, 1992; Collins, 1990). “Design experiments have both a pragmatic bent –‘engineering’ particular forms of learning – and a theoretical orientation – developing domain-specifictheories by systematically studying those forms of learning and the means of supporting them” (Cobb,
Confrey, DiSessa, Lehrer, & Schauble, 2003, p. 9). I conducted a usability study, two pilot studies, anda semester-long experiment. Here I report on parts of the semester-long experiment, for which I
designed web-based de-constructionist practice problems for learning to program with Python.

Context and Participants
The study was conducted in cooperation with the CS department of an urban public research university
in the northeast United States. In 2015 the university had ~28.5k undergraduate students: 51% female;
11% Asian; 13% African American/Black; and 55% White. The study involved Professors Town andPark (pseudonyms). Each taught two sections of an introductory CS1-Python course with (N=87)
undergraduate students overall. Both CS majors and non-majors attended the course. 15% of thestudents were female; 24% identified as a (non-Caucasian, non-Asian) racial or ethnic minority. I was
the lab instructor and developed the activities for the weekly labs.
I developed a problems set for each lab starting with week 4, and iteratively designed problems for thefollowing week for a total of ten labs. I used the Runestone Interactive e-book system to create webbased practice problems (Ericson et al., 2015; Miller & Ranum, 2014). Runestone has a uniquecollection of components that provide interactivity and real-time feedback. In addition to components for
multiple choice and free response questions, several components help students explore code throughinteractive code reading. The clickable component (See Figure 2) allows the student to click on one ormore sections of code to identify key features. The codelens component (See Figure 3) provides
program visualization as the user steps through code line by line (Cross, Hendrix, & Barowski, 2011;
Guo, 2013). Using the drag ‘n drop component (See Figure 4), the user clicks and drags items from one
column to match items in another. With the Parsons problem component, the user re-orders scrambled
code (Parsons & Haden, 2006). Several components require students to write code. The author may
supply starter code (to be completed or changed), or ask the student to write code from scratch. The
author may also supply unit tests, to test the user’s code and supply feedback. Practice problems related
to debugging can be created with any of these components.

Figure 2. Clickable

Figure 3. Codelens (Guo, 2013)

Figure 4. Drag ‘n Drop

As a guide I used an instructional design model for de-construction (Griffin, 2016), which I refined during
the experiment (See Figure 1). Following the model, the practice problems progressed from easierreading and tracing problems (that involved explaining, identifying, labelling, predicting, and comparing)230

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to harder ones that required writing code. Most problems involved short code segments. The problem
sets incorporated distributed practice and interleaving. Students who finished before the lab period
ended were free to leave. Students worked individually but could consult with classmates, with me, or
my teaching assistant. A student who completed all ten labs interacted with 109 practice problems, ~11
per lab.
The treatment group (Bugs) and control group (NoBugs) were each comprised of two lab sections, one
for each teacher. The Bugs group got 22 practice problems with bugs; the NoBugs group got 22 similar
reading/tracing problems without bugs. Thus, ~80% of the problems were common; ~20% were specificto condition. Debugging problems were generally introduced as follows. For a given topic, problems
with correct example code were introduced first. Next, easy problems with bugs were introduced, suchas ones where the bug location was shown, which the student was asked to explain, categorize, or fix.
Then more difficult problems were introduced, such as ones that required finding a bug. (These are nothard and fast rules.)Timeline
The course spanned a 14-week semester, with two 80-min lectures and one 2-hour lab per week. Class
time was devoted to lectures, lab time to practice problems. I met with the professors weekly. Theytaught the same topics with the same textbook and gave similar exams.

Instruments and Procedures
At the end of the semester an online survey was administered to gauge students’ attitudes about thepractice problems. It had the following Likert-scale questions:1) Indicate your level of agreement with the following statement: The "Interactive Python" e-bookexercises during lab helped me to learn Python (strongly disagree, disagree, neutral, agree, strongly
agree).
2) In general, I found the e-book exercises to be: (too easy, about the right level of difficulty, too hard)3) I recommend that in the future, the labs have: (fewer e-book exercises, about the same number,more)4-5) Did you like the {reading/tracing, writing} exercises? (strong dislike, dislike, neutral, like, stronglike)6-7) Do you think the {reading/tracing, writing} exercises helped you learn Python? (not helpful,neutral, helpful, very helpful)
The Bugs group got two additional questions, about liking and learning from the debugging problems.
Additional instruments to measure learning gains were administered; that data will be reported in a
future publication. Throughout the experiment I recorded researcher memos and took field notes about
students’ interactions with the practice problems in the labs.

Results
This section summarizes the responses to the post attitudes survey.
Attitudes About Overall Learning, Difficulty, and Quantity. A majority of students responded positively
about overall learning, difficulty, and quantity of practice problems. Of the (N=76) survey respondents,
92% agreed or strongly agreed that the practice problems helped them to learn Python. 88% thought
the level of difficulty was about right; 8% found them too easy; 4% too hard. Regarding dosage, 62%
recommended the same amount for future courses; 11% recommended fewer, 28% more.
Attitudes about Liking the Practice Problems. A majority of students liked or strongly liked each type ofpractice problem. (N=75) students responded to the question about how much they liked thereading/tracing problems. 83% liked or strongly liked them; 8% disliked them; 9% were neutral. Of the(N=76) students who responded to the question about liking the code writing problems. 87% liked orstrongly liked them; 3% disliked them; 8% were neutral. (N=39) students in the Bugs group responded
about liking the debugging problems. 69% liked or strongly liked them; 5% disliked them; 26% were
neutral.
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Attitudes About Learning From the Practice Problems. A majority of students thought both thereading/tracing and the writing problems helped them to learn, while 38% thought so about the
debugging problems. (N=76) students answered the question about learning from the reading/tracing
problems; 82% thought they were helpful or very helpful; 3% thought they were not helpful; 16% were
neutral. About the code writing problems, 96% of (N=76) thought they were helpful or very helpful; 1%thought they were not helpful; 3% were neutral. For learning from debugging, an extra response
category was added (Detrimental) because the review of the literature suggests that some think errors
may deter learning. Of the (N=39) students in the Bugs group who responded, 38% thought they were
helpful or very helpful; 15% thought they were not helpful; one student (3%) thought they were
detrimental; 44% were neutral.

Discussion
This design-based research study implemented and further developed the emerging pedagogy of deconstructionism. In a 10-week intervention with a quasi-experimental design, undergraduates in a
Python programming class solved sets of practice problems designed with a de-constructionist
approach. This approach emphasizes ample practice with taking apart well-built examples, some ofwhich have intentional bugs. Before writing code for a given topic, students engaged with interactive
reading and tracing code by explaining, comparing, identifying, labelling, matching, tracing, andunscrambling. The treatment group got some problems with intentional bugs.
The study sought to understand students’ attitudes about learning with a de-constructionist approach.
According to the post attitudes survey, a majority of students liked each type of practice problem (forreading/tracing, writing, and debugging). While a majority of students thought the reading/tracing andwriting problems were helpful for learning, only 38% thought so about the debugging problems. Further
research is warranted to determine if it is useful to help students see the potential benefits of learningfrom intentional bugs, e.g. to help one become aware of common errors and learn how to repair them.
As the lab instructor, I observed that students’ interactions with the practice problems were generallysmooth; they appeared motivated to complete the problem sets. I was assured that all students who
attended the lab interacted with the key course concepts. Students appreciated the immediate feedback
and the chance to re-try until they completed a problem successfully. Approximately a third to half ofthe students finished during the first hour of the 2-hour lab period. Usually several took the entire period.
As the instructional designer, my experience with Runestone Interactive was generally positive. Despite
being a fledgling system, it is reliable and responsive, and has a rich variety of components. Creating
the practice problems was somewhat laborious. It required encoding them with the reStructuredText
language, but Runestone is evolving to streamline this process. I found that implementing distributed
practice and interleaved practice was more difficult and time consuming than implementing blocked
practice.
As the researcher, in some respects it was beneficial for me to also have the roles of lab instructor andinstructional designer. This was an early-stage, exploratory study that implemented the Model for DeConstruction for the first time (See Figure 1). The lab exercises were iteratively designed weekly basedon observations from the previous week. In future studies, having two or three people perform theseroles would be advantageous.
Future plans include incorporating constructionist activities into the lab period. Rather than having
students leave when they finish the problem sets, students could work on creative projects. Ideally the
rubrics would be such that students who need more time to master the practice problems are notpenalized for having less time to work on creative projects. This approach is common with the mastery
learning approach (Bloom, 1968; Griffin, Pirmann, & Gray, 2016). Unfortunately, many CS professors
are unaccustomed to designing rubrics for creative projects, such as the professors in this study who
have doctorates in mathematics and computer science. Introducing constructionism into such learning
environments would be valuable for both teachers and students. Students could have motivating design
experiences, and teachers could learn how to design, manage, and evaluate creative projects.

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Additional future plans include adding appealing graphics, and gamification elements. A priority would
be to add these elements to debugging and reading/tracing activities, since students already view writingcode as a worthwhile activity. Having students collaborate on de-constructionist activities, e.g. with Pair
Programming, is worth exploring. Also desirable would be to differentiate instruction based on priorexperience and/or demonstrated mastery.
The constructionist/de-constructionist dialectic is useful for teachers and instructional designers but it is
not all-inclusive. Other pedagogies are also important, such as ones that teach problem solving or
systematic approaches to debugging. De-constructionism draws inspiration from both reverse
engineering and mathematics education. While this study explored de-constructionism as it applies tolearning programming, it can be implemented for a variety of subjects with both physical and conceptual
elements.

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Mind the Gap: Teaching High School Students
about Wealth Inequality through Agent-based
Participatory Simulations
Yu Guo, yuguo2012@u.northwestern.edu
Northwestern University, USA

Uri Wilensky, uri@northwestern.edu
Northwestern University, USA

Abstract
This research paper presents a design-based research study centring on a constructionist curricularunit, called Mind the Gap (MTG), which was designed to help high school students learn about acomplex and controversial social issue in the United States—wealth inequality. The four-day-long unit
was implemented in eight economics classes with a total of more than 200 students across two highschools with vastly different demographics. In both schools, students’ engagement with the unit was
high. Preliminary data analysis has shown that students 1) made connections between their simulation
experience and the real world to reason about wealth inequality, and 2) showed attitudinal changes
favouring more equality after the unit.
MTG revolves around a series of three participatory simulations, which are microworlds that allow
students to project themselves into through their own avatars like in a multiplayer online game andinteract with the virtual environments in order to “figure out” the rules embedded in these simulations.
This work contributes to both the literature of designing constructionist learning environment andpeople’s perception and understanding of wealth inequality. In democratic countries, people’s
understanding of inequality is the key to achieve more equal societies.

Mind the Gap ABPS. Upper left: teacher’s view; the other four: students’ view

Keywords
agent-based participatory simulation; wealth inequality; economics curriculum; complex social
phenomena; artificial society

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Introduction
Today, American people face many controversial social issues that deeply divide the country. Examples
include wealth inequality, racial segregation, and climate change. Although natural and social scientists
have been studying these problems and have provided accumulating evidence for causes andconsequences, when people think about these problems, they tend to form very polarized views basedon the vastly different local environments they are in (e.g., Xu & Garand 2010), political beliefs (e.g.,
Kteily, Sheehy-Skeffington, & Ho, 2017), and media coverage (e.g., Diermeier et al. 2017). The scientificmethods and evidence, which can help people understand the mechanisms and consequences of manysocial issues, remain underutilized in people’s reasoning about complex social problems.
Through the lens of complexity science, wealth inequality, racial segregation, and climate change canbe seen as patterns that emerge from interactions among constituting elements. The system level
patterns at very large scale can be strikingly different from the behavior of the constituting elements at
the local level, making these complex systems unintuitive and hard for people to understand (Wilenksy& Resnick, 1999; Chi, 2005; Penner, 2000; 2001). Agent-based modeling (ABM) is a computationalmodeling method that scientists use to study these complex systems. With the ABM approach, elementsof complex systems are modeled as autonomous agents, which have their own properties andbehaviors. In an ABM, numerous such agents interact with each other and give rise to complex patterns
at the system’s level.
ABM is also an effective way to help students understand complex systems. Extensive research has
been done on using NetLogo (Wilensky, 1999)—an agent-based modeling tool that dynamicallysimulate and visualize complex systems—to create instructional interventions in STEM education.
Evidence show that these curricula supported students’ understanding of complex phenomena in avariety of scientific subjects, including biology, chemistry, and physics (e.g., Wilensky & Reisman, 2006;
Levy & Wilensky, 2008; Sengupta & Wilensky, 2009).
Teaching about complex social phenomena is an understudied area (Hjorth & Krist, 2016). While social
phenomena such as wealth inequality can be treated as complex systems, learning about these
phenomena presents new challenges. Hjorth & Wilensky (2014) designed and taught urban planningand policy reasoning with an agent-based models and found that compared with physical or biologicalsystems with constituting elements like molecules or animals, social systems—composed of people
with wants and needs—are much more heterogeneous and therefore more difficult for students toreason about (Hjorth & Krist, 2016).
A special form of ABM—participatory simulation—can be especially promising for helping students
understand complex social issues. Agents’ behavior in regular ABMs are pre-programmed, but agentsin agent-based participatory simulations (ABPS) are controlled directly by students, who project
themselves in to the models by taking on the roles of the agents and acting out their behaviors. For
example, the Disease participatory simulation (Wilensky & Stroup, 1999) models the spread of acontagious disease across a population. In this simulation, each student uses a computer to control anavatar’s movements in a shared virtual world. When an infected avatar touches a healthy avatar, there
is a certain chance that the healthy one also becomes infected. The simulation starts with nobody being
infected, but when the teacher randomly chooses a student to infect, the simulation usually turns into achasing game, in which the infected student tries to infect as many other students as possible. Onceothers are infected, they in turn chase those who are not infected. This participatory simulation teaches
an important complex systems idea about logistic growth: no matter what the students do at theindividual level, the larger pattern of infection across the population remains the same—the epidemic
starts with a very slow spreading speed, but as it develops, it reaches a breakout point, after which thenumber of infected people skyrockets, and finally the progress levels off, when the whole population is
infected. Such an approach not only teaches students’ the dynamics of complex systems, but also
brings out students’ emotions, desires, and decisions, which can be productive elements that contributeto understanding complex social issues.

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ABPS as Constructionist learning environments
Constructionist learning environments are designed to support students’ constructing knowledge andartifacts (Papert, 1980). Core features of these learning environments include leveraging students’ priorknowledge—tapping into students existing experience, closeness to objects (Papert & Harel, 1991)—
bringing student close to objects that they can manipulate, and epistemological pluralism (Turkle &Papert, 1992)—valuing different ways of knowing.
Microworlds are a genre of constructionist learning environments in which students explore a very
specific aspect of the world. In this sense, a microworld is a model of the real or a hypothetical world, inwhich the rules and regularities that govern the world are beneath the surface as far as the students’direct experience is concerned, but the rules and regularities manifest themselves by transforming thestates of concrete objects as students interact with the microworld (Groen & Kieran 1983). Edwards(1995) citing Piorlli & Greeno (1988) and Pratt (1991) describes a fundamental aspect of microworlds
as “the scientific or mathematical phenomenon which the designer intends to introduce to the learner isinstantiated or embodied in computer code. It is by translating mathematical or scientific regularities into
procedures and computational objects that the designer constructs a microworld, and this process
involves a complex series of choices and design decisions.” (Edwards 1995)
ABPS can be seen as a special type of microworlds, in which models can be executed by participants’following rules in the microworlds and acting out the behavior of elements that they are representing(Colella 2000). Students explore these powerful models by becoming part of them. Students constructspecific runs, or instances, of the model by collectively acting out the rules. These runs become artifacts
that can be replayed and investigated. In addition, in ABPS, students co-construct theories to explain
the phenomena that they are part of. The explanation is grounded in students’ individual experience ofbeing part of the phenomena and interacting with others in the ABPS. Students construct publicly
sharable entities and pay collective attention to these entities, which can either be projected on the big
screen for the whole class to see, or exist ephemerally in the shared virtual space, such as theconfiguration of everybody’s avatar in that space.

Mind the Gap Curricular Unit
We designed an ABPS curricular unit, called “Mind the Gap” (Guo & Wilensky, 2018a), for students tolearn about wealth inequality—a complex and controversial socioeconomic phenomenon.
Previous studies have shown that most people have misperceptions and misconceptions about wealthinequality, such as severely underestimating the extent of wealth inequality in the U.S. and attributing
the problem solely to individual characteristics, such as work ethic (e.g., Bullock 2008; Hauser & Norton2017). Most existing instructional interventions on wealth inequality use multicultural or social justiceapproaches to engage students in knowing factual knowledge, analyzing their social environments,
critically thinking about diversity, and advocating for possibilities for marginalized groups (Nagda, Gurin,& Lopez, 2003; Seider 2011; Mistry, Brown, Chow, & Collins, 2012). These interventions are importantbecause they raise students’ awareness of social issues, empower marginalized students, and help
students avoid deficit thinking. However, students still have difficulties identifying or explaining thestructural problems of wealth inequality after going through these curricula (e.g., Seider, 2011; Mistry etal., 2012).
Building on a body of work on participatory simulations of social complex phenomena (e.g., Sterman
1992; Wilensky, 2002; Maroulis & Wilensky, 2004), we use ABPSs to teach the complex andcontroversial socioeconomic topic of wealth inequality. Participatory simulations in STEM education are
shown to promote engagement (e.g., Colella, 2000; Klopfer, Yoon, & Perry, 2005), improveunderstanding of complex systems mechanisms (e.g., Wilensky & Stroup, 1999; 2002; Stroup &Wilensky, 2014; Berland & Wilensky, 2015), and high classroom participation (e.g., Fies & Langman,2011). However, less is known about the affordances of participatory simulations in social science
topics.
Mind the Gap (MTG) centers on a series of three ABPSs—microworlds in which students’ avatars
interact with the computer-based virtual environments that represent simplified economies. Instead of240

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encompassing all the factors and intricate relations that may contribute to wealth inequality in the realworld, these microworlds highlight three mechanisms of the phenomenon: 1) emergence, 2)
randomness, and 3) feedback loops. These ABPSs serve as computational laboratories of social
systems for students to play the roles of people with different socioeconomic status, explore the rules
and the structures of the system, experience success and frustration, and make sense of the emergenceof complex patterns from individuals’ behavior.
The design of the three ABPSs are based on SugarScape agent-based models (Epstein & Axtell, 1996;
Li & Wilensky, 2009a, 2009b, 2009c) that allow computational scientist to investigate complex social
phenomena such as wealth inequality, migration, trade, and epidemics through a bottom up approach—agents’ following very simple rules. It is surprising for people to see a small set of simple rules, when
followed by each agent, can generate highly complex patterns at the system level, which are similar toreal-world social phenomena.
The original SugarScape models represent a society with two major parts: 1) the “land”, where
resources (sugar) can be harvested to become people’s wealth; 2) the people, which are computational
agents that are pre-programmed to find the highest concentration of sugar around them and to “harvest”
that sugar. The land is represented by a 51 by 51 checkerboard. Each tile on the checkerboard contains
certain amount of sugar. The people have randomly assigned traits and are randomly placed on the
checkerboard.
Our design of MTG ABPSs preserved the basic structures of the SugarScape models, such as the 51
by 51 checkerboard, the random placement of agents, the random assignments of agent traits, andmost of the rules. However, in our ABPS models, agents are avatars that are directly controlled by
students, who can make decisions of what to do next within the constraints of certain rules. Students
are connected to the virtual space through the HubNet architecture—a client-server technology
designed for participatory simulations (Wilensky & Stroup, 1999b). Therefore, in MTG ABPSs, students
not only explore the simulations, they become parts of the simulation.
Figure 1 shows the teacher’s view of the MTG Equality model. At the center of the view is thecheckerboard. The grid is added to this figure to visualize the checkerboard. In the real model, the gridis not visible to either the teacher or the students. The unicolor of yellow shows that each tile contains
the same amount of sugar. However, in the other two models, the shades of yellow can differ from tile
to tile, representing different concentration of sugar—the darker the color, the higher the sugar. Students
are not supposed to know about the resource distribution. The teacher can hide the checkerboard fromthe students by clicking the “hide-world” button, which makes the whole checkerboard grey andstudents’ avatars invisible. After playing the simulation, in the discussion phase, the teacher can use
the “show-world” button to show students what kind of world they were in.
The three plots on the right—a bar graph, a Lorenz curve, and a Gini index plot—show three frequently
used forms of representation of wealth inequality in economics. The plots automatically update basedon real time aggregation of the amount of sugar that students own. The “setup” and “go” buttons at theupper left corner prepares the model and runs the model. The “sugar-mean” monitor shows the averageof sugar that students own, and the sugar distribution plot shows a histogram of tile sugar on the
checkerboard.

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Figure 1. MTG Equal Opportunity model teacher’s view (grid added to visualize the checkerboard)

Students see a different view from the teacher (Figure 2). The red arrowhead at the centre of the viewis a student’s avatar. Because the avatar has only imperfect local knowledge about its surroundings, it
can only see a few tiles away in each direction. The yellow cross represents the field of view of theavatar.

Figure 2. Students’ View

A Student can use the “up”, “down”, “left”, and “right” buttons on the left to move their avatar around andexplore the surroundings. He or she can also use the “harvest” button to collect sugar from the tiles they
are on. The “sugar” monitor above the buttons show the current amount of sugar that the avatar owns.
The “message” bar at the top shows real time tips and warnings about the student’s behaviour. The two
big input boxes on the right are data collection tools, accepting students’ typed answers to two openended questions posted by the teacher during class.
Students have a few attributes:

Vision: how many steps (tiles) away a student can see.
Endowment: how many units of sugar a student starts with
Metabolism: how many units of sugar is needed for moving one step or doing one harvest

Students have some actions they can take:242

Constructionism 2018, Vilnius, LithuaniaMove: by clicking the direction buttons or the keyboard shortcuts, students can move around. Eachclick moves the student by one step and burns metabolism amount of sugar.
Harvest: by clicking the harvest button, students harvest all the sugar on the tile that he or she isstanding on. One harvest burns metabolism amount of sugar.

MTG curricular unit consists a series of three models: 1) Equal Opportunity, 2) Random Assignment,
and 3) Feedback Loop (with education as an example). Each subsequent model builds on the previous
one with added complexity that more closely resembles the real world (Figure 3). At the beginning of
the unit, students are asked an overarching question that drives their inquiry: Why are rich people rich
while poor people poor?

Figure 3. A series of three MTG models.
Left: Equal Opportunity model; middle: Random Assignment model; right: Feedback Loop model

The Equal Opportunity model (Guo & Wilensky, 2018b) allows students to experience that eveneverybody starts with equal opportunities, inequality can still emerge due to individual differences. In
Equal Opportunity, sugar is evenly distrusted, as shown by the unicolour yellow. Students are randomly
placed on the checkerboard with the same personal traits, including equal vision, endowment, andmetabolism. Students strive to become the richest in the class by harvesting as much sugar as possible.
Inequality inevitably emerge due to differences in students’ strategies, understanding of rules, andmotivation. To answer the question about why rich people are rich while poor people are poor in thissimulation, given that everyone starts with equal opportunity, it is fair to say that personal differences,such as intelligence, efforts, and work ethics determine wealth status.
The Random Assignment model (Guo & Wilensky, 2018c) allows students to experience the power ofan uncontrollable force that contributes to wealth inequality. In this model, sugar is unevenly distributed
across the checkerboard. Students are randomly placed on the board and are also randomly assigned
different visions, metabolisms, and endowments. Therefore, the initial conditions that a student starts
with to a large extent determine the student’s course of life in this simulation. Students should realizethat when faced with the force of randomness, the draw of luck, instead of personal abilities, usuallyshapes the course of life.
The Feedback Loop model (Guo & Wilensky, 2018d) allows students to experience another type ofstrong force that shapes people’s course of life. Feedback loops, usually called virtuous circles or vicious
circles, are systematized or institutionalized forces. Unlike randomness, which is not biased against
anyone, feedback loops are usually socially constructed, privileging certain groups of people at the costof oppressing other groups. This model uses education as an example to let students experience that
depending on the cost of education, it can become a force that either closing or widening the gap
between the rich and the poor. This model gives students the opportunity to “go to school” by pressing
a button. While going to school has benefits, such as boosting earning per harvest by 130% andexpanding vision by one step, it also has monetary and opportunity costs. When education is lessexpensive, all students can make use of it to improve their vision and earning. However, when education
is expensive, it becomes a virtuous circle for the rich and a vicious circle for the poor. As the result, therich become richer and the poor become poor, closely reflecting a crucial inequality issue in the realworld.

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For each model, the teacher facilitates the students to go through a five-step inquiry cycle: introduction,2) question, 3) simulation, 4) reflection, and 5) connection (See Figure 4).

Introduction

Connection

Reflection

Question

Simulation

Figure 4. A Five-Step Inquiry Cycle.

In Step 1, students are introduced to the interface and rules of the model. In Step 2, students are asked
to think about what makes rich people rich and poor people poor in that specific model. In Step 3,
students play the simulation and compete to become the wealthiest person. In Step 4, whole classdiscussions are held for students to reflect on their experience in the model: who was the richest in thisround? How did s/he achieve it? What was his or her starting condition? Who was the poorest? Why
did s/he end up being poor? The richest and the poorest students are asked to share their stories in thesimulation with the whole class. In Step 5, group discussions and whole class discussions are held forstudents to make connections between their simulation experience and the real-world: What are thereal-world analogies of vision, endowment, metabolism, and the color of the starting tile? What is a realworld version story of the richest student? What is a real-world version story of the poorest student?
Students go through these five steps of inquiry cycle in each activity and keep revisiting the question
about why the rich are rich while the poor are poor in different models. The increased complexity in eachactivity is expected to contribute to students’ more sophisticated thinking about the question.

MethodThe central goal of this design-based research study (Cobb, Confrey, diSessa, Lehrer, & Schauble,2003) to investigate high school students’ learning processes and outcomes in the MTG unit.
Research questions
With this curriculum design, we ask two research questions:How do students connect their simulation experience with their real-world experience?
Does the unit improve students’ understanding of wealth inequality and change their attitude
toward it?

Participants
We implemented this curricular unit in eight high school economics classes with a total of more than
200 students across two schools with vastly different demographics. So far, we only finished preliminary
analysis on the data from one school with two participating classes, so in this paper, we only report theimplementation, analysis, and findings based on this corpus of data.
The two classes are in a public high school located in a highly ethnically diverse suburb of a large
Midwestern city in the U.S. Students in these two classes are mostly sophomores and seniors, who are244Constructionism 2018, Vilnius, Lithuania

taught by the same teacher for their regular economics curriculum. All 51 students were invited toparticipate in this study. Fifty (50) students consented to participate. Forty-four (44) completed thedemographic survey: There are 19 females and 25 males. Twenty-one (21) self-identified as Asian, 14
White, 4 biracial (2 Asian and White, 1 Black and White, and 1 unspecified), 1 African American, and 1
Hawaiian or other pacific islanders. 13 students speak a language that is different from English at home(including Assyrian, Bangla, Cantonese, Gujarati, Romanian, Serbia, Farsi, Tagalog, Telugu, and Urdu).
A majority of 32 students identify with the Democratic Party, 3 with the Republican Party, 4 with Other
parties, and 6 with None.
Implementation
The implementation of the MTG unit spanned four days with 42 minutes of class time on each day. The
first 20 minutes of the first day and the last 15 minutes of the last day were used for students to take thepre- and post- questionnaires. The first author taught the unit following the 5-step inquiry cycle describedin the section above.
Data collection
Multiple types of data were collected. The pre- and post- questionnaires collect data on students’knowledge and attitudes toward wealth inequality before and after the unit. During the unit, students’
typed responses to questions about each simulation are collected through input boxes built in the MTG
models. Computer log data of all students’ interactions with the models (including key strokes andperformance) were collected. Whole class video recordings captured the class dynamics throughout thefour days. A total of 20 students (10 from each class) were selected in consultation with their economics
teacher as focal students to represent the racial and gender diversity. Two additional types of data werecollected from the focal students: 1) Pre- and post- clinical interviews, in which researchers probed
students’ thoughts and attitudes toward wealth inequality through structured and follow-up questions.
2) Screen recordings: Focal students’ computer screens were recorded using Camtasia screen
grabbing software, which records on-screen behaviour and conversations between students.
Analysis
We analysed the pre- and post- questionnaires for evidence of attitudinal change towards wealthinequality before and after the unit. In both pre- and post- questionnaires, students were asked to rate
a series of statements about wealth inequality on Likert scales. Questions include “On a scale of 1-7,
with 1 being extremely unfair and 7 being extremely fair, please rate”: 1) How fair is the wealthdistribution in the U.S.?, 2) “The rich deserve their economic status”, 3) “The poor deserve theireconomic status”, 4) “In America, anyone can advance, regardless of their family of origin, economic
status, or ethnicity”, 5) “It is possible to move from poverty to affluence thorough hard work”, and 6)“Poverty is a sign of personal failure”. Students’ ratings were summarized with two histograms, one forthe pre-, one for the post. Because 44 students completed the pre-test and 43 the post-test, thehistograms were normalized (converted to percentages) for easier comparison. Summary statistics
were generated to describe the mean and the standard deviation of the pre- and post- results. The preand post- means were then compared.
In addition, we conducted interaction analysis (Jordan & Henderson, 1995) based on the whole classvideo data. The first author watched the videos to identify interesting moments during students’ classdiscussions, in which students utilized resources from both their experience gained from the simulations
and their real-world experience. These interesting moments were then transcribed. Below, we provide
an excerpt of class video transcripts that illustrate these moments.

Findings
Changing attitudes
Overall, students’ ratings shifted toward a more pro-equality attitude in the post questionnaire. Morestudents rated wealth distribution in the U.S. as less fair and showed stronger disagreement toward thesubsequent statements. We calculated and compared the means of pre- and post- ratings based on the245Constructionism 2018, Vilnius, Lithuania

assumption that the 1-7 scale is an even continuum, and the same rating shows the same degree ofagreement for all students.
Figure 5 shows the comparison of pre- and post- rating distribution of the fairness questions.

Figure 5. Pre- and post- comparison of students’ rating distribution of the fairness question.

No.

Statement

pre

post

shift

1

fairness

2.95

2.28

0.67

2

The rich deserve

4.25

3.74

0.51

3

The poor deserve

2.66

2.56

0.1

3.8

3.4

0.4

5 Get rich by hard work 4.93

4.47

0.46

6

2.02

0.18

4 Anyone can advance

Poverty is failure

2.2

Table 1. Attitude shifts on all six Likert scale questions.

In Figure 5, the left bars (blue) show students pre- ratings and the right bars (orange) show post ratings.
Although most students came to the unit already believing that wealth distribution in the U.S. wassomewhat unfair (an average rating of 2.95), after the unit, students believed it even more strongly (postmean = 2.28). The average rating changed by 0.67 towards left, which means students think the wealthdistribution is more unfair. The biggest change occurred in the lowest rating category (extremely unfair).
Before the unit, only 11% (5 students) thought the distribution was extremely unfair. After the unit, thepercentage increased to 28% (12 students).
Shifts in attitudes toward all statements followed a similar pattern, as shown in Table 1. These shifts areevidence that after the unit, students had more realistic perceptions of the fairness (Q1) and theopportunity structures in the U.S. (Q4 and Q5) based on understanding of mechanisms of wealthdistribution and exposure to real-world data during the unit. Minimum changes were observed innegative statements about poor people (0.1 in Q3 and 0.18 in Q6). Students’ pre-unit ratings of thesequestions were already very low (2.66 and 2.2, respectively). The minimum changes may have less to
do with understanding the mechanisms that students learned from the unit, but have more to do with
empathy that the students already have toward the poor.
Making connections between simulation experience and the real world
Below we present an excerpt of whole class discussion about the real-world meaning of students’experience in the MTG Randomness model. Kate was one of the richest persons for this round. The
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teacher asked her to share her experience in the simulation and asked the class to find a real-world
story that matches Kate’s simulation experience (all names are pseudonyms).
Teacher: Can You share your story?
Kate: So I was kind of very near the dark yellow, so I just moved over and a started harvesting.
Teacher: Ok, so basically you spawned and you saw dark yellow right next to you. You moved
over and started harvesting, right? And what was your vision?
Kate: One.
Teacher: One? OK, Interesting! Very interesting.
…
Teacher: Now let’s get into groups and talk about: Is there a real version of their stories in thereal world. let’s think about her story. She started right next to the darkest yellow. She just went
over and started harvesting.
[Students discussed in groups of three for 2 minutes]
Teacher: Which group wants to share their story of the rich in the real world? Go ahead.
Jack: We kind of thought of like really specific, like Sam Walton’s kids have a bunch of money
coming from Walmart and so as long as they don’t mess up and move away from the wealththey have, they can keep that wealth and build on it and like how Kate just moved over one spot
to build on the wealth that she already had.
Teacher: You guys agree? That’s pretty much true right? It is like a kind of the inheritance thing,right? So, if your parents are super wealthy, they will give you a lot of resources. Just make sure
you don’t do too stupid things to move away from them. Make use of it, then you’re probably
pretty well off, right? Ok, yeah, I think that’s a great story. Any other things to add? Any otherthings about the rich story?
Amy: Not about the rich, but sort of in general, I think that it’s interesting that even if she was ina very good neighbourhood, but she had very limited vision. I think that was like kind interesting,because I think that’s almost less realistic rather than more realistic, because I think if thesimulations were more like real life, if you have a high vision, you are more likely to be in a very
yellow area and have a low metabolism. it wouldn’t be randomly assigned.
Teacher: I think this is a great point. In [MTG Randomness model], everything is random. It’s
possible that you are born in a very rich place but with a very limited vision. In the real world, do
you think that happens that often? Not really, right? Because if you were born in a very
resourceful place, you probably get better education. Your parents are probably well-educated,and you probably know a lot of people. Your vision is actually a lot better than a lot of otherpeople. I think that’s a great point. Thank you for bringing it up.
This excerpt shows that students’ engagement with the simulation is quite sophisticated. Jack and his
group understood that spawning near a dark yellow area in the simulation is analogous to being born toa rich family in the real world. It is easier for those who were born rich to build on the wealth they
inherited to obtain more wealth. The MTG Randomness simulation helped students relate randomness
in the simulation and in the world that can make life easier just by chance. However, Amy had deeper
thoughts about the analogy between the simulation and the real world: she pointed out that random
assignment in the model was not realistic, because things are correlated in the real world. If thesimulation were more realistic, then “if you have a high vision, you are more likely to be in a very yellow
area and have a low metabolism”, which can form systematic, rather than random biases that work foror against people.
In this case, the less realistic feature of the simulation is not a drawback of the design. Instead, it isprecisely the nature of a microworld and a computational model, which foreground certain aspects ofthe system being modelled to highlight its basic mechanisms. The MTG Randomness model highlights
randomness as a strong mechanism that contributes to inequality. The students were able to identify its
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limits and provide real world mechanisms in contrast. The discussion triggered by comparing thesimulation with the real world is a valuable learning opportunity about the mechanisms of wealthinequality and the scientific practice of modelling for the whole class.

Conclusion and Discussion
Mind the Gap agent-based participatory simulation curricular unit is our attempt to address people’s
understanding and attitude toward complex and controversial social issues through designing
constructionist learning environments. The implementation of this unit in eight high school economics
classes showed students’ high engagement with this type of design. Preliminary evidence has shownthat students’ attitude shifted toward a more pro-equality stance after learning the unit. Students also
showed sophisticated thinking about rules and conditions in the simulation and in the real world in their
discussions when making connections between the virtual and the real world.
Wealth inequality is an example of a myriad of social issues that people face in today’s society. This
pioneer work shows what learning pathways and learning outcomes are possible to achieve with thistype of ABPS constructionist learning environment. We will continue data analysis to discover moreevidence of students’ knowledge and attitude change. With a better understanding of the learning
affordances of ABPS learning environments, we can use it to teach other complex and controversialsocial topics, such as racial segregation, climate change, and gun control. Equipped with better
understanding of mechanisms of these social issues, people in democracies can exercise their rights
more responsively to achieve more equal societies for everyone.

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To Assess or Not to Assess: Tensions Negotiated in
Six Years of Teaching Teachers about
Computational Thinking
Daniel Hickmott, Daniel.Hickmott@uon.edu
School of Education, The University of Newcastle, Australia

Elena Prieto-Rodriguez, Elena.Prieto@newcastle.edu.au
School of Education, The University of Newcastle, Australia

Abstract
Coding and computational thinking have recently become compulsory skills in many school curricula
around the world. This presents a major challenge for teachers, as many of them would not have hadformal education on the underlying concepts needed to apply these skills, or the pedagogies to teachthem. A range of professional development opportunities are currently being offered to teachers toaddress this challenge. One notable example of a model of PD designed to include constructionist
learning experiences is ScratchEd, which was designed by Karen Brennan. Upon reflecting on her
experiences designing and running ScratchEd for seven years, Brennan identified five tensions thatprofessional development providers may need to negotiate, and proposed that these tensions could be
used for scrutinising and critiquing professional development.

The five ‘tensions negotiated’ by Karen Brennan and our additional tension

In this paper, we analyse the process we have followed in the past six years to design, evaluate andimprove our professional development through the lens of Brennan’s ‘tensions negotiated’. While wehave experienced the same tensions, we contend that the extent to which we assess teachers’ learning
is a new tension that extends those identified by Brennan. There are strong reasons to assess teachers’
knowledge, however, quantitative measures of learning could be at odds with Constructionism: as
Papert argued in Mindstorms, constructionist educators should study their learning environments asanthropologists. Consequently, we have called this new tension the tension between anthropology andassessment.

Keywords
teacher professional development; constructionism; computational thinking; programming;
pedagogical content knowledge.
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Introduction
Recently, while working with a colleague to develop a joint research project on teaching quality, heasserted, ‘just add coding or computational thinking somewhere, it’s the only way to get funds thesedays’. This colleague is a successful academic in the field of physical education and has what one could
consider an impressive track record with funding applications. While this comment is not entirely
reflective of the funding situation in the field of educational research, and admittedly was pronounced injest, it provides an interesting insight into how prominent coding has become for education authorities
worldwide. Many countries have recently introduced, or are planning to introduce, curricula that include
the teaching of Coding and Computational Thinking throughout K-12 (Webb et al., 2016). Australia hasrecently introduced a Digital Technologies learning area within its new national curriculum (Falkner,
Vivian, Falkner, & Williams, 2017), England introduced a Computing Curriculum in 2014 that is
mandatory for all K-6 students (Sentance & Csizmadia, 2016), and in the United States there have beennational efforts to introduce K-12 Computer Science education into all of the states (Brown & Briggs,
2015; Fisher, 2016).
However, Coding and Computational Thinking (C&CT) are not new to schools and there have been
many attempts to bring these skills into mainstream K-12 education since the 1970s. Many of these
efforts were led by constructionists, who encouraged students coding in Logo, and similar programming
languages, to explore “powerful ideas”, as Papert (1980, p. 138) had envisioned. The teaching of coding
in Logo did not become widespread in K-12 education in the 1980s and 1990s, due to a complex mix
of social, political and technical issues, and a “lack of subject-matter integration” (Agalianos, Whitty, &Noss, 2006; Kafai & Burke, 2013, p. 61). Papert (1987, p. 24) lamented that many researchers andeducators had taken a “technocentric” view of Logo and that they had ignored many of the powerfulideas from Mindstorms (Papert, 2000).
The renewed interest in C&CT has motivated educators and researchers to work towards fulfilling
Papert’s dream (Resnick, 2017), and although Seymour has recently passed away, his legacy lives onin the 21st Century. His ideas have had a large influence on the design of Scratch, which is widely
available, free and commonly used in schools (Kafai & Burke, 2013). The Maker Movement, which
encourages learners to construct digital and physical artefacts that are personally meaningful to them,has also been influenced by Papert’s Constructionism (Blikstein, 2013). Despite the wide availability offree tools for learning C&CT, there are still some major challenges that educators and researchers arefacing when introducing C&CT in K-12. One of the main challenges is the preparation of teachers, as
C&CT is unlikely to have been part of their K-12 or tertiary education (Falkner et al., 2017; Yadav,
Sands, Good, & Lishinki, 2018).
To overcome the challenges that the introduction of C&CT in school curricula presents for teachers,
many professional development (PD) initiatives are being developed and implemented worldwide(Garneli, Giannakos, & Chorianopoulos, 2015). These initiatives focus on different aspects of C&CT
and typically provide a range of experiences and knowledge for teachers to take to their classrooms.
While there is general consensus that content knowledge (CK) is essential in order to effectively teach,
there is also general acceptance of the importance of pedagogical content knowledge (PCK), as
introduced by Lee Shulman (1986). PCK is widely used in the education literature as a means ofunderstanding the particular types of knowledge unique to teachers. This type of knowledge includes,for example, knowledge of student misconceptions about specific topics and how teachers might
respond to these and knowledge of effective analogies for illustrating concepts or ideas. PCK has beenstudied in the context of C&CT both in primary and secondary school settings (Angeli et al., 2016).
However, while PCK is recognised as being of crucial importance for effective teaching in C&CT (Saeli,
Perrenet, Jochems, & Zwaneveld, 2011), research about ways to understand and promote PCK in
C&CT is still in its infancy (Cooper, Grover, Guzdial, & Simon, 2014). There are still many lessons tolearn about providing appropriate and effective PD to pre-service and in-service teachers (Guzdial,
2015; Yadav et al., 2018).
Interestingly, in the special issue on ‘Constructionism and Creativity’ of the journal Constructivist
Foundations, Karen Brennan reflected, “I am often asked ‘What lessons have you learned from your[PD] work?’ I have to appreciate that my experiences and understandings are more aptly described as
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Constructionism 2018, Vilnius, Lithuania‘tensions negotiated’ than ‘lessons learned’” (Brennan, 2015, p. 293). In her article, she describes thetensions she has encountered when running the ScratchEd model of PD. Brennan (2015) provides
examples of when these tensions have occurred in each of the PD formats and explains the approaches
she has used to negotiate these tensions.
Our experiences running PD over the last six years have taught us what we thought were invaluablelessons about what effective PD is and how certain aspects of the PD can be evaluated. However, it
has become that apparent our lessons learned are much more like the tensions negotiated by Brennan(2015), and that our latest lesson learned is that we have not really learned any lessons - we have justdiscovered ways to negotiate tensions.
In this paper, we analyse the process we followed in the past six years to design, evaluate and improve
our PD through the lens of the tensions negotiated by Brennan (2015). Our analysis highlights theimportance of evaluating PD and developing metrics that can help us evaluate teachers’ learning during
and after the PD. As we have designed the most recent PD workshops with Constructionism as a“framework for action” (DiSessa & Cobb, 2004, p. 83), we often find that it is difficult to decide between
measuring the changes of teachers’ PCK and CK, and not measuring these changes. We contend thisdifficulty is a new tension that differs from those identified by Brennan (2015), which we call the tension
between anthropology and assessment.

Professional Development Design
As stated in the previous section, many PD initiatives have been established around the world toaddress the challenge of preparing educators for teaching C&CT in K-12 (Menekse, 2015). These
include Massive Open Online Courses (Falkner et al., 2017), face-to-face workshops (Menekse, 2015),
and the development of local hubs that provide teachers with support from peers, known as ‘Masterteachers’ (Sentance, Humphreys, & Dorling, 2014).
One notable example of a model of PD that has been designed to include constructionist learning
experiences is ScratchEd (Brennan, 2015). Initially, Brennan (2015) developed an online community foreducators to share their experiences teaching with Scratch. After the establishment of the online
community, Brennan developed Scratch educator meetups that are run for three hours on a Saturday
morning each month in Boston. Brennan (2015, p. 293) stated that the online community “cannot
provide constructionist experiences”, and, consequently, the meetups were intended to provide theseexperiences. Brennan also developed an online workshop titled the Creative Computing Online
Workshop (CCOW), which was available to teachers globally. The CCOW lasted six weeks and involved
a variety of activities, including the development and sharing of design journals. The ScratchEd model
combined these three formats of PD: the online community, monthly face-to-face meetups and theCCOW.
We have been developing and running face-to-face PD workshops at our university for six years. The
main aim of the workshops has been to help prepare teachers for teaching the Australian Digital
Technologies curriculum. In 2013 and 2014, the workshops were only available for High School
teachers, but in 2015 we began to include Primary School teachers. These workshops have involved avariety of sessions: activities with step-by-step instructions, collaborative problem-solving exercises andlesson-planning activities, and presentations by academics and industry representatives.

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Figure 1. Participants in 2017 workshop

The design and implementation of the workshops has evolved each year as a result of participants’feedback, which has been collected through validated surveys. As discussed later in the Tensions
Negotiated section, the feedback that we received in the surveys, particularly the feedback in responses
to the surveys’ open-ended questions, has influenced the changes that we have made to the PD. For
example, the feedback from our first workshop in 2013 indicated that there was too much theory andlectures in the sessions, which led us to begin the inclusion of more hands-on and constructionist
sessions in the PD in the years that followed. We have also been informed by the general PD literature,in which there has been extensive research about what factors are present in effective PD (for example,
Desimone, 2009). Unlike Brennan (2015), we have not had any online components in our PD yet, andwe would argue that face-to-face PD will always be valuable and essential. The availability of face-toface PD is particularly important for teachers who are only beginning to learn C&CT and have lowconfidence about teaching them, as argued by Sentance and Csizmadia (2017).

Tensions NegotiatedSix years after beginning the ScratchEd project, Brennan (2015) reflected on her experiences andobservations when running the different forms of PD and also analysed interviews with 30 of theteachers that participated in her PD. In her analysis, Brennan (2015, p. 294) concluded that her
experiences and understandings would be best described as “tensions negotiated”, rather than “lessons
learned” and described the five tensions that she considered to be the most pressing. She also
explained how these tensions were experienced and negotiated in the different formats of the ScratchEd
model.
Like Brennan (2015), we have sought to provide constructionist learning experiences in our PD, as we
believe that learning environments that are influenced by constructionist ideas should be encouragedin K-12. However, the format of the PD that we have run over the last six years has been very different
to the forms of PD in the ScratchEd model. Our PD has mainly been run as two-day workshops thathave been planned weeks or months beforehand and have not had an online component like theScratchEd online community or CCOW. Like Brennan, when we have reflected on our experiences
about running the workshops, we have often thought about the lessons that we have learned that could
be useful for other PD providers. But, also like Brennan, we have found tensions negotiated to be amore appropriate description than lessons learned. In the conclusion of her article, Brennan argues thatthe tensions she identified are not specific to Scratch or her ScratchEd model, and that they could be
scrutinized or critiqued by other PD providers.
In the following sections, we explain how we have experienced each of the five tensions identified by
Brennan when developing and running our PD. To determine how the PD was impacted by the different
tensions, we initially analysed the open-ended responses to the feedback surveys (n = 137) through thelens of the five tensions identified by Brennan (2015), by thematic coding the responses in NVivo 11.
We also reflected on our experiences during the PD and discussed how these related to each of thedifferent tensions. We found that teachers have been satisfied with our workshops, and some aspects
have improved as a consequence of responding to feedback. However, we found that this feedback
provided only insight on teachers’ general satisfaction with our workshops and, to a certain extent, theclassroom applicability of the PD content, but was not enough to give us a full picture of their learning.
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Also, our funding bodies have started to require measures of impact that go beyond self-reported selfefficacy outcomes. Consequently, we determined that we are experiencing a tension that was not
identified by Brennan, which we have called the tension between anthropology and assessment, after
the argument made by Papert (1980) that educators should study their learning environments asanthropologists.

Tension between tool and learning
The tension between tool and learning refers to balancing the PD’s focus between teaching about
concepts/tools (CK) and helping teachers create learning environments for students to use theseconcepts/tools (PCK). On one side of the tension is a focus on teaching a tool, such as Scratch, andthe essential concepts needed to use and understand that tool. On the other side of the tension are thepedagogical practices and different approaches to classroom activities, such as the “creative design
activities” described by Brennan (2015, p. 294).
In our workshops, we have conducted sessions with a variety of software and hardware tools and haveusually focused on imparting CK. The rationale for including a variety of tools has been to make teachersaware of the many options that they have for teaching C&CT at different levels of K-12. Each year, theteachers that have attended the workshops have taught at a variety of levels and subjects and,
consequently, we always try to provide a variety of options of activities that are relevant to them.
We have often focussed on imparting CK in the majority of the workshop sessions because manyteachers have not learned about C&CT during their K-12 or tertiary education and much of this CK islikely to be new to them (Falkner et al., 2017; Yadav et al., 2018). Consequently, we expected that theworkshops participants would not have much CK and that they primarily needed to acquire CK before
they acquire PCK. Although the workshop sessions always involve the teaching of certain tools, we tend
to have central concepts that we aim to impart in the sessions and theme the sessions accordingly. For
example, we titled the session that we introduce Scratch in, “Visual Programming with Scratch”. Duringthat session we explained what visual programing is, gave examples of how visual programming canbe included in K-12, and described visual programming tools that could be used instead of Scratch.
Despite the workshop sessions having had a concept as a central theme, we have found that teachers
usually responded to questions about applying the concepts learned during the workshops with answers
about introducing the tools to their students, rather than introducing the concepts or particularpedagogical approaches in their classes. For example, in response to the question “Do you think you
will you apply what you learned in the workshop? If so, what?”, many teachers responded with a list oftools, such as, “Yes, makey makeys, scratch at a deeper level than I've been teaching it and sphero”.
Comments such as this led us to include a combination of sessions that focus on a particular concept
and/or tool, and sessions that focus on particular pedagogical approaches. For example, in the 2017
Primary School workshop we introduced C&CT concepts in Scratch in a session on the first day andthen ran a session on the second day that focussed on teaching CT through design activities with theCreative Computing Curriculum guide (Brennan, Balch, & Chung, 2014), which has activities that use
Scratch.
Another difficulty encountered during our PD, which Brennan (2015) does not explicitly mention, andthat we consider to be encompassed in the tension between tool and learning, is the technical issues
we experienced when using certain tools. For example, we encountered technical issues when using
the MIT AppInventor software with Android tablets during the “Building Mobile Apps” activity, due to theway the network was configured at our university. Although we resolved these issues quickly during thesession, this troubleshooting detracted from the activity. Teachers may encounter these issues
themselves with their classes, so it could be argued that it is beneficial to include instruction on how totroubleshoot these issues in PD. Despite the difficulties faced, we believe that the tools that can bedifficult to use in some environments, are conducive to constructionist learning experiences.
Consequently, we intend to include more instructional time that addresses the potential technical issues,
so that teachers can troubleshoot these issues themselves and assist their students to troubleshootthese issues.

255Constructionism 2018, Vilnius, Lithuania

Tension between direction and discovery
The tension between direction and discovery refers to the balancing by instructors to provide guidance
and resources to learners, while also allowing learners to discover resources and concepts on theirown. This is similar to the “play paradox” defined by Noss and Hoyles (1996), which they used to referto the balancing of exploration and guidance when students are learning in a microworld (for example,
Logo).
When we design our PD, there are certain learning outcomes that we aim to address in each of thesessions, which align with concepts from the Australian Digital Technologies curriculum. These
concepts include what Brennan and Resnick (2012) call computational concepts, which include
concepts like sequencing and loops. Additionally, Primary School teachers have also been encouraged
to integrate C&CT across different subject areas (NSW Education Standards Authority, 2018), and toteach C&CT to assist development of students’ “general capabilities”, for example, Literacy, Numeracy
and Creative Thinking (ACARA, 2018). Our past PD workshops have been focussed on upskillingteachers’ CK and consequently many of the sessions’ planned learning outcomes have been related tothe essential computational concepts from the Australian Digital Technologies curriculum.
Brennan (2015, p. 293) designed the ScratchEd PD model with the main assumption that “teachers
should have learning experiences that are comparable to their students’ learning experiences”. Like
Brennan, we believe that if we are encouraging teachers to include constructionist learning experiencesin their classes, we should be including these experiences in our PD. In recent PD workshops, we havemade changes to provide more constructionist learning experiences, but we have often had to negotiate
the tension between direction and discovery when designing and running the PD activities. In pastevaluations of our workshops we found that teachers have enjoyed the sessions that had step-by-step
exercises more than the sessions that involved self-guided activities (Prieto-Rodriguez & Hickmott,2016). For example, one teacher thought “it was very useful to have printed sheets to follow…” during
the sessions. Conversely, some teachers responded to the surveys with suggestions to include more
self-directed activities and “problems to solve with minimal guidance on how to solve them”.
Consequently, we have had to learn to negotiate the balance of providing different types of instruction
or pedagogical approaches, as the teachers themselves are likely to do in their own classes.
In the 2016 and 2017 workshops, we began to include sessions that allowed the teachers to spend
more time on self-guided exploration and play. For example, in the 2017 Primary School workshop we
included a session themed around the Creative Computing curriculum guide and included the “10Blocks” activity from that guide in the session. In the “10 Blocks” activity, learners are asked to create a
Scratch program but are limited to 10 different types of blocks, which the instructor can learn about
before they run the activity. These types of activities can help teachers believe that it is “…ok to notknow everything as the teacher”, as stated by one of the survey respondents,
Another difficulty that we have experienced designing our PD, that we consider to be encompassed
within the tension between direction and discovery, is related to the limited amount of time for the PD.
In most of our workshops, 20-30 local teachers have attended six-hour long workshops for two
consecutive days. The workshops take place during the school term and at a local university, so theteachers have to be away from their classes during this time and their schools need to cover the costsof substitute teachers. Consequently, we plan many of the sessions prior to the workshop, in order tomake the best use of the time we have with the teachers, and this planning has often resulted in activities
that do give learners specific direction. We are aware of approaches to PD that solely involve selfdirected, constructionist activities, such as the four-day Constructing Modern Knowledge workshops run
by Martinez and Stager (2013). However, we have typically had 1-2 instructors during the workshops,
which would have made it difficult to provide guidance to 20-30 teachers working on open-ended
projects, and the two days has not been enough time to cover essential CK and to also run design
activities. There are also difficulties that are discussed in the tension between expert and novice andtension between actual and aspirational sections, which prevent us from only including self-directed,
constructionist sessions in our PD. To address this tension, we intend to run after-school sessions overa term in our PD. This structure would allow teachers to work on design projects during their free timeand for us to provide guidance during the sessions or through email.
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Tension between individual and group
The tension between individual and group refers to the challenge of facilitating productive collaboration
between the teachers that attend the PD. As Brennan (2015) argues, learners should be encouraged
to connect with each other and learners working in groups can learn new perspectives from one another.
We have not had to negotiate this tension to the same extent as Brennan, which could be due to thecollaborative and face-to-face nature of our workshops. There are a few aspects of our workshops thatallow for collaboration to occur with minimal intervention from us, such as introduction sections, meal
breaks, and collaborative hands-on or lesson planning activities.
One challenge, that we consider to be part of the tension between individual and group, is the sustainingof knowledge sharing and connections after the PD has been completed. Unlike the ScratchEd model,
we do not have an online community for our PD for teachers to share resources or discuss ideas in.
Additionally, teachers that have attended the workshops have usually been from different schools andit has been rare for more than two teachers from the same school to attend the same workshop. To help
address this challenge, we decided to design a workshop in 2018 that is focused on assisting
experienced C&CT teachers acquire the CK and PCK they need to establish local professional learning
communities (PLCs).

Tension between expert and novice
The tension between expert and novice refers to the tension between the teachers who are considered
to be knowledgeable about the content in the PD activities (the experts) and the teachers who have
only begun to learn the content (the novices). Like Brennan (2015), the teachers that have attended ourPD have had a wide range of CK and experience teaching C&CT. Some of the teachers that haveattended our PD were professional software developers before their teaching careers, whereas someother teachers had not learned anything about C&CT prior to our PD. However, the ways in which wehave experienced this tension seem to differ from (Brennan, 2015), which could also be due to thedifferences in the PD format. Brennan (2015) gave examples that were mainly experienced in theinteractions between the teachers when they were learning together. On the other hand, we ourselves
have experienced this tension when trying to choose planned learning outcomes, session themes andactivities that are appropriate for teachers that have varying levels of CK and PCK.
We consider the difficulty of providing resources and instruction that are suitable for a wide range ofteachers’ CK to be encompassed by the tension between expert and novice. We have encountered thisdifficulty since our first PD in 2013 and, consequently, negotiating this tension has been the precursor
to several of the major changes we have made to our PD. For example, prior to 2015 we had only run
one workshop per year, with activities that involved high school Digital Technologies concepts.
However, in 2015, we decided to run two different workshops, the Introductory and Advanced
workshops, which had activities that were designed to cater for teachers with different levels of CT CK.
This decision was a result of reflecting on our observations of teachers’ varying levels of CK and
responses to the feedback surveys in the previous years’ workshops. We have also given the teachers
the choice to participate in different activities that are suitable to their level of expertise, which are similar
to the breakout sessions in the Meetups described by Brennan. For example, in the 2016 High School
workshop, teachers chose between a session that introduced the teaching of Data Science in R and an
Introduction to General-Purpose Programming with Sonic Pi.
As we have often presented short talks before the activities in each PD session, we have often been
positioned as the experts during the PD. However, some of the teachers have had expertise in areas of
CK and PCK that we did not have. Consequently, we have invited some teachers to assist us whenrunning activities or present their own sessions, and also encouraged the expert teachers to share theirknowledge with the rest of the group during the PD. For example, in the 2017 Primary School workshop
we invited a teacher that had attended a workshop in an earlier year, to help us run a Collaborative
Lesson Planning Activity. That teacher had extensive knowledge about relevant K-6 curriculum
outcomes, that we did not have ourselves, and consequently was able to help the teachers map theirlesson plans to these outcomes.

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We have also encountered the tension between expert and novice when we have considered thechanges to make to the PD from teachers’ suggestions in the feedback surveys. Although we have runworkshops that have had activities that aim to cater for different levels of teachers’ CK, we still havereceived feedback that indicated we may have needed to differentiate the content more. An example ofthis was found in the feedback responses of the 2017 Maths workshop, which was intended to besuitable for teachers with some CK and some experience Coding in a Blocks-based language, like
Scratch. One teacher stated that the workshop content was “All well delivered and at right level”,
whereas another teacher responded that “As a beginner of coding, I found the pace of the coding
activities too fast.” Despite the changes we have made to the PD to address the challenge ofdifferentiation, negotiating this tension seems to be inevitable, as the teachers that have attended the
PD have always had a wide range of CK. As discussed in the tension between anthropology andassessment section, one way to address this would be to measure the teachers’ CK before the PD,
which could help us plan sessions that are appropriate for teachers with different levels of CK.

Tension between actual and aspirational
The tension between actual and aspirational refers to the difficulty of providing constructionist learning
experiences to teachers that they can replicate in their classes. Brennan (2015, p. 295) argues that“constructionist learning experiences are fundamentally at odds with the lived reality of K-12 education”
in many ways. However, we contend that teachers that have attended our PD would be able toincorporate some constructionist learning experiences into their classes, particularly in view of theemphasis that creative thinking has in the general capabilities of the Australian Curriculum (ACARA,
2018).
Unlike Brennan (2015), the changes to our PD have been largely been motivated by the challenges
reported by the teachers, which would be considered to be on the actual side of the tension between
actual and aspirational. The feedback from the teachers in our workshop surveys, particularly those in2013 and 2014, indicated that the teachers wanted resources that could be directly used in their classes,
such as lesson plans. Furthermore, the current consensus is that PD is effective when it is aligned with
the needs of the participating schools and involves collaboration with school administration (Desimone,
2009; Menekse, 2015). Therefore, we have often designed PD which has addressed the actual natureof K-12 education, rather than the aspirational nature of a more constructionist approach.
In recent years’ workshops, we have also included sessions that involved open-ended, constructionist
learning experiences such as the Creative Computing session in the 2017 Primary School workshop.
Although there is a climate of high-stakes testing in Australia, there are ways to introduce design
activities that align with outcomes in the relevant curricula. For example, our local state education
authority states that, “Designing, making, data collection and analysis” are part of the Science andTechnology subject area (NSW Education Standards Authority, 2018). Additionally, in the national
curriculum there are general abilities, such as Critical and Creative Thinking (ACARA, 2018), which
could be addressed through the inclusion of design activities in teachers’ classes.
One of the other challenges that we consider to be part of the tension between actual and aspirational
is related to the inclusion of C&CT into a curriculum that is already considered to be overcrowded(Polesel, Rice, & Dulfer, 2014). Ideally, teachers would have sufficient time to learn and teach C&CT in
addition to other subjects, but it is challenging for teachers to find this time. To address this challenge,
local educational authorities and researchers have recommended that teachers find ways to integrate
C&CT across existing subject areas (Barr & Stephenson, 2011; NSW Education Standards Authority,2018). One way we have found to negotiate this tension is the creation, in 2017, of two workshops thatwere focussed on integrating C&CT with mathematics: ScratchMaths for Primary School teachers andNetworks for High School mathematics teachers. The contents of these workshops were aligned withspecific curriculum outcomes, from mathematics and C&CT.

Tension between anthropology and assessment
One of the tensions that we have experienced, which was not identified by Brennan (2015), is thetension between assessing the teachers’ CK and PCK and restricting our evaluation to quality
assurance and self-reported measures of self-efficacy or impact. Brennan (2015, p. 295) does not
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mention the assessment of the teachers during the ScratchEd PD directly but does state that there is a“lack of meaningful metrics for assessment and evaluation” when defining the tension between actualand aspirational. However, we contend that there are metrics for assessment of CK and PCK that canbe meaningful and appropriate for suitable contexts and, consequently, we consider the tension
between anthropology and assessment as separate to the tension between actual and aspirational. We
also recognise that the formats of different PD in the ScratchEd PD may be more difficult to assess thanworkshops. For example, it could be impractical to assess teachers’ CK and PCK before and after abreakout session in one of the ScratchEd Meetups.
Although the teachers that have participated in our PD have not suggested that we include assessment,
we contend that there are two main reasons why a more rigorous evaluation of CK and PCK would be
beneficial. Firstly, in order to improve the effectiveness of our PD, we need to know whether we have
had a positive effect on teachers’ CK and PCK. Presently, we base improvements to our activities basedon self-reported measures given by participating teachers. Secondly, organisations and governments
spend significant funds on PD and, consequently, there is a need to identify whether the PD has had apositive impact on teachers’ classroom practices beyond teachers self-reporting. For example,
researchers from Google, who have funded the CS4HS (Computer Science 4 High School) program,
have recently begun investigating the long-term impacts of their programs on teachers’ self-reported
knowledge of C&CT and their beliefs about teaching C&CT (Ravitz, Stephenson, Parker, & Blazevski,2017).
One of the challenges that we have encountered, that we consider part of the tension between
anthropology and assessment, is the difficulty of identifying appropriate instruments to measureteachers’ CK and PCK. There have been instruments developed for assessing understanding of CK,
but much of this work has been done in tertiary education (Guzdial, 2015), and these instruments may
not be appropriate for measuring the CK that K-12 teachers need. In his review of PD studies conductedin the United States of America between 2004 and 2014, Menekse (2015) reports that seven of thestudies involved some assessment of teachers CK. However, a deeper reading of these studies
uncovered that only three of them involved measurement of CK that was not self-reported. Furthermore,these three studies used instruments that were developed by the authors or by an unidentified source,and did not report reliability of scales. There is also currently limited research into the assessment of
PCK specific to C&CT (Saeli et al., 2011; Yadav et al., 2018). A form of PCK, which is referred to as
Computational Pedagogical Content Knowledge (CPACK), was present in one of the studies reviewed
by Menekse (2015). However, in the study in which Yasar and Veronesi (2015) introduce CPACK, they
do not report measures of teachers’ CPACK or include details on how data can be collected or analysed
to measure teachers’ CPACK. Similarly, while the research conducted by Yadav et al. (2018) examines
teachers’ PCK through teaching vignettes, their methods do not include quantitative measures.

Discussion and Further Research
In this paper, we have analysed our PD through the framework of the ‘tensions negotiated’ defined by
Brennan (2015). In this section, we summarise our findings and discuss the implications for PD design.
We also outline our plans for future research.
To negotiate the tension between tool and learning we have found it is helpful to include a balance ofactivities that address both tools and learning, as we recognise that both CK and PCK are essential.
We have also identified potential technical issues that can be encountered when using the different
tools, and believe it is important that teachers are made aware of these issues.
To negotiate the tension between direction and discovery we have observed it is valuable to include
activities where teachers can explore concepts without direct instruction from a resource or instructor,
such as the 10 Blocks activity. We have also found that it is beneficial to provide teachers with choicesof activities in the form of parallel sessions, or to let them choose their own direction for learning andthen offer instructional support. In response to survey feedback, we have included sessions where
teachers worked together to create an artefact unaided, such as a unit plan, or sessions where teachers
were given a problem to solve with minimal guidance. These sessions were very well received insubsequent years.
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To negotiate the tension between individual and group we have included an increasing number ofcollaborative activities, such as collaborative lesson planning and problem solving. For our PD designfor 2018, we have planned to train and support teachers who are interested in establishing PLCs. These
PLCs could encourage the sharing of knowledge and collaboration between teachers after they haveparticipated in the PD. The development of the workshop was influenced by the work to develop Master
Teachers in England (Sentance et al., 2014).
To negotiate the tension between expert and novice we observed that it is useful to design extension
activities for the teachers that complete the PD activities quickly. We have differentiated the activities tocater for different levels of CK within workshops and created different workshops to accommodate
different PCK needs according to the level of schooling that participants teach at. We also found that
inviting expert teachers to share their experiences implementing C&CT with their classes was highly
regarded by all participating teachers.
To negotiate the tension between the actual and aspirational we have started running workshops that
address existing subject areas and specific curriculum outcomes. It is assumed that the teachers
participating in these workshops already have subject knowledge in that domain, and that they would
be able to assess those subjects already. These workshops are narrower in scope but provide greater
depth of learning. We have made changes to the PD that are intended to address the aspirational
aspects of constructionism as well. We have found these changes have provided teachers with skills
and resources that can help them introduce design activities to their classes, such as the Creative
Computing curriculum guide (Brennan et al., 2014). Another PD project planned for 2018, which may
address this tension, involves running the PD over a longer period of time. This could allow for longerterm open-ended projects that teachers can, in turn, develop with their students.
The new identified tension, the tension between anthropology and assessment, extends the tensions
negotiated by Brennan (2015). Although there are arguments for measuring outcomes in our PD, as
explained in the previous section, it could also be argued that PD with constructionism as a framework
for action should not involve any assessment of learning outcomes. Papert and the MIT Logo groupwere known for being opposed to standardised testing (Agalianos et al., 2006), and Papert (1987)
argued that focusing on assessing outcomes could lead to technocentrism. However, there areexamples of constructionist research that involve the collection of quantitative measurable learning
outcomes, including the study of children designing instructional mathematics games conducted byHarel and Papert (1990). Harel and Papert (1990, p. 10) referred to these outcomes as “thinner results”
because, while they could be used to assess the students’ performance, they are not as in-depth andrich as the qualitative data collected in the study. While we agree with this sentiment, the analysis ofquantitative data helps us understand what we can do to improve our PD. Furthermore, the reporting ofquantifiable measures helps address the concerns of funding agencies and government bodies
regarding the impact of our PD on teachers.
We believe that learners, whether they are teachers or students, should be encouraged to explore ideas
through the self-directed creation of artefacts that are personally meaningful to them. However, as Nossand Hoyles (1996) argue, this does not mean that we should not plan for learning outcomes or that
there should be no assessment of these outcomes. Thus, we need to negotiate the tension between
acting as anthropologists and assessing teachers’ learning in CK and PCK with quantitative measures.
Our next PD workshops will integrate our desire for authentic constructionist experiences and the desire
to improve these experiences by ensuring that the PD has had a measurable positive effect on teachers’
CK and PCK.

Acknowledgements
The authors would like to thank Google Inc. for funding the CS4HS workshops held at our institution.
We would also like to thank the university academics who presented talks and gave laboratory tours
during the workshops, as well as the administrative staff of our institution.

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Sharing is Caring in the Commons – Students’
Conceptions about Sharing and Sustainability in
Social-Ecological Systems
Arthur Hjorth, arthur.hjorth@u.northwestern.edu
Center for Connected Learning and Computer-Based Modeling, Northwestern University, USA

Corey Brady, corey.brady@vanderbilt.edu
Department of Teaching and Learning, Vanderbilt University, USA

Uri Wilensky, uri@northwestern.edu
Center for Connected Learning and Computer-Based Modeling, Northwestern University, USA

Abstract
In this paper, we present the design and analysis of a high school environmental science participatory
simulation-based activity about sharing natural resources. In particular, we focus on this activity’s utility
in surfacing students’ diverse ways of thinking about Social-Ecological Systems, offering entry points
for Constructionist design. The activity was implemented as part of a three-week unit in anenvironmental science AP class. At the core of the activity was a participatory NetLogo simulation inwhich students played the role of dairy farmers. Through their interactions with the simulation andcollective and individual decision-making, students struggled to reason and argue productively about
the difficulties involved with sharing natural resource systems.

The virtual grazing ground and accompanying data interface gives students insights into how their individual andcollective decisions affect the ecosystem.

We gave students two written assignments, in which they were asked questions about true, historicaldescriptions of communities sharing natural resource systems. Here we analyse the responses,
identifying four distinct ‘thinking patterns’ across student responses. We discuss how these four patterns
were productive in so far as they helped students to reason about the historical cases, but also how
these patterns in their thinking restricted them from thinking productively about the full nature of thecase studies. Finally, we discuss how future designs might address the less productive aspects of thesepatterns.

Keywords
NetLogo; agent-based modelling; HubNet; participatory simulation; social studies; complex systems

Abstract
This paper presents data from a high school implementation of a unit on common-pool resource sharing
dilemmas in Social-Ecological systems. We designed and implemented a computer-simulation-based
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classroom activity in a high school Environmental Science course. Students took on the role of cattle
farmers who had to maintain and share a virtual grazing ground and prevent a “Tragedy of theCommons.” We present an analysis of students’ responses to questions about how best to coordinate
collective action and ensure sustainable utilization of the commons. We identify four patterns instudents’ thinking and discuss how these patterns were simultaneously productive and a hindrance toreasoning about different aspects of this complex problem. Finally, we discuss the impact of our findings
on our own iterative design-based research, as well as wider implications for future learning design andresearch on social-ecological systems and sustainability.

Introduction
The United States’ National Council for the Social Studies’ recent C3 framework (NCSS, 2013) proposesa new set of standards for Social Studies, including the use of computer simulations to test andunderstand the effects of policies and collective action. Despite decades of using computer modellingin science education (Wilensky & Jacobson, 2014), there have been relatively fewer applications insocial studies classrooms. In this paper, we present data from a 3-week high school classroom
implementation of activities focusing on the challenge of sustainable food production and featuringcomputer-based network-supported participatory simulations, or PartSims (Klopfer, Yoon, & Perry,
2005; Wilensky & Stroup, 1999b). Our core PartSim activities took Hardin’s (1968) seminal Tragedy of
the Commons paper as a point of departure. Hardin argues that collectively shared resource systems
are doomed to end in “tragedy” because individual actors within the system will act in their short-term
interest to the detriment of the collective. However, Elinor Ostrom’s Nobel Prize-winning work
(Brondizio, Ostrom, & Young, 2009; Ostrom, 1990, 2007) has shown that while true under certain
conditions, other conditions make it possible for communities to sustainably share and maintain
resource systems. In such contexts, collectives can work to discover and sustain solutions to commonpool resource sharing problems. Ostrom and colleagues thus refer to a drama of the commons (TheDrama of the Commons, 2002), which may end either in “comedy” or in “tragedy” depending on policies
and practices adopted by participants.
Given the societal importance of the sustainable use of natural resource systems like oceans, aquifers,
and populations of fish or game, it is critical to prepare students to envision and engage the potential
impact of policies oriented towards addressing the drama of the commons. In this paper, we present ananalysis of a study in which we designed and implemented a 3-week PartSim-based unit on sharing
natural resource systems in a high school Environmental Science class. Our focal PartSim activities,
and our unit as a whole, gave the classroom group the opportunity to role-play a village of cattle farmers.
Together, they collaboratively explored possibilities for sustainable growth and for collectively
maintaining a “virtual commons” in a simulated world. Our analysis focuses on students’ written
responses to two sets of questions on this topic. We identify four patterns in student thinking, explain
how they both helped and hindered students in making sense of the topic, and discuss why futurelearning design and research would benefit from addressing these thinking patterns.

What is hard about sharing
Following Hardin (1968) and Ostrom (1990), we can think of the difficulties of sharing a resource systemfrom the perspective of an individual, and from the perspective of an individual’s larger community. Fromthe perspective of an individual, the dilemma is a classical free-rider problem: the long-term benefits ofcollaboration are shared between all community members, whereas “cheating” benefits only theindividual cheater. This leads to an individual-centred logic which dictates that cheating creates a net
gain for the cheater. From the community’s point of view, the difficulties of sharing stem from the very
nature of conditionally self-replenishing resource systems:
Resource systems can best be thought of as stock variables that are capable underfavorable conditions of producing a maximum quantity of a flow variable without harming
the stock or the resource system itself. (Ostrom, 1990, p. 30)
To exemplify, let us imagine a shared fishing pond: the stock variable is the amount of fish in the pond,
and flow variable is the amount of fish being “produced” in the pond (through reproduction). Favourable
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conditions include natural or anthropogenic factors like weather or nutrition levels in the water that affecthow much flow the system produces. Finally, the last part of the quote alludes to the fact that thesesystems are often fragile and can be harmed or even driven to collapse if not cared for properly. The
challenges facing communities who depend on sharing sustained access to these systems are then
two-fold: First, how does a community collectively manage a system in order to create and sustain theset of ‘favourable conditions’ that optimize the amount of flow resource? This difficulty is exacerbatedby the fact that the cost to maintain favourable conditions sometimes cannot be spread easily amongcommunity members. (For instance, while the community could decide to share the costs associatedwith feeding the fish, the time-cost involved in physically feeding the fish or measuring current nutritionlevels will fall on one or a few individuals.) Second, and related, how does a community set up a system
for sharing the produced resource that makes individual community members feel that their efforts are
rewarded fairly? This difficulty involves a number of social challenges: for instance, establishing ameans to ensure compliance with group norms that enables members of the community to “rest easy”
that their fellow citizens are not cheating.

Research questions
In this paper we offer a preliminary analysis, through which we aim to better understand how students
reason about communities that share resource systems. We ask,
1. How do students reason about the problems facing communities who rely on shared natural
resource systems?
2. What kinds of community rules do students imagine would help address these problems, andwhy?

Design, Implementation, and Data
The study presented here emerged from the first year of a multi-year design-based research project(Cobb, Confrey, Lehrer, Schauble, 2003) in partnership with a classroom teacher at a suburban highschool in the US Midwest. Together, we collaboratively designed a unit to run over 15 periods in a highschool Environmental Science classroom. The 22 students in our partner teacher’s class wereconsented, and all chose to participate in the study. In addition to fitting into the larger themes of thecourse, we designed the unit to target the NCSS’s standards for social sciences (NCSS, 2013), and theNGSS science standards (NGSS Lead States, 2013), focusing on the use of computer simulations of
ecological systems, to test the viability and effectiveness of policy interventions and collective action.
Seven periods out of 15 during the first two weeks of implementation revolved around PartSim activities
and are the focus of this study.

The Virtual Commons Sharing Activity
PartSims are socially shared computer-mediated simulations in which a group of students takes on therole of agents in a system whose aggregate behaviour emerges in real time as the students interact. As
such, PartSims provide a means for a group of learners to experience a phenomenon from both themicro-level (as individual participants) and the macro-level (as a collective group experiencing the
emergence of these outcomes). PartSims have a long history in the design of Constructionist learning
activities, and have been used to teach topics as diverse as chemistry (Brady et al, 2015), geography
and policy decision-making (Gilligan et. al, 2015), and as a tool for teaching complex systems principles(Brady et al, 2017; Guo & Wilensky, 2016).
From a Constructionist perspective, the purpose of a HubNet activity is to give a group of students ashared, manipulable object with an underlying set of complex systems interactions, and a sociallymeaningful purpose for these manipulations. Our PartSim was programmed in NetLogo (Wilensky,1999) using the HubNet (Wilensky & Stroup, 1999a) architecture. The purpose of the PartSim was togive students the experience of sharing a “virtual commons” with collective responsibilities. The Virtual
Commons HubNet activity began on day 3 of the unit. During the first two days, we ran two activities
focusing on the Tragedy of the Commons. In the first, a non-virtual activity, students had to share a265Constructionism 2018, Vilnius, Lithuania

fishing ecosystem consisting of candy fish. In the second, students had to share a grazing ground in a
HubNet NetLogo model. Both of these activities were designed with Hardin’s original constraints inmind: students were not allowed to speak or coordinate with each other, nor were they permitted toshare responsibilities. In both of these activities, students inevitably “crashed” the ecosystem andenacted the Tragedy of the Commons.
In contrast, and reflecting an important difference between Hardin’s thought experiment and Ostrom’s
empirical research, our subsequent Virtual Commons PartSim was designed to give students incentives
to work together to maintain their resource systems: Students played the role of dairy farmers who relied
on a shared grazing ground. For each “week” (turn) of the activity, students held a town hall meeting inwhich they assessed the state of the commons by looking at data together, and agreed on what tasks
would need to be done in the following week as well as, by whom.

Figure 1. Each student’s private view of the Virtual Commons PartSim model.

Each student could take on one of four tasks (see Figure 1): (a) spreading fertilizer (accelerating the
regrowth of grass on the commons); (b) repairing fences (ensuring that the village’s cattle do notescape); (c) herding their own cattle (increasing their own milk production for the week); or (c) monitoring
their peers (confirming that they were doing what they promised to do). Importantly, the simulation
allowed students to “cheat” – by promising to engage in one task, while secretly doing another. This
made monitoring an important task, as monitors enabled the village to catch cheaters.
During the town hall meetings, students gathered around the shared view (Figure 2), which displayed
relevant information, including the number of cows on the field, the amount of grass on the grazing
ground, the total milk production, the Gini coefficient for the farm community, and the general state of
repair of their fences. Based on these data and their shared experiences up until this point, students
discussed the dilemmas they faced, both as individuals and as a group, and devised means to address
them through collective action. In addition to pursuing collective farming goals, they also conducted“experiments” to learn information about their environment. These included varying the number ofpeople who spread fertilizer (to assess how quickly grass grew back on its own or to estimate “carrying
capacity”) or repaired fences (to assess the rate at which fences deteriorated).

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Figure 2. Students' collective view of the grazing ground.

Our aim with the Virtual Commons PartSim activity was to give students a contrasting experience to theTragedy of the Commons, so that they might come to understand problems facing sustainable sharing
as well as to explore the viability of potential solutions. By exploring the aggregate outcome of theircollective choices, students experienced the potential problems that commons-sharing communities
face, while also confronting the strengths and weaknesses of different solutions. We hypothesized that
this activity would support students in reasoning about other, similar, situations in which social
collectives share a commons.

The Elicitation: Two Real Cases of Resource Sharing
At the end of each of the three weeks of the unit, students were asked to respond individually toquestions about written case studies in which communities share resource systems. These assignments
were given as homework over the weekends, and questions were distributed via Google Forms. The
data presented in this paper come from the first and the second of these assignments. As mentioned
above, our purpose analysing these data is more to identify patterns in students’ ways of thinking about
resource systems, than to argue for the effectiveness of our unit in creating conceptual change orlearning in this area.
These two homework assignments presented students with historical cases from Ostrom’s (1990) work.
We decided to use these two cases for a variety of reasons: first, because Ostrom’s own treatment ofthem provided us with an “expert analysis” with which we could compare student responses. Second,
both these cases took place years ago before technological innovations would offer easier solutions tosome of these problems. By taking out technical solutions, we forced students to reason about the socialand ecological aspects of the dilemma, instead of coming up with intricate technical solutions likesatellite surveillance or GPS tracking. Finally, these cases involved self-replenishing but fragile resource
systems with similar features to the one that students experienced in the Virtual Commons, but werestill different enough that we could see whether students would reason about these systems by drawing
on their experiences from the Virtual Commons.
The first case described a community of farmers outside of Valencia, Spain who shared an irrigation
canal system in the Middle Ages. Maintenance of the canals relied on the coordinated effort of many
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people and more resources than any one farmer could afford. Additionally, the amount of waterfluctuated each year due to differences in precipitation.
The second case described a community of fishermen in Sri Lanka living by a bay, whose livelihood
depended on the use of large seine nets. These nets were so costly that seven families typically had toco-own each one, and so large that only one boat could fish in the bay at a time. The nets, then, posed
both financial and logistical constraints, forcing people to cooperate. In addition, maintaining the healthof the bay (a fragile ecological resource system) was yet another factor requiring community
collaboration.

Analysing the Responses
The questions (9 questions for the first case and 8 for the second), probed student thinking about thedifficulties that these communities might face, and asked students to think about what rules thecommunity could instate to address the difficulties, and probed their thinking about why those rules
would help. We changed the number of questions between the two cases as part of our iterative design
process, because a preliminary look at student responses after the first week of implementationsuggested to us that two of the questions overlapped in a confusing manner. Our second iteration ofquestions collapsed these two questions into one and rephrased the prompt.
Because we were still in the exploratory stages of our research, we took an open-ended approach tocoding students’ responses. We used “ways in which students identify challenges at the community or
individual level” as “sensitizing concepts” (Miles & Huberman, 1994) in our initial coding. The first two
authors coded all student responses individually, and we then converged on four interesting patterns
that both researchers noticed across many student responses.

Findings
In this section, we will describe four patterns in students’ thinking that emerged from our open-ended
analysis, and that we believe future learning design and research should focus on. We describe eachpattern and how it relates to the specific details of each of the cases; provide examples of student
responses that exemplify each pattern; and, finally, discuss how each pattern seemed to participate in
the broader reasoning of the students who exhibited it. These patterns proved to be two-sided in theireffects on student reasoning about common-pool resource sharing. On one hand, they acted as
productive tools to support students in thinking and in articulating ideas; on the other, they seemed toforeground particular aspects of sharing dilemmas at the cost of backgrounding other features, thus
tending to limit student thinking in some ways.

The “Fixed Flow” Pattern
We found that when asked to identify a potential tragedy of the commons in each of the two cases, itmany students reasoned primarily about short-term aspects of sharing the flow resource. In contrast,
very few students mentioned long-term, stock-related aspects relating to optimizing the yield of thesystem.
For example, consider this typical flow-focused response to Case 2 (fishing):
By cheating […] that group would get the most fish and […] affect the other groups by forcing
them to split a smaller number of fish and depriving them of equal opportunity. (S12)Compare this thinking with the following typical stock-focused response, also to Case 2. (Note: Hereand elsewhere students’ spelling and grammar are maintained.)
If one of the fisherman caught more than assigned then the it effects the rest of thecommunity because thre wouldn't be enough fish to reproduce and have enough for next
‘harvest.’ (S21)Interestingly, both students are talking here about overfishing. But the function of overfishing is different
and reflects the distinction between fish as stock and fish as flow. In the former response, the student
identifies the problem with overfishing as there being an immediately lower number of fish to split
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between the rest of the fishermen at that moment. In the latter, the student identifies the problem withoverfishing as diminishing or threatening future yield.
This illustrates the importance of thinking about both stock and flow. However, we often saw thatstudents focused only on flow. In Case 1 (irrigation), 21 students brought up potential flow-related
problems with people taking too much water and not leaving enough for the rest of the community, and
14 students brought up potential rules to address these problems. In contrast, only 10 students
mentioned stock-related problems, and only four students mentioned solutions to them. Likewise, inCase 2, 20 out of 22 students mentioned potential flow-problems - relating to taking more than one’s
share by fishing too much - and 12 brought up rules to prevent them. In contrast, only 3 student
responses brought up problems relating to stock – the maintenance of the system and its ability to
produce future fish – and only 2 identified potential rules to address these problems. Moreover, those
who did reason about long-term stock issues were very likely also to reason about short-term flow
issues, but not vice versa: of the 10 students who mentioned stock problems, 8 also mentioned flow
problems.
We call the thinking pattern that attends to flow-aspects at the expense of stock-aspects the “fixed flow”
pattern, because responses exhibiting this pattern treat the shared resource as an invariant quantity offlow – water in the canal, or fish in the bay. That is, this thinking pattern attends to “fair sharing” of theflow, possibly making a hidden assumption that the group’s access of the resource will not damage thesystem’s capacity to produce future flows. This pattern of thinking can be problematic if it prevents
students from considering how to preserve the “favourable conditions” that optimize the long-term
availability of flow resources to be shared, either through maintenance of the stock or the system’s
infrastructure.

The “Social, Not Ecological” Pattern
We also observed a pattern of thinking in student responses that emphasized solving the social
problems of freeloading, at the expense of considering ecological problems. Students’ responses
exhibiting this pattern often assumed that if the community managed to collaborate, then everything
would be fine. For example, consider the contrast between this response to Case 1:
If the farmers collaborate, they ensure some level of food security and economic security.
(S7)…and these responses (to Case 1 and Case 2, respectively):If they collaborate, each farmer will have enough for his on family. (S16)
If the fishing groups cooperate then they will each get some fish and there will be asustainable population (S20)
In the first response, the student explicitly reasons that while collaborating will ensure some level offood security, it will not absolutely guarantee it. In the last two, the students seem to assume that if the
farmers or fishermen prevent each other from cheating, everything will be fine. But there are no
guaranteed ‘happily ever after’ scenarios in the commons, even for communities that share resources
equitably. Even if everyone gets their fair share, a community can run out of fish or grass if they do notsolve the long-term, yield-related stock problems. Because of its emphasis on the social dimension of
the sharing dilemma, we described this pattern as a “social, not ecological” way of thinking. We saw thispattern in 9 responses in Case 1, and 8 in Case 2, with four students exhibiting the pattern in both their
responses to both cases. These responses suggest that students may forefront the social coordination
problems without taking into account the ecological dimensions of the system. While the social side ofproblems is important, it cannot stand on its own, and future design iterations of our unit will try toforefront the ecological side more in an attempt to address this pattern.
The “Social, Not Ecological” pattern and the “Fixed Flow” pattern share similarities in the features ofcommon-pool resource sharing that they background. In particular, both are focused on concerns about
equal access to flows at the expense of questions about the sustainability of stocks. However, for us
they represented different ways of thinking, because of the way they engaged with social dynamics andnorms. As we seek to engage groups of learners in broadening their perspectives, these ways of269

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thinking represent different entry points and leverage points for the design of activities and learning
environments.

The Profit and Competition Pattern
As we have noted, our student responses focused on short-term, flow-based problems, with almost all
students in the population mentioning these challenges. One pattern in students’ characterization ofthese problems had to do with an interpersonal source or motivation for cheating in the commons. Thispattern grounded reasoning about why people cheat in an implicit or explicit belief that people in groups
are competitive, such that community members compete over the commons and attempt to generate
the most individual profit. Responses exhibiting this pattern focused on motivations for cheating thatpertain to people’s desires rather than their needs. Consider these three responses:
Farmers would [cheat] to get ahead and water more and more crops to make money. (S12)and,They want to produce the most and be the best farmer. They want to be known as having
the best most consistent product. (S6)and,By cheating in a way like going out to fish earlier than everyone, that group would get themost fish and be the most wealthy and profitable. (S13)
Twelve responses to Case 1 and twelve to Case 2 brought up a competitive profit motive for cheating,with 7 students responding in this way to both cases. However, Ostrom’s research shows that
community members may cheat for a wide variety of reasons beyond the desire to profit over others.
For instance, community members experiencing temporary financial or health-related difficulties may
cheat out of necessity. Alternatively, community members may feel an incentive to cheat if they perceivethat others are cheating and getting away with it. Thus, preventing cheating can be a very complex
matter. But students exhibiting the “profit and competition” pattern tended to reduce this problem to aninterpersonal competitive dynamic.
Ostrom’s work suggests that designing collaboration rules for preventing cheating requires a deep
understanding of the underlying reasons for why people cheat, as these motivations are what the rules
must target. Thus, while the “profit and competition” pattern may be productive for reasoning about
preventing one kind of cheating, it may draw students’ attention away from solutions to othermanifestations of flow-sharing problems.

The “Fixed Human Nature” Pattern
Another prominent pattern in student responses that addressed flow-sharing problems appeared to be
based in an image of human nature as having essential qualities independent of context or situation.
This pattern was distinguishable from the “profit and competition” pattern in which the competitive nature
was seen as coming out of the interactions between people. In contrast, in the “fixed human nature”
pattern, students downplayed the impact of social arrangements and conditions in preventing cheating,and instead saw rule-breaking as inevitable. Humans (or some types of humans) were seen as
essentially predisposed to pursue antisocial behaviour that would benefit them individually. We provide
examples of this pattern of thinking from responses to Case 1.
In some instances, students posited essentialized features of human nature in particular individuals ortypes of individuals within the broader population. For example, the following response indicates a belief
in the existence of an antisocial element:
A few rotten apples out of the farming bunch may misuse the water and everyone's fields
would collapse. (S10)
In other responses, students expressed essentialized understandings of human nature in general, or
they bridged from behaviours exhibited by individuals to traits of all people. Consider the following
response:
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Certain farmers will always ask for more than they need due to the selfish nature of humans.
(S22)
Here, while the phrase “certain farmers” suggests a conception about a subset of the population, “theselfish nature of humans” points toward a more general human trait. Similarly, in reflecting on the impact
of variability in the flow resource of water in Case 1, another student remarked:
I think this because people worry about themselves first and foremost, then secondly comesthe idea of sharing. (S18)
Under this conception of human nature, it is possible to make context-independent statements about
how humans will behave and interact. This pattern in student thinking stands in contrast to an alternative
conception of human nature, which holds that it is malleable, and that human behaviour is highly
context-dependent. A “fixed human nature” pattern tends to underestimate the potential for policies,
rules, and information to alter collective patterns of action. Thus, this pattern may hinder students’ ability
to conceive of creative responses to the dilemmas and challenges that constitute the “drama of thecommons,” limiting their capacity to imagine solutions for sustainable common-pool resource sharing
and management.

Discussion, Limitations, and Conclusion
This paper has presented an analysis of high school students’ reasoning about communities sharing
natural resource systems as part of a 3-week unit on sustainable food production featuring participatory
simulation activities. Based on our analysis, we identified four patterns across students’ responses.
These patterns proved to be productive for students to reason about some aspects of the cases, but we
speculate that they also hindered students in reasoning about other important aspects.
We have taken a design-based research approach to the iterative construction and implementation ofthis unit, and the design changes to our subsequent iterations have aimed to better identify and address
the problematic aspects of these thinking patterns. We have implemented the activity sequence
analysed here two additional times, including the following refinements, responsive to our analysis ofpatterns of thinking.
In order to address the “fixed flow” pattern, our current design gives students increased access to thedata produced by the model in order to provide the classroom group with opportunities to explore how
their behaviours, both as individuals and as a community, effect changes in the “size of the pie” fromwhich they are taking equal or unequal shares.
To address the “social, not ecological” pattern, we have made two changes to the simulation itself. First,
we reduced the carrying capacity of the system, and second, we have sped up how quickly thesimulation runs. Both of these changes increase the likelihood that student villages will experience acrash, even with a modest number of cows. Our aim with this is to let students discover that even a wellcoordinated community can experience crashes because of scarcity.
Finally, in order to address the “profit and competition” pattern and the “fixed human nature” pattern, we
have developed a set of activities within and around the PartSim experiences, in which students runcollective self-defined “experiments” in and on their commons. While the groups spontaneously thoughtof some of their actions as providing information about their environment even our first-year
implementation, our subsequent design refinements have amplified this tendency. Data from theseexperiments have allowed student groups to make better decisions about how many cows should beallowed to graze, and how many people should repair fences or fertilize the grazing ground each week.
In addition to serving the more general learning goal of using computer simulations and data to makedecisions, our hope with these activities is that students can experience both the challenges andbenefits of working together to manage shared resources and to gauge their success in doing so.
An important limitation of our study is that our sample of students comes from a predominantly white,private, suburban, parochial high school from which 99% of graduates go to college. Importantly, thisstudy focuses on reasoning about sharing, and we believe that these students who often come fromvery high-resource homes will have a particular set of experiences of sharing as a result of their271

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upbringing, not representative of the high-school aged population as a whole. To address this limitationin the makeup of our population, we hope soon to implement our unit in other socio-economic settings.
In analysing student response data, we identified substantial variation not only across students but also
within students across the two cases that students reasoned about. However, it is important to keep inmind that the two cases are quite different, exhibiting distinctive features of the natural resource systems
that communities share. In order to tease apart (a) how differences in students’ responses to the cases
stem from genuine differences or changes in thinking about the cases on the one hand, from (b)substantive differences between the social-ecological systems in the cases on the other hand, we are
varying the order in which students experience each of the two cases.
Finally, we believe that participatory simulations offer a particularly powerful approach to learning aboutcommon-pool resource sharing dilemmas in a classroom setting. PartSims give students the dual
perspective of individual and group in experiencing social-ecological dilemmas and allow them toengage in deliberation about resource systems sharing. Given the importance of this topic to the survivalof our species and planet, we feel that collective resource systems sharing and maintenance is an
important area of focus for education research and design. In this paper, we have presented what we
see as early educational research on this topic.

ACKNOWLEDGMENTS
We are grateful for the support from Loyola Academy in Wilmette, IL, USA and all students who
participated. The authors gratefully acknowledge the support by NSF Grant #1441552. Any opinions,findings, and conclusions or recommendations expressed in this material are those of the authors anddo not necessarily reflect the views of the NSF.

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Urban Planning-in-Pieces: A Computational
Approach to Understanding Conceptual Change
and Causal Reasoning about Urban Planning
Arthur Hjorth, arthur.hjorth@u.northwestern.edu
Center for Connected Learning and Computational Modeling, Northwestern University, USA

Uri Wilensky, uri@northwestern.edu
Center for Connected Learning and Computational Modeling, Northwestern University, USA

Abstract
In this paper, we present a computer-simulation-based classroom unit designed to improve students’causal reasoning about the effects of urban planning. Based on data collected in three separate
implementations of this unit, we use Association Rule Mining (ARM) to assist in Knowledge Analysis(KA) of students’ responses to pre- and post-questions. We first define causal-nodes as a construct,and then qualitatively identify distinct causal-nodes in students’ responses. We then validate thesenodes quantitatively within and between students’ responses. Finally, we compare changes in theassociation rules between these causal-nodes between pre- and post-responses in order to findchanges in students’ explanations. This paper makes two distinct contributions: first, it shows a
productive use of an existing computational method to aid in the analysis of conceptual change. Second,
it contributes towards better understanding how to design for and analyse causal reasoning in social
science education.

Keywords
knowledge analysis; netlogo; social studies; complex systems; causal reasoning; computational
analysis; computational methods

Causal Reasoning in the Social Studies
The College, Career, and Civic Life (C3) Framework for Social Studies State Standards (NCSS, 2013)presents a new framework for K-12 social studies, emphasizing ‘student explanations’ and ‘complex
causal reasoning’ in Social Studies. However, in contrast to a similar move in science education (NGSS
Lead States, 2013), design and implementation of social studies curricular activities that focus on causal
reasoning are presently understudied. This leaves two gaps that we think are important in the literature:
first, how do we design to encourage and strengthen causal reasoning in social studies; and second,
how do we study this thinking in a social studies context?
In this paper we hope to contribute to these currently understudied goal by presenting a computationallyassisted Knowledge Analysis (diSessa & Sherin, 2015) of students’ causal-explanations in response topre- and post-questions as part of a unit of our own design, in which students used a computer
simulation to learn about urban planning. The simulation and accompanying activities were designed tohelp students think causally about the social impact of urban planning decisions. Analysing students
pre- and post-question responses, we identify a set of causal-nodes and use Association Rule Mining(ARM) (Agrawal, Imieliński, & Swami, 1993) to look at how, at the classroom-level, these nodes arereorganized between pre- and post-responses. Based on our findings and discussion, we claim that
ARM is a particularly well-suited approach to helping us in the analysis of student reasoning with apiecemeal view of knowledge and learning, and that it potentially can help us scale up Knowledge
Analysis to larger dataset than it has currently been applied to.

Research Questions
In this paper, we address the following questions,274

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1. How can we design activities that improve students complex causal reasoning about social
issues?
2. What are the constituent parts of students’ causal-explanations when responding to questions
about city planning?
3. How are these parts assembled into larger explanation-structures, and how can ARM help us makesense of change in these explanatory structures?

Why Association Rule Mining for Knowledge Analysis?
Knowledge Analysis takes a piecemeal approach to understanding the mental representations ofknowledge, and views reasoning as the assembly of these pieces in-the-moment (diSessa, 2002;
diSessa & Sherin, 2015; diSessa & Sherin, 1998; Sherin, 2006; Sherin, Krakowski, & Lee, 2012). The
purpose of KA is to provide an analytical space for better understanding knowledge and learning: In thisview, briefly, learning is the acquisition of new knowledge pieces into a learners’ repertoire, and/orchanges in the assembled constellations of these pieces. An important feature of a single knowledge
piece, is that it can be combined with other knowledge pieces into essentially different, larger structures,and that the meaning of a network of knowledge pieces is an emergent property of these manifold
structures.
Likely due to the fine-grained nature of KA, most KA-oriented studies are based on somewhat small
sample sizes. Recent work has moved to include more computational methods in the Learning Sciences(Martin & Sherin, 2013; Sherin, 2013). In contrast to Educational Data Mining’s focus on quantitatively
assessing student thinking, this work has primarily focused on using computational methods toqualitatively better understand the process of learning or the constituent parts of conceptual change(Berland, Baker, & Blikstein, 2014; Blikstein, 2011), and on utilizing the power of computation to scale
up the size of data. In this paper, we propose to use Association Rule Mining for a similar purpose: asa method for assisting us in a Knowledge Analysis, both as an approach to validating the causal-nodes
that we identify in students, and as a way of scaling up KA to help us look at learning at the classroomlevel.
Association Rule Mining (Agrawal et al., 1993) is a method for understanding co-occurrences between
elements in a set of data. More specifically, an ARM-analysis takes a set of ‘transactions’ that each
include some elements, and then calculates, across all these transactions, how well the presence of
one particular element in a transaction predicts the presence of another element in a transaction. We
believe that ARM’s focus on looking at the co-occurrence of individual items makes it particularly wellsuited for helping us in analyses of students thinking with a piecemeal view of knowledge: If we view
student whole explanations as transactions, and knowledge-pieces as the individual elements in thesetransactions, we can use ARM to better understand the changes in students’ reasoning both at theindividual- and at the classroom-level by calculating if and how these pieces are reassembled over time.

Previous use of simulation in urban planning education
Simulations have been used for SimCity in particular has received attention since the early-1990s as ateaching tool in formal education that involves urban planning or thinking about cities (Adams, 1998;
Dorn, 1989; Gaber, 2007; Kolson, 1996; Pahl, 1991). Early uses of SimCity focused on two different
aspects. The first sought to make a general case for using commercial computer simulations games in
education (Shaffer, Squire, Halverson, & Gee, 2004; Shaffer, 2006; Shaffer, 2006; Squire, 2003) andsought to show how the well-designed and engaging commercial games would make school fun forchildren. The other focused on simulation literacy (Gee, 2003, 2007; Turkle, 1997), and its specificinterest was in how (or if) children engaged with the ideological assumptions programmed into the city.
More recent work has focused using SimCity in formal urban planning education at the college or
graduate level: how to align SimCity with formal curriculum and use it as an introductory tools at thecollege and graduate level (Bereitschaft, 2016; Devisch, 2008); how SimCity can help urban planning
students engage with their own creativity (Kim & Shin, 2016); or as a reflective tool to improve on urban
planning pedagogy itself (Kim & Shin, 2016).
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However, while simulation of causal mechanisms are at the very core of the representational power ofsimulation games (Frasca, 2001, 2003; Squire, 2003), none of the reviewed work has focused
specifically on causal reasoning. Thus, while simulations in general and SimCity in particular have seenuse in education, this work does not address the specific need created by NCSS (2013) for a conceptual
framework for analysing and measuring learners’ complex causal reasoning. One aim and contributionof this paper is to fill out this gap in the literature and explore how to design simulations to help students
improve on their complex causal reasoning about complex social phenomena. We previously reported
on qualitative findings from pilot data from this study (Hjorth & Wilensky, 2014a, 2014b; Hjorth & Krist,2016), but have now collected enough data to take quantitative approaches like this as well.

The Design & Study
The data in this paper come from a unit that we designed on Urban Development and Regional Planningand which we implemented during two quarters at the undergraduate level at a mid-sized, private
research university in a metropolitan area in the American Midwest. The course was called, ‘Introduction
to Social Policy’ and is required for Social Policy majors. Students were given course credit equivalent
to one course essay for participating. In this paper, we focus on students’ pre- and post-responses to aquestion about how urban planning affects the distribution of commute times for different income groups.
Over the span of three implementations, a total of 60 students consented. We sent out the prequestionnaire 10 days before class in which we used the model, and we sent out the post-questionnaire
10 days after class had finished, and students typically responded within two days. Not all students
responded to our questions, and we had some attrition between pre- and post-, and during students’
responding to questionnaires due to technical problems. In the end, we had 41 students who responded
to both the pre- and the post-question.

Our Design and Activity
We designed a unit that ran over the span of two class periods in which students use a NetLogo(Wilensky, 1999) simulation to build cities. Before the simulation activity, the professor (who was notpart of the research team) first led a 45-minute discussion about why people live where they live. The
purpose of this discussion was to cue students’ causal reasoning about the role of human decisions,and how these decisions play a causal role in the emergence of economically segregatedneighborhoods. Students then worked together in groups of three over a period of about an hour and ahalf, using the simulation.
The simulation and activity were designed to help students iteratively improve their causalunderstanding. We did this by letting students iteratively articulate a causal-explanation, test it, andpotentially revise it. This was achieved by designing an iterative, four-phase activity:

1. The Design Phase
The primary purpose of the design phase was to prompt students to reason causally about how todesign a city that meets a set of measurable policy goals. Students were first asked to write a set goals
for their city. This included specifying which one of three policy outcomes – commute times, local
neighbourhood school funding, and access to parks & leisure areas – to focus on, and setting
measurable goals for that outcome. Examples include, “We want everybody to have less than 30-minutecommute time”, or “We want the poorest 20% to have as much funding for schools as the wealthiest
20%”. They would then be asked to describe how they would achieve these goals. Students responded
along the lines of, “We will put highways everywhere so there are enough roads for everybody”, or “We
will put parks all over the city to make it attractive for wealthy people to live anywhere so their property
taxes are spread out across the city.” (Schools are primarily funded through property taxes in most of
the US.)

2. The Implementation Phase
During the Implantation Phase, students built their cities inside the simulation. The simulation isdesigned to let students do this in various ways: Students designate zoning in the city, specifying the276Constructionism 2018, Vilnius, Lithuania

density of dwellings, and making certain areas more or less desirable and relatedly more or lessexpensive to live in. They can also put parks in the city, or build railroads or highways for people tocommute on. Finally, they designate certain parts of the city as ‘Industrial areas’ where jobs will belocated.

3. The Growing Phase
The purpose of the Growing Phase was to let students see the dynamic effects of their design decisions.
During the Growing Phase, the computer model simulates and visualize how (AI-based) computeragents move into the city that students built. These agents have different income levels and different
job locations and make decisions about where to live based on affordability, desirability, and distanceto their job. When agents move into an area, their income is reflected in the house prices of nearby
areas, so if a wealthy person moves into a neighbourhood, house prices go up, and vice versa. Agentsalso use the roads near them to go to work, and the more agents use a road, the more congested it
gets, making nearby areas less attractive.

4. The Data Analysis Phase
The purpose of the Data Analysis phase was two-fold: first, it was for students to use data as a meansof assessing the success of their city; and second, for students to potentially revise their causalunderstanding of the model in the cases when their cities did not meet their policy goals. There are twodifferent ways in which data can be visualized in the model: one is spatial, and the other is with bar
charts. They provide different perspectives on the same questions, but it is often necessary to look atboth in order to really make sense of how the city evolved. Bar charts showed how each income decile
was affected by each of the three policy outcomes measures, and the map helped students visualize
the geographic distribution of how people were impacted by the policy outcomes.

Data & Analysis
The student responses that we focus on in this paper were all in response to the question,“Can you explain why a wealthy person’s income might make their commute time longer thana poor person’s?”
A conventional explanation, consistent with real-world data, could include a variety of factors and soundsomething along the lines of, “In American cities, higher-income people often live in suburbs, because
they can afford to buy houses there, and because they can afford to own cars that allow them tocommute between their workplace in the city and the suburbs that often have no public transit options.
They choose to do so because they want to live in places that they perceive as safe, and have wellfunded public schools. High paying jobs are often located in the downtown areas of cities, andconsequently high-income people must make the commute in and out, often on congested roads dueto the number of people who also commute in and out at the same time”. While it is true that some highincome people live closer to their jobs than some low-income people – in part because they can afford
more freedom to choose where to live, and where to work – the result is nonetheless that higher income
people typically have longer commutes, but with a large spread in a bimodal distribution. We werecurious about how students would reason about it exactly because it contrasts many people’s initialassumption that a higher income leads to more desirable outcomes by all measures.
While some KA-approaches have strict, conceptual selection criteria for the pieces they identify(diSessa, 1993), in this study, we take a pragmatic and somewhat promiscuous approach to identifying
the individual knowledge-pieces. Rather than looking for a particular grain size or ontogenetic origin, weused ‘causality’ as a sensitizing concept (Miles & Huberman, 1994) when identifying knowledge-pieces
in students’ explanations, and looked for parts of their explanation that we could put “because… “ infront of. Because our knowledge-pieces relate to students’ reasoning about causality, and inspired by
Sherin, Krakowski and Lee’s (Sherin et al., 2012) node-mode approach towards a more permissive
inclusion of students’ knowledge-pieces, in the following, we will refer to them as ‘causal-nodes’.
Further, we will refer to the process of using a casual-node when constructing an explanation as
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Constructionism 2018, Vilnius, Lithuania‘activating’ that causal-node, and we call the process of combining causal-nodes into larger explanationsas ‘co-activating’ those causal-nodes.

Causal-Nodes and Student Response Examples
To give the reader a sense of what causal-nodes in our data look like, we provide some examples ofstudent responses and connect them to our causal-nodes. The student responses are always
reproduced in full, and as the student wrote them. Across all 82 responses (41 pre- and post-responses)
our first round of coding identified 21 different causal-nodes. We iteratively condensed this set twice,eliminating similar codes, eventually arriving at a total of 9 different causal-nodes. Table 1 provides a
description of all nine causal-nodes, grouped by what we think of as three interesting types: the first
relates to the geographic location of people and their jobs. The next group relates to how a person’s
income affects their actual commute – either by influencing their available modes of transportation, or
things that affect their commute speed. The final type relates to the wants or desires of people, and how
having money better allows high income people to fulfill them.
As Table 1 (next page) shows, the most frequent causal-nodes were the ones that deal with thegeographic location of people and jobs. Consider the following response,A wealthy person has greater freedom to choose where to live and often wealthier neighborhoods arein suburbs away from urban areas. (S2-post)
In this response, we see two different causal-nodes: first, that money gives people more choice, andsecond, that suburban areas in which wealthy people choose to live are geographically far from urban
areas. However, the response does not explicate exactly how these two causal-nodes end up resulting
in longer commute times for higher income people. In contrast, consider this elaborate student
response,
Often wealthy people live in a suburb outside of the city because they can afford a house
out there and the schools are often better. They also often work in the city at a job that ispaying them enough to be able to afford to live in the suburbs. Another big thing is that more
wealthy people can afford a car or are able to pay for the train everyday so that they canlive far away from their work. (S7-post)
This response contains the same two causal-nodes as the previous response: people with more money
have more choice, and they choose to live in suburbs. However, we see additional causal-nodes in this
response: first, the response explicitly states that their jobs can be in the city; second, that this causes
them to have to travel a larger geographical distance; and finally, that owning a car is expensive andthat wealthy people can afford to own one (or afford to take commuter trains.) These were somewhat
typical responses, but they show how he different constellations of casual-nodes can result in different
responses, or in similar kinds of responses with variations in what students focus on. We also saw
responses that focused more on practical issues relating to the process of commuting,A wealthy person probably commutes by driving his or her own car. A person in a car is
subject to traffic from stoplights and other vehicles. Trains, on the other hand, can travel ata quicker rate and also don't have to stop at lights or wait for other vehicles. (S37-post)
This response does not activate any of the geographic location-related nodes, but focuses purely on
the commute-related ones. However, we also saw some responses that seemed to activate different
causal-nodes that included perceived differences between why wealthy people choose to do what theydo, even in the face of a longer commute time,
If they choose to live in a suburb or nice area that is farther for work because of theneighborhood or home or school system. They also more likely have ways to commute
comfortably and efficiently. (S23-post)
Students that reasoned about why wealthy people make the choices that they do often focused on theirperceived better school systems, more green areas or nicer houses in suburbs. But we also saw a few
responses that essentialized different characteristics or desires in low- and high-income people. For
instance, in this response,
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They might have more time to spend on leisure, and might not be in a rush. Since they makeenough money, they don't have to work as much. (S10-pre)Node_ID

Causal-Nodes (‘Because…’)

Prefrequency

Postfrequency

Withinperson
stability

Combinability

High income people live in suburbs/ low income people live in thecities

0.73

0.83

1.13

8/8

1

Jobs are located in cities, not in
suburbs

0.46

0.49

1.19

8/8

2

High income people may live
further from workplaces / Low
income people might live closer toworkplaces

0.63

0.59

1.12

8/8

Group 1: Geographic/Spatial Location
0

Group 2: Commute-Related
3

Buying/owning a car is expensive

0.1

0.1

5.12

7/8

4

High income people more likely to
own and commute by car

0.1

0.05

5.12

5/8

5

Expressways/highways can becongested, or driving can be slow

0.34

0.2

2.56

7/8

Group 3: Desires or Possibilities relating to Income
678High income people have morechoice / low-income people haveless choice

0.59

0.59

1.14

8/8

Wealthy people care less aboutcommute time / are willing tocommute longer

0.12

0.07

2.73

6/8

People want to live in safe areas orplaces with more space or green
areas or better schools or nicer
houses

0.39

0.34

1.1

7/8

Table 1. Causal-nodes and descriptive statistics

the student seems to construct an explanation around an assumption that higher income people
somehow don’t feel the same sense of urgency, or that they have a different set of preferences aroundtheir choices when it comes to commute times.
As the examples above hopefully illustrate, we saw a large variety in the types of student responses,
and in the ways in which we see them activate and co-activate various causal-nodes. Some student
responses only included causal-nodes from one or two of the three causal-node groups, while others
mixed them across groups.

Validation: Are these Knowledge-Pieces?
KA views knowledge as constructed in the moment through the assembly of knowledge pieces.
However, it does of course not view this process as random. Rather, taking a Knowledge Analysis
approach to making sense of student reasoning, we would expect to see three different properties
relating to the consistency of the nodes that we identify:279

Constructionism 2018, Vilnius, Lithuania

The pieces should be found somewhat frequently in a specific population that reasons about a given
domain or question (i.e. ‘frequency between people’). This would indicate that these are more generalthinking-bits, and not only parts of one person’s idiosyncratic way of thinking.
The pieces should be generatively combinable, meaning they should be combinable into different kindsof explanations (i.e. frequency between different explanations). This would indicate that they are truly‘pieces’, and not in themselves larger explanations. Finally,
Even though individuals might acquire new nodes and/or reorganize their existing ones, we would
expect there to be some degree of stability in the knowledge-nodes that students activate when
responding to a similar question in a short timeframe (i.e. ‘frequency within people’). This would indicatethat these are parts of somewhat stable knowledge structures, and not completely randomly selected
when the student is prompted to answer a question.

Validating Frequency Between People
This criterion is straight forward to validate. We calculated the frequency of each node in pre- and postresponses. The results can be seen in Error! Reference source not found. in respectively pre- andpost-frequency. We see that even the least frequent of our codes appear in at least 10% of responses
in either pre- or post. While the exact cut off for this rule is contestable, we believe that seeing theactivation of a causal-node at some point in time across 10% of responses seems like a reasonable
number.

Validating Frequency between Different Explanations
There are two different ways in which this can be validated. First, a very simple quantitative statistic
showing with how many of the other 8 causal-nodes that we see each node co-occur. In the‘combinability’-column in Error! Reference source not found., we show how many other causal-nodes
we see each causal-node co-occur with across all responses. Even the least frequent causal-nodes (3and 4) are used respectively with 7 and 5 of the other 8 casual-nodes, suggesting that these nodes canbe mixed and matched in many different ways. Second, as we showed in the previous section, causalnodes were combined into different constellations of explanations that changed the function of the of
the individual causal-node in the larger reasoning structure. In other words, we see this combinability
both quantitatively and qualitatively.

Validating Frequency Within People
Finally, we expect there to be some stability in the causal-nodes individuals activate in their pre- andpost-responses. What we should address then is, does activating a causal-node in a pre-response
better predict that a person also activates it in their post-response than we would expect to see if people
randomly activated causal-nodes in their responses. For each of the causal-nodes, we calculated theconditional probability that people who activated it in their pre-response also activated it in their postresponse and divided by the frequency of that causal-node in post-responses. If this ratio is greater than1, we see a higher than expected frequency amongst people who also activated the node in their preresponse. As can be seen in the “stability” column, all causal-nodes had a higher than expected degreeof stability between pre- and post-responses.

Findings: Association Rule Changes
Now that we have identified students’ causal-nodes and hopefully made a convincing argument thatthese are, indeed, knowledge-pieces of some sort, we can run an Association Rule Mining on our data.
The primary output of an ARM are so-called association-rules. They take the form of, “if a student
activated causal-nodes X and Y, we observed that they also activated causal-nodes A and B with aconfidence of P, a support of S, and a lift of L”. ARM calculates the association-rules for all possiblecombinations across all responses. Since we have 10 different codes, and each of them can either bepresent or not, we end up with a total of 2^10 = 1,024 combinations – too many rules to read through inany meaningful way. ARM assists us in navigating this large analytical space by providing three metrics,
confidence, support, and lift, that each help identify interesting and important rules: Confidence
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calculates how well the presence of one set of causal-nodes predicts another set of rules, or moreformally: the conditional probability that a set of causal-nodes are activated, given the activation ofanother set of causal-nodes. However, confidence does not consider the aggregate frequencies of
causal-nodes, and thus it often overestimates the confidence with which a less frequent causal-node
predicts a more frequent one and vice versa.
Lift

Rule

Confidence

Support

Pre

Post

Delta

Pre

Post

Delta

Pre

Post

Delta

R1

1->2&6

0.84

1.17

0.3

0.37

0.40

0.03

0.17

0.20

0.02

R2

1->0&8

1.21

1.37

0.15

0.47

0.40

-0.07

0.22

0.20

-0.02

Table 2. Two Examples of Association Rules (bolded text indicates why they were chosen)

Support and lift help us filter out rules in two related ways: support simply calculates how often, acrossthe dataset, we see a particular combination of causal-nodes. Lift enhances the confidence
measurement by calculating the ratio between observed confidence and the expected confidence, given
the independent probabilities of each of the two sets of codes. (This is the same approach we used for
calculating the within-person stability in the previous section.) Consequently, as we are interested inlooking at changes in how students co-activate causal-nodes, the immediately most importantmeasurement is change in lift between pre- and post-responses, but looking at lift isolated in pre- or inpost- can also help us understand what kinds of constellations of causal-nodes students bring to theunit, and which ones they leave with. We ran an ARM for pre- and post-responses separately, and thencalculated the changes to confidence, support and lift between pre- and post-rules. To find interesting
and frequent rules, we filtered out rules that had less than .15 support, and included only combinations
that we observed in at least 7 out of 41 responses in both pre- and post-responses. A full account of allinteresting rules is outside the scope of this paper, but in the following, we will give two examples ofinteresting rules, explain why we think they are interesting and why we chose them, and discuss what
they tell us about changes in students’ thinking at the classroom level.

R1: Connecting Job Location with Choice and Relative Distances
R1 in Table 2 shows that students who reasoned that jobs are located in cities and not in suburbs weremuch more likely in the post-response than those who said so in the pre-response, to also say that highincome people may live further from workplaces, and that high-income people have more choice. We
chose this rule because it had the highest change in delta in our ARM. Of course, even when lift takes
into account the expected frequencies, this change could have happened simply because fewer people
activated any of the three causal-nodes in their post-responses, and those that did could be the ones
spuriously “pulling up” the association rule. However, Table 1 tells us that the frequencies of all threecodes are fairly stable from pre- to post- at the classroom level, and in Table 2 we even see a modest
increase in support for R1, showing that in absolute numbers, more people co-activated the threecausal-nodes in their pre-responses. To us, this indicates that R1 points to a robust change in students’
thinking at the classroom level towards connecting the location of typically higher-paying jobs with the
choice and relative commute distances of higher income.
Qualitatively, this is a particularly interesting rule to us, because it gets at the part of the question thatmost people find counter-intuitive: that high-income people often have longer commutes that lowincome people. Indeed, one of the reasons we designed the simulation activity was to let studentschange the infrastructure and the zoning of the city – including the relative position of residential zones
and workplaces – to see how common city design patterns (e.g. dense city centres, “green” suburbs faraway) lead to this distribution of commute times. While any firm conclusions about causality in thischange in thinking would rely on a closer analysis of students’ activities during the simulation unit, we
speculate that the focus on placement of zones in the model helped make this aspect of thephenomenon more salient to students.

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Rule 2: Connecting Job Location & And the Desirability of Suburban LifeR2 speaks to the relationship between causal-nodes 1, and 0 and 8. We chose this rule for two reasons:first, R2 has the highest lift in post-responses that we identified in any rule, and second, it is interesting
because while we see a positive change in lift, we see a drop in confidence between pre- and post-. R2
shows that students who said that jobs are located in cities and not in suburbs were more likely than
expected to also say that wealthy people live in suburbs and that people want to live in places withgreen areas or good schools. However, as mentioned, we see an absolute drop in confidence, orpredictive power, for this rule between pre- and post-. The drop in confidence on its own could meanthat more people are activating causal-node 1 in the post-responses without an increase in the coactivation of causal-nodes 0 and 8 in post-, or that fewer people are co-activating causal-nodes 0 and
8 in the post-responses without a corresponding drop in the activation of causal-node 1. Table 1 showsthat we do see an increase in the activation of causal-node 1 and a drop in causal node 8 between preand post-. However, we see an increase in causal node 0. To us, this is a good example of why looking
at lift tells a more nuanced story, and why it is sometimes easier to read association rules backwards
when trying to make sense of them: While slightly fewer people co-activated all three causal-nodes in
the post-response, the strongest predictor of whether someone did was whether they activated causalnode 1. In other words, this shows a convergence across the classroom on the inclusion of causal-node
1 by those who also co-activated causal-nodes 0 and 8.
Qualitatively, this rule is interesting to us, because it shows how students reason not just about therelative position of jobs and residential areas or about high-income people having more choices. It also
shows how they perform a kind of meta-reasoning or perspective-taking: reasoning about how otherpeople reason about where to live, and how students then activate this reasoning with the rest of causalnodes. While we do see a strengthening of this particular association rule, we see an overall drop in theactivation of causal-node 8. We think perspective-taking is important when reasoning about policy
outcomes, and had hoped that our design would have encouraged more of this thinking. We speculatethat this might be due to how the underlying logic of the AI agents in the simulation activity was hiddenfrom students, and will in future implementations explore how we can forefront the AI and make this
aspect more visible to students.

Discussion, Limitations, and Conclusion
Using Association Rule Mining helped us better identify interesting patterns in changes in students’assembly of causal-nodes into larger explanatory structures, and gave us both some statistics and a
vocabulary for measuring and discussing which of these changes were interesting and significant. We
found that students’ responses seemed converge around particular co-activations, and we speculated
how the collaborative simulation activity might have influenced their thinking, and how we could improveon the design. We hope to have provided evidence for our assertion that Association Rule Mining can
be a powerful addition to the qualitative researchers’ toolbelt when taking a Knowledge Analysis-inspired
view of knowledge. In particular, we hope to have shown that ARM can be used in combination withmanual qualitative coding to both validate the knowledge-pieces identified at the level of individual
students, and show changes in the assembly at the classroom level.
In contrast to KA’s focus on conceptual change at the level of individuals, we only looked at withinstudent changes when we validated the stability of individual causal-nodes. In future work, we hope to
use ARM to first identify important changes at the classroom level, and then use this as a starting point
for a more in-depth analysis of the reorganization of knowledge-pieces at the level of individual students.
In additional contrast to KA, our knowledge pieces are somewhat less fine-grained, and as we
mentioned previously, we took a very permissive approach to coding students’ responses. We hope in
the future to apply ARM to a both more fine-grained, and more conceptually coherently uniform set ofknowledge-pieces.
Because this use of ARM is new, we have limited understanding of exactly how to interpret the stability
and change in changes in thinking at the classroom level. Our baseline for comparing changes is always
the expected outcome, i.e. the general frequency of a set of causal-nodes in the post-responses.
However, we hope to do similar kinds of analyses on more data from this and other reasoning tasks
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over longer periods of time, and start work on better understanding and measuring the change andstability in knowledge structures, and potentially develop more generally applicable measures.
An important limitation of this study is that students were all attending a private mid-western
university, and almost all students reported to have grown up in suburbs. Consequently, the causalnodes that we identified should be considered expressions of a particular, and fairly limited experienceof the world. We hope to expand on this by collecting similar data from other socio-economic or
geographic groups.
We do not wish to claim that an ARM can stand on its own as an analysis of student reasoning. But webelieve that it provides a tool for measuring and discussing changes in thinking at the classroom level
while still anchoring the unit of analysis in a KA-approach to knowledge and thinking that respects themultitude ways in which students can assemble their knowledge. Consequently, we hope that it will find
an appropriate place in the computational methods toolbelt of the Learning Sciences.

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How High is the Ceiling? Applying Core Concepts ofBlock-based Languages to Extend Programming
Environments
Sven Jatzlau, sven.jatzlau@fau.deFriedrich-Alexander-Universität Erlangen-Nürnberg, Germany

Ralf Romeike, ralf.romeike@fau.deFriedrich-Alexander-Universität Erlangen-Nürnberg, Germany

Abstract
Since the emergence of block-based visual programming languages, they have been developed andimproved to become increasingly accessible, intuitive, and easy to use. Over the course of thisevolution, both uncommon and entirely new language concepts have been introduced, such as thecloning of objects, or the nesting of sprites. This paper provides a selection of core concepts anddescribes a categorization model. It is proposed that the concepts found in block-based languages are
the reason they lend themselves to constructionist learning approaches. To illustrate this point, thefundamental computer science concept of image nesting will be selected, its background and origin
explained, and its incarnation in current block-based languages outlined. Following this, theconstructionist task of modifying a programming environment will be implemented. Using the
aforementioned concept proves that even high-level solutions according to the “high ceiling”-principle
can be implemented using the basic concepts of block-based languages.

Keywords
visual programming languages; Snap; Scratch; GP; block-based languages; language concepts;
nesting; composite objects

Context
Introduction to block-based languages
In 2006, the programming language and environment Scratch made its debut. Developed by the Lifelong
Kindergarten Group at MIT, it represented a new approach to introducing programming to learners:
based on the ideas of “low floor, wide walls, high ceilings” by Seymour Papert (Resnick, et al., 2009),
its popularity grew rapidly. At the time of writing, some 32 million projects, guides, animations, andgames have been shared by a continuously growing user base of 28 million users. In schools, blockbased languages display a similar success: Compared to text programs, block-based environments
enable students to achieve better results on average (Weintrop & Wilensky, 2015), and while students
like to shift to text sooner or later, they can nevertheless appreciate that block-based programming isequal to text-based programming (Bau, Bau, Dawson, & Pickens, 2015). Block-based languages arecharacterized by a number of common features, such as a stage, and code blocks that snap together
to create scripts and replace the syntax of text-based languages. These features open up new
possibilities and approaches to solving problems. In this paper, we give examples for these core
concepts, and elaborate upon the origins of the image nesting concept in particular. Within this context,
the proposition that block-based languages enable users to solve problems in new ways will beillustrated with an example: extending a programming environment, which may seem to be one of themost complicated tasks users could undertake. However, given the possibilities of the core concepts
found in block-based languages, how complicated will it ultimately be?
The example reinforces the claim that block-based languages such as Scratch, Snap!, or GP, support
constructionist ideas, and demonstrates the complexity of projects users can create with them. For thispurpose, the GP environment itself will be modified and extended (using its easily-accessible source
285Constructionism 2018, Vilnius, Lithuaniacode) to include a feature that makes it more meaningful to us: the ability to search for certain blocks
within the program code.

Constructionism in programming
The ideas of constructionism and block-based languages have always been closely linked: theprogramming language Scratch was built on the constructionist principles of Logo and Etoys (Maloney,
Resnick, Rusk, Silverman, & Eastmond, 2010). The three basic principles “low floor”, “wide walls” and“high ceilings” laid the foundation for the way Scratch and its family members behave and are displayed
to the user (Resnick, et al., 2009).
However, whether Scratch supports the principle of “high ceilings” (high threshold for more advanced
programmers (Weintrop & Holbert, 2017)) is a point of frequent discussion: feedback shows thatlearners and educators alike feel as though Scratch is too simple to enable complex, high-level projects.
For this reason, GP was designed with the idea of enabling users to create significantly more complex
solutions (Maloney, 2018).

Core concepts in block-based languages
Block-based languages, and the environments used to program with them, enable new solutions toknown problems, while creating the opportunity to produce entirely new tasks and problems. The reason
for this is that in multiple ways, these languages are fundamentally different from typical text-based
languages and environments used for teaching programming novices.
To understand the ways in which these languages are different, it is important to recognize the aspects
that constitute a programming language. To characterize these aspects, the term “core concept” will be
used; these core concepts embody the nature of programming environments and languages and makethem both graspable. They should also be central for the way programming languages are taught. The
concepts found in these languages separate them from text-based languages used for introductoryteaching situations. In the following, several examples of new and promising, or less well-known
concepts will be provided and outlined. These examples were identified inductively by analyzing userand developer-made demo projects, and deductively from the programming environments of Scratch,
Snap!, and GP.
Manual control of program flow (abbreviated as “Buttons”)
Most block-based languages enable the user to start, to stop, and (in some cases) even topause the currently-loaded program. This results in a feeling of directness for the user,
making it possible for them to stop and observe their program, which offers interesting
strategies for debugging.
BroadcastingSending messages between objects is based on the one-to-many communication model, i.e.
a single object sends messages to all other objects in a program. Other objects can thenreact to the received message – or not. This type of messaging is commonly used forsynchronization and signaling purposes, as it ties into the event-driven program paradigm(outlined below).
Sensing
In many block-based languages, objects (or “sprites”) are able to detect a number of different
factors, including their own position on the stage, the direction they are facing, whether they
are overlapping with any other objects on the stage, or whether a color is touching anothercolor.

This sensing ability makes objects able to react autonomously to different situations,alignments, or conditions.
Prototyping
Instead of conceptually abstract classes, many block-based languages utilize a system of
prototypes and clones of prototypes in order to create new objects. The concept ofprototyping can be further extended to include prototypical inheritance or “delegation”(Lieberman, 1986), a form of inheritance that does not require classes. For this reason,
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prototyping and delegation can be perceived as more intuitive and easy to understand thanother forms of inheritance.
Event-driven programming
If an event occurs, the corresponding script is executed. If multiple events occur simultaneously, or if
multiple scripts react to the same event, multiple blocks are executed at the same time. This behavior
results in an implicit type of concurrency that is intuitive and simple for learners to grasp. Events canoriginate from the user (i.e. key presses, or mouse clicks), or from within the environment (i.e. an object
sensing overlap with another object, or messages being received).
Weak typing
One variable can hold different types of data; the same variable can store strings of letters, boolean
values, or integers without being limited to a single type of data that is set at the time of its creation.
Stage
Most block-based programming languages feature a stage that can visualize the world of the project tothe user, showing objects, and their (inter-)actions. Possible applications include using it as a canvasfor drawing graphs, or as a playing field for various games.
Delayed execution of code
Typically, block-based languages slow down the execution of blocks within scripts. This is done forvisualization purposes: without an inherent delay, objects could fly off the stage instantly without theuser being able to observe the process. The concept is based on a non-atomic interpreter, and istypically found in block-based programming environments for didactical reasons.
Drag & drop (abbreviated as “D&d”)
While most of these languages support keyboard editing and input, learners will typically utilize theconcept of dragging-and-dropping blocks from the sprite palette into the scripting area to create scripts.
The same process also deletes scripts, moves objects on the stage, and enables the user to resize
parts of the graphical interface to fit their personal preferences.
Image nesting (abbreviated as “Nest”)
The attachment of visual objects to other objects can be compared to the way objects are grouped invarious software tools (such as Microsoft Powerpoint or OpenOffice Impress). Objects become parts ofother, greater objects, while still retaining their individual identity. As a real-life example, a hand can beconsidered an attachment to the arm, of which it is a part. When the arm moves and rotates, so does
the hand. However, both the hand can also still rotate and move (somewhat) independently. Therefore,
while the hand is part of the greater object “arm”, it retains its individual nature, and can perform actions
independently (such as rotation, and grabbing).
Note: this concept does not refer to the idea of nested loops, or code nesting through the use of syntactic
elements such as brackets; it deals with the visual representation of objects and their composition into
greater composite objects.
This is merely a non-exhaustive selection of core concepts found in block-based languages. As they
make up the foundation of how programming languages function, their impact on teaching needs to beconsidered when designing tasks. These concepts are also the reason block-based languages lendthemselves to constructionist solutions and tasks. It is important to note that not all of them are entirely
new ideas; many (such as weak typing) have been part of programming languages and environments
for decades.
This paper proposes that the core concepts of block-based languages characterize the way they areused and, in the same way, characterize the way they should be taught.

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Concepts can be categorized into three overlapping categories:
Figure 1: Categorization model for core concepts found in block-based languages

1. Presentation concepts“What do users see and how?”This category includes all concepts regarding the presentation of the user interface. Thesedesign choices were made in order to support the learner's ability to interact with theenvironment, and to improve their understanding of the underlying programming concepts. The
stage, for example, helps learners understand the nature of objects by offering a visual
representation in the form of sprites.
While traditional environments make them seem abstract, or “ethereal” to many students, thisvisualization makes them more graspable and concrete. A common trait of most block-based
languages is that for pedagogic reasons, several blocks (such as loops, all motion blocks andbroadcasts) have an inherent delay associated with their execution. This delay was implemented
to slow down program flow and further support its visualization.
2. Interactional concepts“How do users interact with the environment?”“How do objects interact with each other?”
The second category includes concepts concerned with the way users interact with theprogramming environment and its user interface, while also including concepts that deal with theway objects interact with each other.
An example within this category is the buttons a user can use to start, pause, and stop the
program at any desired point. They enable the user to get a better feel for the program flow. Theinteraction of objects with each other includes concepts such as broadcasting, and sensing.
3. Programming concepts“How do users implement their solutions?”

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This category comprises both typical programming constructs (such as variables, loops andconditionals) and consciously-designed language traits that were implemented with accessibility,
intuitiveness and ease of use in mind. For instance, an event-driven program flow (found in all
members of the Scratch-family) enables users to implement projects that rely heavily on
concurrency – intuitively, while encountering fewer of the typical problems and difficulties
associated with concurrency (such as visibility, race conditions, etc.).
Certain concepts do not fit a single category. Dragging and dropping blocks to create scripts is a hybrid
concept: while it fundamentally influences the way users interact with the programming environment tocreate meaningful projects, the possibility of doing so is suggested through the shape, shading, andhighlighting of blocks. As another example, image nesting fits all three categories: presentation (due tothe visual connection of nested sprites/objects), programming (after nesting, objects can refer to eachother as “owner” and “part”), and interactional (as nesting creates a hierarchically-structured part-wholerelation between two objects).
The proposition is that these core concepts enable users to implement constructionist solutions easily– they are the reason block-based languages support constructionism to the extent they do. To illustrate
this proposition, the concept of image nesting will be elaborated upon in more detail.

Image nesting: a core concept
The concept of nesting visual objects into others is based on the idea of composition. Naturally, thecomposition of individual, smaller parts to form a greater entity is by no means a novel concept; In theprocess of identifying fundamental ideas of computer science, Schwill deemed the idea of structured
dissection a master idea (Schwill, 1994). Two of its sub-ideas are hierarchization, and the visualization
as a tree. All these factors are closely related to the concept of nesting, leading to the conclusion thatimage nesting is an essential idea of computer science.
Despite its basic nature, nesting has the potential to enable very high-level solutions to complex
problems (“high ceilings”), making it a core concept in block-based languages. As has been shown(Resnick, et al., 2009), block-based languages such as Scratch make it simple for users to create
projects within the framework of constructionism. Commonly-identified traits of such frameworks are“low floors”, “wide walls”, and “high ceilings”. For Scratch, these three traits have been expanded toinclude “more tinkerable”, “more meaningful”, and “more social” (Resnick, et al., 2009).
How do these traits relate to image nesting, and other core concepts of block-based languages? Using
the core concept of image nesting, a seemingly very complex, high-level task can be achieved: theprogramming environment GP will be modified to include a search feature.
This search feature, and the algorithm that is used to implement it, rely entirely on image nesting,therefore showcasing that block-based languages accommodate the ideas of high ceilings, more
tinkerable and more meaningful.
In the following chapter, to gain a more substantial understanding of the concept in question, the originof image nesting will be analyzed in more detail.

The origin of image nesting: Morphic
Morphic is a user interface construction environment. Originally developed as part of the language Self,
it would eventually play a central role in the Scratch-language family: the first versions of Scratch used
Morphic for the user interface creation, which may be the reason for the existence of nested sprites in
Snap! and GP.
The first central principle that acts as the foundation for the entirety of Morphic’s functionalities is themorph (Greek for “shape”). Every visual object is graphically represented by a morph (Bouraqadi &Stinckwich, 2007), which is the base class of everything that can be displayed in a “world”. As graphicalentities, morphs are able show behavior and handle events: they can sense and react to user input,
periodically perform actions, detect their own position and size, and sense overlap with other morphs.
The second major concept found in Morphic is “composition”. Any morph can be made into a composite
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morph through the attachment of others. The attached morphs are then considered submorphs, whichkeep a pointer to their owner they are part of. When the composite morph moves, turns, is copied, ordeleted, the same action is applied to all its submorphs – and their submorphs recursively. While they
depend on their owner in many ways, they retain their individuality: they are given a chance to handle
events (such as button presses) before their owner does (Maloney & Smith, 1995).
The entire IDE of Morphic is based on this special structural relationship between morphs. As eachmorph has an owner (be part of a greater composite morph) or can have submorphs (be a composite
morph), a compositional hierarchy is created. In this tree-like structure of part-whole-relations, the“world” is at the root, ultimately containing all the visible morphs within itself (Maloney & Smith, 1995).

How does Morphic relate to block-based languages?
Nesting in Snap! and GP is based on Morphic’s structure of composition, owner-morphs and parts.
Comparing both systems yields the following central aspects:

Nested objects behave the same way composite morphs do (act as a single entity when
performing tasks).



Individual parts know their owner, owners know all their parts; both can communicate with eachother using these connections.



Individual parts have a degree of independence from their owner (can perform actions andhandle events with a higher priority than their owner).

Both systems create a part-whole hierarchy between objects, and can therefore be visualized
by a tree structure

Due to these similarities, it appears that composition in Morphic was used as the foundation of the way
nested objects are handled in certain block-based languages; therefore, nested sprites are a new
incarnation of composite morphs. The central ideas of composite morphs and image nesting are vital tounderstanding of how nesting works and what is meant by it.
GP particularly adheres to Morphic’s design philosophy. Each part of the user interface is a morph
attached to an owner: the script editor, as an example, is a composite morph.
Its components are the individual scripts, which are composite morphs themselves. They are comprisedof the individual blocks. Blocks consist of several parts: a colored frame, a text label, and typically one
or more input slots. Blocks react to mouse clicks by running their associated code (the so-called“handler”). It becomes apparent that the hierarchical structure and functionality of morphs is part of GP’s
design.
In the following chapter, these central aspects of image nesting will be utilized to add a search feature
to the programming environment GP. The implementation of this feature will demonstrate how image
nesting, and other core concepts found in these languages, enable new and intuitive solutions topreviously complex problems.

How do concepts enable new solutions?
Block-based languages such as Scratch, Snap!, or GP, enable new solutions to problems due to theconcepts they use. The very same concepts are also the reason block-based languages lendthemselves to constructionist learning approaches. To give an example of this proposal, theprogramming environment of GP itself will be modified by utilizing its application of the nesting-concept.
The implementation will highlight that image nesting enables three aspects in particular: “high ceilings”,“more tinkerable”, and “more meaningful”.
To illustrate this point, we will solve a two-fold problem outlined by the developers of GP: 1) Coderepresented by blocks typically takes up more space than it would with a text-based representation.
This makes it harder to get an overview of the code at a glance (Mönig, Ohshima, & Maloney, 2015).
2) The visual stimuli, such as colors, borders and other graphical elements of blocks, can make it harderfor the user to scan the actual labels of the blocks. These label texts, however, hold most of the meaning.
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Text-based language editors typically solve this issue by providing a search function that can locate
certain code snippets based on a given search term. For block-based language environments, such a
feature does not yet exist – even if the user knows the block they need to find in the program, they mustvisually scan the entire scripting area to find it (Note: most block-based environments provide a searchbar for the blocks palette. This searches the library of available blocks based on a search term; it does
not search the code blocks in the scripting area).
The proposed solution to this problem, therefore, is the addition of a “search in class”-function thatenables the user to find a block containing a given search term within the currently selected class. Thisshould operate similarly to the “search”-feature of a text-based language editor, scanning the code
contained in a project for a matching search term and pointing the user to its location.

Implementation of a search function in GP
In this step, the programming environment of GP itself will be extended to include a “search in class”function. But what is the role of image nesting for this extension?
Image nesting as a core concept of several block-based languages applies not only to objects on thestage (so-called “sprites”): it can also be found within the graphical interface of GP itself. It is one andthe same core concept – a hierarchical part-whole relationship between two objects.
Due to GP’s graphical interface structure relying on image nesting, searching for a specific block label
only needs to follow a basic algorithm.
However, there are several terms and their relation to each other that need to be clarified (Figure 2):

Figure 2: Interface of GP

ScriptEditor (red box): The area that contains all the code of a class. The ScriptEditor is a composite
morph; its parts are the individual scripts (blue box).
Class (green box): GP uses classes and objects. The ScriptEditor always displays the code blocks forthe currently selected class; this code is shared among all its objects (or “instances”). The currently
selected class for the example in Figure 2 is “Grass”.
Scripts (blue boxes): Any number of blocks (>= 1) attached to each other form a script. Every script is
a composite morph; among its parts are the individual blocks. The selected class has two scripts: “whenI receive”, and a method definition for the “initialize”-block.
Blocks: Parts of a script, internally attached by nesting. The “when I receive”-script has one blockattached.
labelText: A part of a block, contains the actual readable text on the block. The labelText of the “delete”block is the text string ‘delete’.
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The hierarchical structure of the graphical interface that is created by image nesting can be visualized
by a tree (Figure 3). This tree shows the position of the label texts in the hierarchy of the graphicalinterface: the texts are nested into the individual blocks, which in turn are nested into the scripts. Finally,
the scripts are nested into the ScriptEditor, which is the part of the GP interface that holds all the scripts
of a class.

Figure 3: Hierarchical structure of the interface

The algorithm below (simplified) is outlined in pseudo code. This representation was chosen due to theinevitable simplification of the algorithm in place, although a block-based representation may have been
more fitting.
To find the matching string, we need to compare the text labels of all visible blocks. To this end, we
utilize the concept of image nesting. We will not, however, nest objects into other objects ourselves.
Instead, we will utilize what happens when code blocks are snapped together: they are nested into one
another.
This impacts our approach to a solution significantly: we merely need to descend the hierarchicalstructure (Figure 3) that makes up the graphical interface in order to find the label texts of blocks.
1 set answer to (ask “Enter search term”)
2 set searchTerm to answer
3
4 for every script of ScriptEditor
5
for every block of scripts
6
for every part of block7
if (searchTerm == part)8
return true

After the user’s search term has been stored (lines
1-2), the concept of nesting becomes important.
Descending the hierarchy of the graphical interface
is done in lines 4, 5, and 6. In each of these lines, thecomposite object (ScriptEditor, scripts, and block,respectively) is prompted for a list of all its parts, thatmeans other objects that have been nested into it.
This hierarchy is followed downwards until the label
text becomes available (line 7). The algorithm istherefore no more complicated than parsing a tree

and its nodes.
As an example, breaking down the “delete”-block yields three parts – a blue puzzle-shaped image, thelabel text “delete”, and an image of a black arrow. In this manner, every block of every script shown in
the ScriptEditor is decomposed into its nested parts in order to find the label text.
This label text can then be compared to the searchTerm entered by the user. If both strings match, theresult has been found. The search menu is accessed through the context menu by right-clicking thescripting area. After a matching block label has been found, the ScriptEditor scrolls to it and a highlightis added to signal its position.
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Discussion
The reason for the simplicity of the algorithm described here is the architecture of the user interface: aseach visible object (like the ScriptEditor, the scripts, the blocks, etc.) is a part of a larger (composite)
object, and is in turn made up of parts (making it a composite object), a hierarchical structure is created.
Descending this hierarchy downward to the point where the label text of blocks becomes accessible
makes it possible to keep the search algorithm simple and intuitive. Therefore, the search algorithm
makes use of the nesting of images that are used to make up the user interface of GP. Furthermore, italso makes use of three central ideas of constructionism in block-based languages:High ceilings: Image nesting enables simple solutions to complex problems: An example could
be implementing an autonomous car: the task is to create a car that is able to follow a race
course reliably without user input; the issue is that to turn appropriately, the car needs to be able
to determine where exactly it touches the edge of the course. In block-based languages, thiscan be implemented simply by nesting sensor objects onto the car; these sensors notify theirowner if they touch the edge of the race course, to which the owner reacts by turning according
to the notifying sensor.
At the same time, however, nesting also enables solutions on a much higher level; as describedin this paper, the seemingly complex task of modifying a programming environment can be doneby utilizing its reliance on image nesting.
More tinkerable: As GP is open to modifications, the search algorithm described above can be
implemented entirely in blocks through the usage of GP’s system class browser.
More meaningful: By extending the programming environment of GP according to our individual
needs, we modified it to become meaningful for us (Resnick, 1996).

The search feature is by no means indicative of the limits to image nesting: with some adjustments andextensions, this feature could also be used to parse blocks in a project for various purposes, forexample, an analysis tool examining the block types users tend to use; the “high ceiling” has not yet
been hit. Furthermore, while the language of this implementation is specific to GP, the algorithm itself
is not; the algorithm is based on the usage of image nesting. Therefore, similar implementations are
possible in other programming environments that utilize nesting to create their graphical interface, suchas Snap!.

Conclusion
As the example illustrates, the graphical representation of elements in block-based languages enables
the reduction of algorithms to their basic principles. For the user, this means that the implementation of
personally-meaningful, constructionist projects is simplified. In the case of image nesting, which
originated from Morphic’s composite morphs, the unique part-whole hierarchy it creates between
objects makes it possible to solve a wide range of different tasks. Among these tasks are typicalprogramming projects implemented by novices in introductory courses, such as autonomous cars. Evenon a higher level (“high ceilings”), however, block-based languages enable constructionist solutions toknown problems, as has been demonstrated with an extension of the GP programming environment. In
conclusion to the posed question, “how high is the ceiling?”: we have not hit the ceiling quite yet. Witheven basic concepts such as image nesting, high-level solutions to problems can be implemented in a
simple manner. Indeed, the particular feature implemented in this paper may be part of future GP
versions.

References
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Bouraqadi, N., & Stinckwich, S. (2007). Bridging the gap between morphic visual programming andsmalltalk code. Proceedings of the 2007 international conference on Dynamic languages: in conjunction
with the 15th International Smalltalk Joint Conference 2007, 101-120.
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Environment for Creating and Sharing Active Web Content. Proceedings of the 5th international
Symposium on Wikis and Open Collaboration , 9.
Lieberman, H. (1986). Using prototypical objects to implement shared behavior in object oriented
systems. ACM Sigplan Notices, 21(11), 214-223.
Maloney, J. (2018). GP: A New Blocks Language for CS Education. Proceedings of the 49th ACM
Technical Symposium on Computer Science Education, 1110.
Maloney, J., & Smith, R. B. (1995). Directness and Liveness in the Morphic User Interface Construction
Environment. Proceedings of the 8th annual ACM symposium on User interface and software
technology, 21-28.
Maloney, J., Resnick, M., Rusk, N., Silverman, B., & Eastmond, E. (2010). The Scratch Programming
Language and Environment. ACM Transactions on Computing Education (TOCE), 16.
Mönig, J., Ohshima, Y., & Maloney, J. (2015). Blocks at Your Fingertips: Blurring the Line Between
Blocks and Text in GP. Blocks and Beyond Workshop (Blocks and Beyond), 2015 IEEE, 51-53.
Resnick, M. (1996). Distributed constructionism. Proceedings of the 1996 international conference on
Learning sciences, 280-284.
Resnick, M., Maloney, J., Monroy-Hernández, A., Rusk, N., Eastmond, E., Brennan, K., . . .
Kafai, Y. (2009). Scratch: programming for all. Communications of the ACM 52(11), 60-67.
Schwill, A. (1994). Fundamental ideas of computer science. Bulletin-European Association for
Theoretical Computer Science, 53, 274.
Weintrop, D., & Holbert, N. (2017). From Blocks to Text and Back: Programming Patterns in a Dualmodality Environment. Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer
Science Education, 633-638.
Weintrop, D., & Wilensky, U. (2015). Using Commutative Assessments to Compare Conceptual
Understanding in Blocks-based and Text-based Programs. 11th Annual ACM Conference on
International Computing Education Research, 101-110.
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Programming Environments. Proceedings of the 2017 Conference on Interaction Design and Children,
183-192.

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The Direction and Possibility for Social Justice inInformatics Education based on Bebras Challenge
in Republic of Korea
Ungyeol Jung, purnagi@gmail.com
Korea National University of Education, Republic of Korea

Young-jun Lee, yjlee@knue.ac.kr
Korea National University of Education, Republic of Korea

Abstract
In addition to high interest in computational thinking ability, the 2015 revised national curriculum of
Republic of Korea has set as an emphasis on revising skills for the 4th Industrial Revolution era throughthe enhancement of the ability, and has made informatics mandatory for the middle school. As a result,regional, gender, teacher level, and students' prior knowledge levels are pointed out as importantenvironmental variables affecting informatics education and raise of students' computational thinking ofcomputing, and as a threat to the social justice of informatics education.
In this study, based on the tasks and the results of the Bebras Challenge, the applicability of Bebras
challenge for social justice in informatics education was analyzed from two perspectives (internal andexternal) and suggested implications. As a result of the research, the tasks of the Bebras challenge
reflected educational elements for social justice. Also, the results of the Bebras contest showed that
informatics education based on Bebras challenge could contribute to the social justice.
The results of this study will provide the basis for the direction and possibility of Bebras challenge forsocial justice in informatics education.

Keywords
keyword; informatics education, software education; social justice; Bebras Challenge

Introduction
The 2015 revised national curriculum of the Republic of Korea was developed on the basis of thenational and social needs for cultivating creative and convergent talents and the awareness of the
problems of the 2009 revised national curriculum. Especially, the curriculum provides the education
paradigm that cultivates talented people with core skills required by the future society and implements
happy learning by improving the quality of experience for the students’ real life. This means that oursociety is being innovated so as to bring about changes in education. Especially, for the future of thenation, the influence of artificial intelligence, internet of things, cloud computing, big data, and mobile
technology on the social, political, economic, cultural, artistic and educational fields is inevitable.
Accordingly, in the 2015 revised national curriculum, software (SW) education aimed at cultivating
computational thinking ability was imposed in the K-12 in order to strengthen the future capacity of thestudents who will live in the intelligent information society brought the 4th industrial revolution. On theother hand, it is not the first time that computational thinking was introduced in the 2015 revisioncurriculum in Republic of Korea. Computational thinking has been included as an educational goal
through the 2009 revised informatics curriculum.
However, this is limited to secondary schools in in the elective courses, not mandatory. Of course, inthe 2015 revised national curriculum, only the minimal time and contents of SW education were
presented without independent subject for elementary school. However, SW education in middle
schools and high schools is the essential education through informatics for professional and systemic
education. In particular, since the first national curriculum has been implemented, it is the first case thatthe elective subject has become mandatory. Therefore, the software education as compulsory and295

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informatics as mandatory have great significance for educational history in Republic of Korea. The factthat the informatics has become a compulsory and mandatory subject leads to the expectation that it
can cultivate various kinds of abilities such as collaborative problem solving ability and information
culture literacy as well as computational thinking in various aspects. The role and impact of the national
curriculum in Republic of Korea is extensive and powerful because the curriculum presents thecharacteristics, goals, content system, achievement standards (content elements, skills), teaching andlearning methods as well as teaching times of the subject relatively in detail.
However, unlike this expectation, there is a concern that the effect of SW education varies dependingon the infrastructure, the capacity of the informatics teacher, the students’ gender and prior knowledge
level. In particular, the Korean education market is growing because of the concerns of the Korean
people, who have a high level of education and passion for education. These concerns can be a greatrisk to social justice, a very important value in education.
Therefore, in this study, we aimed to search and analyse for social justice in informatics education
through the Bebras challenge, which is developed for the purpose of motivating K-12 students for
informatics, and promoting their computational thinking ability. The results of this study are expected toprovide the basis for the direction and possibility of Bebras challenge for social justice in informatics
education.

Directions of Social Justice in Informatics Education
based on Bebras challenge
Social Justice in Education
The concept of social justice is closest in meaning to fairness, emphasizing communication andunderstanding, humility, compromise, and tolerance rather than emphasizing one aspect of the public
good or the private good. Justice is some similar and somewhat different from equality. In other words,
everyone should be educated without discrimination, but the difference of results should be
acknowledged only by the factor of the ability. On the other hand, it cannot be denied that the purposeof education is to realize social justice, but the ignorance and unawareness of these concepts andvalues have made the view that education is for social inequality.
Paulo Freire (1970, 2009), who advocated education for social justice, said that students in literacy
education critically read and critically rewrite the world and that participation for individual liberation isimportant. Applying Freire's liberation to informatics education, students should be taught and prepared
informatics to think critically about the oppressive part of the social structure or environment through theconcept or technology of informatics and computational thinking. In this respect, D'Ambrisio (1999)
stated that students should be taught to apply and infer knowledge of the subject within the social
context. In other words, we need a curriculum for leading students' interest, curiosity, and creativity.
Skovsmose (1994) and Frankenstein (1983, 2001) presented the teaching and learning direction ofsocial justice in mathematics education in two perspectives. It is 'mathematics for social justice' and'critical mathematics' education. 'Mathematics for social Justice' means to understand the world and to
improve problem-solving ability through mathematics. It can be interpreted as a goal and an effect ofmathematics education. In addition, 'critical mathematics' means to cultivate positive cultural and social
identity through learning of mathematics. It means to cultivate critical awareness through teachingmathematics.
Choi (2015) presented the teaching topics and contents for social justice. It is possible to teach 'criticalmathematics' through the topics and contents focusing on social problems and it is possible to teach'mathematics for social justice' the contents focusing on mathematical problem solving.
Through this literature analysis, 'Informatics for social justice' means that everybody should be able togain the opportunity for computational thinking. In addition, 'critical informatics' means to have a positive
viewpoint on different races, classes, genders, etc. through learning informatics.

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Bebras Challenge, Informatics Education, and Social JusticeBebras challenges are an educational model and an initiative that is designed to motivate K-12 students
in informatics and to improve computational thinking. The challenge is based on tasks that anyone canparticipate and challenge regardless of gender, region, prior knowledge and so on.
All the tasks of the Bebras challenge involve interesting and familiar real-life problem situations, andparticipants experience ‘abstraction’ that analyzes problem situations and designs problem- solving
models as well as automation that develop and apply problem-solving procedures. Through thisprocess, logical thinking ability and computational thinking ability can be cultivated. This means that it
is possible to educate 'informatics for social justice' through teaching and learning informatics based on
Bebras challenge.
Jung and Lee (2017) analyzed the educational effects of the Bebras challenge and found its applicability
to the 2015 revised national curriculum of Republic of Korea. According to them, the Bebras challenge
can measure whether it is possible to deeply understand and apply the basic concepts of informatics
beyond simply knowledge acquisition or memorization. It also improve students’ affective ability thoughsolving attractive and challenging tasks, motivation, attitude, interest and so on.
In addition, it is possible to diagnose computing thinking ability, which allows to provide appropriate
feedback for students. Furthermore it can be applied to various teaching, learning and evaluation
models based on competition and collaboration and it can improve students’ ICT literacy andabstraction, algorithm design, and programming ability through interactive tasks based on IBT withvisualized and simulated environment.
All the tasks of Bebras challenges include various multi-cultural elements as they are developed throughcollaborative development and reviewing from the experts of various countries.
In particular, there is no discrimination against specific countries, classes, and races because they place
an emphasis on attractive problem situations that ‘everyone’ can challenge. This means that it ispossible to educate 'critical informatics' education through Bebras challenge.

Possibilities of Social Justice in Informatics Education
based on Bebras challenge
The purpose of this study is to analyze the effectiveness of informatics education based on Bebras
challenge with the perspective of social justice. To do this, we set up the tasks and the test-results ofBebras challenge 2017 in Republic of Korea. The challenge was conducted by Bebras Korea, anonprofit organization composed of Korean experts in the field of informatics education, and 7,208
students from five age groups, from Group II (Primary) to Group VI (Seniors), participated in thechallenge.
The analysis of this research was done in two aspects. The first thing is about the social problem factors
included in the tasks of Bebras challenge 2017 in Korea so that we explore the possibility of 'criticalinformatics education'. The second is about the participants' results of the challenge so that we find thepossibility of ‘informatics for social justice’. Therefore, the research problem to be solved through thisstudy is as follows.
Is Bebras challenge suitable for 'Critical Informatics' education?
Is Bebras challenge suitable for 'Informatics for social justice' education?

Internal Analysis: Bebras Tasks for Social JusticeIn order to figure out whether the Bebras challenge is suitable for critical informatics education, it isnecessary to analyze whether the tasks of this challenge contain the elements for social justice.
Therefore, this can be said that the internal analysis on Bebras challenge.
Choi (2015) and Lim (2017) suggested five important topics and contents for teaching and learningsocial justice. Therefore, it is necessary to search whether the tasks of the Bebras challenge includes
the following elements for solving the 1st research problem.
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No.

Topics or Contents

1about Social Unjust

2

about Equity and Public Virtue

3

about Consideration of Social Minorities

4

about Students’ Interests and Concerns

5

about Multicultural and Global Materials
Table 1. Topics or Contents for Education of Social Justice

Frist, The content about social unjust means to construct educational contents in such a way as to findout or solve unfair problem situations. The following is a task of Bebras challenge that explores theappropriate police actions to catch a pirate (a bad man) under given conditions.

Figure 1. Pirates (2015-SI-07; Age group: Juniors; Difficulty: hard)

Students try to catch pirates who are unjust in the solution of this problem, and through this process,they become police officers and endeavor to realize social justice. The examples like this task can befound in "Find the Thief (2016-BE-02; Age group: seniors; Difficulty: hard)" to search the thief who stolea diamond and "Intrusion (2017-DE-03; Age group: cadets; Difficulty: hard) ' to prevent unauthorized
intruders from the museum.
Second, the contents about equity and the public virtue refer to educational contents that encourage torecognize or solve the problem for the more people. The next task is to find a way to ensure that allislands are able to communicate even if one thin cable is cut off.

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Figure 2. Honomakato MC (2017-DE-06a; Age group: Seniors; Difficulty: medium)

In solving this problem, students try to find ways to create a situation in which everyone cancommunicate equally. The examples like this task can be found in 'Soda Shoppe (2017-CA-07; Agegroup: seniors; Difficulty: easy)' to find ways to drink all your friends' favorite drinks and 'Bebragram;Age group: seniors; Difficulty: easy)' to find out how all your friends can buy music.
Third, the contents about consideration of social minority means the educational contents to recognize
the problem situation for the handicapped, the elderly and children, or to induce them to solve theirdifficulties. The following is a Bebras challenge task to find a way to make delicious food (twigs) for a
one armed Bebras.

Figure 3. One Armed Bebras (2017-CH-08b; Age group: Primary; Difficulty: medium)In the process of solving this problem, the students have a desire to help the one armed Bebras. The
examples like this task can be found in 'Grandmother's jam (2017-RU-04; Age group: Primary; Difficulty:
medium)' to find a way to help grandmother who want to make jam and 'Bird House (2017-RO-03a; Agegroup: Primary; Difficulty: easy)' to prepare a daughter’s birthday present.

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Fourth, the contents about students' interests and concerns are related to the use of familiar materials,
pictures, or real-life problems. The following is a task to find a suitable picture combination with a Ninja
Bebras which is attractive for students.

Figure 4. Stick and Shield (2017-JP-02; Age group: Cadets; Difficulty: medium)

Students are interested in the action and facial expressions of cute ninja Bebrass holding a stick and ashield in solving this problem. The examples like this can be found in "Irrigation system (2017-AT-05;
Age group: Cadets; Difficulty: easy)" to fix the real-life problem, and "Color the flowers (2016-SK-04 ;Age group: Primary; Difficulty: easy) ' with beautiful flowers.
Fifth, contents about multicultural and global material means constituting educational contents in theway of problematic situation of various countries and races as material. The following is a tasks to findways to change the lighting conditions of skyscrapers.

Figure 5. Skyscraper art (2017-IT-06; Age group: Cadets; Difficulty: hard)

In the process of solving this problem, students in Korea will be questioning the beginning of the building
from ‘0’ (not 1), and realize that this is a difference between culture and custom. There are many otherproblems like this, but the students especially have looked at Beavers’ different shapes, facial
expressions and color, and clothes from different countries in the tasks and imagine the people of thecountry.
These results indicate that the elements for critical informatics are included in the tasks of Bebras
challenge. Therefore, 'critical informatics' education based on Bebras challenge is possible.

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External Analysis: Results of Bebras Challenge 2017 in Korea
In order to find out whether the Bebras challenge is suitable for informatics education for social justice,
the focus should be on whether students will be given opportunities to improve their computational
thinking through the challenge. Therefore it is necessary to analyze the participants and results of theparticipation. These approach is can be called the external analysis on Bebras challenge
Dagienė & Futschek (2008) and Dagienė & Stupuriene (2016) said that everybody can be participants
to Bebras challenge regardless of gender, region, or prior knowledge and all tasks of this challenge arebased on interesting and familiar real-life problems. Thus, participants in the Bebras challenge can be
motivated for informatics and can enjoy their computational thinking regardless of their different
backgrounds.
On the other hand, Jung et al. (2018) presented the points to consider for the informatics by analyzing
the background factors influencing the secondary school participants’ results (N = 5,874) . The students
participated in Bebras challenge 2017 in Korea. As a result, the following conclusions were obtained.
First, as a result of analyzing the difference in the percentage of correct answers of all participants
according to their genders, the result of male students was higher than female students. However,as a result of analyzing the difference for each group, there were statistically significant differences
only in Group VI (Seniors), and there was no difference between Group IV (Cadets) and Group V(Juniors)
Second, as a result of analyzing the difference according to the region, it was found that 'Rural >Big city > Small city'. These results are somewhat different from those of previous studies thatworried about the educational gap between urban and rural areas (City > Rural).
Third, there was no statistical difference in the percentage of correct answers according to theevaluation areas (ALP, DSR, CPH, COM, ISS) and task-types (Multi-choice, Interactive). However,
we should pay attention the result that the percentage of correct answers (N = 5874, m = 51.77,
df = 36.41) in the CPH area was very lower than the overall average (N = 5874, m = 34.49, df =18.89).

In this study, to find out the difference of the percent correct according to the region in more detail, we
analyzed the difference based on the results of the students of each. The results are as follow.
First, the average (avg.) of the big city (N = 221) was 69.05 and the standard deviation (SD) was
39.84 among the students in the group IV (Cadets). The avg. in the small city area (N = 1443) was
55.05 and the SD was 37.95. The avg. of rural (N = 91) was 81.14 and the SD was 35.39. The Fvalue was 30.46 and p-value was .00 when the significant level of this research was .05. The
results of the post-hoc analysis (Scheffe) are as follows. This result implies that a statisticallymeaningful interpretation can be made for 'Rural > Big city > Small city'.
Comparative Group(GroupⅣ)mean
differenceSEp

Big city vs. Small city

14.01

2.75

.00

Big city vs. Rural-12.094.74.04

Small city vs. Rural-26.10

4.11.00

Table 2. Results of Post-hoc analysis (GroupⅣ)

Second, the avg. of the big city (N = 256) was 40.82 and the SD was 35.10 among the students in
the group V (Juniors). The avg. in the small city area (N = 574) was 48.94 and the SD was 39.21.
The avg. of rural (N = 23) was 44.74 and the SD was 26.77. The F-value was 4.12 and p-value
was .02 when the significant level of this research was .05. The results of the post-hoc analysis(Scheffe) are as follows. This result implies that a statistically meaningful interpretation can bemade for 'Small city > Big city’. However, there was no difference between ‘Big city and Rural’ and‘Small city and Rural’.
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Comparative Group(GroupⅤ)

mean
differenceSEp

Big city vs. Small city

-8.12

2.84

.02

Big city vs. Rural

-3.92

8.21

.89

Small city vs. Rural

4.20

8.03

.87

Table 3. Results of Post-hoc analysis (GroupⅤ)

Third, the avg. of the big city (N = 964) was 53.76 and the SD was 34.33 among the students in
the group VI (Seniors). The avg. in the small city area (N = 2258) was 47.94 and the SD was 34.46.
The avg. of rural (N = 44) was 54.05 and the SD was 26.70. The F-value was 10.06 and p-value
was .00 when the significant level of this research was .05. The results of the post-hoc analysis(Scheffe) are as follows. This result implies that a statistically meaningful interpretation can bemade for 'Big city > Small city’. However, there was no difference between ‘Big city and Rural’ and‘Small city and Rural’.
Comparative Group(GroupⅥ)

mean
differenceSEp

Big city vs. Small city

5.82

1.32

.00

Big city vs. Rural

-.28

5.29

.99

Small city vs. Rural

-6.10

5.22

.51

Table 4. Results of Post-hoc analysis (GroupⅥ)

These results show that it is difficult to generalize the test-results of Bebras challenge according to
specific background factors. Instead, it emphasizes the importance of detailed analyzing the individual’s
computational thinking ability or misconceptions and providing appropriate feedback to help them.
Therefore, 'informatics for social justice' education based on Bebras challenge is possible

Conclusion and Discussion
The purpose of this study is to find out how Bebras challenge can contribute to social justice ininformatics education based on the analysis of the tasks and the results of the Bebras challenge 2017
in Republic of Korea. Based on the results of this study, the following conclusions can be drawn.
First, there are two educational directions for social justice in informatics education. The 'Informatics forsocial justice' education is to provide all students with opportunities to develop computing thinking skills
that understand the world and solve problems from the perspective of informatics. 'Critical informatics'
education is aimed at improving positive social and cultural identity for all students.
Second, the Bebras challenge includes various elements necessary for 'Critical informatics' education.
This is based on the developing principles and processes of the tasks that allows for consideration ofdiverse cultural and ethical issues. Therefore, it is possible to educate 'critical informatics’ education
based on Bebras challenge.
Third, the goals and characteristics of Bebras challenge are appropriate for 'Informatics for social justice'education. The results of the challenge 2017 in Korea also show that informatics education is not always
beneficial to a particular group or subject. In particular, through detailed analyzing, we can practice'Informatics for social justice' education.

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Figure 6. The directions and possibility for social justice in informatics education
based on Bebras challenge

As the number of countries and students participating in Bebras challenge grows, various possibilities
using Bebras challenge are being discussed. However, considering the goals and characteristics of thechallenge, we should more focus on the social justice in informatics education based on this challenge.
Therefore, we hope that this study will be the basis for the direction and possibility of Bebras challenge
for social justice in informatics education.

References
Bebras Challenge. (2018). http://bebras.org
Choi, J., An, S. and Lee, Y. (2015). Computing Education in Korea-Current Issues and Endeavors. ACM
Transactions on Computing Education (TOCE) - Special Issue II on Computer Science Education in K12 Schools, 15(2), Article 8.
Choi, S. (2015). Study on the development of elementary school mathematics program with a focus onsocial issues for the mathematically gifted and talented students for fostering democratic citizenship:based on the teaching mathematics for social justice. Ph.D. thesis, Ewha Womans University, Republicof Korea.
D’ Ambrisio, U. (1999). Ethnomathematics and its first international congress. Zentralblatt fur Didaktik
Mathematik, ZDM, 31(2), 50-53.
Dagienė, V., & Futschek, G. (2008). Bebras international contest on informatics and computer literacy:
Criteria for good tasks. International Conference on Informatics in Secondary Schools-Evolution and
Perspectives, 19-30.
Dagienė, V., & Stupuriene, G. (2016). Bebras-a sustainable community building model for the concept
based learning of informatics and computational thinking. Informatics in Education-An International
Journal, 15(1), 25-44.
Frankenstein, M. (1983). Critical mathematics education: An application of Paulo Freire's epistemology.
The Journal of Education, 165(4), 315-339.
Frankenstein, M. (2001). Reading the world with math: Goals for a critical mathematical literacy
curriculum. Proceedings of the Eighteenth Biennial Conference of the Australian Association of
Mathematics Teachers, Canberra, Australia.
Freire, P. (1970, 2009). Pedagogy of the oppressed. New York: Continuum.
Jung, U. and Lee Y. (2017). The Applicability and Related Issues of Bebras Challenge in Informatics
Education. The Journal of Korean association of computer education, 20(5), 1-14.
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Jung, U., Kim, H., Lee, M., Lee, H. and Ahn S. (2018). A Study on the Factors Influencing Computational
Thinking Ability of Secondary School Students in Bebras Challenge 2017. The Journal of Korean
association of computer education, 21(3), 21-33.
Kim, J. and Park, M. (2015). The Influences of Teaching Mathematics for Social Justice on Students'
Interest towards Mathematics and Perceptions of Mathematical Values. Journal of Elementary
Mathematics Education in Korea, 19(3), 409-434.
Korea Ministry of Education. (2015). The 2015 revised national curriculum.
Lee, M. (2017). Computational Thinking: Efforts in Korea. In: Rich P., Hodges C. (eds) Emerging
Research, Practice, and Policy on Computational Thinking. Educational Communications andTechnology: Issues and Innovations. Springer, Cham.
Lim, H. (2017). A Study on the Realization of Contents of Mathematics Education for social justice.
Master thesis, Ewha Womans University, Republic of Korea.
Park, S. C. (2012). A Study on Social Justice in Multicultural Education. Center for Educational
Research, 1-26.
Seo, K. (2017). A Narrative Inquiry on a Teacher’s Struggle to Teach for Social Justice. The Journal ofCurriculum Studies, 35(3), 129-156.
Skovsmose, O. (1994). Towards a philosophy of critical mathematical education. Dordrecht,
Netherlands: Kluwer.

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Interconnection between Computational Thinking
and Digital Competence
Anita Juškevičienė, anita.juskeviciene@mii.vu.lt
Vilnius University, Lithuania

Valentina Dagienė, valentina.dagiene@mii.vu.lt
Vilnius University, Lithuania

Abstract
The European Commission Science Hub has been promoting Computational Thinking (CT) term as animportant 21st century skill or competence. However „despite the high interest in developing
computational thinking among schoolchildren and the large public and private investment in CT
initiatives, there are a number of issues and challenges for the integration of CT in the school
curricula46.“ From the other side, the Digital Competence (DC) Framework 2.0 (DigCom) is promoted in
the same European Commission Science Hub portal47. It shows that both topics have may things incommon. Thus there is the need of research on CT relationship with digital competence.
The goal of this paper is to analyse and discuss the relationship between DC and CT, and help
educators as well as educational policy makers to make informed decisions about how CT and DC canbe included in their local institutions. We begin by defining DC and CT and then discuss the current
state of both phenomena in education in multiple countries in Europe. By analysing official documents,
we try to find the underlying commonness in both DC and CT, and discover all possible connections
between them. Possible interconnections between both approaches components groups is presented
in Fig. 1.Fig. 1. The interconnections between DC and CT

Fig. 1. The interconnections between DC and CT

Keywords
Computational thinking, digital competence, education

46
47

https://ec.europa.eu/jrc/en/computational-thinking
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Background
Computational Thinking (CT) and Digital Competence (DC) are indicated by many education policy
makers as important 21st century skills. The European Commission Science Hub has promoted CT and
has launched the Digital Competence (DC) Framework 2.0 (DigCom) in its portal. Nowadays CT and
DC are essential skills and young generation should learn them for life.
During the last years, a lot of research work was devoted to the both topics, and enormous amount ofstudies and practical experience have been implemented. Nevertheless, there is the huge need ofresearch in these topics on many aspects. One of them is the computational thinking relationship withdigital competence.

Digital Competence
The invention of computer and internet has changed our lives and education sector. It requires
individuals to improve their competencies, especially be digitally literate.
Digital competence is the most recent concept describing technology-related skills. Digital literacy isoften seen as a synonym of digital competence, however there are some stages in development of thisconcept, for example: computer skills -> ICT skills -> digital skills -> digital competences (Ilomäki et al.,2011; Laar et al., 2017). Some researchers argue that digital skills concept, as a more holistic
phenomenon, involves more features than digital competence (Pérez-Escoda, Rodríguez-Conde,2015).
The number of content (e. g. media, tools, technologies) in the internet is growing every day. Thus, it
requires individuals to deal with more abilities. JRC technical report (2012) analysed fifteen frameworks
on DC and developed the following definition: “the set of knowledge, skills, attitudes (thus including
abilities, strategies, values and awareness) that are required when using ICT and digital media toperform tasks; solve problems; communicate; manage information; collaborate; create and sharecontent; and build knowledge effectively, efficiently, appropriately, critically, creatively, autonomously,
flexibly, ethically, reflectively for work, leisure, participation, learning, socialising, consuming, andempowerment”. Being digitally competent implies the particular abilities, such as, understanding media,
searching for information and be critical about what is retrieved, and communication with others using
a variety of digital tools. Seven areas of DC were identified: (1) information management, (2)collaboration, (3) communication and sharing, (4) content and knowledge creation, (5) ethics andresponsibility, (6) evaluation and problem solving, and (7) technical operations. The results of that reportcontributed to the DigCom project. Lately, contribution to the better understanding and development of
DC in Europe was presented as DC framework involving five competence areas (DIGCOMP, 2013): (1)information, (2) communication, (3) content creation, (4) safety, and (5) problem solving. These areas
consist of 21 competences. It was meta-framework for existing frameworks, initiatives, curricula andcertifications. DigComp 1.0 has become a reference for many DCs initiatives at European and Member
State levels. DigComp 2.0 keeps the same overall structure of 5 competence areas however slightly
renamed (DigComp, 2016): (1) information and data literacy, (2) communication and collaboration, (3)digital content creation, (4) safety, and (5) problem solving.
One of the best known frameworks among educators and academics is the European Framework for
the Digital Competence of Educators (DigCompEdu) which has six areas focusing on different aspectsof educators’ professional activities: (1) Professional Engagement  organisational communication,
professional collaboration, reflective practice, digital continuous professional development, (2) Digital
Resources  selecting, creating, modifying, managing, protecting and sharing digital resources, (3)Teaching and Learning  teaching, guidance, collaborative and self-regulated learning, (4) Assessment
 strategies, evidence analysis, feedback and planning, (5) Empowering Learners  accessibility andinclusion, differentiation and personalisation, actively engaging learners, and (6) Facilitating Learners’
Digital Competence (DigCompEdu, 2017).
DC involves the confident, critical and responsible use of, and engagement with, digital technologies forlearning, at work, and for participation in society. It includes information and data literacy,
communication and collaboration, digital content creation (including programming), safety (including
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digital well-being and competences related to cybersecurity), and problem solving. The concept “digitaltechnologies” is employed as an umbrella term for digital resources and devices, thus comprising any
kind of digital input: software (including apps and games), hardware (e.g. classroom technologies ormobile devices) or digital content/data (i.e. any files, including images, audio and video).

Computational thinking
In 2006 Wing presented the concept of CT: “Computational Thinking involves solving problems,
designing systems, and understanding human behaviour by drawing on the concepts fundamental tocomputer science” [Wing, 2006]. Based on this it can be concluded that CT involves three key
components: algorithms, abstraction, and automation. However, the rice of this concept can be related
to Seymour Papert by introducing it in the context of suggesting an alternative, computationally-based
mathematics education [Papert, 1996]. Thence the researchers are very interested in CT approach andits application in education. However, it is still at an early stage of maturity [Lockwood & Mooney, 2017]
and the steady definition of CT is not provided [Voogt, 2015]. In the attempt to define CT, researchers
often focus on the core components of CT. As argued in [Voogt, 2015] it is more important try to findsimilarities and relationships in the discussions about CT rather than try to give an ultimate definition.
They provide the discussion about the definition and core concepts of CT only in the Computer Science
domain (excluding other domains), examined by covering literature from 2008 to 2013. Similarly, theanalysis made in 2016 [Kalelioglu et al, 2016] provide the word cloud of CT definitions used in analysed
papers covering 2006-2014. The generated ‘Wordle‘ by Kalelioglu and others was based on thedefinitions provided by analysed researchers and not included some core concepts of CT. Additionally,
CT definitions explained in the analysed papers were analysed and presented in percentage form of thewords used to describe the meaning of the CT: problem solving (22%), abstraction (13%), computer(13%), process (9%), science (7%), data (7%), effective (6%), algorithm (6%), concepts (5%), ability(5%), tools (4%) and analysing (4%). Later, 59 definitions were analysed and classified into 7 themesin [Haseski et al., 2018] and they concluded that current limitations in the CT definition is that it is shapedby technology-aided problem solving. Thus further dimensions needed to be explored especially studies
on personal, environmental, social, affective, psychological and ethical factors needed to beinvestigated.
CT definition challenge was analysed also in others domains, such us mathematics and science by[Weintrop et al., 2016]. They review the literature on CT and situated it historically till 2013. Thus they
propose a definition of CT in the form of a taxonomy consisting of four main categories: data practices,modelling and simulation practices, computational problem solving practices, and systems thinking
practices. Additionally, the set of ten CT skills were proposed:
1. Ability to deal with open-ended problems
2. Persistence in working through challenging problems
3. Confidence in dealing with complexity
4. Representing ideas in computationally meaningful ways
5. Breaking down large problems into smaller problems
6. Creating abstractions for aspects of problem at hand
7. Reframing problem into a recognizable problem
8. Assessing strengths/weaknesses of a representation of data/representational system
9. Generating algorithmic solutions
10. Recognizing and addressing ambiguity in algorithms
This set was the developed based on literature review on CT with a focus on applications to mathematics
and science.
The focus on CT skills rather than definition meaning was presented in [Curzon et al, 2014]. They useda simplified set of skills: algorithmic thinking, evaluation, decomposition, abstraction, generalisation andimplemented them in the developed workshops for teachers on CT themes in order to fill teachers’
knowledge gaps about CT and as a useful practical way that they can teach computing to school
students. Very similar set of CT concepts were used to develop the relationship of CT concepts, student
activity and curriculum subjects example [Catlin& Woollard, 2014]: abstraction, decomposition,
algorithmic design, evaluation, and generalizations.
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However the researchers and educators were searching for more CT elements in order to find out how
CT definition can be interpreted. The literature review made by [Grover& Pea, 2013] showed that there
are nine core elements widely accepted as comprising CT: Abstractions and pattern generalizations(including models and simulations); Systematic processing of information; Symbol systems andrepresentations; Algorithmic notions of flow of control; Structured problem decomposition(modularizing); Iterative, recursive, and parallel thinking; Conditional logic; Efficiency and performance
constraints; Debugging and systematic error detection.
Most of the researchers argue that CT is an activity, often associated with, but not limited to, problem
solving [Beecher, 2017, p.8; Haseski et al., 2018]. For example, the International Society for Technology
in Education (ISTE) and the Computer Science Teachers Association (CSTA) define CT as problem
solving process that (but is not limited to) the following characteristics [CSTA&ISTE, 2011]:• Formulating problems in a way that enables us to use a computer and other tools to help solvethem• Logically organizing and analysing data
• Representing data through abstractions, such as models and simulations• Automating solutions through algorithmic thinking (a series of ordered steps)• Identifying, analysing, and implementing possible solutions with the goal of achieving the mostefficient and effective combination of steps and resources• Generalizing and transferring this problem-solving process to a wide variety of problems.
These skills are supported and enhanced by a number of dispositions or attitudes that are essential
dimensions of CT, including:• Confidence in dealing with complexity• Persistence in working with difficult problems• Tolerance for ambiguity• The ability to deal with open-ended problems• The ability to communicate and work with others to achieve a common goal or solution
Additionally, they defined the vocabulary for CT in the purpose to explain the operational definition bylisting nine CT concepts implicit in the operational definition such as: data collection, data analysis, data
representation, problem decomposition, abstraction, algorithms and procedures, automation, simulationand parallelisation.
As mentioned previously, CT can be seen as a problem solving process. It is in line with many aspectsof 21st century competencies such as creativity, critical thinking, and problem- solving [Lye & Koh,2014]. Thus it can be seen as one of the constructionist method (like the problem-based learning) whichallows students to learn about a subject by exposing them to multiple problems and asking them toconstruct their understanding or objects of the subject through these problems. The pioneer of theconstructivist theory, S. Papertas, has expanded the theory of constructivism by stating that learning isbest when the learner actively develops objects of the real world (for example, a sand castle, anautomatic watering system), and not just ideas or knowledge that deliberately engages in design. The
learning process itself is improved by improving the conditions that learners can construct.
Additionally, constructionism can be seen as the theory of particular relevance when considering lifelong
learning. Lifelong learning is defined as "all learning activity undertaken throughout life, with the aim of
improving knowledge, skills and competences within a personal, civic, social and/or employment-related
perspective" [CEC, 2001]. Similarly, the CT is nowadays fundamental and children should learn it forlife because it involves essential digital age competencies.

CT frameworks and existing practical implementation solutions
A framework for studying and assessing the development of CT was developed by (Brennan & Resnick,2012). Three key dimensions based on studying activity in the Scratch online community and in Scratch
workshops were identified: computational concepts (the concepts designers engage with as they
program, such as iteration, parallelism, etc.), computational practices (the practices designers develop
as they engage with the concepts, such as debugging projects or remixing others’ work) andcomputational perspectives (the perspectives designers form about the world around them and about
themselves). Based on these three dimensions, the suggestions how to assess learning programmingwere developed.
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The College Board (2017) developed the CT framework for a Computer Science Principles course forhigh schools in the USA. They identify six CT practices: Connecting computing, Creating computationalartefacts, Abstracting, Analysing problems and artefacts, Communicating, Collaborating. It is believedthat these practises help students make sense of knowledge in order to accomplish the task, learn
collaboration and communication principles.
Computing at School (CAS) presented the conceptual framework of CT by identifying key concepts(logic, algorithms, decomposition, patterns, abstraction, evaluation) and approaches (tinkering, creating,debugging, persevering, collaborating) involved in CT process as well as techniques (reflecting, coding,designing, analysing, applying) employed to demonstrate and assess CT (CAS, 2015). Each of theconcepts of CT can be identified with approaches and techniques. This gives the opportunity to
implement CT in classroom.
Framework for CT as a Problem-Solving Process was developed by (Kalelioğlu et al., 2016). It has fivemain categories of process that consist of the actions extracted from literature analysis. These
categories are as follows: identify the problem; gathering, representing and analysing data; generate,
select and plan solutions; implement solutions; assessing solutions and continue for improvement. They
argue that this framework could help to teach, learn and practice CT and informatics concepts withinmany courses.
In the book by Krauss & Prottsman (2017), the CT framework of four categories: decomposition, patternmatching, abstraction and automation, is presented. Each has subcategories. This framework could
help plan computer science lessons. The detailed descriptions of lesson plans for each category is given
as well.
Scratch, App Iventor, LegoMind Stroms, CS Unplugged activities and various games are widely
available tools and resources for CT development. CS Unplugged activities are adopted in different
ways: videos, shows, outdoor activities, competitions. Another adoption is an internationally recognizedchallenge on informatics and CT called by Bebras (Beaver). Recently there have been over 60
participating countries. The Bebras challenge is an informatics education community-building model
designed to promote informatics learning and CT at schools by solving short informatics concept-basedtasks (Dagiene & Stupuriene, 2016). Alongside the initial goal of the Bebras project is to motivate
students to be more interested in informatics topics, there is a strong intention to deepen algorithmic
and operational thinking and extended to CT (Dagiene, Sentence, Stupuriene, 2017).

Interconnection between Digital Competence and Computational
Thinking
The characteristics of CT and DC approaches based on results of literature review were analysed. The
analysis showed that CT skills are overlapping and relatively broad in context. The eight CT’s
components groups were identified. Additionally, the list of abilities was identified.
The first CT concepts group - Data analysis & representation (DatAnaRep) involves processes of datacollection, analysis and representation. The concept of generalization is also included in this group.
Generalization is a way of gaining extra information by finding similarities between items (Krauss,Prottsman, 2017). Thus the concept of pattern recognition also belongs to this group. Because
generalization can be defined as an activity that identifies patterns among individual sub-problems andsimplifies them (Beecher, 2017). This group is mostly related to (CSTA & ISTE, 2011) vocabulary andframework for CT proposed by (Atmatzidou & Demetriadis, 2016).
The second group - Computing Artefacts (ComA) involves the creative aspects of computing and isdeveloped based on six CT practises framework. It means the process of designing and developing
computational artefacts as well as applying computing techniques to creatively solve problems.
Decomposition (Decom) is the third group. This concept is identified in the most of the literature on CT
and simplified means the process of breaking down the task into smaller manageable parts.

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The fourth group – Abstraction (Abst) can be defined as the solution for a more general problem byignoring certain details (Krauss, Prottsman, 2017). It also involves developing and representing modelsof the real world (Yadav & Hong & Stephenson, 2016).
The core concept of CT is Algorithms (Algo) and mainly devoted to the process of algorithm design. It
also involves automation concept as it can be defined as the process of plugging pieces into analgorithm to help with a result (Krauss, Prottsman, 2017). Similarly, these two concepts were grouped
together in (Duncan, Bell & Atlas, 2017).
The sixth group is Communication & collaboration (ComCon), the ability to communicate and work withothers to achieve a common goal or solution are essential dimensions of CT. In order to successfully
design, build, and improve computational artefacts the application of teamwork and collaboration, basedon effective team practices, is important (College Board, 2017).
Practice that relates to the influence of computing and its implications on individuals and society is called
Computing & Society (ConSoc). It can be seen also as responsible use of technologies by
understanding the impact of computing, connection between society and computing. It involves
concepts such as cybersecurity concern, privacy, self-protection in the Internet, potential beneficial andharmful effects of computing innovation.
The Evaluation (Eval) group involves the process of ensuring that solution, whether an algorithm,
system, or process, fit for purpose (CAS, 2015). Simplified: evaluating the appropriateness of the
proposed solutions and artefacts (College Board, 2017). It can be done systematically (through criteria
and heuristics) make substantiated value judgements (Catlin & Woollard, 2014).
Digital competencies selected from the European Framework for the DC of Educators (DigCompEdu,
2017) were analysed. Facilitating Learners’ Digital Competence area consist of five groups: Informationand media literacy (InfMedLit), Digital communication and collaboration (DigComCol), Digital content(DigCon), Responsible use (ResUse), Digital problem solving (DigProSol).
Visualization of the interconnection between CT and DC components groups is presented in Fig. 1.
These interconnections were developed from a matching of relationships between the details ofcomponents and features of CT and DC gathered from their respective literature reviews. First,
component groups were identified and definitions collected for CT and DC. Then the related abilities ofeach component group were listed. Based on definitions and abilities, main concepts involved in eachgroup were identified.

Fig. 1. The interconnection between DC and CT components

The interconnections presented in Fig. 1 are a visual portrayal of matches identified from analysis ofinterconnections between DC ant CT components. It was quite clear that the Information and media
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literacy group that involves abilities to manage information has interconnections with CT groups thatinvolves similar abilities: Data analysis and representation, Decomposition, Abstraction, and Evaluation.
It is interesting to note that it has interconnection with the Computing and Society group that relates tothe influence of computing and its implications on individuals and society (College Board, 2017). As it
was thought, the second group of Digital communication and collaboration which requires learners toeffectively and responsibly use digital technologies for communication, collaboration and civic
participation has interconnection with CT groups related to information exchange: Data analysis andrepresentation, Communication and collaboration and, Computing and Society. Additionally, it connects
to Abstraction group that is a way of expressing an idea in a specific context while at the same time
suppressing details irrelevant in that context (Beecher, 2017) and looks like has nothing in common tocommunication or collaboration at a first sight. The Digital content group involves abilities to deal with
content development strategies and licenses. It is not surprise that it is interconnected with four CT
groups related to content procession, development strategies and social wellbeing: Data analysis andrepresentation, Computing artefacts, Algorithms and, Computing and Society. Fourth DC group,
Responsible use involves abilities to empower learners to manage risks and use digital technologies
safely and responsibly has relation to very similar CT group - Computing and Society. Finally, the lastDigital problem solving group is interconnected with four CT groups of Computing artefacts, Algorithm,
Communication and collaboration, and Evaluation, involve abilities that are essential for problem
solving. Thus it is quite clear that the last, Digital problem solving group, is interconnected with thesefour CT croups.
The presented interconnections of DC and CT groups were identified from similarities between abilities
in each group. Patterns were spotted by looking for concrete descriptions, nouns and verbs thatappeared in both cases. This enabled a simplification of them in order to identify interconnections. The
findings from the literature review, generalisation and analyses are described below.
Information & media literacy group has 6 abilities. First Information & media literacy ability is to search
information, thus ability to gather information is very similar as well as Abstraction ability to information
filtering for solution development, and in order to articulate information needs the Computing and society
ability of understanding connections between computing is needed. Second Information & media
literacy ability to develop search strategies has relation with ability to gather an appropriate informationand making sense of data. Additionally, in order to update search strategy, the expansion of existed
solution to cover more cases could be used. Information filtering is needed for personal searchstrategies updating as well as ability to explain connections between computing concepts. The third
Information & media literacy ability to adapt search strategies has the same interconnections with DC
abilities as the second Information & media literacy ability. Fourth Information & media literacy ability is
to evaluate the content. Thus, the analysis and comparison of content is related to patterns finding andconclusions making, as well as evolution of proposed solution and appropriateness, correctness
justification. To organize content in digital environment (sixth Information & media literacy ability) is
directly related to ability to depict and organize data in graphs, charts, images. The last listed Information& media literacy ability - information processing and organizing in structured environment has
interconnection with two CT abilities. To identify the patterns and commonality between content could
be one of the steps for information organizing as well as breaking down the content into smaller parts
to easier manage them.
The second DC group Digital communication & collaboration has twelve abilities. Eleven of them isrelated to two CT groups, mostly to Communication & collaboration. Of course, ability to interract andability to share content has connection to ability to exchange knowledge. Ability to understandappropriate digital communication means for a given context is could be related to ability to explain themeaning of a result in context. It is clear, that ability to know about referencing and attribution practices
ability to participate in society through the use of digital services, ability to create and manage one or
multiple digital identities, ability to protect one’s own reputation) are connected to Computing & Society
gruop ability to identify impacts of computing and describe connections between people and computing.
This ability deals with privacy and security concerns, self protection in Internet, the way people connect,computing innovation (social, economic, cultural) effect, benificial and harmful effects of computing(College Board, 2017). The sixth Digital communication & collaboration practise to seek opportunities
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for self-empowerment and participatory citinzenship could be related to practise of sharing the workload
to collaborative effort by providing individual contributions. Seventh Digital communication &collaboration ability to collaboratively create content is interconnected with ability to collaborate inproducing content and problem solving. Ability to foster a constructive collaborative climate by resolving
conflicts and facilitating the contributions is related to eighth and ninth Digital communication &collaboration abilities because in order to foster the constructive collaborative climate one must beaware of behavioural norms and cultural diversity. Ability to deal with the data that one produces throughseveral digital technologies, environments and services has connection to CT Data analysis &representation group‘s ability to deal with data in appropriate formats and Abstraction gruop‘s ability to
explain how content is represented for computation.
The third group Digital content has six abilities. First is ability to manage digital content in different
formats. It is related to ability to create content with a personal intent. Ability to express themselves
through digital means and to be aware of copyright and licenses application is connected to ability todescribe people and computing connection. Third ability is partly related to Data analysis &representation group’s ability, because in order to modify, refine, improve and integrate information into
an existing body of knowledge one could find similarities between items as a way of gaining extra
information. On purpose to create original and relevant content, knowledge (fourth ability) the abilities
to select appropriate techniques or information-management principles are useful. Sixth ability toperform a task or solve a problem by developing a sequence of instructions for computing system hasfour interconnections with abilities to use appropriate algorithmic principles, to identify sequence of
events, to plug pieces into algorithm and to control a process by automatic means.
Responsible use group’s all abilities that deal with safety and security understanding, protection andprivacy, wellbeing in digital environment are interconnected with ability to identify impacts of computing,describe connections between people and computing. Digital problem solving group has seven abilities
that are interconnected with nine CT abilities. First ability to identify and solve problems when using
devices connected to ability to identify the processes of events. On purpose to identify, select, use andevaluate possible technological responses to solve task all Evolution group abilities are needed:
evaluate proposed solution, correct errors, explain solution functions and justify appropriateness andcorrectness. Fourth ability to create knowledge by using technologies in innovative can be seen as ause of technologies with practical, personal or societal intent and appropriate management principles.
Additionally, in order to create a high quality content, the review and revise of own work is needed. Also
it is related to competences self-improvement needs recognition and self-development regulation. Sixth
ability to support others in competence development has connection to ability to facilitate thecontribution of a team member in a constructive way thus enable their improvement.

Conclusion and Discussion
The analysis of possible interconnections shows that both Digital Competencies (DC) andComputational Thinking (CT) have a lot of in common. Many abilities and competencies are overlapping.
Only one of DC listed abilities (Digital problem solving - to adjust and customise digital environments topersonal needs) has no direct connections to CT abilities. This ability is from personalization researcharea thus it could be concluded that it is out of the CT focus area at the moment. Digital problem solving
is very important and huge area, so more detailed investigation in connection to CT is needed.
Five CT abilities were left without connections: ability to explain how abstractions are used incomputation or modelling; ability to identify abstractions; ability to describe modelling in a computational
context; ability to describe computation with accurate and precise language, notations, or visualizations;ability to summarize the purpose of a computational artefact. These abilities are related to abstraction
and computation processes, actually, that are included in DC framework indirectly.
The discussed framework of CT components group and interconnections between DC and CT could befurther developed, especially the quality research based on interviewing experts is needed. Evaluation
study of experts’ opinions of different countries for the proposed framework is in our future plans.
Limitation of this work is that there was no focus on age groups or educational course subject. As arguedin (Shailaja & Sridaran, 2015) work, CT skills could be grouped by age, e.g. visualization, pattern
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recognition and generalization can be learnt in K-2, abstraction and critical thinking in grade 6 to 8.
Additionally, deeper investigation should be paid to integration of CT in different subjects. CT can benefit
students studying in any area, academic and work lives and can be successfully taught in any subjects(Lockwood & Mooney, 2017). The similar questions can be applied to DC.

Acknowledgment
This research is/was funded by the European Social Fund under the No 09.3.3-LMT-K-712“Development of Competences of Scientists, other Researchers and Students through Practical
Research Activities” measure.

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AI Programming by Children
Ken Kahn, toontalk@gmail.com
Department of Education, University of Oxford, UK

Niall Winters, niall.winters@education.ox.ac.uk
Department of Education, University of Oxford, UK

Abstract
The idea of children constructing artificial intelligence programs goes back to the early days of Logo
(Papert & Solomon 1971; Kahn 1975; Kahn 1977). After decades of little activity recent efforts to support
students in making AI programs has come from Stephen Wolfram (Wolfram 2017b), Google (Google
2018a, 2018b), the Machine Learning for Kids website (Lane 2018), and the eCraft2Learn project(eCraft2Learn 2018b). Two technological developments underlie the feasibility of these efforts: (1) AI
cloud services and (2) mainstream laptops and desktop computers that now can run sophisticatedmachine learning algorithms. And all of these developments can be made accessible in a web browser,
thereby running on many platforms without the need to install software.
Given appropriate programming tools children can make apps and intelligent robots that rely uponspeech and image recognition services. They can add custom capabilities to their programs by using
machine learning training and prediction. In doing so they may learn about perception, language,
psychology, and the latest empowering technologies.
We describe the addition of new programming blocks to the Snap! visual programming language(Harvey & Mönig 2010) that provide easy-to-use interfaces to both AI cloud services and deep learning
functionality. Interactive learning materials have been developed and preliminarily trialled with students.
We anticipate in future trials to observe children creatively using these new blocks to build very
impressive programs. Children are likely to be even more motivated to program when the results are
such capable programs.

Keywords
Visual programming; machine learning; block languages; Snap!, AI services; cloud services; speechsynthesis; speech recognition; image recognition

Introduction to AI programming by children
From the early days of Logo research (Papert & Solomon 1971; Kahn 1975; Kahn 1977) there wasinterest in supporting children in creating artificial intelligence programs. The decision making,perception, learning, and natural language understanding in these programs was very simple due to theinabilities of the computers of those days. In the process of programming AI systems one naturally
reflects on one’s own thinking processes. This provided a very good fit for the constructionist ideas oflearning through construction and reflection.
Among the many reasons for supporting AI programming by children are
1. Students may become better motivated and empowered to produce very capable artefacts
2. Students may learn about perception, reasoning, psychology, and animal behaviour in theprocess of building perceptive robots and apps
3. Students may learn about cloud services, machine learning, artificial intelligence, and otheradvanced technologies
4. Students may reflect more deeply upon their own abilities to hear, see, learn, and respond
appropriately.

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AI cloud services
Today’s AI cloud services provide a new opportunity to support a new class of student AI projects –those that rely upon state-of-the-art AI “subroutines” (Kahn & Winters 2017). These services include
speech synthesis and recognition as well as image recognition. Children can design and build
impressive intelligent artefacts by composing and customising components provided by world-class AI
teams.
Several companies are offering AI cloud services via a web connection. These include Google’s
machine learning services, IBM Watson cloud services, Microsoft cognitive services, and Amazon AI
services. Many of these services include recognition services for obtaining descriptions of what is being
spoken or seen. Other services analyse text for content, tone, and sentiment. They all include machinelearning services that find patterns in data.
These are commercial services that cost a few dollars for a thousand queries. Fortunately for schools
with limited budgets, free quotas are provided which allow a hundred recognition queries per day or a
few thousand a month.
The service providers support accessing these services from many programming languages.
Unfortunately, these are complex interfaces designed for use by professional programmers. In thispaper, we show how to provide easy-to-use interfaces to these services, opening up their potential tochildren who are learning to program.
Students today are often doing physical computing projects involving micro-controllers such as
Raspberry Pi, Arduinos, or Micro:bits. They are also often programming pre-built robots. In many cases
these projects could benefit significantly from the ability to recognize what is being spoken or what is in
front of a camera. For example, a student could build a robot that when it hears “push the red ball” will
move to the ball and push it. This could be accomplished by sending the output from a microphone toan AI cloud service, picking out the keywords in the response, then repeatedly turning and sending
images from a camera to a service until the response is that a red ball is in the image and then headingin the direction the camera is facing.
There is a long tradition of children programming language-oriented programs. In the early days of Logo
children programmed poetry generators, silly sentence makers, chatbots, and more (Papert & Solomon
1971; Kahn 1975). The appeal of these kinds of projects increases when speech input and output
replaces reading and typing, an area of research that has been neglected but can now be revisited tousing the power of cloud-based AI services. In addition, student projects can use other AI services
including sentiment analysis of what is spoken to respond in appropriate or amusing ways. This opens
up the potential of AI to children in a simple and interactive manner, an emerging area of research weare exploring.

Machine learning
AI cloud services are created by large teams of experts using complex algorithms, massive data, andvery large collections of servers. Systems are trained to recognise speech, images, video, sentiment,
and more. Clients of these services have no control over the training. The services are black boxes totheir users. In contrast, when children use machine learning software (Wolfram 2017b; Lane 2018) theyneed to provide the training examples before the system can do any classification or recognition.
Programs can be created that respond to hand gestures, individual faces, or odd sounds that AI cloudservices are not trained to recognise.
While there are cloud services for machine learning, we are not aware of any that have free quotas orare inexpensive enough to be used by school children. The Machine Learning for Kids website (Lane2018) has a special arrangement with IBM for limited use of their machine learning cloud services.
Consequently, the site requires registration and limits the number of projects allowed.
An alternative to cloud services appeared recently (tensorflow.js 2018). Tensorflow.js (formerly named
deeplearn.js) is an open source JavaScript library that is able to support both training and prediction
using deep neural nets. It runs in a browser and is accelerated hundreds of times by its use of graphicalprocessing units (GPUs). These are now common on modern laptops, desktop computers, tablets, and316

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smartphones. For example, a high-end laptop can process up to ten images per second during training
and make up to twenty predictions per second. Programs using tensorflow.js on devices that run at one
tenth of that speed can still support a wide range of applications.

Machine Learning for Kids
The Machine Learning for Kids website (machinelearningforkids.co.uk) provides an interface whereusers can define a number of labelled buckets and fill them with images, text, or numbers.

Figure 1. Training to classify a hand as rock, paper, or scissors at Machine Learning for Kids

After the buckets have been filled they are sent off to the IBM Watson servers to generate a model forclassifying new images. It then adds new blocks to the Scratch programming language (Resnick et al2009) that can be used to determine which category a new costume image belongs to. Another Scratch
block is available to send new data for additional training.
The Machine Learning for Kids website has many suggested projects. For example, one implements
sentiment analysis using textual data. Other projects involve learning to play Pac Man or Tic-Tac-Toe(Noughts and Crosses) using numerical training data. The Pac Man program learns to play better byadding additional training data whenever a human playing the game makes a move. The Machine
Learning for Kids website and project ideas inspired the machine learning work described in this article.

Machine Learning in Wolfram Language
Stephen Wolfram wrote a blog post entitled Machine Learning for Middle Schoolers (Wolfram 2017b)
about the new extensive section of his Wolfram Language textbook (Wolfram 2017a). (The Wolfram
language is the language of the Mathematica system.) In this blog post he describes a variety ofmachine learning programming exercises including classification, clustering, text recognition, image
recognition, feature extraction, feature spaces, and training. The textbook explains how neural nets canbe defined and visualised.

Teachable Machine and Model Builder
Google recently released Teachable Machine (Google 2018c) which demonstrates image training andclassification in a web page. It directly inspired the machine learning work reported below.

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Google also recently released the Model Builder (Google 2018b). This is a website where one candesign and train a deep neural net and run experiments on it. While one can learn a great deal aboutmachine learning from this site, it doesn’t provide a programming interface for creating apps that use
the trained models.

AIY Projects
Google has begun to release a series of do-it-yourself AI kits they call AIY kits (Google 2018a). The kits
are based upon Raspberry Pi computers. The first kit enabled one to build a box with a button that can
do speech recognition and synthesis. It can answer questions the same way “OK Google” does. The
second kit is for building a box that can do image recognition. Sample programs written in Python areprovided.

Creating Child-friendly Programming Interfaces
Our efforts as part of the eCraft2Learn project are not to create a new programming environment forchildren, but instead to enhance existing ones. We have added speech input and output to ToonTalk
Reborn (Kahn 2014) and to Snap! (Harvey & Mönig 2010). We have also added image recognition andmachine learning to Snap!. This paper reports on our Snap! efforts.
Why was Snap! Chosen?
Snap! is a superset of the very popular children’s blocks-based programming language Scratch(Resnick 2009). It is well-suited to our efforts because
1. It is a powerful language that supports first-class data structures and functions
2. It is easy to define new blocks using JavaScript without touching the source code
3. It runs in every modern browser
4. There are versions that connect to Arduinos and Raspberry Pis

Speech synthesis
All the popular browsers except for Internet Explorer support the Speech Synthesis API. (There is aproblem with Chromium that no voices are installed.) The API utters the provided text with control forthe pitch, rate, volume, language, and voice. An issue arises because the API instructs the browser only
to begin speaking. The new Snap! block that invokes this API returns at once and yet some projects
need a way to respond to the speech finishing. This was accomplished by passing to the Snap! blockan optional function that is called when the browser signals the event that the speech has ended.

Figure 2. Simple speak command takes a text argument and an optional continuation functionMore advanced users can use a full-featured ‘speak’ command that exposes most of the Speech
Synthesis API functionality. While it is more difficult to use, all but the first parameter are optional andcan be ignored. During testing students were clearly amused by entering different values for the pitch,rate, and voice. The underlying API expects the language parameter to be given as a BCP 47 language
tag. This is a string that specifies the language and dialect. E.g., fr-FR is French as spoken in France
while fr-CA is the Canadian dialect. This is not very user friendly. We addressed this by providing a
Snap! reporter that supports search terms for installed voice names (e.g., “English”, “UK”, and “Female”
in Figure 3). We also defined a way to change the default language for speech synthesis and recognition
by giving the name of the language in the language or in English.

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Figure 3. Advanced speak command that exposes nearly all the functionality of the Speech Synthesis API

Speech recognition
While speech recognition is part of the Web Speech API, as of this writing only Chrome and Opera
support it. However, AI cloud services for speech recognition are available from Google, Microsoft, IBM,and others.

Figure 4. Simple speech recognition block for receiving text recognised and errors(Empty fields here are shorthand for the argument passed to the command. The first one is what was heard andthe second is the error message.)

The asynchronous nature of recognition services forces a reliance upon continuations (also called“callbacks”). Continuations are ideal from a technical point of view; however, we are concerned thatstudent programmers may find them difficult. However, when students learn that the two fields of thelisten command are simply places to put commands that run when and if the speech is recognised orwhen an error occurs. The combination of handling failures, errors, multiple outputs, and the asynchrony
make the use of continuations more appropriate than Snap! reporters.
As an easier alternative to the use of continuations, we also implemented a block using the ‘listen’ blockthat supports event broadcasting and uses global variables:

Figure 5. A block that broadcasts speech recognition results

The underlying complexity is hidden (but available for ambitious students) so that one needs only to callthe ‘broadcast speech recognition results’ block and then receive broadcasts when something washeard and read a global variable containing the last thing spoken. For example, the following program319

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repeats what was spoken, prefaced with “I think I heard”. Besides a questionable reliance upon global
variables this technique makes it very difficult to respond to an utterance that wasn’t expected with a
response such as “I don’t understand …”.

Figure 6. Using broadcasting to respond to recognised speech

For more advanced uses of speech recognition, a user may want access to partial results as one isspeaking, to receive alternative interpretations of what was said along with their confidence scores. And
one may want to specify which language is expected. The following block provides this functionality. In
Figure 7 while speaking partial results are displayed in a thought bubble (unless there is an error).
French is expected. Up to five alternative interpretations of what was said will be displayed.

Figure 7. A full-featured speech recognition block

Image recognition
There are no standard APIs for image recognition so the block we implemented supports the Google,
IBM, and Microsoft APIs. The simple image recognition block has a parameter specifying which cloudprovider, a continuation that will receive a description of the image, and a flag as to whether the image
should be displayed.

Figure 8. A simple block for obtaining a list of labels of what is in front of the camera

The responses from the vision recognition services are tables containing tables sometimes containing
tables. And each provider has different structures. We provide blocks for accessing all the information320

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a vision service provides. For example, The Google vision API supports more than labelling what is in
the image. One can access text recognition, face detection, and image properties results as well.

API Keys
All the AI cloud vision services require that requests be accompanied by API keys. These keys are easy
to obtain and entitle the user to a moderately generous free quota of requests. It is not wise to sharewidely a project that contains API keys since the free quota will be exhausted quickly. The first solution
we implemented was that the keys are provided as URL parameters. This wasn’t very child friendly andbecame too clumsy for projects that rely upon more than one AI service provider. We eventually settled
on using Snap! reporters to hold the keys. Our Snap! blocks use keys if provided and if they are missing
offers to take the user to a page documenting how to obtain keys.

Machine Learning Snap! Blocks
At the time of this writing the machine learning Snap! blocks are limited to images (and a crude learningalgorithm for audio) The underlying technology can be enhanced to support audio, video, text, and data.
The interface to machine learning requires a second browser tab in addition to Snap!’s. Technically thisis necessary since the machine learning software uses the GPU and Snap! uses it for graphics via
WebGL. It is also motivated from a user interface perspective since the second tab is well-suited forboth training and prediction feedback.
The following command launches a tab for training whether you are leaning to the left or the right. It
overrides the default text on the page.

Figure 9. Launching a training tab

The tab created includes buttons for adding new images to the training. It also provides feedback as tohow confident it is that it can label the current image.

Figure 10. A machine learning training window321Constructionism 2018, Vilnius, Lithuania

Upon returning to the Snap! tab one can send an image from the camera to the training tab to obtain alist of the confidences that each label applies to the image.

Figure 11. A block to obtain label confidences

An alternative to training using the camera is to use blocks that use the costumes of the Snap! sprites.

Figure 12. Blocks to use costumes for training and prediction

A block has been defined that uses the ‘Add costume …’ block to send all the costumes of a sprite to
the training tab. These costumes can be drawn, imported, or captured by a camera. Currently theunderlying library providing the machine learning capabilities (tensorflow.js 2018) has no general way
to save a model after training so applications need to run training before doing classification.

Student and teacher guide to AI programming
An extensive interactive guide to using all of these new Snap! blocks has been created. There is a
student and a teacher version (though they are over 90% identical) (eCraft2Learn Project 2018b). The
guides are web pages with dozens of Snap! projects embedded as iframes. Some are simple interactive
introductions to a single block while some are more complex demonstration programs.
In addition to the technical content the guides include discussions of how the underlying technology
works as well as societal impact. They also include exercises and project ideas.
The guides can be viewed via a local web server. This is useful when the Internet connection is slow orintermittent. If there is no Internet connection about half the guide’s interactive elements are still
functional. The machine learning chapter works fine locally without a network connection.

Internationalisation
Google Translate has been integrated with the AI programming guides, documentation, and the training
tab interface. The speech synthesis and recognition blocks by default use English but there is a block
for setting the default to any of the many languages that Chrome supports. Snap! itself supports several

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dozen languages. Unfortunately, the labels and help messages of the AI blocks themselves are nottranslated.

Sample programs using AI blocks
Currently ten sample programs are available that use these new blocks. They are designed to bereadable and editable by students. Many of them implement only part of what a full app would containleaving the missing parts for students to add.
1. Speak with random pitch, rate, voice, and language (Speech synthesis)
2. Speak commands to a sprite (Speech synthesis and recognition)
3. Generate funny templated sentences (Speech synthesis and recognition)
4. Generate template stories (Speech synthesis and recognition)
5. Talk to Wikipedia (Speech synthesis and recognition)
6. Report what is in front of the camera (Speech synthesis and image recognition)
7. Speak what is in front of the camera when a cloud service is asked to (Speech synthesis, speech
recognition, and image recognition)
8. After training watch the turtle move depending on which way your finger is pointed or sounds you make
(Machine learning)
9. After training watch the turtle move left or right depending on which way you lean (Machine learning)
10. Play a Rock Paper Scissors game by configuring your hand in front of the camera (Speech synthesis andmachine learning)

All of these programs and the new blocks are available at (eCraft2Learn Project 2018b).
Using NetsBlox (NetsBlox 2018), a Snap! variant that supports multi-player projects, and together.js(together.js 2018), a library for creating multi-player JavaScript programs, we created a two-player
version of Rock Paper Scissors. In the first phase both players connected via the Internet can train thesame model to recognise whether a hand is showing rock, paper, or scissors. In the second phase theprogram says “1, 2, 3, go” and then the trained model and cameras on both computers are used toclassify each players moves. Speech synthesis is used to inform the players of the outcome.

Field testing
The speech input and output blocks have been tested with 25 Singaporean undergraduate students, 18
children (aged 7 to 13, Sri Lankan and Singaporean) in afterschool programs and 40 Indonesian highschool students. This preliminary testing has been very encouraging. The new Snap! blocks were easily
understood and the users indicated that they enjoyed adding speech to their programming projects. 12of the 18 children and all the high school students were also introduced to machine learning Snap!
blocks and mastered several machine learning programming exercises.
A formal study of 40 Indonesian high school students using speech synthesis, speech recognition, andmachine learning has been submitted for publication. Teachers in Finland and Greece have reported
successfully introducing the Snap! AI blocks.

Conclusion and Discussion
We began by describing the history and current efforts to support AI programming by children. We thenpresented programming constructs we have developed that are suitable for use by beginners for speechsynthesis, speech recognition, image recognition, and machine learning (of images). Artificial
intelligence is much broader than this and includes common sense, planning, reasoning, understanding
language, unsupervised learning, translation, and more. The research reported herein is just the startof an effort to give children the ability to construct AI applications.
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The goal of this research is to demonstrate that AI programming need not be limited to those withadvanced degrees. Even young children can be empowered to creatively use AI programming to makeapps that speak, listen, see, and learn.

Acknowledgements
This research was supported by the eCraft2Learn project funded by the European Union’s Horizon
2020 Coordination & Research and Innovation Action under Grant Agreement No 731345.

References
Tensorflow.js (2018) https://js.tensorflow.org/.
eCraft2Learn Project (2018a) http://www.project.ecraft2learn.eu/.
eCraft2Learn Project (2018b) https://ecraft2learn.github.io/ai/.
Google (2018a) AIY Projects. https://aiyprojects.withgoogle.com/.
Google (2018b) Google Model Builder. https://deeplearnjs.org/demos/model-builder/.
Google (2018c) Teachable Machine. https://teachablemachine.withgoogle.com/.
Harvey, B., Mönig, J., (2010) Bringing “No Ceiling” to Scratch: Can One Language Serve Kids andComputer Scientists? In Proceedings: Constructionism, Paris, France.
Kahn, K. (1975) A Logo natural language system. Technical report, MIT AI Lab, LOGO Working Paper
46.
Kahn, K. (1977) Three Interactions between AI and Education. Machine Intelligence 8.
Kahn, K. (2014) ToonTalk Reborn: Re-implementing and re-conceptualising ToonTalk for the Web. In
Proceedings: Constructionism, Vienna, Austria.
Kahn, K., Winters, N. (2017) Child-friendly programming interfaces to AI cloud services. In Proceedings:
EC-TEL 2017: Data Driven Approaches in Digital Education, 10474, 566-570.
Lane, D. (2018) Explaining Artificial Intelligence. Hello World 4. Raspberry Pi Foundation.
https://helloworld.raspberrypi.org/issues/4.
NetsBlox (2018) https://netsblox.org/.
Papert, S., Solomon, C. (1971) Twenty Things to Do with a Computer, MIT AI Lab,
http://hdl.handle.net/1721.1/5836.
Resnick, M., Maloney, J., Monroy-Hernández, A., Rusk N., Eastmond, E., Brennan, K., Millner, A.,
Rosenbaum, E., Silver, J., Silverman, B. and Kafai, Y. (2009) Scratch: programming for all.
Communications of the ACM, 52(11), 60-67 DOI=http://dx.doi.org/10.1145/1592761.1592779.
Together.js (2018) https://togetherjs.com/.
Wolfram, S. (2017a) An Elementary Introduction to the Wolfram Language, Second Edition. Wolfram
Media.
Wolfram, S. (2017b) Machine Learning for Middle Schoolers. Stephen
http://blog.stephenwolfram.com/2017/05/machine-learning-for-middle-schoolers/.

Wolfram

blog.

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VISURATCH: Visualization Tool for Finding
Characteristics of Teaching and Learning Processof Scratch Programmers
Nobuko Kishi, kishi@tsuda.ac.jp,
Mari Yoshida,
Minori Yoshizawa,
Tsuda University, Japan

Aoi Yoshida, aoi@si.aoyama.ac.jp
Aoyama Gakuin University, Japan

Abstract
We are developing an interactive visualization tool, VISURATCH, for Scratch projects using Python andD3.js. This tool collects a set of programs, or projects, from the Scratch site and reveals each program’s
characteristics, such as the number of blocks, as a line chart and a heat map. By visualizing a set ofprograms in chronological order of creation, it enhances an instructor’s or a learner’s ability to quickly
find the characteristics of their teaching and learning process. The tool’s visualization model is basedon the Visual Information-Seeking Mantra: Overview first, zoom and filter, then details-on-demand. We
observe that the tool shows some characteristics of a teaching plan when applied to a set of sample
programs shared in a Scratch studio. Moreover, the tool shows some characteristics of a learner, suchas the pattern of programming blocks frequently used, when applied to a set of programs specified by
the learner’s id.

Keywords
information visualization; Scratch programming; computer science education

Introduction
Teaching children to program or encouraging them to learn to program is becoming a major trend ineducation. However, there are no simple methods for assessing children’s ability with programming orcomputational thinking. In software development classes for adults, we can often evaluate students’skills based on the programs they create. In programming classes for children, we need to focus onwhat they learn, rather than what they create. In the constructionist approach to learning programming,
we encourage children to create programs meaningful to them, but we need to assess various aspectsof children’s learning, not just the programs that they create.
Brennan and Resnick (2012) proposed a general framework for the assessment of computational
thinking. In their paper, they propose three key dimensions: computational concepts, computational
practices, and computational perspectives for assessing computational thinking. They also described
three approaches—project analysis, artifact-based interviews, and design scenarios—and six
suggestions for further research.
Our work is motivated by their work, based on their suggestions #3: Illuminating processes.
Conversations with the learner and real-time observation of their activities are mentioned in their paper
as possible methods for studying the learning processes. However, it is time-consuming to use thosemethods with many children, or when instructors have limited time.
We have decided to try a well-known technology, information visualization, to ease the task of finding
the characteristics of the process of children learning to program. Scratch is an online programming toolfor children, or for people of any age, developed at the MIT Media Lab. (Resnick, 2009). Scratch projects
are shared by Scratch users at the Scratch site http://scratch.mit.edu. Various project metadata suchas a project creation date, modification date, publication data, and the user id of the creator are also
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available at the Scratch site. By obtaining a set of projects created by a single user from the Scratchsite and putting them in chronological order, we can get a series of programs in order of creation dateby the same user.
In this paper, we first describe the information visualization model we used with VISURATCH. Next, we
describe its system design and its main function. After that, we report on the findings we obtained withthe tool in the case studies. Although VISURATCH is still in the development stage, we made several
interesting observations with the sample programs used in Scratch books and the programs created byyoung learners. Finally, we discuss the possibility of program visualization in assessing the
programming skills of young learners and future works.

Information Visualization Model for Scratch Projects
Information visualization or data visualization is a technology to explore large amounts of abstract databy creating images or diagrams to represent a fact or communicate a message. The term information
visualization originates from the works at Xerox PARC (Card, Mackinlay, Shneiderman, eds., 1999).
There are a variety of techniques for information visualization, and Shneiderman summarizes it with the
Visual Information-Seeking mantra: overview first, zoom and filter, then details-on-demand(Shneiderman, 1996). We have decided to apply this mantra to a set of Scratch projects that are createdby a single learner, or to those that are included as sample programs in a Scratch book or online tutorial..

A Scratch project
The Scratch 2.0 programming environment is available as an online programming tool at the Scratchsite: http://scratch.mit.edu. The Scratch projects, programs written in Scratch, are created by snapping
graphical programming blocks together at the Scratch site. As of March 31, 2018, the number of Scratch
projects shared was 30,302,556. These Scratch projects are stored in JSON format and available
through the Scratch API. Figure 1 shows some of Scratch’s graphical programming blocks, which arepart of a Scratch project. Figure 2 shows JSON data corresponding these blocks. Each Scratch project
also contains graphics data and sound data, in addition to the Scratch program. Figure 3 shows an
online programming screen for a Scratch project.

Figure 1. Scratch programming blocks

Figure 2. Scratch programming blocks in JSON format

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Figure 3. A Scratch project

The Scratch site and studio
At the Scratch site, the metadata for Scratch projects are also available. By metadata, we mean thedata about the program author, the program creation date, the modification date, and the date when the
program was shared. The site also offers mechanisms for sharing projects. When a user id is specified,
the site shows a list of programs created by that user and made publicly available by the user
himself/herself. Figure 4 shows an example of the screen that is returned after a user id is specified. Itshows one featured project and five additional projects from 18 projects shared by a single user. The
Scratch site also has a studio mechanism that can hold a set of projects created by different users.

Figure 4. Projects created by a single Scratch user

Overview of Scratch projects.
By using the Scratch API and the Scratch site, we get two sets of Scratch projects. With a Scratch user
id, we get a set of projects created by a single user corresponding to that user id. With a studio id, we
get a set of projects created by a group of users with a common interest, such as creating sample
programs for a workshop.
Each Scratch program has the following metadata:“created” date: the date and time when the project was first created“modified” date: the latest date and time when the project was modified“shared” date: the date and time when the project was made publicly available on the community
site
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To observe the changes among programs over intervals of time, we decided to extract the following
statistics to characterize the program.
 The number of each type of block The number of blocks in each category of block Total number of all blocks
 Total number of sprites
To get an overview of these data, line charts are used. A line chart shows a trend in the total number ofblocks, or the number of blocks in each category of block over the dates of creation, modification, andsharing. To give a detailed view of each project, a heat map is used to show the use of each type ofblock. In addition, a list of the project title and the thumbnail image of the screen is shown so that theuser can run the Scratch program easily. Left part of Figure 6 shows an example of the VISURATCH
main screen. It shows a visualization of the Scratch projects created by a single user, corresponding tothe ones shown in Figure 4. It shows a line graph in the upper part, a heat map in the bottom left, anda project list in the bottom right. When a project in the list is double-clicked, a Scratch project window,
as shown in Figure 3, is opened and the program can be run in that window.

System Design
VISURATCH comprises two main components (Figure 5). One is an analysis tool written in Python. This
tool obtains Scratch program data by scraping the Scratch site and by using the Scratch API. It thenanalyses the program data and sends the statistics of the program, such as the number of blocks andthe program creation date, to a web browser in JSON format. The other is a visualization tool
implemented in JavaScript. It runs on a web browser and uses D3.js, a JavaScript library for visualizing
data (Bostok, 2017). The term D3 comes from Data-Driven Document. When the statistical data fromthe analysis tool are obtained as a document, the images of the data can be interactively modified. For
example, scrolling and zooming of a line graph can be done interactively using the brush function (Figure
6).

Figure 5. VISURATCH system design328Constructionism 2018, Vilnius, Lithuania

Figure 6. Zooming and scrolling of line charts using brush function in D3.js

Case Studies
To evaluate the effectiveness of VISURATCH, we have conducted three case studies. We first studied
a model user, a hypothetical learner who creates sample programs exactly as given in a website or ina book. We then studied Scratch projects created by real learners of Scratch. Although we are aiming
for visualization of a large set of Scratch programs in the future, we need to start with a small set ofabout 20 programs so that we can study each program in detail.

Case 1: Scratch tutorial at the Scratch site
The Scratch site has an online tutorial for beginners, which has a series of sample projects (Figure 7).
We created all the sample programs and put them in a single Scratch studio. Figure 8 shows thevisualization of these sample programs specified by its studio id. With the line graph we can see thatthe number of blocks increases over time, but each project is not always larger than the previous project.
We also notice that some blocks are used rarely. The Pen category blocks are not used at all, and theMore category blocks are not used until the end of the tutorial. This means the tutorial excludes a sample
program for drawing graphics using x-y coordinates. It also means that the blocks for creating a function,
which are the More category blocks, are not used in the beginning part of the tutorial.

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Figure 7. Scratch Tutorial at http://scratch.mit.edu

Figure 8. Visualization of sample projects in Scratch Tutorial

Case 2: Scratch book on creating a shooting game
Creating a shooting game is very popular among Scratch learners. A book was published in Japan
aimed at having the reader learn Scratch by creating a shooting game (Sugiura, 2015). The book author
made 16 projects from the book available in the Scratch site. Figure 9 shows the visualization of these16 projects. We found that the number of the total blocks gradually decreased over time in the line chart,
and the use of category blocks also gradually decreased in the heat map in chronological order of thedate of sharing. Figure 10 shows the visualization based on the creation date and modification date.
We presume that the author uploaded the final project, a complete shooting game, first, followed by therest of the programs in reverse order of their appearance in the book. We also presume that the author
was very careful that each sample project be created by adding a small set of blocks to the preceding
sample projects.

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Figure 9. Sample projects in the order of their being shared

Figure 10. Sample projects in the order of “modified” date and “created” dateCase 3: A real learner of Scratch
A Japanese TV station, NHK, broadcasts a TV program to introduce Scratch to children in Japan (Japan
Broadcasting Corporation, 2016). On that program, contests are regularly held. One of the contest prize
winners was a girl with two years of experience with Scratch. She put 18 shared projects on the Scratchsite. Figures 11 and 12 show the visualization of her projects. We applied a filtering function to the line
chart to create three line graphs: a purple line for for the number of Looks category blocks, a orange
line for Control category blocks, and green line for the total number of blocks (Figure 11). We observedthat these three lines show almost the same trend. This means she used the blocks in the Looks andthe Control category together quite often. Next, we used the detailed heat map of the blocks in thesetwo categories. We found one block, wait x secs, in the Control category (Figure 12), and three blocks:
say x for y secs, show, and hide in the Looks category (Figure 13) quite frequently. These four blocks
were often used to change the scenes of the stories in Scratch projects. With this information, we canpresume that she frequently created stories with Scratch. We confirmed this by running her projects on
the Scratch site and by observing that many of her projects were story-oriented.

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Figure 11. Filtered line charts

Figure 12. Control Category block found by the detailed heat map

Figure 13. Looks Category blocks found by the detailed heat map

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Conclusion and Discussion
We have developed a preliminary version of an interactive visualization tool, VISURATCH, for Scratch
projects using Python and D3.js. We also conducted case studies to evaluate its effectiveness. The
initial case studies suggested that VISURATCH could help an instructor or a learner of Scratch toidentify the characteristics of teaching and learning processes.
After conducting several more case studies, we plan to improve VISURATCH and make it publicly
available as an online tool. By doing so, we can conduct a data-based evaluation of our visualization
model at large scale. We hope to improve VISURATCH so that it can be used easily by both instructors
and learners to understand the learning process.
We also hope that it can serve as a basis for research on educational data mining in the future. Scratch
is excluded in the literature review and case studies paper on educational data mining and learning
analytics (Ihantola, P. et al., 2015), probably because Scratch is not a textual language and is intended
for children to learn computational thinking, not programming. However, it can be a good starting point
for educational data mining because it offers a large set of program data with an IDE available toeveryone.

References
Brennan, K., and Resnick, M. (2012). New frameworks for studying and assessing the development ofcomputational thinking. Proceedings of the American Educational Research Association (AERA) annual
conference
Card, S.K, Mackinlay, J.D. and Shneiderman, B. (Eds.), (1999). Readings in Information Visualization:
Using Vision to Think. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA
Ihantola, P. et al. (2015). Educational Data Mining and Learning Analytics in Programming: Literature
Review and Case Studies, Proceedings of the 2015 ITiCSE on Working Group Reports, July 04-08,
Vilnius, Lithuania
Japan Broadcasting Corporation, NHK (2016). Why? Purograminngu (in Japanese),
http://www.nhk.or.jp/gijutsu/programming/
Resnick, M. et al. (2009). Scratch: Programming For All. Communications of the ACM, Vol. 52 No. 11,
Pages 60-67
Shneiderman, B. (1996). The Eyes Have It: A Task by Data Type Taxonomy for Information
Visualizations. In Proceedings of the IEEE Symposium on Visual Languages, pages 336-343,
Washington. IEEE Computer Society Press,
https://www.cs.umd.edu/~ben/papers/Shneiderman1996eyes.pdf
Sugiura, M. (2015). Scratch de hajimeyou, purograminng nyumon (in Japanese), Nikkei BP, Tokyo,
JAPAN

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Curriculum Intervention for Learning Programmingin Python with Turtle Geometry
Eva Klimeková, klimekova@fmph.uniba.sk
Department of Informatics Education, Comenius University, Bratislava, Slovakia

Abstract
Programming as part of computing education at upper secondary schools in Slovakia has a long
tradition. When a new trend of teaching programming in Python emerged, many schools adopted it. In
this paper we focus on deeper understanding of teaching programming basics in the Python language.
Our aim is to find out how we can help to teacher for easier transformation to new language. We
designed a curriculum intervention for learning programming in Python in which we decided to exploit
the motivation of turtle geometry. This supports learning by exploring, creating and discovering. Bymeans of it pupils learn the basic concepts and constructs as variables, loops, functions and conditions.
We developed, implemented and verified our materials iteratively, using design research. In this paper
we also present the process of development of our curriculum intervention from the first steps ofcollecting initial insight to implementing the materials into practice at upper secondary schools in
Slovakia.

Photo of a pupil’s “rose” created by using turtle geometry in Python

Keywords
Python; turtle geometry; curriculum intervention

Introduction
Computing in upper secondary school education (pupils aged 15 to 18) in Slovakia has a relatively
strong tradition. Despite the fact that educational reforms often modified the content of the generalacademic subject Informatics, programming is an integral part of this subject for more than twenty years.
The aims of teaching programming are the development of algorithmic thinking, problem solving skills
and the acquisition of programming skills. In Slovakia, National curriculum does not explicitly specify aparticular programming language, therefore the teachers choose the programming language. Currently,
the most widely used programming language in Slovakia is Pascal (Delphi or Lazarus), previously Logo
was used. In the last few years Python is more commonly amongst the languages recommended forteaching programming basics. Research shows that Python is suitable as the first programming
language for pupils at upper secondary school (Atteq, 2014; Goldwasser and Letscher, 2008; Krpan
and Bilobrk, 2011). Manilla and De Raadt (2006) after extensive analysis of programming languages
concludes that “...the most suitable languages for teaching, Python and Eiffel, are languages that havebeen designed with teaching in mind“. According to Grandell (2006), the benefits of Python fully support
the aims of teaching programming, the development of algorithmic thinking, and that the characteristics
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Constructionism 2018, Vilnius, Lithuaniaof this language are consistent with the requirements for the programming language for upper
secondary schools. Therefore, more and more universities and secondary schools switch to teaching
programming basics in Python (Hromkovič, 2016; Blaho, 2016; Belan, 2013). However, when we started
our research there weren’t any official textbooks for learning programming basics in Python in Slovak
language. Teachers had to develop materials for their own use, where these materials are aimed forspecific group of pupils (Belan, 2013), or using very similar or identical examples as the ones used for
Pascal (Delphi / Lazarus) (Kučera, 2016) based on working with the graphical canvas using components
as an input fields or buttons. However, Python also offers other techniques, such as turtle geometry,
which promotes learning by exploration and discovery or using command mode which gives immediate
feedback for pupils.
Before designing a curriculum intervention is we examined the currently most used textbooks in ourcountry, textbooks of the Logo and Pascal (Delphi) languages: Algorithms with Logo and Programmingin Delphi and Lazarus (Varga, 1999; Blaho, 2012). Both textbooks' major topic is the work with graphics
while other terms and concepts are explained through it so pupils can visually see the solution of thetasks. We have found out that the order of some of the first topics coincide in both textbooks, whichmeans that loops, conditions and procedures can be taught both by turtle geometry and graphicalcanvas. When designing our materials for teaching programming basics in Python for upper secondary
schools, we will base it on the examined textbooks as much as possible and use the tasks created by
the experienced and respected authors of these textbooks.
When designing methodological materials, it is advisable to keep in mind the stages of the cognitive
process defined by Hejný (2009), and also the advice of Linn and Dalbey (1988), whom suggests thefollowing learning process of programming: 1. learn elements one at a time; 2. learn design skills by
combining old and new strategies, and 3. learn general skills to solve problems. In our design process
we also keep in mind the advice for designing the right materials for learning. Salanci (2011) points outthat the correct textbooks emphasize that the learner collects the experience, while textbooks are notencyclopaedic they gradually explain the basic concepts of programming; emphasizing motivating
examples and tasks, tracing and the gradual discovery of the algorithmic rules. As we follow the Fuller’s
spiral taxonomy of cognitive learning (2007) we proceed from basic programming concepts.

Methodology
In our research we focus on the teachers. Our aim was to find out how we can help to teacher for easiertransformation to a new language. The pre-survey showed us that teachers need material in Slovak
language compatible with our learning system (Mészárosová, 2015). We decided to develop acomprehensive curriculum intervention for the Python programing language to teach the basics ofprogramming. Our materials will include remarks, tips, and notices that will make it easier for teachers
to transition from the Pascal programming language, which they have experience with, to Python. Our
materials will differ from most of the materials for Python by focusing on algorithms and concepts, but
not on characteristics of the language. Our goal was to minimize the amount of commands pupils needto learn and to gain new programming concepts and knowledge so we chose turtle geometry. We also
wanted to create material that uses didactic methods and tasks that teachers already know and make
pupils learning easier by using the smallest number of commands.
Our curriculum intervention consists of methodical materials for teachers and worksheets for pupils.
These materials contain educational goals, developed competencies by students, selected methods,
content of lessons with solutions of the tasks and recommendations for the teacher and they are inaccordance with the National Educational Program of Slovakia. Therefore, we had a research question:
What should be specific objectives, content and form of curriculum intervention for teaching
programming basics in Python to help teachers with transformation to this language?
We used design based research with four iterative cycles (Creswell, 2002) as a main research strategy
and we used qualitative methods of data collection and data analysis (Lichtmann, 2012), including
semistructured interview, observation (transcriptions and field notes), and audio-visual materials(photographs and recorded videos of pupil’s work and their products). We conducted our research inordinary upper secondary school in capital city of Slovakia. In the first and third iteration of the335Constructionism 2018, Vilnius, Lithuania

verification we cooperated with the teacher who taught their pupils within compulsory school subject
Informatics according to our curriculum intervention. The pupils from these iterations haven’t met with atextual programming language before. The teacher was experienced with teaching programming in
Pascal (Lazarus) and C++, but had no experience with the Python programming language. During theverification we observed the teacher’s questions about the course and the Python language, as well as
pupil’s questions, mistakes and misconceptions. The second iteration we performed during our teachingactivity at an upper secondary school with the pupils of first grade. This group consisted of pupils who
were selected at the admission process of the school on the basis of stricter acceptance criteria(success rate about 1:10). Pupils were also encouraged to work independently, which allowed them tolearn even faster and solve more challenging tasks. They had experience with childrens programming
environments of Imagine or Scratch. One pupil had experience with Python (but not turtle geometry)and one of them had experience with C#.
In one group there were from 9 to11 students, aged from 14 to 16. These groups included boys andgirls in different ratio, see Table 1.
Iteration Girls Boys Total
1.

4

5

9

2.

4

6

10

3.

9

2

11

Table 1. Pupils participated in our researchFirst steps of designing the curriculum intervention
Before designing materials, we have gone through several steps of acquiring experience with Python.
Since, we also transformed to Python language from Pascal, these steps could be in part similar to
those of teachers when they switched to Python. By having experienced it ourselves, it helped us tobetter understand the situation of the teachers. (Mészárosová, 2016)

Pedagogical activity
A PhD study also includes teaching activities of students at the university. During our research whichlasted about a year and a half (4 terms) we conducted exercises for the subject Programming in Python
for students of the Applied Informatics unit, each semester in two groups of around 30 students. Thanks
to this, we gained not only programming skills in Python, but we saw lectures, led by an experienced
university pedagogue right in practice (Blaho, 2016). Also gained valuable observations about what kindof mistakes and bugs that pupils make. Our pedagogical activities continued during further iterations,where we continued in practice teaching programming in Python for Computer Science Teacher
students as well as pupils of an upper secondary school.

Overview of teaching programming in Slovakia
Obtaining an overall view of the current state of teaching programming in Python should have helped
us to better conceive the possibilities of teaching programming in Python at upper secondary schools.
Within, we talked with teachers who shared their experience of teaching programming basics in Python.
In this iteration of our research we also implemented a case study aimed at describing of a teachers
practice while she switched to teaching in Python. Obtaining a general overview of the current state ofteaching programming in Python in Slovakia should help us to imagine and to better understand thepossibilities of teaching programming basics in Python language at upper secondary schools in Slovakia
as well as teacher’s requirements for methodological materials. (Mészárosová, 2015)

Selecting the motivation of turtle geometry
After creating a comprehensive overview of teaching programming basics in Python, we have a variety
of motivations to choose from: 1. Turtle geometry; 2. Using graphical canvas (Tkinter); 3. Task withmathematical context without graphical motivation. We have chosen the motivation of turtle geometry336

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that supports learning by discovering and exploring. Our goals were to create a curriculum intervention
for learning programming basics in Python for the pupils in the first year at upper secondary schools.
We have based on the assumption that pupils coming from lower secondary schools have experience
with different programming environments for children as Imagine or Scratch. Our goal is that they meeta higher level programming language in the upper secondary school, which is suitable for learning the
programming basics. At the same time, we have tried to find ideas for our materials so that the pupils
get acquainted with more demanding algorithms in this programming language.

Selection of the primary resources
We selected some existing textbooks as guidelines to follow in developing our materials. As the primaryresource we chose the textbook Algorithms with Logo (Varga, 1999), which is intended for upper
secondary schools and is using turtle geometry. When choosing the didactic procedure and tasks wewere motivated by lectures on programming in Python and C# from the Comenius University in
Bratislava; also by the textbook for Delphi (Blaho, 2012). While these materials are not intended for ourtarget group or they focus on another programming language, we were motivated by the selection of
didactic procedures, methods, approaches and motivations used in these materials. Also we have tofollow the National Education Curriculum which defines the knowledge and skills pupils have to acquire
before they graduate.

Developing and verifying our curriculum intervention
The process of the materials development contained three iterations of verification in three different
groups of pupils at upper secondary schools. In one of the groups we took lessons by ourselves andalso cooperated with a computing teacher who taught the lessons according to our curriculum
intervention in two different groups. During the verification we followed the teacher’s questions about
the course and the Python language, as well as pupil’s questions, mistakes and misconceptions.

1st iteration
The design of the pilot version partly went on simultaneously with its integration to practice andvalidation, so we were able to respond more flexibly to the needs of the pupils and teachers, and to theencountered deficiencies and problems as well. When designing our materials, we mostly followed the
textbook for the Logo language. However, we encountered a problem with the order of the topics: loops
and variables. In the pilot design we decided to devote ourselves to the topic of loops first. We preferthem because of the lesser level of abstraction than variables, and also because we followed theprocess of learning programming with turtle geometry used in practice. The problem of the variable
inside of the looping construct still remains even though we were trying to not use the looping variablein tasks. After the evaluation we decided to change the order of the topics: variables and loops. A similar
problem was encountered by researchers Hromkovič et al. (2016), who stated in their publications thatthey have designed a course of programming basics using Logo tutorials with turtle geometry. As oneof the positive attributes of the Logo language they mentioned the repeat loop, which according to theauthors, is the simplest loop to learn the looping construct without the looping variables or conditions.
They solved the problem of how to position the topic of loops without the topic of the variables, byimplementing the repeat loop into their own programming environment for Python.
We found out that the materials need to be in two parts: materials for teachers and worksheets for
pupils. The teachers need an extensive description of the lessons and also to solve the tasks before thelessons, so we added the solutions of the tasks to the materials to make this process easier. Materials
also include the aims of each lesson according to the National Education Curriculum to help teacherswith school plans and documents. In order to simplify the preparation of the teacher before lessons, we
added a list of used commands as a brief reminder of commands the pupils already know and the syntaxof the language.
We also found out that the teacher who does not have experience with teaching programming in alanguage that is new for her has difficulty finding errors in pupil’s code. Therefore, we have added
recommendations to the materials on what kind of mistakes pupils can do, so the teacher after reading
the material was ready to find errors in pupil’s codes.
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2nd iteration
This iteration of the validation gave us valuable experience with the organization of the lessons andprovided an insight of the teaching in a group of pupils who have different programming experience andalso different cognitive abilities than the first group. In this group there were pupils who progressed
faster than most of the group. This is the reason why we adjusted the pupil’s worksheets to include
examples intended for a common solution with teacher, while more skilled pupils (in our case, pupils
who attended other programming courses) did not have to wait for slower pupils. They were also able
to work independently and when they encountered a problem and needed to know a new command or
knowledge, they asked the teacher. In this group we met with such internal motivation of pupils, where
we did not have to give homework, some of the pupils solved the remaining tasks from the worksheets
themselves at home.
We were also enriched with an experience of teaching without a projector, using one part of thewhiteboard for code and the other part for drawings to support explanation. However, we noticed that
pupils had more negative reactions to the error messages when they ran the code, compared to thepupils from the first iteration. In this group, we noticed that pupils asked for help immediately (or faster)
after the error report, while in the first group they asked for help just after they couldn’t fix the problem.
These differences between reactions on error messages could be attributed to the fact that while in thefirst group pupils saw on the projector how the teacher corrects bugs in the code, in the second groupthey did not see the teachers’ reaction on the error messages because she wrote the code on thewhiteboard. In this group, we had to positively support pupils when they encountered error messages
in order to avoid being scared and individually explained to them how to interpret the error message
and fix the program.

3rd iteration
According to the analysis of frequent mistakes, we have adapted the methodological material so as to
avoid similar mistakes and misconceptions of the pupils. We have added explanations, reminders andalso changed the order of some tasks, in which we firstly failed at the determination of the grading.
During the verification, we had to devote more space to the tasks of using nested loops. Among thecommon mistakes of pupils was that they executed the program only after they implemented the wholesolution of the problem even in the case of difficult tasks. That is why the program often did not draw
the desired picture and it was not possible to see directly from the picture in which step the error is (seeFig. 1). (Jašková et al., 2017)

Figure 1. Picture on the left: the task; Pupils most common mistakes on the right.

In case of these errors, pupils needed help from the teacher, but mostly only needed to correct a valueof a parameter in a command or the number of repetitions. We tried to focus their attention to solving
difficult tasks by dividing them into smaller parts, to gradually solve and verify their solutions. After
analysing these observations, we came to the conclusion that it would be more appropriate for the pupils
to learn functions and solve these or similar tasks using them, instead of using nesting loops. In thisway, the components of informatics thinking – decomposition and pattern search – would be accessible
and developed in programming lessons. The solutions themselves would be more comprehensive,
understandable and would help pupils to find errors easier. In such tasks, where they already usedfunctions in solutions, we didn’t meet such problems.
After analysing the observations from these iterations, we finally enriched the materials withrecommendations for teachers to eliminate pupil’s errors, for example, specifically, in Python, we needto carefully explain the indentation of the commands in loops, conditions and functions. We present
frequent pupil’s mistakes in the syntax of the language, such as forgetting to enter command parameters
or skipping parentheses for some commands. We also warn them to keep in mind the work of pupils to338

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be able to orient themselves in an environment with three separate windows, using REPL andprogramming mode, or to teach pupils how to read the error messages.

Results and Discussion
Before the design of the curriculum intervention we needed to take some steps to get the necessary
knowledge and collect experience. Without these steps, we may have overlooked some importantelements. We consider these steps to be important before designing a textbook or methodical materials
for teaching programming:
1. Learn the language as much and deep as possible, to recognize the positive and negative aspectsof the language. Without this step we can easily overlook the strengths of the language and make a
mistake where we just adapt a book to another programming language without taking advantage of the
programming language options that would make coding easier and thus focus on algorithms.
2. Pedagogical experience is necessary to design lessons for programming in a new language.
3. Literature overview is needed to have insight into what materials exists, to draw inspiration andmotivation from the best, which is used in our schools, country and abroad for programming courses.
Didactic knowledge is also necessary for designing valuable materials.
4. Co-work with teachers and monitor their needs in order to be able to develop a material that is
helpful for them. During the verification, teachers can point out such problems and mistakes of which
the creator wasn’t aware. It’s also necessary to verify the materials in groups of pupils with different skill
level and teachers with a different amount of experience.
We identified some key elements and features a material should contain to help teachers transform to
a new language:
materials for pupils (worksheets)
recommendations for teachers what mistakes are being made by pupils, as one of the mostchallenging elements of teaching in a new language is to find mistakes in the pupil’s code recommendations for the assessment
 key differences in the new language vs. language used before (in our case Python and Delphi)
 list of used commands helps the teacher especially at the beginning of the course
The output for teachers is the created curriculum intervention which we describe in the following sectionof the paper.

Curriculum intervention
The materials are divided into six parts by topic (introduction to Python, variables, loops, functions,randomness and conditions) and consists of a methodical material for teachers, supplemented withworksheets for pupils. In each part of the material we defined the required knowledge and skills of pupils.
The structure of the lessons (for 45 minutes), pedagogical procedures and the description of its agenda
are also part of the material, along with tasks for pupils, their solutions and recommendations forteachers. When designing tasks, we also tried to select programs that do not need a large set ofcommands such that we don’t unnecessarily burden the students. We focused on the concepts andconstructions defined in National educational curriculum. In terms of the process of lessons, we follow
the scheme: Motivation → Collecting experience → Exercising the knowledge, while in the Motivation
section the teacher, along with the pupils, solves an example at the board. In the other parts the pupils
work independently on their own computer (that. Tasks had a constructionist approach, pupils got
pictures which they had to draw using turtle geometry.

Introduction to Python and turtle geometry
The aim of these lessons is to familiarize pupils with the Python programing language and its
development environment IDLE, as well as with the idea of turtle geometry. The first two lessons of thecourse are aimed at learning to work with IDLE – command and programming mode too. They are also
designed to help pupils to learn basic commands for navigating the turtle. We want to achieve pupils beable to control the basic commands automatically in order to make the following lessons, when they willmeet new constructions and concepts, easier. Working with the three separate windows they encounter339

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in this environment can be challenging for some pupils. That is why we have reserved two lessons forthis topic so that students have enough time to practice and the teacher has the opportunity to makesure all pupils are able to navigate through this environment. Another aim of these lessons is to make
pupils think about the properties of the performer – turtle. Pupils draw simple pictures in these lessons
which are intended to automate the basic commands for the movement of the turtle (see Fig. 2).

Figure 2. Examples of the tasks for practicing the navigation of the turtle.

Variables
For the topic of variables, we designed only a single lesson, but pupils use them in the following lessons
as well. In explaining variables, we used a didactic method widely used in practice, representing thevariables by drawing boxes that contain the values stored in these variables. It is important to show theprocess of assigning a value to a variable step-by-step. Our goal was that pupils understand themeaning of a variable and its use in Python, learn to assign a value to a variable and later use it, and toable to identify from the task, which data must be remembered, respectively vary and therefore require
the use of variables.

For loops
We divided the topic of for loops into five lessons, ranging from loops with enumerated values for thecontrol variable, for loops with the function range(value) to loops with the control variable used in the
body of the loop. The aim of these lessons is that pupils are able to recognize recurring patterns, andto generalize and write a solution using loops. Similarly, to the topic of variables, we make reckoning ofa thorough tracing of the program, with which pupils can search for and find certain patterns. It can also
be helpful in generalizing. This topic contained two lessons devoted to nested loops, but after analysing
pupil’s mistakes, we have decided to leave out nested loops from the course and give prepared tasks
to pupils after the lessons of functions.

Functions
The aim of these lessons is that pupils can define and call functions, and also to use functions withsimple parameters. New concepts and knowledge gained at these lessons are: definition of the function,function call, function name, parameters, function body, how the parameters work and the use offunction parameters inside the function body. We proceed from functions, loops in the function body tofunctions with parameters. In the tasks for functions with parameters we focus on explaining parameterdelivery, creating a local variable for the function parameter, and the disappearance of the variable after
the function body has been executed.

Figure 3. Examples of the tasks for practicing functions.

Turtle position and randomness
In this topic, we used the motivational example of drawing a starry sky. During this exercise, we taught
the pupils to change the turtle's position using the setpos(x,y) command and to generate random
values using the random module. Including this topic in the curriculum intervention made available theuse of motivational tasks with random values.

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Conditions
The aim of these lessons is to understand how branching the program behaves, to recognize situations
and conditions when branching is needed, and what part of the algorithm has to be performed before,
within, and after the condition. After these lessons, pupils should be able to solve tasks that combine
loops and branching. Among the tasks, we included known ones as throwing coins or generating a large
number of dots on the canvas at random positions and colouring them according to their location.

Figure 4. Example of a pupil’s projectAt the end of the course, we included a project to retain the acquired knowledge and for the summative
assessment by the teacher. We also give recommendations for teachers for the formative andsummative assessment over the course.

Discussion
The use of Python and turtle geometry have been discussed in the literature rather extensively. The
authors of papers (Hromkovič, 2016; Cho, 2016; Ayock, 2015; Vidal Duarte, 2016) highlighted thestrength of learning programming basics using turtle geometry. According to Cho (2016), “Python thatstarts with turtle modules is linked to the exploration of microworlds at primary and middle schools, thus
serving to lower the entry barrier for students in performing exploration at high schools”. Researchers
also mentioned CS1 courses starting with turtle geometry, continues with game engine (Ayock, 2016)or extensive projects (Vidal Duarte, 2016). In Slovakia, however, turtle geometry is used in earlier
education (using Imagine Logo), but it is also appropriate for higher education, upper secondary
schools, where pupils are learning programming in textual programming languages. In our research we
met with increasing motivation among pupils to learn programming, confirming the suitability of our
choice of motivation with turtle geometry.
There are several books and online courses for Python using turtle geometry, but these materials arerather suited for self-study, e.g. (Payne, 2016). In the learning process we prefer the scheme: Motivation→ Collecting experience → Exercising the knowledge, where we need a motivational example that theteacher solves together with pupils by discussion. Consequently, the pupils solve the tasks to exercise
the new knowledge, where we need a lot of graduated tasks. Our materials also differ from most of thematerials for Python by focusing on new algorithms and concepts, but not on language characteristics.
Materials are aimed for teachers and the worksheets for pupils include only tasks without a huge amountof text. Our goal was to minimize the amount of commands pupils need to learn and to gain new
programming concepts and knowledge, we used just a fraction of the commands compared to othermaterials (Belan, 2013; Kučera, 2016). This goal is also confirmed by the fact that during the verification,
pupils easily remembered the used commands. A similar material exists in Switzerland (Kohn, 2016),
where researchers created an environment that offers a repeat loop instead of a for loop, therefore thematerial follows a different approach in teaching.
We distributed the curriculum intervention to computing teachers in Slovakia. We extended the materials
to teacher’s awareness thanks to the PyCon SK 2017 conference, where we organized a section forteachers. On this conference, we gave teachers copies of the materials, and also made it available on
the internet. Then we asked them to comment the materials. We have received positive feedbacks fromteachers, such as “I appreciate the performance of the curriculum intervention in my class very
positively, even though the breaks they have completed their solutions without any other motivation,although they did not want to code before.” or “it works! especially in girls I saw increased motivation
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for programming”. Also the fact, that some teachers chose this material for their teaching is positive
confirmation too. It should be noted though, that they choose the motivation according to thecharacteristics and possibilities of the group of pupils, their style of teaching and habits. This feedback
is very valuable for us for further development of the materials.

Conclusion
In our work, we focused on teaching programming basics in Python. Our goal was to develop acurriculum intervention for learning programming basics for upper secondary schools helping teachers
to transformation to a Python programming language. In it we targeted basic concepts and constructions
as variables, loops, functions and conditions. We developed the methodical material in iterations,realized and verified through the development of three groups of pupils at upper secondary schools withteachers who haven’t experienced with teaching in Python before. The material has been distributed toteachers and we will continue to collect feedback from them and monitor their experiences with the useof the material in practice. When we were developing the curriculum intervention, we cared that we did
not overburden pupils with too many Python commands, but rather let them learn the basic concepts
and constructions, and motivate them to further learn programming. Our materials include
recommendations and notices that will make it easier for teachers to transform from the Pascal
programming language, they have experience with, to Python. We believe that the materials created by
us will serve the development of pupil’s algorithmic thinking, important knowledge and skills, and last
but not least the fulfilment of the educational goals of computing.

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The Impact and Effectiveness of Technology
Enhanced Mathematics Learning
Einari Kurvinen, emakur@utu.fi
University of Turku, Finland

Valentina Dagienė, valentina.dagiene@mii.vu.lt
Vilnius University, Lithuania

Mikko-Jussi Laakso, milaak@utu.fi
University of Turku, Finland

Abstract
What happens inside the classroom is crucial for pupils’ learning. Mathematics is mostly hard work andonly small portion is accounted by talent. However emotions and enjoyment can facilitate the hard workand make it easily done. We can use technology to help pupils to complete more exercise, makepracticing more interesting and motivate pupils, which in turn will improve learning performance andlearning results.
A 15-week research study was conducted by research team of University of Turku, Finland and Vilnius
University, Lithuania. Three schools were selected for this study. From each school we had two 3rd
grade classes, one with control group and one treatment group. There was altogether 140 pupils(N=140) of which 69 (N=69) formed the treatment group and 71 (N=71) formed the control group.
Figure 1 describes the research setup. The test started with a pre-test and was followed by 15 week
research phase. The research was concluded with a post-test. During the research period, pupils fromtreatment group had one mathematics lesson per week transformed into technology enhanced
mathematics lesson using a virtual learning platform called ViLLE. The control group followed typicalteaching without any changes.

Figure 1. Research setup

Identical tests for pre-test and post-test were used in the study. The test consisted of two parts. The firstpart measured mathematics performance in topics taught in 3rd grade. The second part was anarithmetic fluency test that measures how fast pupils can solve basic arithmetic facts. The study clearly
showed that the treatment group outperformed the control group pupils with statistical significant
difference both in mathematics performance and in arithmetic fluency. When we combine these resultswith our previous findings there is solid evidence that the digital learning path in ViLLE makes a radical
improvement to pupils’ math learning. What makes it more amazing that we only replaced one lesson
in a week.

Keywords
gamification; mathematics; primary education; technology enhanced learning; TEL

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Introduction
Using technology to help students learn is complex. Technology enhanced learning (TEL) provides aninteresting addition to traditional teaching methodologies (Goodyear & Retalis, 2010). We are going to
use the term TEL as umbrella, but other terms, such as computer-aided learning (CAL), or e-learning
carry similar connotations. There are many tasks where computers excel against humans. For example,
assessing unambiguous tasks, asking repetitive random questions or giving feedback to multiple pupils
at the same time. Using TEL enables educators to duplicate parts of their pedagogical knowledge andtake the burden of simple time consuming tasks from teachers whose time is valuable and scarce.
Mathematics is at the core subject of science, engineering and technology. The 2015 results of theOECD Programme for International Student Assessment (PISA) show that many countries need to
improve mathematics education in schools (Gurria, 2016). Too many students in schools still perceive
mathematics as an educational stumbling block. How can educational technologies influencemathematics education and improve pupils’ mathematical skills? First, technologies can improvestudents’ technical skills in mathematics. By technical skills, we consider the know-what and the knowhow. For example, students recognize formulas and notation (know-what) and apply procedures to solve
different types of problems (know-how). How could be possible to improve the traditional learning
outcomes in mathematics that are expected by most policy makers, and, at the same time, develop
other important skills for innovation, such as reasoning, understanding, posing problems?
In this paper, we provide evidence that TEL strategies are effective both for traditional and for complex,
unfamiliar and non-routine math problems. According to Goodyear and Retalis (2010), TEL activities
that promote active learning are considered constructivist. When pupils are active, they are involved in
the learning process, more motivated and interactive with the material provided. These aspects makelearning deeper instead of superficial strategies.
We report the experiences in Lithuania utilizing a digital learning path in mathematics in Finnish learning
platform called ViLLE. The aim of this research was to find out how regular, weekly technologicallyenhanced ViLLE-lessons affect the learning performance and arithmetic fluency of Lithuanian 3 rdgraders compared to pupils receiving traditional instructions. ViLLE provides ready-made weekly
lessons with interactive and gamified exercises supporting the Finnish math curriculum that can beeasily adapted into any curriculum. We have conducted multiple studies in Finland (e.g. Kurvinen et al.,2015a, 2015b) with success.
This paper is organized as follows: First we introduce previous research in the field of TEL and previous
findings using ViLLE in Finland. Methodology, tests and content used by the pupils are introduced inresearch setup, which is followed by results. Finally, this paper is finished with conclusions anddiscussion.

Previous WorkThe research community has studied TEL for years. Most researchers seem to agree, that using
Information and Communication Technologies (ICT) in teaching and learning can have a positive effecton learning. Still, there is room for doubt, and some researchers demand for more robust methodology
and evidence for benefits of TEL (e.g. Drijvers, 2016).
There are multiple meta-analyses conducted on TEL in education, all showing a positive impact onlearning, motivation and confidence of the pupils (e.g. Edwy & Vodanovich, 2017; Vogel et al., 2006; Li& Ma, 2010). Li and Ma (2010) found out that there is a statistically significant positive effect of using
educational technology, especially in primary education. However, the researchers remind us that
introducing technology does not automatically yield for better results, but there must be a well-planned
strategy on how to utilize them in teaching and learning. For example, in drilling-based games it istypically easy to just try different answers without thinking. Pupils relying on this superficial “learning
strategy”, will probably not learn much. Li and Ma divided technology enhanced solutions into two
categories. First category is traditional approach, which means teacher-centered whole-class
instruction. The second category is constructivist approach, which is student-centered. This approachputs more emphasis on active learning of the individual student through problem-based and discovery345

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based learning. They found out that both approaches lead to positive effect, but the constructivist
approach leads to significantly and consistently higher impact on learning.
Repetition and immediate feedback are two strong factors which are easily implemented in TEL andcan clearly provide great improvement in learning. Brosvick, Dihoff, Epstein and Cook (2006) studied
automated feedback and its effects on learning. They used paper-based automatic feedback andcompared that to immediate feedback given by an educator. Both were found to be effective and there
was not significant difference between the two methods. Based on this, we can draw a conclusion thatthe feedback does not have to come from a human being to be effective, and the same effect can beprovided automatically by software. Humans are of course able to give more in-depth feedback invarious topics but on the other hand computers are tireless and will offer feedback for as long as you
require. The researchers concluded that immediate feedback increases interest and involvement inassessment, helps drilling to be more effective and promotes active learning by maximizing time on task
(Brosvic et al., 2006). In addition to this, Attard and Curry (2012) found that immediate feedback helps
building pupils’ self-confidence, thus making pupils feel safer to make mistakes and try again. This helps
building pupils’ persistence.
Pilli and Aksu (2013) conducted a three month study on fourth graders in North Cyprus. They assigned
26 pupils into a control group and 29 into a treatment group. Their research setups are similar to theresearch setup presented in this paper. They also compared TEL to traditional teaching and verified
their results with identical pre- and post-tests. In final results, the pupils using technology outperformed
the control group in multiplication and division of natural numbers. In fractions the difference was notstatistically significant. They concluded that computer-assisted instruction, or TEL in this context, is agood supplementary method compared to traditional mathematics instruction.
In previous studies conducted in Finland, we have found similar positive effects on learning by using
TEL. We have conducted several studies in primary schools of using ViLLE for one weekly lesson formathematics teaching. We have followed a similar research setup in all cases. The test starts with apre-test, which is followed by the research period, where one technology enhanced mathematics lessonin each week (accompanied with homework) is utilized. This technology enhanced lesson is a traditional
mathematics lesson transformed into an electronic lesson, thus the treatment group is not provided any
extra instruction. After the research period, a post-test is conducted and the results are determined bycomparing the treatment group and control group between the two tests.
One of our earliest studies is on first graders. Two classes participated in a 10 week research period.
The improvement of the treatment group in post-test was statistically significant (Kurvinen et al., 2014).
Another 18-week long study was then conducted on four first grade classes from two different schools.
Again, the treatment group achieved statistically significantly better results in the post-test, while nosignificant difference in the pre-test was seen (Kurvinen et al., 2015b). During the same time, we also
conducted a similar study on third graders. Two classes participated in the 18 week long study. Again,
without a statistically significant difference in the pre-test, the treatment group achieved statisticallysignificantly better results in the post-test (Kurvinen et al. 2015a).

Virtual learning environments for learning mathematics
There are many virtual learning environments for learning and some are specialized in mathematics.
Many publishers have their own learning environments to supplement exercise books. One of the most
famous non-profit platforms is Khan Academy (https://www.khanacademy.org/). Another famous
system is Mathletics from Australia (Stephan, 2017; Nansen et al., 2012). Mathletics did not provide
statistical significant improvement on standardized tests, but it was said to improve pupils’ motivationand make differentiation easier for teachers (Stephan, 2017). Brasiel et al. (2016) compared 11
platforms to supplement mathematics teaching in K12 education in Utah. They found out, that all theplatforms, except for one, had a positive impact on learning. However, only two platforms (ALEKS and
i-Ready) had a statistically significant impact, but only when pupils were using the platforms on
recommended level.
The problem of using virtual learning environments is more about choosing a proper one than just finding
existing solutions. Many solutions provide only the platform without any content. Others provide some346

Constructionism 2018, Vilnius, Lithuania

content but the content might not be customizable or compatible with local curriculum. Because there
are so many solutions already available, the choice is usually made by seemingly small features that inteachers’ everyday life make huge impact. For example, compatibility to national curriculum, localizationor the scope of the content are crucial for teachers.
ViLLE is an exercise-based digital learning platform, developed at the University of Turku, with over 100
different exercise types. Most of the exercise types are automatically assessed and give immediate
feedback to pupils. The progress made by pupils is stored by ViLLE and comprehensive statistics andlearning analytics based on pupils’ answers are presented to the teacher. ViLLE provides all necessary
tools for teachers to create their own content in any subject they choose in ViLLE (Laakso et al., 2018b.)
We have created a gamified digital learning path for mathematics for grades 1-9 (ages 7-15). The pathhas one digital mathematics lesson for each school week and it covers all the topics in the national
curriculum in Finland (POPS, 2014). Altogether, the path contains more than 15 000 exercises. Eachlesson contains 25-30 exercises that are customizable by the teacher. This path can be easily
customized and tailored to meet needs of different national curriculums. Exercises in the digital learning
path have been created in close co-operation with teachers, to ensure the suitability and quality of theexercises.
The path is gamified ie. the pupils collect points from the exercises and we give trophies for achieving
certain amount of points. The general guideline is that the pupils’ goal is to achieve at least 50% of thepoints available in each lesson. If the goal is not met during the lesson at school, pupils continue thework at home. We provide four different trophies as markers for goals for pupils. By default, the limits
are set as follows: bronze is the first trophy (50%), then silver (75%), gold (90%) and diamond (100%).
Limits can be customized by the teacher either for the whole class or for individual pupils. Customizing
limits per pupil offers an easy way to differentiate pupils. We also provide two exercise categories, called
warm-up exercises and bonus exercises to further differentiate pupils. If a pupil is added to either ofpreviously mentioned groups, they will see also differentiated tasks in addition to the normal lesson
content. In some exercises pupils have the opportunity to choose from three difficulty levels (easy,
normal, hard). Selected difficulty level does not affect scoring but the choice is recorded for the teacher.
Typically each exercise contains 10-15 problems or tasks. Most of the exercises have adaptive
capabilities. If a wrong answer is given, the same question is asked again from the pupil. Besides
remembering wrong answers, all answers are recorded and automatically analyzed to identify typicalmisconceptions in mathematics.
ViLLE has build in mechanism for group work. One or more pupils can work together on one devicecollecting points for their own account. After learning session, pupils can continue their work individually.
We have very good results working in pairs from university level programming courses (e. g. Kaila etal., 2016). Anecdotal evidence from observation in class rooms show that working in pairs is also
beneficial for primary education. Pupils are able teach one another and discuss about the problems.
Exercises used in the mathematics learning path can be divided roughly into two categories: (1) gamebased exercises and (2) more traditional exercises. Figure 2 shows an example of both of these
exercises. The first one is gamified multiple choice question, which has a time limit. Pupils choose thecorrect lane to answer the question shown in the blue container. The question in the figure translates
into “Which number is greater than six”, and the pupil should select the rightmost option “8”. On the rightside of Figure 2 is a traditional columnar addition. The exercise has 6 steps, each step containing a new
problem to be solved.

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Figure 2. Examples of two different kind of exercise types used in mathematics learning path

At the moment of writing this paper, ViLLE has over 120 000 users, mostly in Finland. The number ofusers has more than doubled during the last year (Laakso et al., 2018a). The number of users is growing
at huge pace, mostly by mouth of words marketing among teachers in Finland.

Research Setup
The research was conducted as a joint project between University of Turku, Finland and Vilnius
University, Lithuania with support of UPC (Lithuanian Education Development Centre). UPC had aproject with 10 schools around Lithuania. Three schools were selected to take part in this research.
Schools were selected based on two criteria: the school should have enough IT-resources to participate
and at least two classes on third grade.
Two classes from each of the three schools participated in the research - one class being randomly
assigned as the control group and the other the treatment group. Altogether we had three control
classes and three treatment classes. Independent variable is the use of ViLLE in one weekly
mathematics lesson. Study was started in November during week 45 (2017) by conducting the pre-test
with all classes. Treatment groups also had their first ViLLE-lessons during that week. The post-test
was conducted during week 12 (2018) in March. Note that the time period includes Christmas and winterholidays, so we used 15 weekly ViLLE-lessons in total in treatment group. All pre-tests were conductedon the same week, but different schools had tests on different days. Post-test were conducted in thesame manner. In all schools both groups conducted the test at the same time. A single 45 minutes
lesson was used for pre-test and later for post-test.
During the 15-week time period teachers in treatment groups replaced one mathematics lesson a weekwith a ViLLE-lesson, meaning there was no extra time allocated for teaching in treatment groups
compared to control groups. Pupils in treatment group also got homework in ViLLE. In total, pupils
completed 15 lessons in ViLLE. All lessons were translated from Finnish to Lithuanian. Participatingteachers, together with UPC personnel, selected suitable contents for Lithuanian third graders basedon the Finnish 3rd grade mathematics content. Lithuanian pupils worked on the same exercises as
Finnish pupils, except for the translation and localization of content.

Participants
All together 140 third grade pupils participated in the research. Treatment classes had altogether 69
pupils (N=69) and control classes 71 pupils (N=71). Number of pupils in each class is shown in Table
2. Before starting the research, research permission was asked from guardians. One pupil from control
group did not get permission and the results were hence excluded from the study.

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Table 2. Number of students in each class
SchoolN (Treatment group)

N (Control group)

School 1
School 2
School 3

20
25
24

24
26
21

Content in ViLLE
We selected the content used with pupils together with teachers. The content used by Finnish third
graders was used as a template. Teachers were requested to choose and arrange 15 out of 48 lessons
so that they would match topics they were going to teach pupils during the research period. Each lesson
contains approximately 25-30 exercises and each exercise contains approximately 10-15 problems.
Table 3.2 Lessons selected by teachers#

LessonNumber of
exercises

Number of
exercise visible

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15

Numbers 0-1000: Revision of addition and subtraction
Revision of multiplication tables 2, 3, 4, 5 and 10
Numbers 0-1000: Columnar addition and subtraction 1
Multiplication: multipliers 10 and 100
Multiplication tables 6 and 8
Multiplication tables 7 and 9
Multiplication: Order of operations
Clock: Application
Columnar multiplication: No carrying, carrying once
Division: Multiplication and division
Division: Order of operations
Measurement: Counting with units
Measurement: Unit conversion
Geometry: Perimeter, area
Numbers 0 - 10 000: Columnar addition and subtraction

44
44
44
44
42
43
47
42
45
47
41
43
43
44
44

30
26
25
26
25
27
26
25
26
27
26
26
27
26
26

The research was designed to measure the actual impact of ViLLE on learning performace and fluency.
For the scope of this research, teachers did not create any new material, but used exercises designed
by Finnish teachers and researchers.
. Participating teachers had all the tools teachers would normally have, including hiding exercises orrevealing more, if needed. Teachers also had the possibility to assign easier tasks for some pupils, if
they thought it was necessary.

Pre-tests and post-test
Effects of using weekly ViLLE-lessons were evaluated by using a two-part test. We used identical tests
in pre-test and post-test to ensure comparability between them. All tests were conducted traditionallyon pen and paper, thus the treatment group was not favored in tests.
First part of the test was an arithmetic fluency test. The test, called “a 3-minute test”, contains 160 basicarithmetic facts (addition, subtraction and multiplication with operands from 0-10). Pupils had 3 minutes
to solve as many calculations as they could. Papers were given to pupils upside down. When the time
started, they turned the papers and started solving calculations. After three minutes, they wrote in theirnames as well. For the results, we counted the number of correct answers and wrong answers. Eachcorrect answer was worth one point.

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Second part of the tests was a mathematics performance test. The test used in this research is basedon a standardized test that we have been developing. The test has been previously used with Finnish
3rd graders (Kurvinen et al., 2015a). The test measures topics mentioned in Finnish mathematics
curriculum for third graders (Table 4.33). Some topics are more advanced than basic requirements forthird graders, for example decimal numbers. Before conducting the pre-test, the math performance test
was tested on seven pupils. After testing we also collected feedback on how well the test measures
topics taught in Lithuania for 3rd grades. In the pre-test we found that the test should be difficult enoughto use in both pre-test and post-test. Pupils completed the mathematics performance test right after thearithmetic fluency test and they had time until the end of the 45-minute lesson. Only two pupils from thetreatment group would have needed more than 45 minutes to finish the test in time.
The mathematics performance test contains nine exercises. Table 4.34 describes each of the exercises
and gives one example.
Table 4.3 Exercises used in pre-test and post-test.
#

Type

Example1

Arithmetic calculations (addition, subtraction, multiplication,
division). Order of operations(5+2)*7; 120*3

2

Calculations with decimal numbers and order of operations

0.8+1.1; 500/2+3*12

3Addition and subtraction in columns

597+484

4

Multiplication in columns

142*9

5

Missing numbers (find the value of symbol)@+@ = 6@+# = 8#+& = 12

6

Continue number sequence1012, 1009, 1006, ___, ___, ___7

Convert units (length, time, volume)

2cm = ___ mm

8Circle the greatest (integers, decimals, fractions, currency)

1€ 9c 1,5c

9Rounding

17 to nearest tens

Exercises 3 and 4 were open problems in the Finnish version of the test. To make assessment easier,the exercise was converted into a fill-in-the-missing-numbers exercise. Exercise 5 is a new exercise. All
problems are not part of 3rd grade curriculum in Finland or in Lithuania, but the test was designed to bea difficult one to measure how much even the best pupils can improve their results.
Each separate task in the test was assessed as correct or incorrect. Each exercise was then assessedwith regards to the percentage of the tasks the pupils answered correctly. For example, the first exercise
has 10 calculations. If pupils got 7 correct and 3 incorrect, the total score would be 0.7 (as in 70%). The
maximum score from the whole test was therefore 9 points. The internal validity of the test wasmeasured with Cronbach’s alpha (α=0.88).

Results
Identical tests for pre- and post-tests were used. This enables us to compare groups using absolutemeans and the differences between each pupil’s pre-test and post-test performance. The results section
is divided into two sections. The first section presents the results for the second part of the test,
mathematics performance test (skills), and the second section presents students’ results in arithmetic
fluency, which was the first part of the test.

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Mathematics performance
Each exercise in the test was scored between 0 and 1 based on the correctness. The test has nine
exercises, meaning the maximum score is 9 points. Table 5 lists the pre-test results from each school
separately and all control groups and all treatment groups combined.
Table 5. Pre-test results. C=control group, T=treatment group
Group

Mean

p

MedianMin

Max

Std. dev.

School1 C

3.13

School1 T
School2 C

2.50
3.49

0.06

2.99

0.94

6.35

1.19

2.77
3.45

1.01
0.82

4.14
5.73

0.88
1.34

School2 T
School3 C

3.66
2.72

0.65

3.41
2.89

0.63
1.23

6.57
4.00

1.34
0.90

School3 T
Control All

3.17
3.14

0.15

3.07
3.10

0.56
0.82

6.57
6.35

1.27
1.20

Treatment All

3.17

0.88

3.07

0.56

6.57

1.27

An independent samples t-test was conducted to compare the mathematics performance in the pre-test
between two groups, in each school and in total. There are no statistically significant differences
between any of the classes. There is a near-significant difference in School 1. From minimum andmaximum scores we can see that both control group and treatment group have high-achieving and lowachieving pupils. Best-achieving pupils got over two thirds of total score in pre-test. There was no
statistically significant difference in scores between control group and treatment group (p=0.88, p>0.05).
The results show that the pupils are on average equally skilled and there is no difference between
treatment and control groups. Figure 3 shows this result in a more graphical way, showing the averagescore achieved by each group.

Mean
4,00
3,50
3,00
2,50
2,00
1,50
1,00
0,50
0,00

School 1 (C)School 1 (T)School 2 (C)School 2 (T)School 3 (C)School 3 (T) Control group Treatment
group

Figure 3. Average score by groups in pre-test

Figure shows clearly that School 1 and School 3 have the biggest differences between control andtreatment groups. In School 1 the control group achieved higher scores on average and in School 3 thetreatment group achieved higher scores. All control groups and treatment groups combined, thedifference is very small.
The post-test was conducted 17 weeks after the pre-test (15 lessons). The post-test was identicalcompared to the pre-test. Table 6 combines results from the post-test. In addition to absolute test
results, the improvement of individual pupils between the tests is included. The improvement iscalculated by subtracting pre-test result from post-test result. Difference was only calculated, if the pupil351

Constructionism 2018, Vilnius, Lithuania

had completed both tests. In control group there were 63 pupils (N=63) with both tests and 60 pupils intreatment group (N=60).
Table 6. Post-test results. (*=statistical significance)
Group

Mean

School1 C

4.49

School1 T

4.00

School2 C

4.37

School2 T

5.25

School3 C

3.44

School3 T

4.73

C

4.17

T

4.72

p

0.35

0.051

0.0097*0.049*MedianMinMax

st. dev.

Mean (difference)

4.54

1.63

6.76

1.34

1.30

4.18

1.30

6.24

1.58

1.61

4.73

1.98

7.37

1.44

0.89

4.85

2.58

8.61

1.61

1.55

3.62

1.12

5.23

1.23

0.81

4.87

1.41

7.21

1.61

1.86

4.31

1.12

7.37

1.44

1.00

4.85

1.30

8.61

1.66

1.66

p (difference)

0.38

0.032*

0.0019*0.0006*

When the absolute post-test results from individual schools are combined, the only statisticallysignificant difference is between the treatment and control groups in School 3 (p=0.0097, p<0.05). The
difference in School 2 is statistically near-significant. When all groups are combined, there is astatistically significant difference between control group and treatment group (p=0.049, p<0.05). It is
worth noting, that the treatment group from School 1 decreased the difference between the two groups,
by decreasing the statistical significance from nearly significant (p=0.06) to not significant (p=0.35).
These results show that the treatment group achieved higher scores from post-test with statisticalsignificant difference. Because the pre- and post-test is the same test, more proper measure to use is
to compare the difference in score (post-test score – pre-test score).
The effectiveness of ViLLE is solid when we compare the improvement of pupils between tests. Every
treatment group has greater improvement than the control group. The difference is also statisticallysignificant in schools 2 and 3. All groups combined, the improvement is statistically very significant(p<0.001), meaning pupils using ViLLE have greater learning in results than pupils not using ViLLE.
Figure visualizes the difference between all groups.

Improvement
2,00
1,80
1,60
1,40
1,20
1,00
0,80
0,60
0,40
0,20
0,00
School 1 (C) School 1 (T)School 2 (C) School 2 (T) School 3 (C) School 3 (T) Control group Treatment
group

Figure 4. Improvement between tests

Figure shows clearly the greater improvement in all treatment groups. On absolute scale the overall
improvement of the pupils between the groups was 11.7%. However, the improvement in the treatmentgroup was 39.8% higher than the improvement in the control group.
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Arithmetic fluency
In addition to skills in mathematics, we also tested the arithmetic fluency of the pupils. We used a 3minute test, which has 160 basic arithmetic facts. Pupils solved as many calculations as they could inthree minutes. Only correct answers were counted, but errors were also tracked. Table7 presents theresults from the pre-test.
Table 7. Pre-test, arithmetic fluency (*=statistical significance)
Group

Mean

MedianSt. Dev

School1 C

60.91

59.50

12.58

School1 T

53.35

55.00

16.62

School2 C

62.00

60.00

15.77

School2 T

60.96

60.00

14.01

School3 C

49.33

51.00

10.58

School3 T

59.30

59.00

13.61

C

57.74

57.50

14.29

T

58.16

59.00

14.82

p

Errors on average
0.77

0.1

2.90
0.80

0.8

1.00
0.95

0.01*

2.22
0.84

0.86

1.97

There was a statistically significant difference between treatment and control group only in School 3(p=0.01). With all treatment groups and control groups combined, there is no statistically significant
difference between the two groups (p=0.86). On average, the pupils are on same skill level in bothgroups. Every treatment group made more errors and pupils from treatment groups made on average
twice as many errors in calculations than pupils from control group.
In the post-test we compared the absolute results and improvement between the two tests. Post-test
was identical to the pre-test. Table 88 describes the post-test results from arithmetic fluency test.
Table 8. Post-test, arithmetic fluency (*=statistical significance)Group

Mean Median St. Dev. p

School1 C 69.48

67.00

18.48

School1 T

69.83

69.50

15.63

School2 C 75.12

76.00

19.67

School2 T

78.92

78.00

19.57

School3 C 61.11

62.00

School3 T

83.37

C
T

Errors on average Mean (difference) p (difference)
1.74

8.14

1.33

17.50

1.29

12.54

0.50

17.96

13.95

0.78

13.17

87.00

16.06 0.00007*

0.42

27.11

69.35

67.00

18.60

1.31

11.25

77.62

77.00

17.96

0.72

20.57

0.95

0.5

0.01*

0.014*

0.056

0.00037*0.000005*

Differences between groups were tested using independent samples t-test. In School 1, there is no
statistical significance (p=0.95). In pre-test there was a statistically significant difference (p=0.01), whichmeans that there is no more difference between groups in School 1. Using ViLLE had enabled the pupils
to catch up to their peers in arithmetic fluency. In School 2, there is no statistically significant difference(p=0.5). However, in School 3 the difference between control group and treatment group is statisticallyvery significant (p<0.01). Difference between all control groups and all treatment groups is statisticallysignificant (p=0.01), which clearly shows that using ViLLE improves arithmetic fluency.
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Constructionism 2018, Vilnius, Lithuania

Like in mathematics performance, also in arithmetic fluency, the difference between using ViLLE andnot using ViLLE is solid, when we compare the improvement of pupils between the two tests. The
improvement was higher in treatment groups in all schools. Only improvement which was not statisticallysignificant, was in School 2 (p=0.056). Even this result is statistically near-significant. When the groups
are put together, the difference between control group and treatment group is statistically very significant(p<0.001). This result shows solid evidence that pupils working with ViLLE clearly improved theirarithmetic fluency.
In every school, pupils in the treatment group made less errors on average than pupils in the control
group. The situation is the opposite when compared to pre-test. Pupils in treatment group solved on
average 20.6 calculations more in post-test than in pre-test. In control group the corresponding
improvement was 11.3 calculations, making the treatment group’s improvement 45.3% higher. The
treatment group solved on average 8.3 calculations more in the post-test than the control group. This is10.7% improvement on absolute scale.

Conclusion and Discussion
This study provides solid evidence that regular usage of carefully planned and designed TEL improves
learning performance. Statistically significant improvement is observed both in mathematical skills andarithmetical fluency.
During the 15 weeks of the research, teachers were instructed to use ViLLE to replace one traditional
mathematics lesson a week and give homework for pupils. Before the research project, we had agreed
on the contents together with all the teachers participating in this research. Otherwise teachers of thetreatment groups had the freedom to customize the exercise set as they saw fit. Customizing in thiscontext means hiding some exercises or revealing others. Teachers were also able to assign easiertasks for certain pupils. Both of these features are standard tools in ViLLE to enable teachers to matchthe content to their pupils needs. In other words, it gives teachers the pedagogical freedom to fine tune
the learning experience for their pupils. Due to the scope of this paper we did not track thecustomizations made by teachers or their effect, only the overall progress made by pupils.
Teachers worked independently with ViLLE during the research. They had the possibility to ask
technical or pedagogical questions from the research team. The same possibility is available for all
ViLLE teachers. During the 15 weeks, one researcher visited the treatment groups in total four times.
First to conduct the pre-test, two times in the middle of the treatment period and last during the posttest. The idea of the visits was to ensure there are no technical issues and to observe the local school
culture and how well the Finnish model fits in the Lithuanian schools.
The results presented in this paper clearly indicate that regular usage of ViLLE can improve learning
performance, arithmetic fluency and even improve pupils’ arithmetic fact accuracy. In the pre-test all thepupils were on average equal in skills. After the 15 weeks, the pupils in the treatment group were on
average statistically significantly better than pupils in the control group. This result is in line with previous
results and further strengthens the assumption that carefully designed content combined with TEL canlead to better learning results. We consider the pedagogical approach of ViLLE to be constructivist andstudent-centered, in context of how Li and Ma divided the usage of educational technology (Li & Ma,2010). One of the key ideas we promote is active learning. The better learning results can be explainedby increased amount of practice the pupils got, but the quality of practice is also important. As previously
stated, immediate feedback can be a very encouraging and engaging element in TEL (Brosvic et al.,2006; Attard & Curry, 2010). ViLLE gives pupils immediate feedback in all automatically assessed
exercise types, thus shortening the feedback loop for pupils. In traditional exercise book practicing, it
might take days for pupils to receive feedback on their answers. When receiving feedback takes a longtime, it increases the risk of learning wrong strategies and facts.
The verbal feedback from teachers further strengthens these results. With less need for preparation
they got more time to encounter pupils individually and they got more data on the pupils’ learning.
According to the feedback received from teachers, the pupils were motivated and eager to work with
ViLLE. According to our previous anecdotal experiences, this also shows that the teachers had proper354Constructionism 2018, Vilnius, Lithuania

methods for motivating pupils and boosted their confidence and they knew how to use ViLLE in apedagogically sound way.
Most pupils had the opportunity to complete ViLLE exercises from home. However, every school had
arranged the possibility to work on the exercises also in school, either during breaks or after school
hours. This was not considered to be a problem.
We selected applicable content together with the teachers for the 15 lessons covered in this study. The
mathematics performance test used in pre-test and post-test is based on general math skills and it has
been used in various studies. To ensure the suitability of the test, it was tested on seven pupils before
the actual pre-test. The test is designed to measure general mathematics skills. The arithmetic fluency
test is based on general mathematic tasks. The study clearly showed that regular usage of ViLLE will
also improve fluency in basic arithmetic tasks.
To conclude our findings, the results shows that with regular usage of ViLLE, we motivate pupils topractice more and improves their learning performance statistically significantly. For teachers this does
not require extra work and we can actually save teachers’ time from assessment and lesson
preparations.
The new Finnish national curriculum in mathematics includes programming in mathematics. At the
moment ViLLE exercises integrates exercises for computational thinking and programming inmathematics. Further integration of even more computational thinking contents and research in thatfield could be fruitful.

Acknowledgments
The authors gratefully acknowledge the support of the Nordic Research Council through the NordPLUS
programme of transverse actions. In particular the funding of the two year project “Culturally Diverse
Approaches to Learning Mathematics and Computational Thinking”, the project code NPHZ2018/10063.

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Constructionist Approaches to Computational
Thinking: A Case of Game Modding with ChoiCo
Marianthi Grizioti , mgriziot@ppp.uoa.gr
National and Kapodistrian University of Athens, Educational Technology Lab, School of Philosophy,
Faculty of Pedagogy, Greece

Chronis Kynigos , kynigos@ppp.uoa.gr
National and Kapodistrian University of Athens, Educational Technology Lab, School of Philosophy,
Faculty of Pedagogy, Greece

Abstract
Computational Thinking (or CT) involves a wide range of mental processes like problem solving,recursive thinking, abstract thinking etc, which are considered necessary supplies for the 21st century
children. However, despite the wide attention that CT has received, there is still limited research on
pedagogical designs and strategies that can promote the acquisition and development of such skills. In
this paper, we discuss how constructionist approaches can inform and benefit the cultivation ofcomputational thinking by exploring the case of modifying and sharing digital games. In this context
game modding is implemented as a constructionist activity that enables students’ progressive
engagement in computational thinking through their interaction with various affordances such as coding,data processors, graphics editors, etc. To further investigate this approach, we present the results of adesign-based research in which junior-high school students modified games with the digital tool ChoiCo.
ChoiCo (Choices with Consequences) in an online platform that integrates three different affordances
for designing and modding digital games: a map-based (GIS) game scene, a simplified database andblock-based programming editors. The research focused on a) the computational thinking skills thatemerge and the meanings that are generated through students’ engagement with the three affordancesand b)

Keywords
Computational thinking; game modding; block-based programming; progressive engagement

Game Modding for Computational Thinking
Computational Thinking (or CT) was firstly described by Wing as a new way of thinking that drives fromfundamental concepts of computer science but it is applicable to a wide spectrum of scientific fields andto everyday activities. It includes a set of thinking skills, habits and behaviours that are integral to solving
complex computational problems, such as conditional logic, debugging and error detection, algorithmic
thinking, process of information etc. However, despite the big attention that CT has received, there is
still ongoing discussion about its definition and the set of skills that it encloses (Brennan & Resnick,2012). While not exclusive, many researchers across the literature consider the following four skills as
an important part of CT (Barr et al, 2011, Krauss & Kiki, 2017, Grover & Pea, 2013): a) decomposition
which refers to the process of breaking down a problem into smaller parts that are more manageable
and easier to be solved, b) pattern recognition in the terms of identifying and matching similarities(patterns) between different instances as a way of gaining extra information for them c) abstractionreferring to the process of generalizing from specific instances by ignoring unnecessary details andkeeping only their essentials so that a generic set is created and d) algorithm design, which refers to
the ability to create a specific and effective process with step-by-step instructions, intended to be
executed by a machine or even by another human.
Despite the long discussion and debate that has been done on grounding the theoretical basis ofcomputational thinking, little attention has been given to the exploitation of known pedagogical theories
for fostering its skills. In most cases, the development of CT is associated solely with closed coding
activities and puzzles in which the ‘making’ element is limited or non-existing. However, this is a quite357

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strict approach if we consider that CT includes the cultivation of thinking skills and behaviors. To thisend, we approach Computational Thinking through the constructionism lens, in which coding is seen as
an expressive medium that enables the design, creation and sharing of complex personal artefacts(Papert 1980, Kynigos 1995). We also argue that constructionist designs that integrate coding with otheraffordances in a meaningful way (i.e. dynamic manipulation, robotic design, graphics creation etc), canbecome powerful tools for the acquisition and development of CT skills. One strategy that seems to beable to address the above challenge, but still has not been studied in the context of Computational
Thinking is that of game modification or game modding.

Game Modding and Constructionism
Modding is a term coming from the world of gamers and refers to the process of modifying specificelements of a digital game to create a little or completely different version of it, which is called ‘mod’ (ElNasr & Smith, 2006, Moshirinia, 2007). From a pedagogical point of view, modding shares a lot withconstructionism. According to constructionism theory, students build their own knowledge structures
as they are engaged in constructing a public entity (Pappert, 1991). These structures are seen as
externalized expressions of ideas and thoughts which, through the sharing of the artefacts are becoming
objects for discussion and change (Kynigos, 2015). Similarly, during the modding process students
explore, decompose and analyse the game structure (i.e. the code, the skins, the mechanics etc)
acquiring the necessary knowledge to make changes. Then they apply this knowledge to create andshare a personalized artefact that depicts their own ideas and perceptions on the game. This game isthen played, evaluated and discussed by others, probably leading to another cycle of modifications. We
believe that this process can support students to generate and share meanings about the concepts
involved in the game and also promote the development of skills like critical thinking, communication,
abstract thinking etc. One difference from other constructionist activities is the process of deconstructionthat precedes the construction. In game modding, students firstly break down the game structure tomeaningful entities (i.e. the code, the skins, the mechanics etc) for analysis which then are modified
and composed again for the creation of the mod. The process of deconstruction can put an added value
to the learning process in the sense that children will use the deconstructed entities as building blocks
to construct the personal knowledge which may differ from the original one (Boytchev, 2015).
A strong advantage of game modding compared to designing a game from scratch is that the modder
can use the game’s structures (code, database, media etc) as a starting point for understanding its
development, eliminating in that way the learning curve which can be quite big in the case of gamedesign. Moreover, the ‘modder’ is already familiar with the original gameplay and starts modifying it witha specific plan in mind. Depending on the type and the depth of the modifications, modding can require
quite complex actions like analyzing the game structure, identifying the essential parts of the original
game and those that can be modified, designing new game strategies and rules that are in line with theoriginal gameplay etc. Several studies have explored the educational potentials of game modding. As
the studies indicate, through game modding activities students acquired and implemented knowledge
about several programming concepts (variables, event-handling, Boolean logic etc), but also from otherfields like mathematics (3D geometry, vectors), art and physics (rotation, movement). In addition,
students developed a number of skills like teamwork, iteration and refinement, debugging and error
prediction etc. However, even though game modding seems a promising approach for promoting CT
skills, limited related research has been done so far.

Progressive Engagement in CT skills
In order to design students’ engagement in game modding activities, we adapted the three-stage
progression pattern ‘Use-Modify-Create’ proposed by Lee et. al. (2011) for engaging youth in CT skills
within rich computational environments. In this pattern, children are gradually engaged with theaffordances of the environment transforming from users to creators. The approach is based on the ideathat ‘scaffolding increasingly deep interactions will promote the acquisition and development of CT’ (Leeet. al. 2011). In this way, students tackle progressively higher design challenges as their skills andcapacities increase. We argue that for a meaningful involvement in CT skills, game modding process
should also follow a similar model of gradual engagement that would start from playing the original
game, succeeded by small modifications or corrections and end up to the creation of the final mod which358

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can stand as a separate version of the game. Based on the model of Lee we propose a three stages
model for progressive engagement with game modding: “Use-Fix-Mod Creation” (Figure 2).
In their model Lee et.al describe the “create” stage as an iterative process of testing, analysing andrefining until the final artefact is created. We argue that a similar cycle is also followed in game modding
during the stages of Fix and Mod Creation. We see the creation of a mod as an iterative process ofseveral modifications each of which involves a cycle of the 4 steps: Test (Play the modified game) –Evaluate (evaluate the game for further modifications) – Analyse (analyse the game’s structure andcomponents) – Modify (implement the new modification). After one modification is completed the sameseries of steps will be repeated until the final mod is created.

Figure 2. Three-stages of progressive engagement in game modding

In order to achieve the transition from “Play” to “Fix” and finally to “Create” stage, we are using theapproach of “half-baked” microworlds which was introduced by Kynigos (2007) and refers to exploratory
digital environments conceived, by design, to call for modification and change. Similarly, according toKynigos & Yiannoutsou (2018), a “half-baked” game is designed with some characteristics that provoke
students to modify it because they don’t like it. In our case, the ‘buggy behaviour’ of the game works asa trigger mechanism for engaging the students with the different game elements and urge them to makechanges to them.

Related Work
Computational Thinking seems to consolidate its place in educational curricula around the world. A lotof countries, like Israel, Russia, New Zealand and Australia, recently revised their K-12 curriculum so it
includes computational thinking (Grover & Pea, 2016). In addition, several groups are trying to make
Computational Thinking accessible to everyone. For instance, in 2014 ISTE in collaboration with
Computer Science Teachers Association developed the ‘computational thinking toolkit’, a complete
collection of CT resources, from presentations to surveys and graphical animations, free to be used byanyone who wants to learn more about or to advocate for CT. Moreover, popular game design
environments like Scratch and Alice, have been used lately for the development of computational
thinking. For instance, the iGame after-school program aims to engage students in CT by designing
their own computer games using Storytelling Alice (Lee et al. 2011). In addition, recent studies havebeen focused on the development of skills related to CT through students’ engagement in constructionist
designs. For instance, Weintrop and Wilensky explore the emergence of programming abstractions
through RoboBuilder, a constructionist video game in which the player uses block-based coding in orderto play. Similarly, the tool eXpresser has been used for studying students’ engagement in generalization
processes (Geraniou & Mavrikis, 2012). Finally, in some cases instead of designing a game fromscratch, it is followed the approach of game editing or ‘remixing’. The term remix in game design has
been widely implemented by the Scratch community referring to the process of building on an existed359

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Scratch project and repost it (Brennan & Resnick, 2012). Depending on the depth of remixing, it canrequire a high degree of sophistication and CT skills (Kafai & Bruke, 2014).
However, especially in the context of game design, there is still lack of research that studies
computational thinking in the context of a) game modding and b) rich computational environments that
integrate a number of affordances like graphics and media editors, data representations, coding etc.

ChoiCo: A constructionist tool for game design
Based on the above context we designed and developed ChoiCo (Choices with Consequences), an
online authoring tool for designing and playing choice driven simulation games (Grizioti & Kynigos,
2018). In ChoiCo games the player revolves around different map-based settings making selections(these can be items, buildings, actions etc) each of which has specific consequences to a number ofgame attributes (Money, Health, Fun etc). The final target may vary, depending on how the game has
been developed. For example, perfect-Ville (Yiannoutsou et. al, 2014) is a SimCity-like ChoiCo game
which includes a set of city-sites such as “restaurant”, “library”, “work” etc. The player is a citizen of acity and has six attributes (Energy, Money, Hygiene, Fun, Social and Health) which should remain within
specific limits. Every time that the player selects to ‘visit’ a site these attributes change accordingly until
the game is over.

The modding features of ChoiCo
In ‘Design Mode’ the user can design a new game from scratch or modify an existed one. This
environment is based on a carefully designed structure that aims to support computational thinking skills
in multiple levels. More precisely the game design area is divided into 4 tabs each of which implementsa core game element (Figure 3).

Figure 3. Modding of Game Interface in the Game Design Mode of ChoiCo

The first tab, called “Game interface” (Figure 3) contains the game scene, which is a map-based
interactive tool and the game database, which contains information for all the items (rows) and how theyaffect the game attributes (columns). By using these two tools the user can make changes to thegraphical interface of the game such as: change the background (which can be either a static
representation or a map tile), add and modify items, add new areas and new player’s attributes etc. The
three other tabs enclose the code for the game rules and behaviours in the form of block-based
programming. Specifically, in the second tab is implemented the initial code which is executed only
when the game begins i.e. the initial values of the player’s attributes. The third tab contains the codethat describes the gameplay and it is executed every time the player selects an item. This code includes
rules for manipulation of the items, transition between layers, feedback to the player etc (Figure 4).
Finally, the fourth tab contains the ending rules of the game.

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Figure 4. The "Gameplay Rules" in the Game Design mode of ChoiCo

The programming language of ChoiCo has been developed with Blockly Javascript library and apartfrom the basic programming structures it also contains blocks designed especially for gameprogramming such as game over, hide/show item, set background, set active layer etc. The available
blocks are divided into 6 categories( ‘Conditions’, ‘Variables’, ‘Maths’, ‘Game Actions’, ‘Map Actions’and ‘Initialize’) in order to ease the modding of different game elements. Thus, for example in category‘Map actions’ there are blocks for modifying the game scene and its objects such as ‘hide item’, ‘set
active layer’ etc.

Pilot Study: Context and methodology
In order to investigate how computational thinking skills emerge within game modding activities, weorganized a pilot study with Junior High school students in which they modified a game created with
ChoiCo. The main research questions we wanted to investigate with this study were:
1) Which computational thinking skills emerge and what meanings are generated during students’
engagement in game modding activities with ChoiCo?
2) How students’ computational thinking skills are being developed through the three different
stages of engagement?
In the presented study we applied the method of Design-Based Research which includes the design ofa pedagogical intervention and its evaluation in real classroom settings with the aim to refine the initial
pedagogical design and to develop new theories (Barab & Squire, 2004). The research was carried out
through three repeated cycles of design and implementation, utilizing every implementation as anopportunity for data collection, evaluation and review.

The ‘half-baked’ game and the modding activity
For supporting students’ progressive engagement in modding activities we developed a ‘half-baked’
game in ChoiCo which was called “Eating out”. The game represents a neighbourhood of an imaginarycity with a number of different places to eat. Each place also contains a number of food choices. The
aim is to keep the attributes ‘Hunger’, ‘Health’, ‘Money’ and ‘Joy’ between specific red lines that theplayer has to discover while playing. In order to give this game a ‘half-baked’ characteristic, we added
on purpose the following two buggy behaviours in relation to its rules: A) An inconsistent item behaviour.
The item “House of Granny” increases money by 15 without causing any damage to the other attributes.
This behaviour is in contrast with the pattern of all the other items which will cause both positive andnegative results to the score in order to keep a balance in the game flow. Thus the player can selectthis item unlimited times to get money with no consequences, giving to it a ‘cheaty’ role. B) A missing
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ending rule. We didn’t include a check condition for the attribute ‘Hunger’ in the code that implements
the ending rules of the game. This is also contradictory with the game rules for the rest attributes.
We expected that these problematic behaviours would trigger students while playing the game to fix it
and extend it. In addition, the detection and fixing of them require actions related to CT skills likerecognizing patterns in items’ behaviour, debugging and changing the algorithm for the ending rules
etc. Regarding the modding process, it was divided into three main phases based on the three stagesof progressive engagement described before. First students play the original game, then they makesmall changes and finally through repeated modifications they create their own mod. At the end of thesecond (fix) and the third (create) sessions, every team played the game of another in order to give
feedback but also to discuss and exchange ideas about their artefacts.

Participants
The study was divided into three 3-hour sessions and took place in a public Junior High School as anafter-school activity. The participants were 13 students, mixed boys and girls, from the third grade, aged
14-15 years old. They worked collaboratively in small groups of 2-3 in the school’s computer laboratory
using one computer per group. All of the students had little previous experience with block-based
programming in Scratch. The ChoiCo tool had been presented to them during a math class.

Data collection and analysis
During the study, we collected qualitative data which included screen capturing files, audio recordingsof each group and the files of students’ modified games in different stages. Moreover, we followed theapproach of artefact-based interviews for assessing CT skills and strategies (Brennan & Resnick, 2012).
At the end of each session we did a semi-structured interview with every team aiming to urge discussion
around three points: a) the changes that they had done so far to the game b) their involvement tocomputational thinking skills and c) their opinion and suggestions for the ChoiCo tool. Some
representative questions are for instance: ‘Did you change the number of layers and why?’, ‘Did you
group your items in a specific pattern?’, ‘What difficulties did you face in programming the rules?’ etc.
Finally throughout the study students filled out a worksheet with their modding plans, their decisions
and their final implementations.
For the analysis of the data, a qualitative analysis method was followed. More precisely, the transcripts
of audio files and interviews were analyzed with the tool Atlas.ti using a set of codes divided into threemain code categories a) computational thinking skills, b) modding ideas and c) interactions with the tool.
The unit of analysis was the critical episode, which refers to representative moments that indicate
students’ engagement in one or more of the above three categories. In addition, students’ games wereanalyzed according to the progression of the implemented modifications across the different modding
stages. This data was compared with students’ replies in their worksheets about their modding progress
and plans.

Findings
At the end of the study, there were five different mods created, one by each team. The mods are briefly
presented in table 1 with a short description and the modifications they had. It is worth mentioning thatin their final mod all teams had made significant changes to the game elements with the buggy behaviourin the initial half-baked game: The Items’ Values and the Ending Rules. This fact shows that the halfbaked elements of a game are possible to affect the modifications that students will decide to make.
Moreover, four of the mods had new attributes, while in three of them the students also added newlayers and designed a new setting or story. Despite the big or small changes they made, all of the teams
maintained some elements from the initial game especially in the code and in the game attributes, which
helped them compare and evaluate their games both with other teams’ and with the initial one.

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Table 1. The 5 mods created by the students
Mod NameGame description/Attributes

Modified Elements

Eating out 2

Choose between different restaurants in your city.
Attributes: Joy, Money, Health, Hunger

Items Set, Items’ Values, Ending
Rules

I am hungry

Make the right food choices.
Attributes: Joy, Money, Health, Hunger, Free Time

Items Set, Items’ Values, Ending
Rules, Game Attributes

Travelling inEuropeVisit as many European cities as possible.
Attributes: Joy, Money, Tiredness

Items Set, Items’ Values, Ending
Rules, Game Attributes, Layers,
Game Concept

SchoolSurvival

Make the right choices in order to survive a schoolday. Attributes: Joy, Boredom, Health, Knowledge,
DangerItems Set, Items’ Values, Ending
Rules, Game Attributes, Layers,
Game Concept

Our School

Get in different school classes without gettingbored. Attributes: Joy, Education, Health, Stress

Items Set, Items’ Values, Ending
Rules, Game Attributes, Layers,
Game Concept

The data analysis indicated the emergence of CT skills during the three stages of engagement. Table 2
depicts the CT skills that were detected in each stage according to the results of the data analysis. Even
though the skill of decomposition was mostly present during the third stage of mod creation, for thethree other skills there was evidence of a progressive development from the first to the third stage. As
progress, we don’t consider a numeric increase in the number of related critical episodes, even though
there was one, but a gradually deeper and more meaningful engagement with CT skills during themodding process. To further explain this we present this progress through characteristics example foreach of the three CT skills, pattern recognition, abstraction and automation.
Table 2. Emerging Computational Thinking Skills across the three-stages of progressive engagement in gamemodding
Stage

Emerging CT skills

Use

Pattern Recognition, Automation

Fix

Pattern Recognition, Pattern Implementation,
Automation, Abstraction

CreatePattern Recognition, Decomposition, Pattern
Implementation, Abstraction, Automation

From Pattern Recognition to Pattern Construction
Since patterns play an important role in games, and especially in ChoiCo games, students’ engagementwith them was present throughout the study. While they were playing the game, all groups recognizedpatterns related to the behaviour of the items in the game and how they affected their score. For
instance, some patterns expressed by students of groups 2 and 3 are “Health is decreased inverselywith Joy”, “All the tasteless foods increase your health but reduce the joy, while the tasty ones do the
opposite”. Three of the five groups also detected the problematic behaviour of the ‘house of granny’choice which they described it as an inconsistency of a related pattern. Maria from group 3 mentions as
she plays the game “Look! None of the other buildings gives you money except the house of granny.
That’s a cheat!”. She recognizes a common behaviour of all the buildings in the game (pattern) andsince ‘house of granny’ is a building should also follow. George, a student from group 1 says to his
teammates “We can go in and out from the house of granny forever and never lose! Unlike all the otheritems of the game, this one has only positive effects on your score!”. George describes a different patternfrom Maria which should also have been followed by this item as it is a pattern for all the items of the363Constructionism 2018, Vilnius, Lithuania

game. Both students realize that even if a single item doesn’t follow the designed game pattern, it caneasily destroy the gameplay. During the Fix stage students corrected these patterns in the data tablevalues while some of the groups experimented with the patterns of other attributes. In this stage, they
progressively passed from pattern recognition to pattern implementation which in the third stage became
pattern construction. In the stage of mod creation, three of the groups constructed new patterns in the
database values. One example comes from group 2 who created two categories of patterns as shownin Figure 5. The first category included patterns for each attribute by item category i.e. “The city points
will have zero effect on the Joy variable. Hotels will increase it a lot, but Hostels or Motels will decrease
it. Tourist sites will increase it a little bit“. For the design of this pattern, they modified accordingly all thevalues of one column (attribute) in the database and that’s why we called them ‘Vertical Patterns’. The
other category included patterns of relations between attributes either for an item category or for all thegame items. For instance ‘Tiredness will be Money/10 so that they are proportional’. These are the‘Horizontal Patterns’ as students applied the pattern to the related cells for each row (item) of thedatabase.

Figure 5.2 Patterns created in the database values (translated version of the game)

From specific items to abstract classes
In the beginning, students used the skill of abstraction combined with pattern recognition to identify
categories of game elements such as items categories or code functions. Later during the stage ofcreation, they firstly designed and then implemented their own abstract categories (classes) of items
and layers in the game. We will present as an example the mod ‘School Survival’ from group 1. In thisgame, the player is a high school student and has to make choices related to school behaviour(miss/attend a class, sleep, solve an exercise, talk with a classmate etc.) Every choice may reduce or
increase the five attributes: Joy, Boredom, Health, Knowledge and Danger. When this group wasplanning their mod, they described on their worksheet a region category with the name “Classroom”.
Then they explained in the interview that “Classroom” is an abstract model that will help them for thecreation of the school classroom layers because all classrooms must have some common
characteristics. These were: A background image that represents students in a classroom, 3 choicesof actions an 1 point ‘Exit’ for returning to the corridor region. Moreover, the entrance to a classroom
reduces the ‘Danger’ attribute. Students used this model to create three different classroom instances(Figure 6).

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Figure 6. The 3 instances of the abstract category "Classroom" in the game 'School Survivor'

In addition, they created three abstract categories for the actions according to their consequences,
before they add the actual actions in the game. These were: High Risk Actions (They increase a lot the
attribute Danger, decrease the attribute Knowledge but also increase the attribute Joy), Low Risk
Actions (They increase a little the attribute Danger and decrease the Boredom) and Good Student
Actions (They increase knowledge but also boredom and decrease Danger but also Joy). Then, when
they wanted to add a new action they created it according to these three categories. They also tried tokeep a balance between these three categories in the game.

From simple descriptions to complex algorithms
From the first to the last stage, students applied algorithmic concepts in order to describe, modify anddevelop game rules, with the most common being conditional statements (if..else), logical expressions,the sequence of commands, and variables in the form of game attributes. An important outcome for allgroups is the gradual transition from a verbal description of game rules to their implementation with
programming blocks. This process led students to generate meanings about programming andalgorithmic concepts, but also to develop their automation CT skill. To elaborate on this outcome we will(again) use group 2 as an example who created the mod ‘Travelling in Europe’. During the first stage
students detected and wrote down to their worksheet the basic rules of the game such as «If your healthis lower than three you lose» «Every time you make a choice, it will reduce some of your four needs. If
some of them become lower than zero then you lose». In this stage, students make a free descriptionof the algorithmic sequence and of the conditional statement. Then in the Fix stage, they matched theserules to the relevant blocks of the game code. In this stage, they also made some small changes to theexisted rules including modifications to logical conditions and the addition of an extra rule for the variable‘Hunger’. In this stage, they transit from the free algorithm description to the recognition and modificationof the corresponding parts of the code. Finally, during the Create stage, they constructed new rules
based on those they had already explored and modified. Some of the new rules were even more
complicated than the initial ones regarding the use of the conditional statement. Figure 7 summarizes
this gradual engagement in algorithm building.

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Figure 7. Progressive engagement with the conditional statement

During the create stage there was another interesting outcome for the algorithm building CT skill. While
students were designing their mod they imagined and sometimes tested, possible scenarios of play to
improve the game rules and flow. This led them to think the gameplay as an algorithm and to apply
coding techniques such as event handling and error prediction both to the code but also to the databasevalues. This was a complex and extensive procedure which required students to continuously switch
between the three affordances (map, database and code) but also switch between the roles of designer,
player and evaluator.
In the example below, students of group 1 discuss the initial and limit values of the attribute “Danger”.
S1: So, what should be the initial value of Danger (tab 2)
S2: Let’s set it to 0
S1: But if you start with 0 and you choose the point ‘leave school’ first you will immediately
lose…because…look (switch to the 1st tab)… “Leave School” gives +5 to Danger
S2: Yes ok. And why will you lose?
S1: Because here (switch to 4th tab) we have programmed it to lose if Danger is greater or equal
to 5
S3: Well ok. We can just change the condition and make it greater or equal to 100
S1: Yes but not 100! It is too high. (Switch to 1st tab) All the items increase or decrease dangerby 5-10 units. It is too hard for a player to reach 100. Make the limit 50.
In the above episode, students switch between the three tabs trying to predict the possible sequence ofplay, thus possible executions of the game algorithm. Then they discuss on algorithmic concepts which
apply to their program aiming to create a meaningful game. This process indicates a progress inautomation skills and algorithmic thinking. Students not only have the knowledge to use the conditional
statement, but they also express their ideas for it and implement it in a meaningful form for them.

Conclusions and Discussion
In this article, we explored the emerging computational thinking skills through the process of gamemodding with ChoiCo tool. We presented a case study in which students were progressively engaged
in the three stages of modding “Use – Fix- Create Mod”. The modding activity was developed around ahalf-baked game, designed with two intentional bugs to urge students to change and extend it. The
results of the data analysis revealed some significant outcomes regarding CT. First of all, it seems that366

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this gradual engagement in construction process acted as scaffolding for the development of some
important computational thinking skills. While students are transforming from players to creators so does
the way they apply CT skills. In the first stage students used CT skills to understand and analyze thegame structure (pattern recognition, categorization) which in the next stages utilized for meaningful
constructions (pattern generation, the creation of abstractions). Moreover, the analysis showed that thehalf-baked approach can contribute to game modding activities in terms of motivation but also guidance
for the modifications. Especially for the transition from play to fix stage, the half-baked elements of thegame worked as ‘trigger’ mechanisms for the students. After detecting the problematic behaviour theywanted to fix it according to their own solutions and to even to improve it with new ideas. It also urged
them to focus on specific game elements and guided them on avoiding the same mistakes in their mods.
As emerged from the analysis, all of the three affordances of ChoiCo environment played a significant
role in the development of students’ CT skills. The use of the map-based tool for the modification of thegame scene, items and regional layers seemed to foster students’ perception and implementation of
abstractions. In addition, the representation of game items and their properties in the form of a simplified
database urged students to use, process and handle a big number of data. This led them to discover,match and apply patterns, reinforcing pattern recognition and pattern generation skills. The block-based
programming involved students with algorithmic thinking and automation. The categorization of blocks
according to their role in game design as well as the feature of special game blocks, facilitated thecreation of complex programming structures for automating the game rules. The analysis also revealed
some limitations which need further investigation. Students applied the skill of decomposition only during
the third stage, even though it could have emerged earlier. Even in the third stage its presence wasquite restricted and not by all teams. With respect to coding, even though students made extent use of
the conditional structures, the use of other programming structures like repetition or internal variables
was very limited. These outcomes indicate the need for redesigns in the modding activity with more
focus given on the specific aspects.
This study showed that game modding has significant potentials as an educational approach for
fostering computational thinking skills. However, more research needs to be done in this field that would
examine how the game genre and the game elements that are modified affect the development of suchskills. Moreover, the results revealed that the integration of different affordances can provide a richcontext for the development of CT skills. Thus, further research is necessary for designing and utilizing
integrated designs for computational thinking, not only in the field of game modding but also in otherareas that engage students in design.

Acknowledgements
This research has been financially supported by the General Secretariat for Research and Technology(GSRT) and the Hellenic Foundation for Research and Innovation (HFRI) (Scholarship Code: 531)

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Programming Approaches to Computational
Thinking: Integrating Turtle Geometry, Dynamic
Manipulation and 3D SpaceMarianthi Grizioti , mgriziot@ppp.uoa.gr
National and Kapodistrian University of Athens Educational Technology Lab, School of Philosophy,
Faculty of Pedagogy, Greece

Chronis Kynigos , kynigos@ppp.uoa.gr
National and Kapodistrian University of Athens Educational Technology Lab, School of Philosophy,
Faculty of Pedagogy, Greece

Abstract
During the last decade, coding has come to the foreground of educational trends as a strong mean fordeveloping students’ Computational Thinking (or CT). However, there is still limited research that looks
at coding and Computational Thinking activities through the lens of constructionism. In this paper, we
discuss how the knowledge we already have from other thinking paradigms and pedagogical theories,
such as constructionism and mathematical thinking, can inform new integrated designs for thecultivation of Computational Thinking. In this context, we explore students’ engagement with MaLT
(Machine Lab Turtle-sphere)48, an online environment of our design that integrates Logo textual
programming with the affordances of dynamic manipulation, 3D graphics and camera navigation. We
also present a study on how the integration of the above affordances can promote constructionist
learning and lead to the development of CT skills along with the generation of meanings aboutprogramming concepts.

Keywords
Logo geometry; computational thinking; programming; dynamic manipulation; 3D graphics

Introduction
Programming as skill and as a learning activity is connected to technological changes (the shift fromweb to web 2.0, society’s acquaintance with technology) and to cultural trends. Back in the 80’s, many
schools showed great enthusiasm in teaching their students computer programming, while by the mid1990s schools turned away from programming, mainly because of a lack of subject-matter integration
and a lack of qualified instructors (Noss & Hoyles, 1996). When software tools with dynamic
manipulation and icon-driven technology appeared, programming was seen as a kind of unnecessary
noise to doing interesting things with digital media (Kynigos, 2015). However, during the last decade,we are facing a big comeback of programming (Kafai,Bruke & Resncik, 2014) in a new context in which
it is considered as an integral part of a new computational literacy. An advanced view about computer
programming has become popular; that every child can and should learn to write code as a way todevelop their computational thinking. The idea of computational thinking that Wing described in 2006as ‘a set of thinking skills, habits and approaches that are integral to solving complex problems using acomputer and widely applicable in the information society’, is now considered as a necessary supply forthe 21st-century students (Barr, 2011; Kafai,Bruke & Resncik, 2014; Lee et.al. , 2012). However, there
is still need for exploring pedagogical approaches and strategies for the acquisition and improvementof its skills (Brennan & Resnick, 2014).
In this paper we argue that the challenge remains, both for designers and educators, to consider whatkinds of learning processes and to what end students may engage with the new programmable media.
It seems to be a good time, with all this enthusiasm surrounding computer programming, to utilize these48

http://etl.ppp.uoa.gr/malt2/

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new opportunities for learning to code, with a constructionist approach, based on Papert’ s genericvision and ideas for learning (Papert, 1980). Constructionist design approaches for educational tools,
aim to engage users of all ages in the construction of personally meaningful artefacts throughprogramming (diSessa, 2001; Papert & Harel, 1991). To this end, we propose the approach of integrated
constructionist designs in which programming is combined with other affordances forming a meaningful
and enriched computational environment. The affordances are selected and implemented in a way thatthey engage students with scientific concepts and at the same time promote the development of wider
skills, like these included in CT. In integrated designs, students use the different affordances, including
coding, as means for self-expression, communication of ideas and design of personal artefacts.

Rethinking Programming Approaches to Computational Thinking
All this recent enthusiasm around Computational Thinking and ‘coding for all’, has led to a wide
development of new educational software and applications that aim to engage students of all ages with
programming. Block-based programming tools such as Scratch, Alice, ToonTalk and apps like Kodu
and LightBot have become very popular and have attracted many young people, but also teachers andadults, to write simple programs with them. At the same time campaigns like the ‘Hour of Code’ and ‘EUcode week’ are organized every year to introduce young students to programming.
The research on computational thinking points out the importance of active engagement, constructionand exploration in programming activities (Brennan & Resnick, 2014; Resnick, 2014; Stager 2014). In
many cases though (with the exceptions of Scratch and Alice), students’ engagement with programmingis attempted through a series of closed quizzes and puzzles with the “making” and the social elements
being limited. In addition, block-based programming can also have significant limitations for older ormore experienced students (Weintrop & Wilensky, 2015). On the contrary, former pedagogical designs
like textual programming are left aside regarded as obsolete. This is confirmed by the fact that there is
still limited research focusing on the Computational Thinking skills that can be promoted with textual
programming like i.e. Logo language.
The question that arises is how these ‘obsolete’ but yet deeply studied approaches, can contribute to
the design of new coding tools by providing a strong theoretical background. For instance, a large
number of studies have shown the benefits of Logo textual programming and the effectiveness of turtle
geometry in offering rich mathematical experiences and encouraging the construction of meaning(Clements et al. 2007, Kynigos 1995, Papert 1996). We argue that if coding is seen as a new kind of
expression and mediation of meaning, then it may be worthwhile investing on designing ways to makeformalism functional and meaningful through its connection to other representational forms in adependent way. New educational tools for programming could for instance maintain the basic designprinciples of conventional Logo-based designs and be enhanced with new technologies in parts which
are meaningful for the learning process such as dynamic manipulation, a variety of what is programmed(e.g. robots, devices, digital objects with properties, behaviours and interactions in diverse fields), 3D
representations etc. In such tools, the development of learner’s skills would be achieved throughauthentically creative and constructionist activities and coding will be seen a vehicle to enhance them.

Towards an integrated approach
Computational Thinking is strongly connected with other known thinking paradigms such as
mathematical thinking, engineering and scientific thinking and algorithmic thinking (Wing 2008, Hu
2011). Thus, for the development of the integrated designs described above, it is quite important toinvestigate how grounded implementations and approaches from these paradigms may contribute.
Let’s take for example the element of abstraction. Abstraction is considered by many researchers animportant CT skill and the ‘mental tool of computing’ (Wing, 2006). However, the concept of abstraction
and abstract thinking is not new at all. Abstraction and generalization are core concepts of mathematics.
Despite the differences that mathematical and programming abstraction may have (Wing, 2008), thelong year research and the theoretical constructs from mathematics education, can be a strong basis
for the development of tools and methods that foster students’ abstraction skill. An interesting methodthat comes from the world of the Dynamic Geometry Environments (DGE’s) is the dynamic manipulationof geometrical constructions. Dynamic manipulation helps students to understand the properties of370

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geometrical objects and generalize their rules and the relationships between them (Goldenberg & Cuoco
1998, Psyharis & Kynigos, 2009). This is particularly interesting with respect to thinking about
abstraction as a process of defining or approaching a construction as 'how it behaves when manipulated
dynamically'. We argue that dynamic manipulation could also be beneficial in the context ofprogramming abstractions and Computational Thinking, even though it has not yet been studied
enough.
Another example from mathematics that requires a number of mental skills is that of creating andmanipulating 3D geometrical shapes. When students interact with 3D models they have to use spatial
thinking which includes complex mental processes like the capability of understanding and recognizing
the location and shapes of the objects, their relations to each other and their movement in space(Hauptman, 2010; Kynigos, 2007). To achieve that they usually apply skills like pattern recognition or
decomposition which are also included in CT (Lee et al. 2011). Thus the programming and manipulationof 3D geometrical objects in a digital environment could also be a technique for supporting the cultivationof such skills.
The above examples indicate that the integration of digital affordances and concepts from other fields
like mathematics can possibly be beneficial for CT skills. In our research, we try to investigate and reveal
the common elements between mathematical, algorithmic and computational thinking paradigms andstudy their possible development through such integrated designs. In the presented study we focusedon four skills which are considered by many researchers across the literature, as an important part of alearners’ skillset (Lee et. Al. 2011; Krauss & Prottsman, 2017). These are a) decomposition, b) patternrecognition, c) abstraction and d) algorithm design.

MaLT: Logo Programming of 3D dynamic representations
In order to investigate the benefits of such integrated approaches to both programming knowledge andCT skills, we organized a study with MaLT (Machine Lab Turtlesphere), an online Logo-based
application of our design that allows the creation, exploration and dynamic manipulation of 3D
geometrical models with textual programming. MaLT provides a learning environment that endorses
the benefits and the strong theoretical basis of Logo programming but also extends it with newtechnological features in such ways that could enhance the development of CT skills.
More precisely it implements the classic Turtle Geometry of Papert, extending it with the integration of
the following three affordances: 1) The turtle becomes a sparrow. The designed Logo language supports
the coding of 3D drawings by navigating a sparrow (instead of a turtle) in a 3D spherical space. Thisfeature broadens the range and the complexity of objects that can be programmed with the Logo
language. 2) The drawings become alive. MaLT implements a dynamic manipulation system for the
Logo variables, allowing the animation of any 3D model that has been created by a parametric
procedure (i.e. cube :x). To use the dynamic manipulation the user clicks on any drawing on the scene
and activates the “sliders” tool. This tool contains a number of sliders, one for every parameter of the
Logo procedure that creates the clicked model, which can be varied between a max and min limit. The
variation of each slider, will result in an immediate re-execution of the Logo procedure with the new
input and thus to an animation of the model on the scene. The aim of dynamic manipulation in MaLT isto reinforce the process of abstraction by means of kinaesthetically causing the continuoustransformation to a structure described formally as parametric to make better sense of how this may
represent a generality, such as e.g. a property of a geometrical figure 3) The view becomes periscopic.
The application allows navigation in the 3D space where the avatar moves with a periscopic camera.
The navigation in 3D space requires a number of skills like perceptual constancy (the ability to recognize
some properties of an object regardless of its size, position or color), spatial orientation (the ability torealize how an object would seem from a different point of view and recognize it even if it has been i.e.
rotated or dispositioned), visual discrimination (the ability to compare multiple objects in space andrecognize similarities and differences between them) and the perception of spatial relationships(Kynigos & Latsi, 2012; Lohman 1988).

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Figure 2. The different affordances of MaLT: Logo programming, 3D graphics, Dynamic Manipulation and
Camera Perusal

There are three basic elements that comprise the MaLT environment are: The “command editor”, the“3D scene” and the” variation tool” (Figure 2). In the command editor, the user can write programs inorder to navigate the avatar on the scene. The programming language of MaLT is based on MSW
Berkeley Logo expanded with the feature of 3D graphical representations, including special commands
for changing sparrow’s direction in the 3 dimensions (e.g. up 90), importing 3D objects while moving inspace (e.g. cube 100), changing pen color and thickness (e.g. setpenwidth 4) etc. The 3D scene iswhere the sparrow moves as the commands are executed, with the same logic as Papert’s turtle moves
and draws on a 2D canvas. The scene contains a periscopic camera, with which the learner cannavigate in the 3D scene and observe the created models from different points of view. With thevariation tool, the user can provoke dynamic changes to shapes created by the execution of aparametric command (e.g pyramid x w). The tool provides a number of sliders, one for every parameterof the specific procedure, which can be varied between a max and min limit. The above features make
MaLT a rich computational environment which extends turtle geometry beyond classic 2D designs anduniquely leads it to the modern world of online sharing, 3D graphics and animation.

The pilot study
The integration of the above affordances can provide a new constructionist context for the developmentof computational thinking skills through the engagement with programming and mathematical concepts.
To further investigate this we organized and conducted a study in a Greek junior high school. The aimof the study was to answer the following questions: How the integration of the three affordances with Logo programming contributes to students’development of computational thinking skills.
How students use and construct meanings about programming concepts while theycollaboratively create and share artefacts with the above affordances.

The total duration of the study was 9 hours divided into three 3-hour sessions and it was organized aspart of the school’s mathematical club. In the study participated 9 students aged 13-15 years with small
previous experience in Logo programming, high experience and interest in mathematical problems andno previous experience in 3D geometry. The students worked collaboratively in 4 groups of 2-3 in theschool’s computer laboratory using one computer per group.
The research method we used is that of Design-Based Research which is evolved from design
experiments (Cobb et al, 2003) and includes the design of a pedagogical intervention and its evaluationin real classroom settings with the aim to refine the initial pedagogical design and to develop newtheories. The research was carried out through repeated cycles of design and implementation, utilizing
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every implementation as an opportunity for data collection, evaluation and review for the next design.
Design-Based Research focuses mainly on the collection and analysis of qualitative data as theobjective is to identify the main characteristics and the different facets of the designed intervention when
implemented with students.
Description of the activities
For the purposes of the study, we designed a set of activities with MaLT which aimed to foster students’
computational thinking skills through creative construction with all of the three affordances. The activities
were divided into main three phases.
The first phase is more of an introduction to the environment, aiming to get students familiar with MaLT
environment and its affordances through simple Logo coding tasks. They begin by designing shapes in
2D, like a square, and create new functions. Then they pass to 3D space by using their 2D models tocreate 3D ones, like a cube. After that, they design ‘give life’ to their models by adding one or moreparameters to their procedures so that they can manipulate them with the sliders of the variation tool,
creating animations.
At the second phase students design and program a dynamic cube model that animates smoothly froma 2D cube net to a 3D cube. Through this activity, students investigate the transition from two to threedimensional design and implement both mathematical and computational concepts. With respect to themathematics, they have to use the concept of angle and rotation in space. With respect to programming,
they explore the role of variables and constants in a program. They also have to think in an abstract
way in order to design an algorithm that will produce the correct model for every input. The way they
will follow to construct the model is open to the students and they have to decide it throughexperimentation with the environment and communication in their teams. There were no instructions
given to them and the role of the teacher was more supportive.
Finally, the third phase is quite more open as students have become familiar with the tool. Students use
parametric procedures as building blocks to design a 3D animated drawing of their choice. This is thephase where we expect students to express their personal ideas and meanings through programming.
To implement their idea they would need to use more complex programming concepts and also
recognize and combine properties of different 3D objects.
Data collection and analysis
During the study, a set of data was collected for analysis that included screen capturing files, audio
recordings of each group, teacher and researcher observation notes and files of students’ code at the
middle and the end of each session. A qualitative analysis method was followed for the evaluation of all
the data. More precisely, the transcribed audio files and observation notes were analyzed in Atlas.ti toolwith a set of codes related to a) the computational thinking skills of abstraction, pattern recognition,
decomposition and algorithm design, b) mathematical and programming concepts of the activities(variable, parameter, angle etc) and c) students’ interaction with the three affordances (i.e. slider use,creation of new function, camera rotation etc). From the analysis clusters of critical episodes emerged.
As a critical episode, we consider as representative moments of the student’s interaction with MaLT
affordances and of their engagement with CT skills. Moreover, the Logo codes of students wereanalyzed by comparing their progression from the first to the last session with respect to abstraction,
pattern recognition and use of variables and repetition structures.

Findings
The analysis revealed that students applied the skills of abstraction, pattern recognition anddecomposition especially during the second and third phases of the study. All of the teams recognized
and implemented patterns related to the 3D shapes both in the scene and in the Logo code. In addition,
they deconstructed 3D complex models to smaller parts or to 2D shapes (i.e. the cube to squares) whichthen used again to create other models (i.e. a square pyramid). Finally, they programmed and designed
abstract models which, with the help of the sliders, could generate a number of successive instances
creating an animation on the scene.
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Moreover, the students used and discussed programming concepts, with the most common being thevariable as a parameter, the procedures and sub-procedures and the error handling of different inputs.
Students made an extended use of the sliders tool for debugging their code but also for experimenting
with the graphical outcomes of multiple parametric procedures on the scene. In this section, we present
some characteristics examples from the analysis regarding both the emerging CT skills and also theapplied programming concepts. In the presenting episodes we don’t use the real names of the students
but randomly selected aliases.
Uses of abstraction
In the context of computational thinking, abstraction is described as the process of defining patterns,
generalizing from specific instances and capturing of common characteristics or actions into one set
that can be used to represent all other instances (Kafai, Bruke & Resnick, 2014, Krauss & Prottsman,2017). In our case, abstraction was detected mainly in the following cases:a) When students used the sliders to animate an existed parameter of their modelb) When students decided to add a new parameter of animation to their drawing
One characteristic example occurred during phase 2 when students designed a dynamic cube net thatwould be able to transform from 2D to 3D space by dragging the sliders. At this moment all groups had
already programmed non-parametric 3D cubes and 2D cube nets. Thus, they had to find a more abstract
solution that would combine the 3D and 2D model to one. According to the video and audio analysis,
students worked in two ways.

Figure 3. Transition from cube instance to cube net with the use of variation tool

Two of the groups decided to start by analysing the 3D cube shape on the scene and how it can be‘unfold’, while the two others begin by the code of the 2D cube net. One example of the first case comesfrom group 1. In that group students first recognized two important properties of the created cube shape:
its size and its dihedral angle (the angle between cube edges) which is always 90 degrees. After that,they mapped these two properties to the commands of ‘cube’ program mentioning that the commands
forward 100 and square 100 are related to cube’s size while the command up 90 is related to itsdihedral angle (Figure 3). Then they replaced the number 90 with the variable :y in all the up 90
commands because as S1 said: “By changing the up angle we will be able to unfold the cube”. However,their initial cube was created by only 4 squares resulting to a wrong cube net. Thus they added two
extra squares to the procedure.
In this approach, we can see the implementation of abstraction both in the categorization of the cube’s
properties (size, angle) and also in the replacement of the appropriate numbers with variables. Students
mapped the properties of the 3D representation to the code and decided what is important and what is
not for the model they wanted to design. They also realized that the outcome of a procedure may seemright for a specific input (cube) but not for all the others (cube net).
Two of the groups followed a different method for the same problem, starting with the static 2D cube
net instead of the cube. They analysed the shape of the scene and the code on the editor, in order todecide where in their code should add an extra command. As Helen from group 2 mentions “we should
add an up command right before every square so that the bird would turn up some degrees before it374

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draws the next square”. Helen and her teammate tested their idea by adding the up 90 command intheir code, then change it to up 0, and then to up 45 to see if their model is correct for different angles.
When they had found the correct model they generalized their algorithm by replacing the numbers of
up command with variables.
T: Why did you write up 45?
H: Because when we wrote up 90 we couldn’t see clearly what’s wrong
A: And with the up 0 it was 2D and the problem was not visible.
H: Yes… And with that number, we can see better how it would fold when it will animate with the slider.

Figure 4. Creating an abstract model of the cube that would be able to animate in space

The two girls try to build an abstract cube model, by creating and comparing different instances of thesame cube, like the frames of an animated picture. Then they create an abstract parametric procedurethat generates all of these instances and they test it by sliding the slider of the parameter.
In both cases, the final result, which was a parametric procedure that produces any possible form of the
cube model between 2 and 3 dimensions, is a product of abstract thinking as students had to create ageneral model from two static instances (cube, cube net). This process demands to recognize thevariable properties of the graphical model, match the properties with the commands in the code andgeneralize the appropriate commands with the use of variables. In addition, through that process, theyrealize that in an abstract representation, the outcome should be correct for any possible input. During
this process, students made an extended use of the variation tool in order to change the values of theparameters and to cause the animation to their 3D model.
Pattern Recognition
Pattern recognition as a skill of computational thinking is the ability to identify and match similarities(patterns) between different items as a way of gaining extra information. In our study students
recognized and used patterns with respect to the objects’ properties and behaviours in the 3D space.
After Helen and Alice had finished with their dynamic cube net model, they had some extra time andthe teacher asked them to decide what they want to do next.
H: “I was thinking to make a pyramid… I tried to create one at home but it was difficult with theangles etc”
A: “We could try to make the net of a pyramid first which is easier and then put a variable to foldit like we did with the cube”
In the above conversation, Alice finds common patterns between the two 3D objects: their dihedral
angle that transforms them from 2D to 3D space.
The presence of pattern recognition was even more visible during the third phase when students had
to combine different 3D models as building blocks to program a 3D scenery. We will use as an example
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the 3D house that was created by group 3 as part of their scenery. For the creation of the procedure
house, students used as building blocks the procedures “pyramid :x :w”, with :x being the size ofpyramid’s base and :w the pyramid’s height, and “cube :a”, where :a was the size of cube’s edge.
First, they programmed a non-parametric procedure for the house in which they used the subprocedures pyramid and cube with specific numbers as inputs. However, as they continued with theirdrawing they decided that a parametric house procedure would be more appropriate for implementing
their ideas. Thus, they used the sliders to recognize the common patterns between the parameters of
the two sub-procedures and how they could use them in their code. During this experimentation withthe sliders, they realized that the cube’s parameter :a and the pyramid’s parameter :w should be thesame variable in the house procedure because the base of the pyramid was attached to the top side of
the cube. They also added a variable for the height of the house so that they could create a house ofany size in their drawing.
G:

Ok lets put some variables to animate the house. Let's write
here… pyramid…what is the first parameter of the pyramid
procedure?

C:

Move the sliders to see what each parameter does

George moves the sliders of pyramid procedureC:

So, the first one is the base and the second the heightG

Ok.. so… the pyramid’s base must be the same with the sideof the cubeC

It is cube 70

George points on the shape with the mouse
G

This must be variable :a for example and this must also be :a.
Do you understand?

C

They are both 70

G

Yes that’s what I am saying. To replace the 70 with the same
variable

Figure 5. Experimentation with the variation tool for the pyramid function

In the above process, we can see the presence of both abstraction and pattern recognition skills.
Students observe the common properties of cube and pyramid models (patterns) and then generalize
them to the form of variables in order to create a relation between the two 3D models. What is importantin the above episode is that George recognizes the pattern by looking at the drawings in the 3D scene(“the pyramid’s base must be the same with the side of the cube”) while Christina notices the pattern inthe code (“They are both 70”). This indicates that the different affordances that MaLT offers can urge376Constructionism 2018, Vilnius, Lithuania

students’ abstract thinking by allowing the recognition of patterns simultaneously in the Logo code, the3D scene and the sliders tool.
The concept of parameter
The combination of Logo programming, 3D graphics and dynamic manipulation tool seemed to urge
students generate and discuss meanings about programming concepts. More precisely, students
engaged extensively with the concepts of procedural programming and the use of a variable as aprocedure’s parameter. Two group also used a repetition structure (repeat) and one group implemented
a counter by using a local variable. Below we present a characteristic episode that occurred duringphase 3 with respect to the use of parameters. At that moment George and Christine from group 3 hadalready programmed a parametric procedure named “house :width :height” which created a
dynamic house by calling the sub-procedures “pyramid :x :y” and “cube :a”. At the moment theycalled the sub-procedures as “pyramid :width :height” and “cube :width”. In the following
dialogue, students express their ideas about combining the input parameters of the two sub-procedures.
C: I was thinking to make the height of the pyramid to be proportionate with the height of the cube.
Like pyramid :width :height and cube :height*2
G: No…I don’t think it’s a good idea because then we could not do much…we wouldn’t be able tocreate a big variety of houses with the animation
C: Oh! What about not having two parameters in the house but only one, the width one? And thencall the pyramid with both inputs as :width?
G: Or…Maybe the one :width and the other :width/2
C: Yes and then the roof would be related to the cube like the double of it width*2 or the half
width/2, width/3 etc … And it would look nice with the sliders’ animation
In this example, we can see the two students expressing ideas around the concept of the variable whichis quite important in programming parametrical procedures. They seem to situate the concept of passing
variables as inputs between procedures and how in that case variables maintain their numerical value.
They also experimented by merging two parameters in one and creating the same procedure with adifferent number of parameters but with the same output. It is interesting how the dynamic manipulation
affordance affects their discussion by providing an imitate animation of all the procedures. In the end,
they created four alternates of the procedure “house” which had the exact same code and differ only on
the number of their parameters and how they called the sub-procedures. The graphical result on thescene included four animated houses each one with different properties, as they are depicted in thetable below.

2 Variables
To house1 :width:height
cube :width
pyramid:width:heightend

2 Variables
To house2 :width:height
cube :width
pyramid:width/2:heightend

1 Variable
To house3 :width
cube :width
pyramid :width
width /2
end:

1 Variable
To house4 :width
cube :width
pyramid:width/2:widthend

Figure 6. Different uses of parameters for the creation of four different house models

377Constructionism 2018, Vilnius, Lithuania

Discussion and Conclusion
In this paper, we explored an integrated design in which Logo programming is combined with 3D
geometry, navigation in space and dynamic manipulation of the parametric models. Our aim was toinvestigate how this integration of programming and mathematical concepts can be beneficial for thedevelopment of important CT skills. In this context, we conducted a 9-hour pilot study with 7 junior highschool students who got engaged with programming activities in MaLT digital environment. During thestudy students designed from simple to more complex dynamic 3D models and drawings using thedifferent affordances of the environment. The analysis of the data showed that the combination of thethree affordances of MaLT urged students to apply CT skills in an authentic context and to connect themwith different representations. For instance, during the design of the animated cube net, students
engaged with the concept of abstraction not only in their code (replacement of constants with variables)
but also in the animation of the 3D model with the sliders (by moving any slider the model should animate
smoothly between the instances of a cube). Furthermore, the dynamic manipulation feature of thesliders offered a strong tool for code debugging, as it allowed students to experiment with an unlimited
number of inputs and immediately connect the algorithm result to the real-time animated model.
However, apart from the use of variables as parameters, there were limited results related to otherprogramming concepts such as iteration or conditionals. In addition, even though students used
programming concepts in all of the three activities, the majority of critical episodes related to CT skills
emerged during the third activity in which students freely designed their personal 3D drawing. This factindicated the need for including more open and creative activities to our next study. This is also aninteresting outcome for other designs and activities that aim to reinforce students CT skills andreinforces the limitations of closed tasks and quizzes. Our future plans include a large-scale study
which will focus on the emerging CT skills from students’ engagement in a variety of activities with
MaLT. We also plan to include block-based programming in the same environment so that we can makea comparison between the two coding approaches.
We believe that the integration of different affordances can be a strong vehicle for the development ofCT but also a new way to combine programming and mathematics in a meaningful context. The resultsof this study may inform other integrated design, contributing to the ongoing seek of strategies forpromoting Computational Thinking for all students.

Acknowledgements
This research has been financially supported by the General Secretariat for Research and Technology(GSRT) and the Hellenic Foundation for Research and Innovation (HFRI) (Scholarship Code: 531)

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Modeling Time
Kit Martin, kitmartin@u.northwestern.edu
Northwestern University, USA

Gabriella Anton, gabriellaanton3.2020@u.northwestern.edu
Northwestern University, USA

Abstract
We held a two-day workshop where eight 5th graders constructed historical simulations in Unity. We
situate the work in the theoretical positions of constructionist history, figured worlds, and narratives. We
find that students could use Unity to make figured worlds, discussing their construction and the originsof the sources they used to make the environments. This constructionist learning environment
highlighted aesthetics and collaboration.

Keywords
History; constructionism; unity; learning environment

Introduction
Many people are interested in history, it just may not be the history presented in classrooms (Kelly,
2013; Matthews, 2016). Educational researchers argue that what individuals believe about the past is
informed by experiences outside the class (Wineburg, 2001). Regardless of their interests, research
has shown students can benefit from an investigative inquiry-based approach to studying history(VanSledright, 2002). This approach differs from Lee and Dickinson’s (1996) depiction of teachinghistory in primary school, where the primary focus was on the veracity of facts. Their research brought
timely attention to the purpose and quality of history teaching, what students should attain, and in whatorder, along with realistic expectations of children studying the past. They concluded that the focus onfacts can distract from the more important work of validating historical arguments. Common Core
Standards in History/Social Studies has advanced history education through integration of standards
regarding key ideas, craft and structure, and integration of knowledge and ideas (Common Core, 2010).
These advocate that students should progressively analyze, evaluate, and critique the perspective,
claims, and reasoning of historical authors. While these standards situate learners as critical consumersof history, they do not encourage learners to produce their own historical texts or arguments(Herrenkohl, and Cornelius, 2013). In short, historical pedagogy traditionally focused on the veracity offacts, but with the move to online fan-based history, and the idea that history is made, not known,pedagogy should shift towards validating arguments and be led by investigative inquiry.
Current trends in New Media studies seek to capture the everyday practices of people engaging withhistorical content, often focusing on the ways that historical media is understood, critiqued,
deconstructed, and reimagined (Matthews, 2016). These studies showcase the ways in which people
actively construct personal narratives using historical media and texts, and then share these narratives
in online communities. Other scholars studying everyday practices focus on the ways in which people
engage in historical content through historically themed video games, like Civilization, Assassin's Creed,or Call of Duty (Squire, 2004). Researchers have shown that players can meaningfully engage withhistorical content through playing historically situated games (Squire, 2004; Bogost, 2007; Chapman,2013). Ultimately, both threads suggest that new media content that are historically themed provide ameans of creating shared artifacts that allow people to build their own understanding that they can sharewith others and thereby people together and can craft historical understanding.
In this paper, we extend the argument for engaging with history through new media, suggesting that
construction of historical artifacts with new media can be conceptualized as a historical practice situatedin the constructionist history tradition we describe below. We present a constructionist design and studyof an informal learning environment, in which learners were encouraged to construct historicalsimulations using the Unity game engine. We argue that a constructionist design facilitates
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constructionist history practices, including historical sourcing, the development of mental models or
figured worlds, and the creation of personal historical narratives, through the construction of artifacts
that externalize mental models and necessitate learners to concretize their thinking.

Constructionist History
In this paper, we present historiography from the constructionist history lens as studied by Wineburg(1998), White (1982), Lowenthal (1992), Davis (1975), and Foucault (1970). Historians, like children
working in a constructionist learning environment (Papert, 1980), use mental models to produce
intuitions about their subject matter (Schnotz & Bannert, 2003; Schnotz and Kuschner, 2008). White
argued that putting yourself in the shoes of agents lets you see the world from their perspective:
The imagination, however, operates on a different level of the historian's consciousness. It is present
above all in the effort, peculiar to the modern concept of the historian's task, to enter sympatheticallyinto the minds or consciousnesses of human agents long dead, to empathize with the intentions andmotivations of actors impelled by beliefs and values which may differ totally from anything the historianmight himself honor in his own life, and to understand, even when he cannot condone, the most bizarresocial and cultural practices. (White, 1982, p. 123)
Long dead agents seem to be better understood when one takes their perspective. The process of
putting oneself into agents’ perspectives motivates constructionist thought. For example, students
practice geometry from the perspective of the agent: drawing triangles by directing an agent to take aseries of turns (Papert, 1980). In history, this is called “putting oneself in the place of past agents” (White,
1982, p. 123). Natalie Zemon Davis (1975) said in order to defend taking historical agents’ perspectives,“We, current historians of popular culture in preindustrial Europe, have a strong streak of interest in thepeople. But I am not sure we really respect their ways very much; and this makes it hard for us tounderstand their lives, just as it was for our learned forebears” (p. 266). Cronon posited that “to recover
the narratives people tell themselves about the meanings of their lives is to learn a great deal about
their past actions and about the way they understand those actions (p. 1369). Baron (2016) put the useof contextualization pragmatically when he argued that through the use of prior knowledge stimulated
by historical imagery in concert with new information, college students — and other historians, we would
contend — build mental models of historical eras (Baron, 2016, p. 17). Foucault (1970) argued thatthese narratives are what give order to the past: “History gives place to analogical organic structures,
just as Order opened the way to successive identities and differences” (p. 219). That historians andconstructionist tinkerers are both engaged in this same perspective-taking is a powerful motivator tobuild an immersive constructionist historical learning environment.

Frames and Discourse
When children make games, as in Vygotsky’s (1978) depiction, they assign new meanings to common
objects. For example, behind the couch becomes the robbers’ den. The broomstick becomes the
cowboy’s horse. Through these sign manipulations, meanings of one social setting are mapped onto
another imagined place. This remapping is the heart of the notion of figured worlds (Holland et al, 1998).
The process also has a dissociative element. People learn to “detach themselves” (Holland et al, 1998,
p. 50) from their experienced physical surroundings and enter an imagined play world. In this world,
people use collectively developed signs and symbols (Vygotsky, 1978). For example, a prop as simple
as a stick might launch a child into a world as a cow wrangler riding horseback. For us, these imagined
worlds are similar to the historical practice of stepping into the historical agents’ shoes. In our
implementation, we can see these symbolic remappings (figure 1) as children remixed digital artifacts
to make imaginative use of visual primitives, such as a Malian town, an ancient Chinese palace available
for free online, or a simple golf ball model becoming boulders.

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Figure 1. Mali, Ancient China, rolling hills of an imagined landscape decorated with tennis balls as boulders, andAncient Egyptian monuments were some of the historical primitives students used in their constructs.

Narratives
These figured worlds can be contested. Cronon (1992) shows that narrating the primary sources affects
those documents’ meaning. Narrative choice matters because the same events can have different
meanings. Cronon uses the example of different perspectives on the Dust Bowl. He shows how the way“facts” are presented legitimized and discredits. Historical narrative “sanctions some voices and silences
others” (Cronon, 1992, p. 1350). Moreover, the narrative modes people use to order stories have asubtle effect. Particular archetypes of story affect the presentation of history. Cronon presents the twoarchetypes of improvement and tragedy that were noticeable in depicting the Dust Bowl. Improvement
narratives argue that the march of civilization, though ever upward, has some setbacks, but will improve.
The tragic type of story argues that society needs to act differently, otherwise calamities like the onespresented will happen again. From the same set of historical sources, historians of the time narrated
both types of stories.
These types of stories order genres of depicting the past, such as the long struggle to settle the westthat erases the Native American experience and demands continuous improvement. These areascending and descending narrative voices, and they order not just individual authors, but generationsof writers. He points out that the progressive narrative was opposed in the Dust Bowl by New Deal
thinkers, who used tragedy in their tale to “construct their stories so as to place themselves on the center
stage” (Cronon, 1992 p. 1361). This pitted notions of linear progress against statist constructs ofcentralized control. Cronon uses these examples to argue for the power of history to “reframe the pastso as to include certain events and people, exclude others, and redefine the meaning of the landscapeaccordingly” (p. 1364).
Like sculptors cutting stone, historians choose which parts to leave in, how to shape them, and how
best to present the subject. Though history may not be intrinsic to the universe, it is fundamental to
how we humans organize our experience (Cronon, 1992 p. 1368). Cronon gets to the fundamental
challenge of postmodernist thought on viewing the past. If our choice of narrative reflects just our power
to choose a proffered version of reality on a past event, an event that has no ability to prevent ourinterpretation, then what is left of history? From this question he poses three axioms that good history
must follow — and we see these applying regardless of modality — (1) history does not lie intentionally,(2) history exists within the natural world, and the rules of that system form a natural check on what we
382Constructionism 2018, Vilnius, Lithuania

can tell (without obvious fabrication), and, (3) good history exists in a community that others with more
knowledge than ourselves can critique our artifacts and therefore there is a community check. As
Cronon (19’92) said, “We tell stories with each other and against each other in order to speak to eachother” (p. 1374). Lived worlds then are organized around figured worlds, and figured worlds grant
influence to players that they can use in either one (Holland et al, 1998).

Constructionist Digital History Learning Environment
Throughout history, humans have consulted artifacts, such as manuscripts and statues side-by-side
with the orally passed concepts to construct history. From Aristotle to Howard Zinn, we have looked tosources of information about the past to develop new ways of viewing our humanity, our present, andour future. This practice’s importance to history is clear from the focus of common core standards on its
accurate and proficient implementation.
Ever since we started discovering our ancestors’ cave paintings, we have questioned our past: Whodrew it? What does it mean? These questions lead us to even more questions about what happened
before us, and we use this information to rhetorically justify our future actions. The battle over who owns
history is endless; because we spend so much time with rhetoric, we sometimes forget its powerfulsway over our lives. In the rest of this paper, we present a pilot implementation of a constructionist
learning environment of history where students can practice the skills of considering, comparing, andvalidating historical sources. This implementation rests on the idea that Historiography is a contest overwhat history is, why it matters, and for and through who. It asks, on whose authority? In other words, inthis curriculum we posit a design that leverages a fact of history. It is constructed (White, 1982; Cronon
1992; Davis, 1975; Lowenthal, 1998; Foucault, 1970). We argue utility in highlighting the constructednature of history and in providing venues for learners to construct and share their own narratives ofhistorical events. This classroom design builds on earlier attempts to make history in digital
environments (Squire, 2004; Bogust, 2007; De Freitas, 2006; Chapman, 2013; Spring, 2015; UribeJongbloed, Scholz, Espinosa-Medina, 2015). To support this intervention, we will explore how students
engaged with the past and how they collaboratively built history. This work acknowledges historians’
traditional apathy for digital history (Chapman, 2013).

Design of the History Simulation Course
From a computer or a textbook, to a pencil and a compass, or a curriculum and a teacher, people use
tools to learn. Constructionism flips the traditional way of learning, by looking at how creating the tool,or artifact, helps the builder form a deeper understanding than simply engaging with the learning tool.
Papert (1987) coined the name constructionism as mnemonic to describe a species of constructivist
thought. It focuses on the benefits of learning from the external construction of an artifact beside theinternal construction of a mental model or framework. Papert explored its deep connection to computer
programming especially how taking a perspective improves learning and facilitates deeper thinking. The
field advanced and extended the approach (Papert and Harel, 1991; Wilensky, 1999; Wilensky 2001;
Peppler and Kafai, 2007; Blikstein and Wilensky, 2009; Ares, Stroup and Schadmen, 2008; Eisenberg
2007; Worsley and Blikstein, 2013).
The ideas came into the public eye after Papert published his seminal work Mind Storms: Children,
Computers and Powerful Ideas (1980). Since this time, constructionist principles contributed to many
powerful tools for education (i.e., Wilensky, 1999; Resnick et al. 2009). For instance, designers deploy
these environments to make mathematics and scientific exploration tools that afford learners the ability
to act in sequence or simultaneously on multiple representations of a phenomena (Schwarz andHershkowitz, 2001; Kaput, 1992). This approach allows for the comparison of different analogies or
meanings, which allows students to derive their own order to their knowledge, which is deeper thanisolated facts. A motivation for this work is that “Research has shown that many of children’s bestlearning experiences occur when they are engaged in designing and creating things, especially things
that are meaningful to themselves or others around us” (Papert, 1993). In other words, open-ended
play, around complicated ideas, restructures the means learners employ to acquire knowledge.
Therefore, the flipped approach can aid in knowledge acquisition.

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We approached the design of the environment by leveraging the power within constructionist learning
environments. We use the pedagogical approach to reimagine history education. More so, we argue
that the combination of the two may allow learners to more meaningfully engage with history, as theycan virtually construct the figured worlds of history through computer modeling/game design.
In designing the course, we created design principles taken from these two fields. First, we sought toprovide learners the opportunity to construct models and games in the context of history with granularity
and structure of their choosing. Second, we sought to constrain the experience through the “primitives”
that students could access during their exploration. Third, we sought to support flexibility in how learners
used primitives (Figure 1). Fourth, we aimed to provide learners an authentic tool that would allow themto create high-fidelity representations of their figured worlds of history.
We selected the Unity Game Development Engine as the software for the course. While there are othergame development engines or software available for the construction of new media, Unity provides theopportunity for learners to use community developed 2D and 3D assets, or visual primitives, in theirconstructions. David Helgason, Nicholas Francis, and Joachim Ante created Unity. Now it is one of theworld’s largest creative communities and the number one game development platform as measured byinstallations worldwide. The environment is free and has all the tools for students and educators to
produce high quality simulations fast and move on. Though intended for production, the authoring
environment itself is a highly constructionist learning space in which builders can see their vision quickly
using prefabricated assets. The ones we used were mostly free and coupled with easy-to-use code.
To construct a historical time period, participants in our intervention chose periods of time they foundinteresting, sought out images of their period of time using Google Image search, and searched Unity
3D’s online asset store for free 3D assets that aligned with their understanding of the period. Thoughstudents’ understanding of the time period is subject to misunderstanding, this plethora of
historiographical views is exactly what the Common Core standards advocate are required in acompetent historian. Though the intervention was short, in this constructionist project students
participated in historical practice: evaluating and incorporating sources to present their understandingof a historical period. We present our immersive historical simulation as one way to create aconstructionist history learning environment.

Methodology
The historical modeling course took place over a weekend enrichment program. We collected pre-, mid, and post-interviews. Additionally, we captured video of the entire two-day intervention. We had
students screen capture pictures and videos of times during their construction they foundinteresting. We collected the games they created, and screen shots of their process. We conductedinterviews with students to understand their motivations as they built in paper, in the video game engine,
and with each other. Additionally, we coded the data to find moments where students engaged in thecore themes including constructionism, collaboration, and use of historical sources.
Day 1•

Intro to History, Games, Modeling•

2D platformer game•

Phase 1: building level•

Phase 2: adding historical images

Day 2
•

Introduction to 3D games•

Physical modeling of game environments•

3D design•

Showcasing Constructions
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Results
We will look first at the results our constructionist design approach afforded. Students created playable
games that allowed flexible exploration of the concept. They were active in requesting additional
information and this allowed students to build on each other’s work, but each created with an individual
twist. Then we will look specifically at the way students used sources in their design and implementation.
The results will show that some students engage historical practices, including evaluating sources, toconstruct their history simulation in pairs using free resources online, such as Google Images forreferences of pyramids and free 3D assets.

Constructionism Environment
Learners were successful in creating playable games of varying aesthetic, mechanic, and dynamic feel.
The experience with Unity required more scaffolds or constraints than a software explicitly designed foruse with novice designers but also allowed for flexible design and provided dynamic feedback. Students
could amend their work directly after visual inspection of their creations if they discovered a bug or if
something in their design did not look as expected.
The scaffolding of the course provided learners with shared basics and the flexibility to explore
components of the game. Seen in Figure 2, L created a platformer with a focus on narrative. She senta robot back to prehistoric time to thwart evil, time-traveling scientists’ attempt to kill the dinosaurs.

Figure 2. L coded a narrative into her game: a time traveling robot saves the dinosaurs from evil scientists.

This narrative touches on the death of the dinosaurs and added the element of a time-traveling villain.
In order to create her time-traveling robot, L sought out support with coding text when the character got
to particular key frames in her platform. This is one example where a student wanted to introduce anarrative into the simulation world through code.

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Figure 3. Stylized prehistoric era where the appeal of jumps was paramount in design.

Meanwhile, M designed a platformer with a natural aesthetic. In her platformer high-resolution trees
rustle in the wind as the time-traveling robot climbs a prehistoric hill (Figure 3). She enjoyed the naturalelements in the world she created and spent a lot of time timing jumps so that when the robot moved it
was the most visually appealing. Both of these creators largely worked independently after the teacher’s
initial introduction to the world of Unity game creation. Innovatively, M’s use of 3D elements such as therustling trees into the 2D design created a spectacular bridging between the primitives she could
download from the Unity asset store and her own sense of aesthetics.
S focused on the mechanics of the gameplay, working out how to use a 3D character controller in his
games to change his character’s appearance. He frequently sought out information about how best to
approach his tasks, and then diligently worked independently towards his goals. He was a continuous
source of inspiration to his classmates as he shared ideas, techniques, and exciting breakthroughs hemade in his own Unity world through micro-sharing.
P created a tragic narrative: a world in which humans have exhausted all the natural resources and left
behind only now-defunct robots. She used little pink tree primitives from the ancient China package(Figure 1) to build the very last spot of nature in the world (Figure 4).

Figure 4. The haunting construction of a post environmental collapse world.

The haunting construction, like the Dust Bowl era historians (Cronon, 1992) evokes a sense that wemust take action to save the planet. P shared knowledge with peers who were less confident or didn’tknow how to approach the problems. This activity allowed her to present herself as an expert that othersin the class seemed to consult when they got stuck. This role both made her feel good when she was
asked questions, but also made her feel uncomfortable to raise her own questions when she got stuck.

Use of Historical Sources
In constructionism, we are rarely concerned with where the materials come from. We do not ask theorigin of straws for building or code for writing. We are interested in the ways the items are used toconstruct a physical manifestation, an artifact, that felicitously builds a mental model that a builder uses.
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Whereas in history, while we are interested in the construct that emerges through historiographic
reasoning — traditionally the manuscript — we also care very much about the origins of the materials
drawn on to make that manuscript. Historians argue about the historical validity of constructs and thesource materials. As a result, we chose to look at the way students engaged with sources separatelyfrom whether students engaged with the constructionist design principles of the intervention.
Halfway through the intervention we requested participants to construct their own 3D historical model.
During this process some students consulted sources to determine if what they were building appeared
like ancient Egypt. In this process we first demonstrated how to make a 3D prototype of a historicalperiod. We gathered around a table with a piece of poster pad paper. To demonstrate the process, thestudents imagined what existed in the Ice Age: “Huts,” one called. “Ice,” another said. “Saber-toothedcats.” We wrote the ideas in diagrammatic form on the large poster paper without interrogating theirnotions. Afterward, we asked the students to pick their own time period that they wanted to model andfollow this same process to get their ideas down. A1 and J1 chose ancient Egypt.
A1 and J1 constructed 3D versions of ancient Egypt. J1 started the process by placing Post-it notes on
his large sheet of paper indicating where to construct 3D temples and pyramids (Figure 5a).

Figure 5. Designing historical simulation on paper referring to Google Images for sources.

Working from his memory of the referenced Google Images sources, he placed them in an arrangementon what he called the desert. Meanwhile, his partner A1, engaged in a high fidelity rendering of one
pyramid; he drew each block in a multi-story 2D representation.
The design process took place mostly in silence. Fifteen minutes into the activity, the two of them pulled
up images of ancient Egypt on Google Image search (Figure 5). Scrolling through the images, they took
conceptual inspiration deciding what to include in their world and in their simulation. Taking turns, they
pointed at images on the screen they found relevant to their task and kept scrolling. A1 found the imageof the Sphinx particularly interesting. When we asked, “What are you looking at?” A1 responded simply,“Pictures.” When we asked them, “What for?” A1 responded, “Trying to show what ancient Egypt looked
like.” When I asked, “How do you know which are real?” J1 flippantly pointed at a cartoon, “That one’s
real.” A1 more cautiously responded, “I guess (which one is real)." Continuing "You have to decide"(somehow). This moment is an example of considering what counts as history.
At this point, we asked the participants to start building their prototype in Unity 3D. When J1 asked,“How will I build this in 3D?” the first author responded, “First, build a terrain and then place objects onit.” Oh yeah,“ he responded, as if it was obvious. He then logged into the Unity asset store and started
searching for an ancient Egypt model set. When he found one, he celebrated. “Freeeeeeee!” heshouted. He said, I need to search “free”, noting that many assets on the store cost money, but quickly
noted he can use keyword search to discover free resources. J1 started his first download. A1 cameover, having finished his high fidelity drawing of a pyramid. Appearing to speak to no one in particularJ1 said “We are going to make a pyramid.” Then bragging loud enough the whole room could hear “Ihave such a big pyramid!” demonstrating that he is beginning to feel pride and ownership in his
construction.
Right before lunch, with our guidance, J shared the 3D pyramid resource he had found with J1, andthen J1 started chanting “cobra” as he added large 3D stone cobras to his simulation. A1 turned his
computer toward J1 and started viewing what J1 was making. After consulting the screen, he turned his
screen a bit more toward J1 so as to indicate, ‘Look what I’ve made.’ A1 did not lift his eyes from his
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screen. J1 said to no one in particular, “Look what this thing gave me,” and rotated his computer toward
A1. They now seemed to begin a partnership to build their ancient Egypt (Figure 6).

Figure 6. Ancient Egypt as built by two collaborative 5 th graders.

Conclusion and Discussion
Within a two-day course, eight students constructed playable video games, engaged in discoursearound what to include, and generated two genres and four contexts. While we admit that theconstructions in this short course were not accurate representations of the past, we argue that theprocess students employed to include sources into their environment were accurate historical practices(Matthews, 2016). Students created a historical agent in each of these worlds, and through the view
they generated, began to enter sympathetically into the minds of humans long dead. We do not thinkthey began the second part of White’s (1982) activity of beginning to understand, even honor, beliefs
and values that differ completely from their own. That being said, the students pragmatically engaged— primed by prior knowledge and historical imagery — with contexts they created which formed mental
models of how the past may have worked. We found this work ordered their analogues, which opened
the way to the possibility of new successive identities. This process accords with how Foucault (1970)
argued history organizes identity.
Through the process of playfully imagining the past, students engaged within this class in a practical
approach that could be implemented in another classroom. Though the two authors of this paper learned
to use Unity over the past two years, the features we used and had students implement are teachable
in a teachers’ training in a few hours. We did see more question-asking by students at the beginning of
the course than in a traditional classroom, but by the end of the second day students were mostly
independent as they disseminated their technical skills through the classroom through microshowcasing. This distributed skills sharing, as individual students took on the role of competence, wasprevalent.
In the open-ended learning environment our classroom provided, students formed partnerships to build
playable games. These partnerships helped some of the students to work from sources to constructtheir understanding of the past. Often times, these designs were engaged with diligently and mostly
silently. This resulted from the way we organized the design process. This process allowed students toengage in laying out their historical understanding, reference sources, and integrate and evaluate
multiple sources of information presented in a variety of formats and media to address a question or
solve the problem of constructing a past Egyptian pyramid and surrounding environment (as required
by Common Core State Standards, English Language Arts Literacy RH.11-12.7).
This work was engaged in both individually and solitarily. The process allowed each student to add an
individual twist to their constructs while bringing together the various sources. The process supported
friendship formation. For instance, that S sat close to J1 and A1 allowed for easy micro-showcasing.
These results came from the open-ended nature of play. Likewise, the flexibility of the design facilitatedstudents to create playable games. This happened in a dynamic feedback environment provided by
Unity. We looked at both the historiographic and constructionist implications of this short intervention.
We found the environment a useful means of teaching historiographic principles through aconstructionist learning environment for three reasons: the level of production, the students’
engagement, and the vitality of the learning environment. We look forward to scaling the approach to alonger course.
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The design of the learning environment provided the opportunities to engage in these activities. The
class allowed students to make history simulations through the provision of scaffolding constraints tothe otherwise very open Unity authoring environment. This was done through an authentic tool, which
affords high-end aesthetics and functionality so students could foreground aesthetics to express
themselves.
For Vygotsky, fields of human action were games. As a result, social worlds are organized aroundfigured worlds which influence the participants in dialogically contextual constrained world. Different
voices, from Google Images, to online assets, to students’ own prior understanding of Egypt merge
together in these constructs of children in our short course. From the ice-swept slopes of E and L, tothe pyramid-spotted deserts of A and J, 5th grade students constructed histories in a professional gamedesign software.
P’s games mobilized arguments for a particular past. As Cronon (1992) pointed out, narrating sources
affects those sources’ meanings. The historical narrative is a sanctioning of some sources’ and voices’
meanings. Discourse in these games legitimized views of the past by the way the place was laid out.
We showed sanction’s subtle effect, emerging through people talking and deciding what to include in
their past. We noted in P's dystopia a particular ordering of the tools representing Cronon's archetypeof tragedy. This narrative structure was similar to the way Cronon (1992) argued political interests
marshalled documents to describe the environmental disaster of the Dust Bowl to motivate action. This
employment of a tragic archetype motivates viewers to action to save the planet before weenvironmentally destroy it. We also saw how genres of discourse swept through the authors in ourcourse: two made the Ice Age, two made ancient Egypt, and two made the Stone Age. They came
together to agree on what was cool. Like Cronon suggests historians do, our students took the license
to include some voices and exclude other voices. This practice is crucial in the study. We find that themain gain of this approach to history-learning arises through discussing constructing the past in a newmode. Though history may not be intrinsic to the universe, it is fundamental to how we humans organize
our experience (Cronon, 1992 p. 1368). We saw students organize their understandings of the pastthrough the process, drawing on the sources available to them.
When children played the game of “what if I was traveling to the past,” they constructed representationsof the past. They assigned new meanings to the digital authoring space available in Unity to turn it into
ancient Egypt, the Stone Age, ancient Mali, the future, and Revolutionary War America. They chose togo there in their play. This reassignment is very much like how Vygotsky saw students turn the area
behind the couch into the robbers’ den. They used collectively developed signs to detach themselves(Holland, 1998) and enter an imagined world of play.
We organized this experience in a constructionist learning environment. We found many affordances to
opening up the play. We created an opportunity for students to engage in a process of history-making
that aligns with history class standards such as CCSS.ELA-LITERACY.RH.11-12.7. The design afford
the opportunity by mixing this environment with the modern understanding of history as a sociallyconstructed artifact. This design built on the promise that video game design may soon change howhistory is taught and advanced, while keeping an eye on the traditional wariness of history teachers toembrace digital methods.

Acknowledgements
This work emerged from working with Jolie Matthews in her New Media class in 2015 and without her
help it could not have developed. Additionally, we would like to thank the Uri Wilensky and the Centerfor Computer Based Modeling, and the School of Social Policy at Northwestern who provided us many
resources in this work. We would also like to applaud Chastity West, and her copious feedback. Finally,
we would like to thank US Department of Education, Institute of Education Sciences, Multidisciplinary
Program in Education Sciences, Grant Award # R305B140042 for supporting this work.

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Ant Adaptation: A Complex Interactive MultitouchGame about Ants Designed for Museums
Kit Martin, kitmartin@u.northwestern.edu
Northwestern University, USA

Michael Horn, michael-horn@northwestern.edu
Northwestern University, USA

Uri Wilensky, uri@northwestern.edu
Northwestern University, USA

Abstract
This paper describes visitor interaction with an interactive tabletop game on the topic of evolutionary
adaptations of social insects that we designed in collaboration with a large Midwestern museum. Our
exhibit, called Ant Adaptations, takes the form of an agent-based modeling game (ABMG) that
integrates complex system learning with gameplay. We video recorded 38 groups (114 participants)
playing the game and conducted pre- and post-interviews. We propose a methodology, constructivist
dialogue mapping (CDM), to monitor the group-level knowledge as it emerged through interaction withthe exhibit. CDM is useful for analyzing the short session times typical of museums. Our results showthat visitor groups collaborated effectively to discuss and elaborate on their understanding of emergent
ant behavior.

Keywords
Ants; complex systems; informal learning; constructionist epistemology; methodologies; tools andtechnologies; innovative computing education

Introduction
97% of children play video games at some point in their lives (Lenhart et al., 2008). By encouraging
open-ended engagement and exploration, games can support learning across a wide variety of topics
and contexts, providing a powerful way for learners to construct new knowledge and understanding(Harel & Papert, 1991). Constructionist learning games try to strike a balance between open-ended play
and targeted treatment of learning content (Holbert, Weintrop & Wilensky, 2014). Constructionist video
games employ traditional game structures infused with constructionist ideals to create a game
experience that both encourage exploration and engages desired content (Egenfeldt-Nielsen, 2006).
For this research, we develop a means to research this informal education, constructivist dialogue
mapping (CDM). In this paper we describe the design and evaluation of a constructionist learning gamecalled Ant Adaptations that we developed in collaboration with The Field Museum. The exhibit presents
visitors with the opportunity to create an interactive digital ant ecosystem that allows learners to better
understand the world of ants by exploring a microworld (Papert, 1980; Edwards, 1995). We then present
findings to answer two questions. First, how can interactive, agent-based, complex-system-based
tabletop games best be designed for museum settings? Second, how can effect and efficacy of suchinteractions in museums best be analyzed?
Research within the museum space could provide a fecund location for future educational gameinterventions that employ a model-based inquiry of complex systems. Our past work with agent-basedmodels of social insects and microworlds has been effectively used for classroom teaching, but less soin informal museum settings. Examples of this classroom use including: Ant Food Grab, a yearlong
block-based programing curriculum with ants (Martin, Sengupta & Pierson, 2018; Sengupta et al, 2015),
and Beesmart, a curriculum about the hive-finding behavior of honey bees (Guo & Wilensky, 2014a;
Guo & Wilensky, 2014b). These insect Microworlds allowed students to construct their own
understandings of science through exploring and adapting models in a classroom setting. Like othercomplex systems interventions, the students build their understanding of complex systems and392

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recursion in natural systems (Danish, Peppler, Phelps and Washington, 2011; Blikstein & Wilensky,
2009; Klopfer, 2003; Levy & Wilensky, 2009; Wilensky & Reisman, 2006; Sengupta & Wilensky, 2009;
Wagh & Wilensky, 2012; Wilensky, 1997b; Wilensky & Resnick, 1999; Yoon et al., 2013; Wilensky &Novak, 2010). However, there is much less work that leverages the pedagogical value we discoveredin these earlier microworld based interventions in museums (Horn et al., 2014; Strohecker, 1995a,
1995b), a dearth our present work addresses.
Although microworlds can be useful for learners to explore about complex systems, developing robust
understandings of complex systems can be challenging. Wilensky & Resnick (1999) describe thedifficulties people have in “thinking in levels”, exhibiting “levels confusion” and difficulties with distributed
control and stochastic processes. Not only do learners have a hard time thinking across multiple levels
such as disease of the whole body resulting from microscopic pathogens, but they also tend not to think
about phenomena such as the flow of ink dropped into water as the processes of collectives of agents
interacting (Chi, Roscoe, Roy & Chase 2012). If the glass of water changing color is explained by theindividual parts of ink interacting with H2O than the process becomes more intuitive. Most people,
however, are not familiar with ink particles. Building off earlier work in classrooms (Martin, Pierson and
Sengupta 2018), we posit that thinking about ants is a powerful way to address both of these challenges
in museum learning. The game scaffolds thinking in levels such as between the colony and the individual
ant, and seeing success and failure of the colony as a result of ants interacting through processes. To
research this claim, this paper explores how a user made sense of the self-organization of ant colonies
in competition.

Theory: studying learning in informal learning through constructivist dialogue
mapping
We developed a way to study learning in an informal environment based in on constructivist theory. In
constructivist theory, a learner’s mental model drives his or her construction of understanding andinternal cognitive structures. This process includes accommodation and assimilation (Piaget, 1952) andmaintaining a balance between stability and change, continuity and diversity, and closure and openness(Ackerman, 2001) when exploring the world. For Piaget, children are not just incomplete adults. Theirideas function very well for their current context and as a result, their mind changes through experience.
As Ackerman (2001) said, children’s’ conceptual changes are like those of scientists: they happen
through “action-in-the-world” (p. 3) to accommodate for experiences, and most likely through a host ofinternal cognitive infrastructures. “Knowledge is not merely a commodity to be transmitted, encoded,
retained, and re-applied, but a personal experience to be constructed” (Ackerman, 2001, p. 7), likesitting with safari ants.
Mental models are built out of theories of how the world works, or in other words, the sum of lessons
learned from thinking passed obstacles. D’Mello and Graeser (2012) showed how people learn when
they encounter system breakdowns, which cause a cognitive disequilibrium or obstacle. These
moments allow for reflection and reordering of their dynamic memory (Schank, 1999). DiSessa andCobb (2004) argue that from Newton, to Einstein, and Darwin, theories embody generalizations toorganize overly abundant data that is subsequently viewed as part of a new theory. In this way diSessa
and Cobb (2004) posit theory as a lens, “teaching us how to see” (p. 4). How we see the world, is the
crucial bit about these theories since the lenses constructed through experience take actual form. Just
as “[t]he world is not just sitting out there waiting to be to be uncovered, but gets progressively shaped
and transformed through the child's, or the scientist's, personal experience” (Ackerman, 2001),
constructionist thought highlights transformation and molding as the work of mental models.

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This theory led us to construct amethodological innovation that is highly
useful for the short, interactions typical ofmuseums. We develop concept maps
(Chi and Koeseke, 1983) as they emerge
through transcript data. At one level, thesemaps represent what players said. At thislevel, we captured ideas about theunderstandingof
scienceplayers
demonstrate during play in a constructivist
map inspired by Minsky’s society of mind
(1986). For Minsky (figure 1), the humanmind works through a cognitive map ofagents. In adapting Minsky’s frame, we
present simplified concept maps ofplayerselaborating their ideas about
Figure 1. A calendar is an order of processes. Each process
agentsthroughobservation
andhas subprocess that are learned through time. So that ainteraction we call constructivist dialogue
meeting has the "begin", "discuss", and "end" subprocesses.
maps (CDM). Thus, maps will visually
depict the ideas players share throughwhat they say and how they interact with the game. We posit that we can research what people conserve(that is, learn through accommodation) by filling in a map with what people say and do during play.
Constructivist dialogue mapping allows us to track how people’s words and actions indicate learning
through short play periods.

Ant-Based Modelling
Early work on agent-based modeling was inspired by the behavior of social insects (Resnick & Wilensky,
1991; Wilensky & Resnick, 1993, Langton, 1997). Their behavior has inspired games; SimAnt(McCormick & Wright, 1991), which is based on Hӧlldobler and Wilson’s (1990) The Ants. The collective
behavior of ants has been simulated using agent-based models (ABM) many times. StarLogo was used
to model the collective behavior of social insects (Resnick & Wilensky, 1992, 1993). Wilensky (1997a;
1990) modeled food source preferences resulting from pheromones, as well as the formation of ant
trails (Wilensky, 1997b). Bonabeau investigated the role of ABM in pattern formation (1997), and, more
broadly, looked at swarm intelligence (Bonabeau, Dorigo & Theraulaz 1999). Prat et al. (2005) modeled
collective nest selection of Temnothorax albipennis also using an ABM. Sumpter and Pratt’s joint work
explored the importance of group decision making with quorums (2009). Their work showed that whenchoosing a destination together, cooperation reduces the probability that an individual will suffer
predation. Robinson, Ratnieks, and Holcombe (2008) used an ABM to explore attractive and repellent
pheromones in pharaoh ants. Likewise, frameworks, such as Anthill, have been used to support thedesign, implementation and evaluation of technical systems, such as peer-to-peer networks (Babaoglu,
Meling, and Montresor, 2002). Their work drew on examples of complex adaptive systems to justify
engineering and user applications because complex adaptive systems exhibit resilience, adaptation,and self-organization that are seen as valuable in social applications. While these earlier models
provided insights and enjoyment, none of them delivered their lessons in the short interaction times
typical of museums. Taking this previous use of ants as a means to understand, research, and teachcomplex systems, we designed a complex system model to deliver complexity learning in the short,opened interactions normal for museum educational experiences without the mess of installing 20
million safari ants in the Midwest.

The Game: Ant Adaptation, agent-based modeling in museums
In order to provide context for our analysis below we review the game. In the game we created, Ant
Adaptation, ants go out to collect food and return to the nest. As they return to the nest ants lay down apink pheromone that attracts others nearby. Other ants walk toward the strongest chemical smell, which
in most cases is where the first ant just arrived. When ants find a flower, their food source, they return,394

Constructionism 2018, Vilnius, Lithuania

lay down more pheromones, and thus reinforce the pink trail. This creates an emergent feedback loopthat routes more and more ants to successful sites of forage. As the ants exhaust a food source theymust find new locations, and thus repeat a cycle. When two or more ants of opposing colonies encounter
each other, they fight or scare each other away, also leaving chemicals that attract more ants. Ants
either fight and kill each other, or they scare each other away. For the winner, this works to protect the
food source from competing colonies. The ant queen reproduces when the ants in her colony collectenough food. The player interacts with this complex system by adding pheromone trails that the ants
follow, as well as adding sources of food (i.e., flowers) to the system, thus changing amount of food in
the game. Through interacting with the system, students form a functional understanding of the entities
and their mechanisms of action (i.e. agents and their rules) in the model.
This setup scaffolds experimentation. Players must simultaneously make choices. Players can touch
the screen to add pheromone the ants will follow. Alternatively, at the flick of a switch, they can add
more flowers anywhere they like in the game, acting like an ant or a seed, but with a bird’s eye view.
Lastly, they can choose to apply vinegar, which erases trails. Erasing trails was used by some gameplayers (like Thomas discussed below) to get ants out of a feedback loop that was leading them
nowhere. For players with a bird’s eye view to achieve their goals in the competitive environment
requires they understand the emergent consequences of simple ant behavior. Their finger is both asingle agent, and systemic agent.
Furthermore, each player can decide how big its ants are and how aggressive they are. When the sizeof ants increases, they become slightly faster and stronger in a fight. Each level of increase adds up. At
the highest levels they are thirteen times stronger. When players make their ants more aggressive, it
increases the radius in which ants detect opposing ants and thus the probability that they will attack.
Increasing either the size or the aggressiveness also increases how much food is required to raise anant, so the largest ant requires thirteen times as much food to feed to adulthood. This gamification
impacts how much food ants must collect to make a new baby ant. Increases in either of theseparameters reduces the expected population of the colony, though it increases their likelihood of fighting
and winning through emergent interactions of parameters and agent actions. This sets up the main
action of the game as a series of strategic choices: to decide whether to pacifically collect food, thereby
increasing the population, or, to go on the warpath where big, aggressive ants conquer their opponents.
Either method of play could lead to high populations or the elimination of the opponent through better
controlled food resources. For example, after learning about the consequences of strategic choices
through gameplay, players strategize by increasing ants’ size, aggressiveness, or both. This might leadthem to win the game by annihilating the other group’s ant colony. However, bigger and/or moreaggressive ants consume more food to reproduce, and potentially reduce the colony’s population size.
Thus, a player might strategize by adding more flowers and pheromone tracks around the colony tohelp the larger ants survive. This learning and strategy cycle interweaves the learning into the gameplay.
The game has four affordances that support three learning objectives. In Ant Adaptation, playing withparameters allows the player to (1) construct their colony in competition with an opponent, (2) sharestrategies through comparison, (3) discuss what is happening through observer scaffolding such as
parents’ intervention, or interaction between players, including taunts, and (4) learn about the emergent
impacts of colony behavior arising from individual ant behavior in a complex systems game. Thisapproach allows visitors to learn (1) the impacts of adaptation on ant colony life, (2) how attractants
such as pheromones work in ants’ organization.

Designing digital multi-touch tables for museums
Prior research on building interactives in museums informed our design. Current research on multitouch tables for museums suggests several key design elements (Horn et al., 2016; Davis et al., 2015)
such as enjoyment, comparability, and productive conflict. Enjoyment, expressed through affect words
such as “whoa”, “wow”, “cool”, and “hah,” is significantly correlated with learning measures consideredby Horn et al. (2016). Facile comparability aided learning in the case of a tree of life game where players
who drew comparisons between lineages learned more easily and were more likely to use terms ofinterest in open-ended questions on post-tests. Block et al. (2015) and Horn et al. (2015) found thatdyads spend more time at an exhibit and engage more with scientific content than groups of three or395Constructionism 2018, Vilnius, Lithuania

more. Finally, conflict can be productive. Davis et al. (2015) and Falcão and Price (2011) argue thatinterference between users on, and across, a multi-touch interface can be productive for learning whenit triggers argumentation and collective knowledge construction.
To answer our first research question—how to design for the museum context—from our review of thisliterature, we hypothesized that to design interactive, agent-based, complex system tabletop games formuseum settings that expedite learning in these short interaction times, designers should encourage
discussion and comparison in a competitive game mode where the biology and complexity science
weave into the experience. As a result, (1) we implemented turns into the play, as Block et al. (2015)
found that groups who take turns spent longer times, and engaged more with biological content, in thetree of life exhibit. Taking turns both increased the use of learning terms and comparisons with thebiological content. (2) Our design included two teams which allowed players to explore the game’s
possibilities and compare between strategies. These two design elements facilitated comparison anddiscussion between teams. Exploration involves moving their bodies and hands across a digital tabletop
to engage in a game. This process engages the group at play more than mousing at a keyboard; weguessed that a body in motion, talking out ideas would create a rich discussion and a problem-solving
mindset. The game includes hampering the competing colony through players’ dexterity, or at times,
physically blocking other players’ hands to develop another colony’s strength following on earlier workon productive conflict (Falcão and Price, 2011). This competition is mediated through the luck of thestochastic system. Trade-offs of adaptations and complex systems thinking are woven into the game,
which allows users to explore and learn about complex systems and ants by making strategic choices
both in the digital microworld and while standing in the museum. The design encourages talk andcomparison to maximize learning about ant behavior, a complex system, in a short (expected 2 minute)interaction at the museum. In this paper, we demonstrate this design’s learning gain through a novel
methodology. The argument is that highly engaged play with a compelling complex biological systems
model taught a person about ants in a very short period of open ended play.

Agent-based modeling game design framework
By combining the classroom work and general ant-based-models, refined through rigorous testing, we
designed an ant-based modeling game that effectively builds on prior successful implementations ofcomplex systems models for the context of museums. An ABGM is a game environment that adopts
principles of microworlds to 1) actively engage students in an authentic challenge, 2) help themunderstand a model of agents, and 3) to let them explore through the construction of their own colony
and ultimately understand ant colonies. The ABGM fuses a long research line (Egenfeldt-Nielsen, 2006).
ABMG’s allow players to explore a representation of the life of an ant colony and learn by interactingwith the microworld, thus getting to know its limits through playful exploration and experimentation.

Method: constructionist dialogue mappings
In this study, we applied a mixed methods approach to the observations we made during this pilot(Clampet-Lundquist, Edin, Kling & Duncan, 2011). In addition to observing visitors’ interactions with thedisplay and taking ethnographic field notes, we conducted a pre-post semi-structured interview protocol
with participant groups. The protocol asked about groups’ background knowledge about ants, complex
system notions, and feelings about the role of adaptations in ant colony life. This protocol provided awider view of Ant Adaptation players in order to make between-player comparisons (Small, 2011) as
well as better understand what they learned about the complex system through playing with the396Constructionism 2018, Vilnius, Lithuania

interactive tabletop ABMG.
From these two sources, wequalitatively coded the typesof their interactions (described
below), their frequency, andhow they elaborated on theirunderstandingofthephenomena (the entities theyrecognized and their actions)presented during gameplay.
To answer our second
research question—how toanalyze the effectiveness andefficacy of interactions inlearning more about ants in
Figure 2. Constructivist dialogue mapping provides a simple interactive
museums—we developed away of mapping ontological entities, to their functions as demonstrated bynovel map of ontological entities
players in short interaction.
in the game (i.e., agents). The
maps were constructed by building a hierarchical map of players’ utterances (Figure 2) before, during,
and after play. Change in ontological maps over time can be compared, and learning shown as thechange. The coders proceeded through the transcript utterance by utterance. When an entity wasnamed, such as an ant, the coder added a box. When other entities were named—such as six legged
or carries a lot of weight—they determined what that concept modified. If, as in figure 2a, they modified
ant unambiguously, then the coders added subordinated boxes below ant. In our coding, we broke thetranscript into a pre-interview, a game play portion, and a post interview. We analyzed the change in
how the identified entities receive subordinate boxes. For example, in Figure 2, we would look at how
the person changed from describing ants as six legged (Figure 2a) to six legged ants that carry 50x
their weight and follow trails to find food (Figure 2d). This change over time is our measure of learning
during the intervention.
We performed this mapping instead of measuring change through responses to a more classroom-stylequestion about biological facts because people learning in a microworld learn what is allowed in asystem, rather than memorizing. “In this picture, the participants are active theorizers. They gather new
evidence and devise methods to test their theories. Instead of accepting classifications as given, they
see these classifications as provisional theories that are constantly reassessed and reconstructed inlight of the dialogue between theory and evidence” (Wilensky & Reisman, 2006, pp 172). We sought tocapture how talk changes based on interactions with the learning environment of Ant Adaptation. We
propose that learning is demonstrated by what students added to their discussions while playing. Thiselaboration is demonstrated by coding their interactions with a CDM.

Results
We tested the game in a major
natural history museum outside of alarge, popular, temporary exhibit.
The game was used over a six-day
period by 114 museum visitors (87%White, 4% Black, 5% Asian, 3%Latino). Of the players, 60 were male
(53.57%) and 54 (48.21%) werefemale. This contrasts with themuseum-wide
attendance
demographics
of
70%
White
(difference of +16.61% points), 5%

Figure 3. Histograms of play time show the most players
engaged for 400 seconds. The average age was 20 with asizable number of players under 10 and some as old as 55.
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Constructionism 2018, Vilnius, Lithuania

Black (difference of -0.54% points) and 14% Latino (difference of -11.32% points). Figure 3 shows
players ranged in age between 5 and 55, with the age distribution skewed to lower ages. The average
length of time people played was 6:27 minutes as opposed to a museum-wide average interaction timewith digital interactives of 1:45 minutes (as a reported by internal museum evaluations). The result
indicates that the design was a better than average interactive experience in the context of the museum.
Unlike Block et al. (2015) and Horn et al. (2015) we found groups of three or more spent more time at
an exhibit than dyads. Worryingly, the design seemed to appeal to certain demographics. When webifurcated playtime by race and gender, however, we find that non-white users who engaged with thegame were some of the most engaged users. As shown in figure 4, fourteen of the seventeen non-whiteusers engaged with the game for longer than 4 minutes. In other words, though most players in our
sample were white museum patrons, non-white users engaged with the game longer. Because players
could stop playing whenever they want, unlike a standardized test, this longer engagement is aninteresting proxy for interest. More study is required to understand the implications of the design on
audience.

Dialogue elaboration through play
For brevity, we present one demonstrative case of the 38 groups interviewed. Our interrater reliability
with two codes was 85%, coding 20% of the transcript data following the CDM method. Looking at how
one group engaged with this game for seven and a half minutes, we examine four White youth in theremainder of this section: Thomas, age 7; Ed age 12; Mary, age 9; and Sam, age 6 (names changed
for anonymity). The four players were slightly more engaged than the average player of Ant Adaption,
along several measures: 100% of the group members touched the screen, smiled during play, andworked to maximize their ants’ colony population count. This as in contrast to the wider sample where
81% (92 people) touched the screen, 43% (49 people) smiled during play, and 41% (46 people) tried tomaximize their colonies’ population during play. As their was no guidance on the goal of the interaction,
it was surprising to us that so many of the groups chose maximizing their population of ants as theirprimary goal. In the open ended environment, they could just have easily drawn smily faces with theirfingers, or planted a flower garden. We think this goal was so popular because of the competive
arrangement of the exhibit. More study is required to investigate this outcome, which we will do in theanalysis of the ramaining groups in future work.

Pre-Interview

Figure 4. Heat Map of players by race and gender. We see
the majority of players, in the middle were white males andfemales. Notably, of the 17 nonwhite players (0-0-0.0, 0-0-1,
1-0-0.0 and 1-0-1), 14 played for the twice the average
engagement time in museum interactives. 1-1-1.0 meansnon-black, non-white, male.

During the pre-interview, we established aplayer’s prior understanding of ants
through a semi-structured interview. As
shown in figure 5, during the questions hesaid ants carry 50x their weight. In
response to probes about how ants control
traffic, he offered the explanation that antsmake physical paths, to control traffic,
which he later rescinded after playing thegame. Protocol questions on aggressiveroles prompted the seven-year-old toguess that being aggressive simply
increases an ant’s likelihood of getting hurtor even killed. A claim he later amended.
Interviewer: Okay. And then imagine
you're an ant. How do you think being
aggressive affects your life?
Thomas: Uh, if you're aggressive, like,you- you're ... Uh, how do we explain this?
If you're aggressive, like, you- since yougo for more things you have a greater riskof getting hurt or killed, I guess.
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Constructionism 2018, Vilnius, Lithuania

In response to questions about how ants know what to do, Thomas offered animal instinct, or, acommand and control understanding when ants get orders through antenna and/or from their queen.
Interviewer: Okay. That's fair. And then, how do ants know what to do? So you said they- they pickup leaves, or they scavenge, but how do they know to do that?
Thomas:Um, animal instincts.
Interviewer: Animal instincts.
Thomas:Or the queen tells them to, if there's a queen ant. we don't know.
Interviewer: So what's the diff- How does the queen tell them to?
Thomas:
It's something with their antennas.
Interviewer: Okay.
Thomas:I think.
Additionally, he said that ants scavenge food from the ground or maybe eat leaves.
Thomas:Like, they can like go hunt for food. They can like, um, try like, get to some, like, maybe
some food on the ground like in the city or like in a park, or they can just eat a leaf.
After we explained how the game worked, players broke into two teams, Thomas and Ed versus Maryand Sam. At the beginning, the younger Sam and Mary chose to have maximum-sized, not very
aggressive ants (10% aggressive). Thomas and Ed chose to have medium sized though not very
aggressive ants (2% aggressive).

Figure 5. Pre-play cognitive dialogue map of four players’ understanding of ants. For instance,
they think ants (entity) can carry 50 times their own weight (mechanism).

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Constructionism 2018, Vilnius, Lithuania

Ants

Carry 50X weight

Scavenge

Eat a leaf

Animal Instinct

Recieve orders
from queens

Orders throughantenasMake paths

to control Traffic

Smaller ants are
weaker and more
vulnerable

Aggressive

Increases yourlikelihood ofgetting hurt or
killedFind food on
groundFigure 6. post-play cognitive map of four players. Came to the cyclical understanding of pheromonesin food foraging and a more contextual understanding of the role of adaptions utility. Red Boxes
indicate elaboration. Grey ideas that were not mentioned again.

Near the beginning, players agreed through conversation on what strategies matter. Soon, Thomas
informed the older Ed how to play: “add flowers close to the nest.” From this we add a new ontologicalentity, flowers, into the group’s constructivist dialogue map and put a mechanism ‘close to the nest’
under it because the players started planting flowers in close proximity. The players then establishedthe connection between collecting flowers and increasing ant population. They offered this as the way
to win.

Post- Interview
From the game, Thomas seemed to develop an understanding of the feedback cycles inherent in Ant
Adaptation:Thomas:Yeah, you had to figure it out and the- you have to have some flowers, see, and then you
put the chemicals and lead it to there, then they'll bring it back, and like, if you want to
get rid of the chemicals you use the vinegar. So, um, you put some sunflowers down,then you get the chemicals and lead it to the sunflowers and if- if there's too much thenthe ants aren't getting the sunflowers and you- then they'll just like, then you use thevinegar and erase it. But if- if you just do one path that leads to the sunflowers it'll justget the energy and just keep going back and forth and back and forth. And that's how we
got 21 [ants].

In the post-test players’ concept maps became more elaborate (highlighted in the red boxes in Figure 6).
More importantly, he takes on a more cyclical understanding of the role of ants’ paths to attract eachother to flowers. Thomas argued that ants follow chemicals to bring food to “get to 21” ants. He also
saw that sometimes ants can get trapped in their own chemicals or “white spots.” He used vinegar toclear excess chemicals. Predictions of population level effects emerging from the ants’ simple rules
indcated an understanding of intra agent action. The action’s multiscale (Wilensky and Reisman, 2006)
effect was learned through play with the game. Thomas leveraged this new understanding to use themacro-level effects of population size to lead his ants to victory by making predictions based in the agent
based model’s simple rules. Thomas employed complex systems thinking learned in the short interact
to reach his goal of maximizing population. He set this goal in communication with his teamate, in the
open-ended constructionist learning environment afforded by our design of Ant Adaptation.
Through play the player learned that (entities) ants’ (have a mechanism) lay trails to attract other ants
to flowers in a cycle by recursively following the chemicals. The player also realized that sometimes thisprocess can lead ants astray as shown by his use of vinegar to redirect them. He also found that the
food source’s (flower entities) proximity to the colony (mechanism) increased the ant population byincreasing food intake. Interestingly, after he stated as much, the other side started placing flowers close
to their nest.

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Conclusion and Discussion
Through the presentation of a single case, from our 38-group sample, we show that a player added to
his understanding of ant colonies through the elaboration of discussion about a complex system. He
started using vinegar to eliminate local optima where the ants got trapped. Simultaneously, he used thefeedback of pheromone trails to organize his colony’s foraging and adjust adaptations to changing
circumstances.
When working in museums, the short informal interactions with digital interactives changes therelationship between mediating object and the learner. Users can engage with these learning tools how
they like. We developed CDM to measure learning in these less structured environments, and from our
first test of the method here, it is an efficient and reliable way of tracking what a person says, and how
the content of their statements changes during group interactions with a digital interactive. We think thismethod has potential for measuring informal learning in other spaces, such as maker spaces, andmuseum exhibits.
The design of the experience drew users in, with people playing for up to a quarter of an hour withaverage play times over twice the normal interaction times of exhibits in the natural history museum. In
light of the success of this pilot, we conclude it reasonable to extend the design principals of Ant
Adaptation and create complex systems arcade for natural science learning in informal settings. The
approach developed here can allow us to identify and describe learning by examining how players
reconstruct provisional theories considering dialogue between theory and evidence (Wilensky &Reisman, 2006). Through this approach to teaching natural history in short interactions, we can bringtheory building to players, who can, like Newton, Einstein or Darwin, organize abundant data as part of
the theory they are building (DiSessa and Cobb, 2004).
The approach to game-play in Ant Adaptation scaffolded player theories through the learning objectives.
(1) Construction of their own colonies in competition with an opponent afforded comparisons whichallowed for dynamic theory validation: Thomas proposed proximity of flowers to the nested aided
population growth, the other player also started planting flowers close to the nest. (2) Sharing strategies
allowed players to update their operating theory. (3) The social aspect of the multi-touch interactive
allowed discussion to guide the theory exploration. (4) Taken together, these scaffolds facilitated players
exploration and learning about the complex system. People have argued that learning about complex
systems is hard (Chi, Roscoe, Roy & Chase 2012), and for an individual it is. When groups of people
each engage part of a complex system, and attempt their best theories in real time, and received
dynamic feedback from the computer and each other, the overly abundant data gets fit into a theory
that the people test in mediation with the machine and themselves and finds regularities and break into the system. These learning moments may happen most when they notice breaks where to get outof there confusion, learners must engage in effortful problem-solving activities (D’Mello and Graeser,2012). That effortful solving activity is the process of science, and that is the process players in Ant
Adaptation took. While Thomas vocalized most of the learning, the other players tested approaches,
and in future work, we hope to better share the joint sense making mediated through technology andeach other.
The decision built into the main action of Ant Adaptation — whether to peacefully collect food or go to
war to eliminate their opponent — sets up a crucial engagement where the uncertainties make thetesting immediate and productive (D’Mello and Graeser, 2012). The theory building exercise at the heartof the game was engaged with in the process of this motivated play. The Game afforded that type ofplay to happen. This work in short expedites the activities found in earlier agent-based modeling andtheory building exercises that have been used in schools and with swarm intelligence researchers. The
design allowed us to work in a novel context: super-fast interaction times typical in informal learning
environments.
The constructivist dialog map (CDM) approach introduced in this paper was able to capture changes ina player’s understanding of agents’ actions (entities’ mechanisms), learning complex ant systems
through playful interaction with our agent-based modelling game (ABMG). The use of this design was
associated with comparisons and elicited talk, that was associated with learning like our first researchquestion hypothesized. CDM captured the changes as utterances occurred during a short interaction.
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By analysing changes in talk pre and post, we found that a player learned about feedback, andemployed that learning at multiple levels to maximize an ant population. The elaboration took place byforming and testing theories with the ABMG, Ant Adaption. Thus, an agent-based modeling game ofants can be used to learn about social insects in a short intervention, in an informal learning setting.
This method of developing a learning environment has the potential to change how we develop natural
history museum interventions. The ABGM design approach is a scalable means to bring a microworld’s
affordances to informal settings and teach targeted content through games that 97% of children play. If
future designs in this space are attached to powerful ideas (Papert, 1980) that help children understandtheir world, agent-based modeling games can create a powerful means for people to learn about thecomplexity in the world.

Acknowledgements
We would like to thank Robert Grider who wrote NetLogo multitouch for this project. Also, Corrie Moreau
and the Field Museum’s Ant Lab, that gave amazing feedback and support throughout the design of theexhibit as well as their welcome to and assistance at the Field Museum. We would also like to thank the
IEF for their generous support of this work, Multidisciplinary Program in Education Sciences for funding
the project (IES: Award # R305B090009).

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Enabling Collaboration and Tinkering: A Version
Control System for Block-based Languages
Tilman Michaeli*, tilman.michaeli@fau.deFriedrich-Alexander-Universität Erlangen-Nürnberg, Germany

Stefan Seegerer*, stefan.seegerer@fau.deFriedrich-Alexander-Universität Erlangen-Nürnberg, Germany

Ralf Romeike, ralf.romeike@fau.deFriedrich-Alexander-Universität Erlangen-Nürnberg, Germany

Abstract
Version control systems are essential for coordinating teamwork when working in projects. They support
computational thinking approaches such as collaboration and tinkering. Yet, when using block-based
languages, which are an excellent choice for novice programmers, there is no adequate solution thatallows this form of collaboration. This paper presents a concept for a simple and easy to use web-based
version control system as well as an exemplary implementation for the popular language Snap!. This
concept is based on an analysis of existing version control systems and their use in Computer Science
Education. Furthermore, possible use cases for such a version control system in classroom
environments will be outlined.

Keywords
version control system; block-based languages; Snap!; computational thinking; collaboration; tinkering

Introduction
Collaborative learning based on the work of Vygotsky integrates social aspects into constructionistic
learning. Much has been written about the advantages of working in groups early on (e.g. Chase & Okie,
2000). Using Projects and project-based-learning (PBL) is one way to enable collaborative learning anda typical method of CSE. PBL is suitable for novices as well as for more advanced learners (Kastl &Romeike, 2015). When carrying out programming projects, one of the recurring challenges is that often
arises that different versions of code have to be managed, project groups need to coordinate and merge
their code. To work together efficiently, professionals use version control systems. Such systems enable
collaboration by allowing teams to work together on the same project by sharing corresponding files.
Furthermore, they keep track of revisions and, therefore, make it possible to go back to old versions, totrack changes, to fix bugs, or to work in branches, which enables experimenting and tinkering in a
sandbox. In a PBL context, such a version control system therefore enables collaboration and tinkering,which are approaches to Computational Thinking (CT) (Barr & Stephenson, 2011). Collaboration is an
important aspect of working as a computer scientist. It includes factors such as decomposition of tasks
or communication among each other and promotes motivation and commitment. When sharing or
discussing their actions, learners can learn from, reflect and build on the work of others (Laurillard,2009). In CSE, collaboration is considered to be important early on as well as throughout the wholecurriculum. For example, the new ACM/CSTA standards for K12 education (Computer Science
Teachers Association, 2017) require collaborative work already in Level 1b (Grades 3-5). Tinkeringneeds a risk-free environment that supports trial and improvement and fosters confidence, creativity
and independent learning (Resnick & Rosenbaum, 2013).
The use of a professional version control system in the classroom is generally possible, but it is suitable
only for text-based programming languages and comes with a lot of overhead. Even graduate students
are often overwhelmed by the sheer complexity of professional tools (Haaranen & Lehtinen, 2015). For
a lot of purposes, only a few functionalities like version history, merging and committing are needed.
*

These authors contributed equally to this work.

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For novice programmers in Java, the integration of SVN and Git in BlueJ aims to reduce this overhead(Fisker, McCall, Kölling, & Quig, 2008).
Block-based languages like Scratch or Snap! are very popular in lessons with novice programmers for
multiple reasons. As an example, they enable students to build creative programs without needing toworry about syntax (Maloney et al., 2004). Collaboration can take place in two dimensions. Currently,
block-based languages only support collaboration in a sequential sense, by supporting and emphasizing
remixing (Monroy-Hernandez, 2012). However, there are only limited solutions to enable parallel
collaboration in the sense of working on one project at the same time. In a day-long workshop on agile
project management with 9th to 12th graders, we recently had a team consisting of three programming
pairs working in Snap!: Every time they wanted to put together their program pieces (e.g. for a
prototype), every pair had to export their project and download it. Then they needed to transfer the XML
files to one PC, e.g. via memory stick, cloud drive or e-mail. Afterwards, they had to import each projectin Snap!, manually assemble their scripts, sprites, etc., test and fix bugs on this PC. Hence, the wholeteam sat in front of one computer to finish the prototype. This is not efficient, the same counts forredistributing the code to all team members. Afterwards, they needed to export the new project status,
download it and share it to the other two computers so that everyone was up to date. They also had to
manage the versions properly in order to be able to use an old version if necessary. Experience has
shown that this often causes problems. As one might expect, the students named these as major
downsides to the workflow in the concluding reflection phase.
In order to address this problem, we decided to design a version control system for block-based
languages. Therefore, a review of existing professional and didactically adapted version control systems
and their use in CSE was carried out with the goal to identify important findings and adapt those forblock-based languages. Using an exemplary implementation for the block-based language Snap!, theproposed concept is demonstrated and its benefits are highlighted.

Context and BackgroundVersions control systems
Version Control systems offer a variety of functions important to collaborative practices. First of all, they
document changes and their reasons by providing a history for each file under version control. Eachchange can be described and summarized by the user through comments. Furthermore, version control
systems offer the possibility to restore older versions. This way, unwanted or problematic changes canbe reverted. Besides that, they enable coordination by offering features to resolve conflicts with multiple
users working on the same file simultaneously. These features include locks to prevent multiple users
from editing the same file at the same time, the automatic merge of concurrent edits or support for
manual merges, if needed.
All files under version control are located in a repository. If a user adds new files to the repository and/orchanges old ones, they commit their changes. Each commit contains awareness information, which
describe the commit and can be divided into two categories. Internal awareness information includes
changes made, time and date of changes, revision numbers, or names of committing users. This
information is generated automatically. On the other hand, there is explicit awareness information, whichis stated explicitly by the user. It requires explicit action. One example is a commit message, in which auser describes the changes he has made (Fisker, McCall, Kölling, & Quig, 2008). If no one else changed
any files in between the last commit and the new one, this results in a new version, also known as arevision, of the project without any additional action. If someone else has made changes to files in
between, but they are not in conflict to each other, these changes are automatically merged. If there isa conflict, when e.g. the same line of source code was changed by more than one person, it must be
solved manually by choosing for each conflict the version which should be in the new revision. Changes
between revisions (added through commits) can be viewed via diffs. Old revisions can be viewed orreverted to at any time. Furthermore, modern version control systems offer the possibility to branch. A
branch is an alternative path starting from a certain revision, so that changes can be made in parallel.
Branches are used for development of new features or experimenting, without impacting the current
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product and state of the project. A branch can be merged into the master/production branch again lateron, e.g. if the new feature is fully implemented and tested.
Version control systems can be divided into centralized systems (like CSV or SVN) and distributed
systems (like Git or Mercurial). In centralized systems, the repository is kept on a remote server
everyone has access to. Whenever a user wants to introduce changes, they retrieve the latest version
from the server first. As commits are transmitted to the remote server immediately, any recent changes
must be merged, and conflicts have to be resolved before the commit. In contrary, distributed systems
store the whole project history locally on every computer but also on a remote server. Therefore, eachcommit will initially be registered locally. To make changes by a commit available for other people aswell, they have to be pushed to a server. From there, they can be pulled by each collaborator to beavailable locally. This way, merging and conflict resolution are not necessary for commits, but wheninteracting with the server (pushing and pulling). Distributed version control systems are dominant
nowadays. Their main advantages are the local “sandboxes” which enable local changes, reverts etc.
for every user offline, the easy branching and merging, and the independence from just one location
where everything is stored (Somasundaram, 2013).

Version control in the classroom
Version control systems are used both at school and university level. At universities, control version
systems are used frequently. Typical use cases include the provision of course materials or thesubmission of homework. Throughout literature, advantages of the use of control version systems canbe divided in organizational and pedagogical ones. Organizational ones are the easy way to post
assignments and give feedback, the possibility to start with skeletons, revert changes and work
remotely, as well as having timestamps for submissions (Lawrance, Jung, & Wiseman, 2013). The
pedagogical advantages include easier collaboration, the possibility to assess individual contribution,
making the development process visible for the teacher and data security in the sense of a backup (cf.
Reid & Wilson, 2005, Lawrance, Jung & Wiseman, 2013, Glassy, 2006). Overall, it is reported thatversion control systems are considered useful by students and teachers alike (e.g. (Isomöttönen &Cochez, 2014)). Just like the advantages, further experience and especially problems in the use ofcontrol version systems are reported. These hint at obstacles that must be addressed in a pedagogicalversion control system. One reported problem is a non-iterative workflow with long periods without acommit. This is an obstacle especially at the beginning and takes a lot of the advantages away (Glassy,
2006). Overall, professional version control systems are reported as hard to learn (cf. Isomöttönen &Cochez, 2014, Haaranen & Lehtinen, 2015). Students sometimes damage repositories so that tutors
need to repair them, or misuse features, e.g. repeated checkouts instead of updates in SVN (Reid &Wilson, 2005). In some cases students even accessed third-party repositories (Reid & Wilson, 2005).
From a student's point of view, conflicts and their resolution are the most complex and difficult tasks(Isomöttönen & Cochez, 2014). If the students always work in the centralized repository they have more
problems than when they work in their own branches and merge when finishing a subtask (Lawrance,
Jung, & Wiseman, 2013). In addition to students, teachers also need significant competencies to use
version control systems successfully in the classroom. They need to set them up and configure them,and also support the students, e.g. when fixing broken repositories.
Brichzin and Rau (2015) give an overview of typical problems that can be addressed by the use of aversion control system in a school context that matches our experiences. One problem is the pupils
name convention and versioning practice – e.g. filenames like game, game_2, game (copy), game(working). This is an obstacle for collaboration as well as identifying the current state of the project towork on after holidays or a longer break. If the students make no backups of the current or former statusof the project, there is always the danger of deleting the work of up to several weeks by accident. The
next problem they mention is merging partial programs in PBL together regularly, no matter whether it
is an agile project with iterations or a traditional waterfall project. Using a version control systemfacilitates regular merging and therefore helps to identify interface problems at an early stage of the
project and address them accordingly. Another typical problem within the school context is that an entire
team gets blocked because a student has forgotten the current code at home or they lack access to his
account while the student is sick. Furthermore, enthusiastic students can't continue to work on the
project at home, because the code is stored on the schools’ machines.
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However, the introduction of a professional version control system is associated with a large overhead.
Pupils must develop an understanding of the functionality of version control systems, familiarize
themselves with the respective commands and working procedures. The complexity of this task poses
problems even for entry-level professionals and graduate students. Therefore, Fisker et al. (2008)
enabled group work support in the form of a simplified SVN and Git integration for the IDE BlueJ. One
design principle for this was making awareness information available. This is important for group workand to keep track of others’ progress. Another explicit focus was simplicity by reducing overhead: files
no longer need to be added to version control manually. Furthermore, many of the powerful but notessential features (such as branching, tagging, revert, single file functions) of SVN resp. Git are notavailable via the BlueJ IDE to ensure easy access. The same goes for the graphical user interface,which is kept basic and simple. Functionalities such as commit or update are made clearer but stillcontain the standard terminology (e.g. “Update from Repository”)

Block based programming
Traditional text-based programming languages have been used for introductory programming orcomputer science courses but are considered to be major entry barriers. Block based languages takeaway users' responsibility to take care of precise syntax compliance. They allow for easier realizationof creative projects and give direct feedback by visualizing the current program’s state. Known examplesof block-based languages are Scratch, Snap!, or GP. Most block-based languages are capable ofrunning in the browser. Hence, they do not require an installation on student devices, making them areasonable choice for educators. Those languages are used in schools and university courses,
especially for novices. Using block-based languages with a traditional version control system is
unsuitable: Traditional version control systems are built to manage multiple source text files, whereas
in block-based programming environments, students interact with their project in a graphical way. In
Snap!, objects are represented as so called “sprites”. Due to this, each object is represented graphically.
Sprites have scripts, which are a sequence of several connected blocks (see Figure 1).

Figure 1. Snap! user interface

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Certain block-based applications such as Kanto, Blockly, or Netsblox already allow multiple students toremotely collaborate on a project (Broll et al, 2017, Ohshima, Freudenberg & Amelang, 2017). However,they lack essential features that version control systems offer, such as version history, branches, orcommits, which are considered essential in PBL settings.

A version control system for block-based languages
Since existing tools of version control systems cannot be used for block-based languages, we havedeveloped a solution based on research regarding the use of version control systems in the classroom.
Therefore, we conducted a didactic transposition of professional version control systems with explicit
attention to specific characteristics of block-based languages and needs of programming novices. It
follows two guiding ideas:

Visualization. The project status should always be visible at a glance. For this purpose, it should
be displayed in a graphical way.

Easy to use. The number of functionalities should be reduced, and unnecessary settings
removed. The usage should be simple for both students and teachers. The latter usually having
little experience with professional software development tools necessitate this guideline evenmore.

The second objective is consistent with the goals for version control realization in BlueJ (Fisker, McCall,
Kölling, & Quig, 2008). In contrast to their solution, however, the operation is further reduced, and more
simplifications are offered even for teachers. In the following, we describe the concept of the version
control system and use images of our concrete implementation for Snap! to illustrate the concept.

Figure 2. Graphical visualization of a project

Conception
A version control system for block-based languages needs to be web-based. A project is represented
by a graph similar to a Git tree. A node corresponds to a revision of version control systems (see Figure409

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2). It can be classified as a special case of a centralized version control system: there is only one projectstatus stored centrally on a server. However, each user always works in their own branch, which is thenorm for distributed version control systems. That means, if a user starts editing a revision, they implicitly
start their own branch. If two or more users start working on the same revision, one individual branch
per user is automatically created. Therefore, changes to the same revision cannot lead directly to aconflict. This also means that there is no explicit master branch. This addresses the experiences
described by Lawrance that students had fewer problems when using their own branch for each subtask (Lawrance, Jung, & Wiseman, 2013).
To create a project, users can either use an empty project or upload their own templates (e.g. withpredefined blocks or sprites). It is also possible to upload additional files later on. As the version control
system runs in the browser, there is no need to set up an individual server to use the version control
system or configure existing services. This allows the teacher to use version control systems without
specific knowledge in a very flexible way.
Double-clicking a node in the graph opens the respective revision directly in the corresponding
programming system (e.g. Snap!). In doing so, an additional button is inserted into the menu of the
programming system. Clicking this button commits every change made by the user directly. By enablinga commit with only one command directly from the user interface, the described problem of few commits
during long work periods is counteracted. The user is prompted to enter a commit message. In this way,
students are motivated to briefly summarize their changes. Additional implicit awareness informationsuch as timestamps, number of sprites or scripts added and removed, and total number of sprites orscripts are provided for each node. These make it easier for other group members to track changes in
the project. Reverting to an old revision is done simply by opening the specific node and beginning towork from there.

Figure 3. Merge process

The process of merging multiple revisions is initiated by selecting several nodes. If the selection is
confirmed, the merge takes place. As long as there is no conflict, the system will merge automatically,
similar to a professional version control system. This is the case if neither sprites nor scripts have beenchanged or only one user has made changes. The more recent version is identified by the ancestor
relationship in the graph. Therefore, no interaction on the student side is necessary, unless several
students have edited the same script. If this is the case, there will be a conflict. To resolve this conflict,
we use a merge view providing all alternatives of the conflicting scripts side by side with comments
attached. The students can then select the appropriate version or compose a suitable solution. For an
example, see Figure 4: Bob and Susi edited the same script. In addition, Bob added another script. The
new script will be merged automatically, while the existing script they both edited will raise a merge
conflict. Therefore, both scripts are added to the merged version providing commentary details.

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Figure 4. Merge view

Features such as add, push or status, which are known from professional VCS, are not necessary,
because the structure of the version control system and block-based languages make these features
obsolete. The only activities that students must actively do and learn are commit and merge.
The user guidance and interface are deliberately kept simple. The same applies to terminology, whichmust be tailored to the target group. Only two essential features need to be named. In discussions withcomputer science teachers, the use of the term commit was rated as difficult. The term merge, on theother hand, was considered suitable for students of all grades. Accordingly, the original term was usedin this case, whereas the term post to <<project_name>> was introduced for commit. This term provides
a suitable analogy for commit, comes from students’ daily life and is appropriate for all ages.
In summary, key features provided are:
visualization of the project and its history in a graph

automatic branching for each editor of a revision

opening each revision directly in the respective programming system in the browser

easy commit from within the programming system used

merging by selecting the respective nodesvisualizing conflicts in a merge view

providing implicit and explicit awareness information for each revisionsupport for multiple templates and starting nodes

Exemplary Implementation: smerge
With “smerge” (derived from the terms “Snap!” and “merge”), we provide an exemplary implementationof the described concept. The tool is implemented in Python 3 and JavaScript using the Django
framework49 and cytoscape.js50. For running a separate instance, only a server running Apache, Nginx,
or similar is needed51. Instead of handling plain source code as with traditional version control systems,
block based languages require a different approach due to their structure. Therefore, we utilize the XML
file structure of Snap! projects, which differs from languages like Scratch or GP. On opening a certain
revision, the associated XML project file is passed to a Snap! instance. In this step, a custom blockcontaining the commit functionality (written in JavaScript) is injected. On commit, the current state of49

https://www.djangoproject.com/
http://js.cytoscape.org/
51
A running instance can be found at smerge.org, the source at github.com/manzanillo/smerge.
50411Constructionism 2018, Vilnius, Lithuania

the project is exported in XML format and sent to our servers. As soon as the user triggers a merge, thecorresponding project files are analyzed and compared on XML level. While conflict detection is easy
when comparing source code in text form (usually line by line), once more a new solution for blockbased languages is needed. Our solution regarding this conflict detection problem is based on sprite
names and script coordinates. For conflict resolution, revisions and their ancestors are compared on
XML level according to the auto-merge concept described above.

(How to) smerge in the classroom
In the following, we will describe a possible workflow in smerge when using it in PBL. One way toimplement PBL in the classroom is agile projects, which have already been used for PBL successfully(Kastl & Romeike, 2015). In doing so, agile practices such as user stories, standup meetings, pair
programming, sprints or prototypes are adapted for the use in schools (Romeike & Göttel, 2012). We
will use this framework to describe an exemplary workflow for smerge in PBL (see Figure 5).
For constructionist learning in school projects, students first create their own initial draft. Therefore, eachgroup creates their own smerge project. In this way, they have created a place where all changes to thecode are stored centrally. Each programming pair continuously works on one user story at a time. With
the auto branch feature of smerge, every user story or feature is realized in its own branch. Pupils are
therefore encouraged to work on tasks in parallel and can focus on a single feature each. Each pair has
its own sandbox in which they can experiment and tinker.

Figure 5. Smerge workflow

This type of workflow also promotes a more realistic form of collaboration in projects. At the end of asprint, at the latest, the students will assemble their subprograms into a new prototype by using smerge
to merge their branches. With smerge, every programming pair can then test the resulting prototype on
its own as opposed to the described problem of all group members in front of a single pc. The merge isa ritualized team activity. A project also involves constantly improving the inner structure of the program
through continuous refactoring. Smerge supports this with its merge view. By contrasting the individual
parts from different sources with each other in the merge view, smerge motivates students to think about
possible refactoring. For example, in order to make their own code easier to read, students tend tooutsource redundant parts of the code into custom blocks. During the process, the visual representationof smerge, in addition to a possible project board, helps teachers and students keep track of the
progress of the project.
The version control system can also be used highly flexibly in teaching outside of PBL. In the following,
we will describe a lesson to introduce broadcasting in lower secondary education. In this scenario, every
student will create his/her own individual instance of a prototype given by the teacher. Let's assume wewant to make a group of penguins dance to a given beat. The beat is determined in a sprite created by
the teacher and is delivered to the students via broadcasts. The students’ task is to create their own
penguin and let it react to the different bars of the music. For this purpose, the teacher provides atemplate with a simple penguin sprite. The students then rename their own sprite, design its looks andimplement an individual behavior on the rhythm. After the students have finished their task, all individual
solutions are to be combined into a complete work to emphasize the concept of broadcasts with more412

Constructionism 2018, Vilnius, Lithuania

receivers. Without such a tool, it would require teachers to collect all students’ solutions and combine
them manually. In smerge, this combination is reduced to nothing more than the click of a button.
In addition, smerge can be used for a longer teaching sequence. In doing so, students develop multiple
small programs to learn specific CT concepts. The class, or each student, can collect all these subprojects within one smerge project. Every lesson, the students receive a new template in which they
complete a specific task. After several units, the subprojects are combined to form an overall project,
and a greater coherence becomes apparent. One example is the game Breakout, where the paddle,
the ball and the bricks can be considered three sub-projects. So, handling user input, movement andbouncing of walls as well as list for placing bricks are main topics for individual lessons.
It would also be conceivable to use several templates for differentiation. Teachers can provide different
templates for different types of learners. By providing weaker students with other tasks or more support,
e. g. through given blocks, within the framework of a project, they can be supported individually. In
addition, the process of tracking students’ progress and assisting them accordingly is enhanced using
smerge.

Conclusion
In conclusion, this concept addresses all the initially described school-specific use-cases such as
managing files and problems like blocked teams with code forgotten at home or sick students. It offersorganizational advantages such as the possibility to share templates and skeletons in an easy manner,
to start with multiple skeletons for different groups, to revert changes or work remotely from home.
Teachers can concentrate on the pedagogical aspects of their lesson concept, as they are no longer
involved in organizational activities such as setting up a server for a version control system. Regarding
pedagogical advantages, both the current status of the project as well as its development process andprogress become visible to both teachers and students. Because of the graphical visualization and thepossibility to directly open, execute and test each node directly, this goes much further than professionalversion control systems. It allows for great flexibility and a constructionist way of teaching and learning:
it supports PBL, differentiation, decomposing a greater whole in small learning lections, or class-wide
collaboration.
The outlined concept for a version control system enables collaboration in block-based languages. The
version history provides a risk-free environment that invites users to experiment and tinker. Features,design and interface are reduced and adapted to the target group of novice programmers and basedon existing research and experience regarding the use of version control systems. Smerge as an
exemplary implementation of the concept offers all these features and is ready to be used in CSE.

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Racket Programming Material for Finnish
Elementary Math Education
Tiina Partanen, tiina.s.partanen@tampere.fi
City of Tampere, Finland

Pia Niemelä, pia.niemela@tut.fi
Tampere University of Technology, Finland

Timo Poranen, timo.t.poranen@uta.fi
University of Tampere, Finland

Abstract
Programming is becoming a part of basic education in number of countries. 2014 Finnish National
Curriculum (FNC-2014) for K-9 education embeds programming into the subjects of mathematics andcrafts. The change in the curriculum challenges teacher training and causes a demand for appropriate
teaching materials. This paper introduces a free Racket programming material for grades 5 to 9 fully
integrable with mathematics lessons. Racket is a general purpose multi-paradigm programming
language especially fit for education. The material consists of theory sections, corresponding exercises
and answers. Covered math topics comprise natural and rational numbers, arithmetic operations, theorder of operations, rounding, percentage calculations, scaling, polynomials, truth values, coordinate
systems, geometric shapes, angles, etc. We evaluate the material by comparing the math concepts withthe ones in FNC-2014.

Keywords
Racket programming material; computational thinking; computer science education; spiral curriculum;
grades 5-9; mathematics

Introduction
In Finnish schools, programming has been mandatory since August 2016 (The Finnish National
Curriculum, 2014). This retrospective change in teachers’ job descriptions challenges the education
system and creates an urgent demand for in-service training and training material. Unlike a number ofother countries, Finland decided to add programming into the existing mathematics syllabus without
granting time to cover the new material. This approach mandates tailoring the material to be as mathsupportive and mutually beneficial as possible: while mathematical concepts ought not to becompromised, the learned programming concept subset should still be as expressive and exploitable
as possible. As a further complication, the FNC-2014 requirements for programming are on a highabstraction level and defined only for certain grades (i.e., grades 2, 6, and 9; others were left to be
specified locally), which impedes application.
In 2016, to support mathematics teachers in the elaboration of FNC-2014 requirements, the Finnish
Association for Teachers of Mathematics, Physics, Chemistry and Informatics (MAOL) initiated a project
to develop programming material for mathematics lessons. In 2017, the same material was used in a
two-day hands-on in-service teacher training in fifteen cities all-around Finland. This paper presents thecontents of the created Racket material concentrating on grades 5-9. We present the learning trajectoryof the programming concepts and how they were integrated into the mathematics curriculum. The
underlying research questions are:
1. Which kind of programming theory, material and exercises are foreseen beneficial for learning
mathematics?
2. How do the introduced exercises comply with FNC-2014 and its underlying learning theory?
This study follows the principles of educational design research, where the feedback is used forincremental improvements (Akker et al., 2006). The rest of this paper is organized as follows. First, we
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introduce the learning theory behind FNC-2014. Second, we review the programming requirements for
Finnish schools. Third, the Racket programming material is introduced and followed by the comparisonand evaluation with FNC-2014. Then we evaluate the material based on feedback received from teacher
training. Finally, we give some directions for future research.

Constructivism
Learning theory of FNC-2014
The FNC-2014 is based on the constructivist theory of learning underpinning a large family of related
theories. All emphasize an individual’s responsibility of his own learning in structuring knowledge as
consistent constructions, i.e., as schemas and other elaborations. As the most prominent schemabuilding theory, Piaget and Duckworth (1970) launched the theory referred to as cognitive
constructivism. Piaget studied a child’s cognitive development and described learning as the formationof mental data structures as cognitive models, i.e., schemas. When a child faces a new concept, he willplace it in the schema: ’assimilation’ strengthens the schema a new concept is a snap to the sketch.
Inconsistency in schema forces to reconstruct the data structures into a new one, i.e., the schema needs’accommodation’. In particular, Piaget’s genetic epistemology addresses the meaning of knowledge andforming schemes by employing reflective abstraction, the whole process being supported by selfregulation skills (Piaget and Duckworth, 1970). In summary, learning can be characterized as changes
in one’s schemata.
As an extension of student-centred learning, the new winds of phenomenon-based learning are blowing
in FNC-2014. Even if as a concept of phenomenal learning is quite new, it complies with the long
tradition of experimental and active learning traditions, such as ’learning by doing’ (Dewey, 1902) and’learning by making’ (Papert, 1980). Both of these educational luminaries, Dewey and Papert,
emphasize the affective side of learning and aim first at motivating students with engaging exercises.
According to Dewey, the gained experiences must be relevant to a student in order to meaningfullearning to take place. This view is seconded by Papert, who increments the idea with the cultural
resonance, i.e., ’the topic must make sense in terms of a larger social context.’
Papert spent four years working under Piaget at the International Centre of Genetic Epistemology. Thus,
Papert counts among a number of later development psychologists that own to Piaget. Papert’s
constructionism is founded on Piaget’s cognitive constructivism, which Papert felt being too tightly
personified to Piaget's constructivism often reads ’Piagetism’. However, constructionism sharesconstructivism’s connotation of learning as ‘building knowledge structures’, but increments it withexperiential learning, in other words, ’learning by making’. According to Kolb (2014), gaining experienceis yet alone not enough, but the experiences must be elaborated with reflection and analysis todeliberately built deeper knowledge, the process of which Piaget refers to as reflective abstraction.
Experimental and phenomenon-based learning has now-a-days strong advocates in particular in thediscipline of science, its development being based on well-planned experiments.
Constructionism in mathPiaget’s influence has diffused widely into different disciplines, in particular math. More systematicallythan other academic subjects, the syllabus of math relies on the consistent learning progressions andcumulative learning; topics proceed in consecutive steps by constructing on what has been learned
previously. In a comprehensive school, the math syllabus can be divided into such main syllabus areas
as arithmetic, algebra, and geometry, each of which can be examined separately by applying
constructivist theories. First, the gradual possession of algebra fundamentals exemplifies the processof reflective abstraction. Next, geometry demonstrates evolving spatial perception. Last, Euclidean
geometry is expanded as computational turtle geometry by Papert, who regards computers as highly
valuable learning tools, in compliance with his theory about constructionism incrementing constructivism
with computer-based exercises.
In the context of algebra, Piaget’s genetic epistemology has suited as the action-process-object-schema(APOS) theory (Dubinsky and McDonald, 2001). The theory hypothesizes the subsequent steps ofreflective abstraction started by simple actions of, e.g., counting or ordering, which are transformed as
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more complex processes when they are recognized to share common patterns. Learning continues withthe encapsulation of objects once APOS processes are abstracted as functions that contain mutable
numbers as variables. The ultimate target is a constitution of flexible schemas based on the processes
and objects, and fitting these schemas to the larger schemata of a learner.
In geometry, Van Hiele (1999) observed the deepening levels of spatial understanding. Analogously to
APOS, Van Hiele levels of geometry consist of similar learning progressions: a student starts byrecognizing the shape, then the patterns, i.e., analogies in shape properties, and comparison of theproperties triggers an informal deduction phase, where students produce definitions in their own words;
later these initial deductions are formalized. ’Rigor’, the last van Hiele level, anticipates the maturity oftransferring and applying the knowledge in different systems, which is often exemplified by the switchfrom ordinary Euclidean to non-Euclidean geometry. Van Hiele criticizes attempting too much too earlyin reference to the deteriorated learning results of the ’New Math’ movement. Instead, the best learning
outcomes are reached by adjusting the material appropriate to the level of children’s thinking.
While proofing the theory of van Hiele levels of geometric thinking, Clements and Battista (1992)
suggest a new zero level without any conditions referred to as ’pre-recognizing’, where children perceive
geometric shapes, but attend to only a subset of a shape’s visual characteristics. In addition, their study
implicates that the levels are not as discrete as described, rather ’there was instability and oscillation
between the levels in several cases … Continuity rather than jumps in learning was frequently
observed.’ For an early exposure to spatial thinking, a computer is an excellent instrument. In an attempt
to combine the affective and cognitive sides of learning, Papert (1996) launched turtles to stimulate bothlearning geometry and simultaneously exercise computing. To get a turtle to move in a desired direction
and to draw the aimed geometric shapes, children are subtly exposed to the classifications andproperties of shapes. Intendedly, turtle exercises are visually pleasing and capable of engaging
students.
A number of Papert’s principles overlap with Hungarian math referred to as the Varga-Nemenyi method
that, e.g., Tikkanen (2008) introduces in her dissertation. For instance, to use learning tools, a lot ofhands-on experiences and multisensory exercises that are refined as logic-mathematical conceptions
when students progress along the ’way of abstraction’. In the same tune, Papert promotes a more playful
way of nurturing computing basics and practice math in sync. According to his interpretation, computing
is applied mathematics, and playing with turtles provides a gentle way of practicing it and strengtheninga child’s self-efficacy.
In essence, motivation and self-efficacy are accurate predictors of successful learning (Bandura, 2006).
Thus, the instructional setup should focus on the affective side of learning instead of concentrating on
mechanical routine work. Regrettably, in Finland, math attitudes deteriorate throughout the wholecomprehensive school (Tikkanen, 2008), and the gap between poor and good students’ skills to further
widens, which is called Matthew effect, i.e., stronger students become stronger compared with poorer
students (Lampinen and Korhonen, 2010). In contrast, the development in mother tongue is opposite
so that the differences over time are reduced. Likewise, Papert is worried about the increasing numberof math-phobic students that label themselves as too stupid to learn. He hypothesizes that this trend isa consequence of a too stiff way of solving problems and the pressure of getting it right at the firstattempt, or in his words, the ’technology of grading’. Because of this phobia, self-efficacy-impaired
students are mathematical under-performers. Programming experiments can be interpreted as acounteract of previous stiff problem-solving procedures criticized by Papert.

Programming in Finnish Schools
Programming syllabus emphasizes problem solving, algorithmic and logical thinking in particular. In
addition, identifying the importance of math as the foundation for information and communication
technology is a learning goal: future work and economy are dependent on the technologicaldevelopment and digital skills. FNC-2014 emphasizes digital competence as an interdisciplinary skill
throughout all grades. The curriculum excerpts below mention programming explicitly in the objectivesof two subjects, mathematics and crafts:
Grades 3-6
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Constructionism 2018, Vilnius, Lithuania● Digital competence: ”Students learn to program and become aware of how technology
depends on decisions made by humans.”● Mathematics: ”Students plan and implement programs using a visual programming language.”● Crafts: ”Students practice programming robots and/or automation.”
Grades 7-9● Digital competence: ”Programming is practiced as part of various other subjects.”● Mathematics: ”Students should develop their algorithmic thinking and learn to solve problems
using math and programming. In programming, students should practice good coding
conventions.”● Crafts: ”Students use embedded systems, plan, and apply programming skills in order tocreate products.”
The mathematics syllabus has been rather stable over the recent years. The syllabus relies on theconsistent and cumulative learning: topics proceed systematically by constructing on what has beenlearned previously. Traditionally, mathematics follows a spiral curriculum (Bruner, 2009), e.g., each year
students learn some more geometry, and arithmetic and algebra related contents. However, FNC-2014
does not mandate the grade in which specific topics are to be introduced, instead, this can be decided
at the local level.
As a new approach, FNC-2014 emphasizes a general skill-set, which students should acquire during
primary education. Each subject should promote the development of transversal skills, such as, thinking
skills, multiliteracy including digital literacy; ICT competence; working skills and entrepreneurship. In
addition, FNC-2014 requires each school to provide cross-disciplinary learning modules that integrate
multiple subjects, increase project- and phenomenon-based learning, and employ multiple learning
environments also outside the school premises.

Racket Programming Material
Teachers must follow both national and local curricula, otherwise, they are free to select teaching
methods and material. A few major school book publishing companies provide text book series. Due tothe programming addition, the publishers started to update their math books. So far, the range ofprogramming languages covers Scratch, Python and Processing. Free Racket online courses forteachers exist as well (Partanen et al., 2017).
To design, create and publish open programming material for math teachers, MAOL (2018) applied
funding from the National Board of Education and from the Technology Industries of Finland Centennial
Foundation. The founded working group consists of five experienced comprehensive school teachers,a university researcher and a freelance coder. For grades 1-6, the group developed material for Scratch(grades 1-6), ScratchJr (grades 1-2) and Lego Mindstorm (grades 3-4). For grades 5-9, the working
group decided to exploit Racket (2018) language, the justification being the close conceptual linkage
between algebra and functional paradigm. Racket is pure functional if limited to the Beginning Student
language only. In addition, it has a strong pedagogical basis for computer science, and a versatile
support for Windows, Mac OS, Linux and browser-based use. Moreover, the working group knew it welland had gained a strong experience.
The material was implemented in compliance with the following design principles: i) theory andexercises should be related to corresponding mathematical concepts, ii) based on FNC-2014, material
for grades 5-6 should cover about 12 hours and the same amount for grades 7, 8, and 9 each, iii) theemphasis on ‘learning by doing’, iv) freely available material, except the answers which will be sharedwith teachers only, and v) exercises ranging from easy to difficult to enable differentiation. Racket
material was published piecewise starting from fall 2016, and the latest additions were made at the endof 2017 (MAOL Racket, 2017). Altogether, it consists of 37 pages for grades 5-6, 54 for grade 7, 31 forgrade 8, and 55 for grade 9 in print.
FNC-2014 mandates grades 3-6 to learn graphical programming. Complementarily, the material
provides textual programming basics for grades 5-6 to foster fluency with keyboard, mouse, file saving,418

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and file system browsing – by and large, such practical skills they are often observed lacking. For thesegrades, learning targets familiarization with programming environment and basic syntax rules, arithmetic
operations, functions for drawing simple geometric shapes, comparisons and their truth values, andvariables for storing data. These concepts were integrated with math exercises, such as calculating
percentages, fractions and decimals, and problem solving in geometric contexts. The content in moredetail and the related curriculum specifications for grades 5-6 can be found in Table 1.
The material for grade 7 was built with no requirement of prior Racket knowledge. Often, grade 7 classes
are formed of students coming from schools that have used different material, time, and languages for
programming. If necessary, the previous grade 5-6 material can be used as a tutor for these students:
programming content for 5-6 and 7 is analogous, but the grade 7 covers more advanced math problems
including negative numbers, absolute value, powers, square root, a circumference of a circle, etc. Turtle
geometry serves as a means to study the geometric properties of lines, polygons (especially triangles),a coordinate system, and the concepts of reflection and mirroring (see Fig. 1).
grade 7 geometry

grade 8 geometry

grade 9 geometry

Figure 1. Exercise material on drawing Turtle images in grades 7-9

Fig.1 exemplifies geometry knowledge that gradually deepens starting from free drawings andadvancing to more formal 2D shapes and 3D shapes whose properties are examined to induce theunderlying mathematical rules. This gradual abstraction and formalization process demonstrates
deepening van Hiele levels; visiting the same topics in a yearly basis manifests the spiral curriculum
approach. Good coding conventions include a descriptive naming of variables, adding comments, andindenting code to improve its readability. The content in more detail and the related curriculum
specifications for grade 7 can be found in Table 2.
The grade 8 material introduces plenty of core programming concepts such as truth values (binary
values), comparisons, predicates and conditional expressions, function definitions, function parameters
as variables, validity tests for user input, and error messages for invalid input. The listed programming
content is practiced with polynomials (user-defined, tested with different arguments) and with geometry
calculations, such as perimeter and area. User input is validated with conditions to eliminate negativeside lengths and zero dividers, for example. In addition, material exploits turtle geometry to scaffold
perceiving shapes that require repetition (circle) and/or parameterization.
Good coding conventions include descriptive naming for function and parameters, as well as a proper
function signature (domain and range). The content in more detail and the related curriculum
specifications for grade 8 can be found in Table 3.
The material for grade 9 covers more advanced topics such as lists, higher-order functions andrecursion. The math topics origin from statistics (mean, mode, median, proportional frequencies,
drawing bar charts), algebra (study of linear and quadratic functions and graphs), and arithmetic(calculating values using recursion, such as number sequences, loan payment schedules). In geometry,
more advanced turtle graphics illustrate 3D shapes. A couple of quiz-style programs are provided forcustomization. Quiz applications could be implemented as math concept reviews, such as volumes of
3D-shapes, for example.

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Table 1. Racket-material concepts in grades 5 and 6.

ModuleProgramming
concepts

Math concepts

FNC-2014

Arithmetic:
Introduction toprogramming- types (numbers,
strings)- function call(arguments)- simple syntax rules forexpressions- nested function calls- defining variables(constants)- expressions using
variables- natural numbers- arithmetic
operations (+, -, *, /)- order of operations

Practice basic arithmetic
operations in varying situations
and using needed tools.

- positive rational
numbers (decimal
number)Familiarize with the notion of
unknown. Examine equation andfind solutions heuristically. Study
decimal numbers as a part ofnumbers in decimal system andpractice calculations using them.

Logic:
comparisons- truth values- comparison operations(<, >, =)- decimal numbers- truth values of
propositions- equality andinequality

Geometry:
Image
programming- library- calling a function withdifferent arguments to
get a different output- pixel graphics- combining images
from smaller images

Arithmetic:
Fractions andpercentages- fractions(improper/mixed
fractions)- function machine(input, output)- measures inrectangles (width,height)- area and perimeter- reflecting withrespect to an axis
- axial symmetry- rational numbers(improper/mixed
fractions)- percentage(calculating
percentage of anumber)- presenting fractions
visually as images- ratio of distances in
the context of scaling
images(smaller/bigger)

Algebra:Variables

Practice skills in finding
similarities, differences andpatterns. Design and implement
programs in graphicalprogramming environment.
Measure and calculate the areaand perimeter of different
geometric shapes. Observe axial
symmetry.
Learn the notion of fractions andpractice arithmetic operations indifferent situations. Create thebasis for understanding
percentages and percentage of avalue, practice calculating these.
Use the relations between
fractions, decimal numbers andpercentages. Familiarize with ratioof distances and use it in thecontext of reduction andenlargement.

Table 2. Racket-material concepts in grade 7.

ModuleProgramming
concepts

Math concepts

FNC-2014

Arithmetic- integers, decimal
number, fractions(improper/mixed)- function call(arguments)- simple syntax rules forexpressions- evaluation of
expressions- nested function calls- rational numbers- arithmetic operations (+,-, *, /)- order of operations- approximating result- rounding- exponentiation- square root
- absolute value- remainder

Practice arithmetic operations alsowith negative numbers.
Strengthen numeracy in thecontext of fractions and learn
multiplication and division withfractions. Deepen the know-how ofdecimal number operations.
Familiarize with the notion ofmultiplicative inverse and absolutevalue. Strengthen understanding of420

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Algebra:Expressions

Strings

Geometry:
Images- rounding (floor, ceiling)- math functions (expt,
sqrt, abs, modulo,
random)- random numbers- definition of variables(constants) and
expressions using
variables- expression evaluation- comments- variable expressions- value of expression- areas and/or perimetersof rectangles, triangles
and circles- angles in triangles andof intersecting lines- word problems- types (numbers,
strings, images)- string operations- type conversions- library- calling a function withdifferent arguments to
get a different output

Geometry:
Turtlegeometry- loading a libraryextension- using library functions- defining a list of
drawing functions incorrect sequenceGood
codingconventions- descriptive naming ofvariables, code
indentation- measures in basic
shapes (circle, square,
rectangle, triangle)- points and lines incoordinate system- rotation and scaling- angles and side lengths
in polygons- different triangles (right
triangle, isosceles
triangle, equilateraltriangle)- parallel andperpendicular lines- coordinate system- reflecting with respect toan axis and mirroring
relative to a point

the difference between exact andapproximate numbers androunding. Practice calculating
powers and exponents with integer
indices. Learn the principle ofsquare root and its use incalculations.
Learn the concept of variable andhow to calculate the value of an
expression.
Study qualities of lines, angles andpolygons.

Study qualities of lines, angles andpolygons.

Understand concepts of point, line
segment, line, angle [and study theconcepts of path and half-open line
segment].
Practice geometric constructions.
Observe axial symmetry.
Study the first quarter of thecoordinate system and extend it tocontain all quarters (grades 3-6)

Program and practice good coding
conventions.

Table 3. Racket-material concepts in grade 8.

ModuleProgramming
concepts

Math concepts

FNC-2014

Algebra:Function- function definition(names of function andparameters)- function output withdifferent input- understanding error
messages- bits- truth values- comparison operations(<, >, =, <=, >=)- Boolean operators (and,or, not)- polynomials- function (polynomial
function)- calculating the value of afunction with different
arguments

Learn the concept ofvariable and how to
calculate the value of an
expression. Familiarize withthe concept of polynomials.

- binary and decimal
systems- truth values- propositional logic
connectives (and, or, not)- defining intervals in
number line (0 < x < 4)

Practice logic thinking suchas finding rules anddependencies andpresenting themappropriately. Ponder andstudy the number ofalternatives. Practice

Logic:
Truth values421

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Logic:
Conditional
structure- predicates (number?,even?)- parity

logical thinking by reasoning
truth values of propositions.

- conditional structure (ifelse)- testing the validity of the
input and giving relevant
error messages- nested if-expressions
and multiple selection(cond)- defining domain andrange for a function- detecting undefined
situations: division by zero,square root of negative
number, negative radius- calculating percentage(progressive tax of income)- naming angles-area of geometric shapes(rectangle, parallelogram,circle)-circumference ofgeometric shapes (square,
rectangle, circle)-Pythagorean theorem(right triangle)

Practice calculating
percentage of the whole.

Geometry:Plane
geometry

Geometry:
Turtlegeometry- repetition- function that returns a list
as output

Good coding
conventions- descriptive naming offunctions- function signature e.g.
input and output types- regular polygons- approximation of a circle
as a regular polygon- arcsAdopt usage of a larger
number set (real numbers).
Learn to use Pythagorean
theorem and inverse
Pythagorean theorem.
Calculate perimeters andareas of polygons. Practicecalculating the area andcircumference of a circle,
the area of a sector and thelength of its arc.
Study qualities of lines,
angles and polygons.

Program and practice goodcoding conventions.

The rest of the material is targeted for the students who seek more challenge. The exercises include
basic algorithms: recursion (list processing), search (binary search), sorting (bubble sort), andoptimization (greedy algorithm and dynamic programming). Good coding conventions in grade 9 include
writing unit tests for functions. The content in more detail and the related curriculum specifications forgrade 9 can be found in Table 4.
Table 4. Racket-material concepts in grade 9.

ModuleProgramming concepts

Math concepts

FNC-2014

Statistics:
Lists andstatistics- list and list operations- nested lists- range- higher order functions(apply, map)
- sorting- filtering- arithmetic sequence- a bar chart- mean, mode, median,frequency, relative
frequency- divisibility- prime numbers with
Aristoteles sieve

Algebra:Functionsand graphs- loops- vector- recursive functions- test cases for functions- local parameters (let)- graphs of functions(first and second order)- number sequences(arithmetic andgeometric)

Deepen skills for gathering,classifying and analyzing
information. Understand theconcept of mean, mode andmedian. Practice finding
frequency, proportionalfrequency and median.
Study and create different
diagrams. Learn about thedivisibility of numbers totheir prime factors. Deepen
skills to study and form
number sequences.
Describe dependencies
using both graphs andalgebra. Study the conceptof a function.

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Constructionism 2018, Vilnius, Lithuania- creating a list recursively- interest (calculating
loan payment schedule)- compound interest- random numbers- factorial

Geometry:
Turtlegraphics- using higher order functions
and range to create lists

Applications:a quiz gameandcalculator forgeometric
properties- user interactions (usage of asimple GUI library)- modifying a readymade code
to serve a new purpose- dividing code into helper
functions- division of geometricshapes into similar
regions- number sequences inimages (spirals etc.)- 3D shapes (cylinder,cone, cube)- quiz on any suitable
math related topic (forexample functions, 3Dgeometry or unit
conversions)- formulas to calculate
area and/or volume of a
3D-shape- rounding- units for area/volume

Good coding
conventions- test cases for functions

Draw lines and parabolas incoordinate system. Learn
the principles of slope andconstant. Study graphs.
Practice calculating
percentage of the whole.
Deepen skills to study andform number sequences.
Learn to use Pythagoras
Theorem and trigonometricfunctions. Study 3D shapes.

Apply self- or ready-madeprograms into math studies.
Learn to calculate surface
areas and volumes ofsphere, cylinder and cone.
Ensure and enlarge themastery of units of measureand unit conversions.

Program and practice goodcoding conventions.

Evaluation
A part of this material was exploited in the series of in-service math teacher trainings. The majority ofcourse participants were math teachers (95.7%), female (78.1%) and teaching in grades 7-9 (92.4%).
In general, the participants had quite long work histories: >15 years (38.3%), 6-15 years (39.5%), 0-5
years (22.2%). The majority of the participants had some previous programming experience (55%) orlots of experience (6.4%), however, 38.6% had no experience at all.
On the first day of the training, the following material was used: i) Introduction to programming
environment (grades 5-6); ii) Image programming (grade 7); and iii) Functions, Truth values andConditional structure (grade 8). On the second day, the material was: iv) Lists and statistics (grade 9);
and v) Turtle geometry (grades 7-9). The participants were assigned to give a programming lesson totheir students in-between the course dates by exploiting the material. After the second day, the course
participants (N=329) assessed the material in a five-point Likert scale. The statements were: i) The usedmaterial is articulate, ii) The material is well suited for teaching purposes, iii) The material iscomprehensive, and iv) The material contains enough exercises suitable for different types of students.

Figure 2. Racket material feedback after in-service training

Based on the feedback, the comprehensiveness scored the best, in addition, the material was
considered rather articulate, suited for teaching purposes, and to include a sufficient number of
exercises for differentiation. However, the review of the FNC-2014 requirements revealed the lack ofintegrative programming projects. In addition, some mathematics topics are still missing, such as:
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Constructionism 2018, Vilnius, Lithuania-

Interpretation and production of mathematical texts, basic proofs.
Changed and base value, change- and comparison percentages.
The addition, subtraction and multiplication of polynomials.
The reduction of exponentials
Linear, partial quadratic, simultaneous equations, roots.
Graphical and algebraic solving.
First-degree inequalities.
Proportion in problem solving; direct and indirect proportionality.
Concepts of similarity and congruence.
Inscribed and central angles [in circles] and Thales's theorem.
The concept of dispersion, probability.

Conclusion and future work
The examined Racket programming material for grades 5-9 was successfully used in teacher training
and it received positive feedback from the math teachers. Further research should evaluate the current
utilization of the material by the course participants and other mathematics teachers, collect feedback
systematically and analyse students’ learning outcomes both in math and programming, also incomparison with other corresponding materials. After the analysis, the content could then be revisitedand developed further in correspondence.

References
Akker, J.V., Gravemeijer, K., McKenney, S. and Nieveen, N. (eds.), (2006) Educational design research.
London, Routledge.
Arnon, I., Cottrill, J., Dubinsky, E., Oktaç, A., Fuentes, S. R., Trigueros, M., and Weller, K. (2013) APOS
theory: A framework for research and curriculum development in mathematics education. Springer
Science & Business Media.
Bandura, A. (2006) Guide for constructing self-efficacy scales. Self-efficacy beliefs of adolescents, vol.
5, no. 307-337.
Bruner, J. S. (2009). The process of education. Harvard University Press.
Clements, D. H. and Battista, M. T. (1992) Geometry and spatial reasoning. In Handbook of researchon mathematics teaching and learning, pp. 420 – 464. MacMillan.
Dewey, J. (1902) The child and the curriculum. University of Chicago Press, no. 5.
Dubinsky, E. and McDonald, M. A. (2001) APOS: A constructivist theory of learning in undergraduate
mathematics education research. In The teaching and learning of mathematics at university level.
Springer, pp. 275 – 282.
The Finnish Association for Teachers of Mathematics, Physics, Chemistry and Informatics (2018)
MAOL ry., http://www.maol.fi.
Finnish National Board of Education (2014), Finnish National Curriculum 2014 (FNC-2014).
Kolb, D. A. (2014) Experiential learning: Experience as the source of learning and development. FTPress.
Lampinen, A. and Korhonen, H. (2010) Suomessa opitaan matematiikkaa Varga-Neményi -menetelmän
mukaan. Dimensio, vol. 74, no. 2, pp. 24 – 28.
MAOL Racket (2017) Programming in primaryhttps://peda.net/yhdistykset/maol-ry/materiaalit/kpm

school mathematics,inFinnish,

MAOL,

Papert, S. (1980) Mindstorms: Children, computers, and powerful ideas. Basic Books.
Papert, S. (1996) An exploration in the space of mathematics educations. International Journal ofComputers for Mathematical Learning, vol. 1, no. 1, pp. 95 – 123.
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Partanen, T., Niemelä, P., Mannila, L. and Poranen, T. (2017) Educating computer science educators
online - A Racket MOOC for Elementary Math Teachers of Finland. In Proc.: 9th International
Conference on Computer Supported Education 2017, pp. 47-58.
Piaget, J. and Duckworth, E. (1970) Genetic epistemology. American Behavioral Scientist, vol. 13, no.
3, pp. 459 – 480.
Racket (2018) Racket programming language, http://racket-lang.org/
Tikkanen, P. (2008) ”Helpompaa ja hauskempaa kuin luulin”: matematiikka suomalaisten ja
unkarilaisten perusopetuksen neljäsluokkalaisten kokemana. Jyväskylä studies in education,
psychology and social research 337. University of Jyväskylä.
Van de Walle, J. A., Karp, K. S., and Bay-Williams, J. M. (2004) Elementary and middle schoolmathematics. Boston: Allyn and Bacon.
Van Hiele, P. M. (1999) Developing geometric thinking through activities that begin with play. Teaching
children mathematics, vol. 5, no. 6, p. 310.

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Using Agent-based Modelling of Collaboration forSocial Reflection
Evgeny Patarakin, patarakined@mgpu.ru
Moscow City University, Russia

Abstract
This paper presents techniques that unite the production aimed at creation of mutual stories with theresearch activity based on the analysis of the relationship between the participants of the productive
activity. The production is delivered through different mediums where all actions of the participants arerecorded in an electronic log. Log records are used as a base material (substrate) to build sociograms,
which are the start for the research activity. Different cycles of collaboration based on the usage ofsocial objects are described in the paper.

Keywords
Collaboration; ABM; Netlogo; wiki; social reflection

Introduction
Social networks, data on relations between collaboration participants, social network analysis
techniques are hardly used for inquiry-based learning. It comes from the impression that the data is
hard to access or the methods are too complicated. In this paper we present simple methods of learning
analytics based on the data on collaboration as well as on the dynamic NetLogo models. The source
material for the research is log records which are a by-product of digital storytelling creative process.
The side results of productive activity are used as a feed substrate for the network research based on
the analysis of sociograms. It provides an advanced pathway to developing learning programs thatutilize the science of complex networks as a vehicle through which students can learn analytical skills
for network-oriented data analysis.

Agent-based modelling of collaboration
Immersing school students in the field of network science begins with the study of maps that are basedon data from different areas of research. For example, science maps for kids are based on theavalanche of data generated by scientific research today. These maps invite students to see, explore,
and understand science (Börner et al., 2009). Introducing students and teachers to the network science
may begin with mapping their own activities in the networked communities. The advantage of thisapproach is a network lens is used to understand situations in which students and teachers are drawn
into. Consequently the network science shows its strength in the areas of immediate experience andstudents and teachers become researchers of their own activity. The desired future of modern learning
can be described as a situation where teachers and students are active agents that produce knowledge.
A necessity of formation participant’s agency requires mentors of learning to put special attention to the
problems of self-control and self-determination of behavior of participants. Schwartz (Schwartz, 1999)
termed this formulation «productive agency», because it emphasizes production through theenvironment. Schwartz defined productive agency as a recursive system where people take advantageof their available means to produce outwardly, and they see their ideas embodied and modified by thematerial or social world. Production distinguishes between situations that involve external production
and those that do not and in this regard productive agency is akin to constructionism. Constructionism
argues that learning occurs best when constructing a public artifact.
Constructionism shares constructivism's connotation of learning as `building knowledge structures'
irrespective of the circumstances of the learning. It then adds that this happens especially felicitously ina context where the learner is consciously engaged in constructing a public entity, whether it's asandcastle or a theory of the universe... (Papert & Harel, 1991).
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Public entity or a public artifact can be not only examined or discussed but it can also be used by otherpeople. That is to say a public artifact is inherently shareable.
Comparing the known learning communities close to the constructionism theory allows us to see that
almost all of them use the idea of a cycle or a spiral of actions performed by agents over objects ofactions:In the Letopisi.org (Y. Patarakin & Shilova, 2015) teachers and students perform the following
actions with wiki pages: Read -> Create -> Edit -> Connect -> Share -> Read.
 In the Globaloria Social Learning Network (Reynolds & Caperton, 2009) students perform thefollowing actions with computer games: Play -> Plan -> Prototype -> Program -> Publish.
 In the Scratch Social Learning Network (Brennan, Hernández, & Resnick, 2009) students performthe following actions with Scratch projects: Imagine -> Create -> Play -> Share.
 In the NetLogo Modeling Commons (Lerner, 2014) NetLogo modelers perform the following
actions with NetLogo Models: Create -> Run -> Share -> Comment -> Modify -> Create variations.
 In the StarLogo TNG Social Learning Network there are two linked circles (Klopfer, Scheintaub,
Huang, Wendel, & Roque, 2009) and StarLogo TNG modelers perform the following actions with
StarLogo Models. Research circle: Observe/Collect Data -> Generate Questions ->Test/Tinker/Play -> Observe/Collect Data. Design circle: Design -> Build -> Test/Tinker/Play ->Design.
 In the Looking Glass Social Learning Network (Kelleher & Pausch, 2007) 3D modelers performthe following actions with Alice Models: Create -> Animate -> Remix –> Share.
In all the above mentioned communities there is a definite social object (Engeström, 2005) – a page,game, a story, a model or other “virtual chips” over which agents perform their actions. Objects or actions
can be different, but all agents are permitted to carry actions over one and the same object. If agents
perform actions over one and the same object, they become indirectly connected by the social object.
The links are similar to those between movie actors, or scientists creating an article, or Wikipedia editors
working on a page together.
Diagrams methodology help to analyze and discuss situations that develop during a network
collaboration in different domains. A sociogram is a powerful analysis tool, helping researchers identify
points of interest and other structural properties that otherwise would not be obvious in numeric data.
We make maps not just of the physical world but also of our social worlds (Mehra et al., 2014).
The study of dynamic networks greatly benefits from visualizations that can illustrate ideas and concepts
not immediately visible in a static sociogram. Moody and others’ research illustrates the need tovisualize how networks develop and change over time (Moody, Mcfarl, & Bender-demoll, 2005). Amongthe tools developed in the complexity field, agent-based modeling and network analysis are very
important in sustaining the process of bringing complexity to bear on the policy world. The combinationof the two methods can increase enormously the potential of complexity-based policies (Fontana &Terna, 2015). Since agent-based modeling is inherently dynamic, the problems with static networks are
overcome naturally. Agent-based modeling permits the desired richness of behaviors and attributes thatmight bridge the gap between agent-nodes and the real world.

Termites with Logs
It was mentioned in the previous section that actors perform actions over same objects becominginterlinked via this social object in many various situations. Simple actions that students perform over
objects of actions in learning communities are very much alike the procedures performed by turtles in a
famous Termites model: search-for-chip -> find-new-pile -> put-down-chip
As Resnick wrote (Resnick, 1997) each individual termite should obey the following rules:
If you are not carrying anything and you bump into wood chip, pick it up.
If you are carrying a wood chip and you bump into another wood chip, put down the wood chip you're
carrying.
Each turtle performs a sequence of steps in procedures search-for-chip find-new-pile put-down-chip
which leads to the result when chips scattered randomly over the screen are gradually gathered into
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one round pile. Termites model seems most optimal because it contains chips as objects forcollaboration. Experimenting on this model we can get a deeper understanding of collaboration
phenomenon. Let’s imagine that termites record their labor actions over chips in a log. I.e. if an actor
performs a meaningful action on a chip, he leaves a record on this in the log. To understand how we
can benefit from collateral records we have modified the original text of Termites model by adding new
variables and rules. We have inserted a variable called list WIKILOG where turtles make records of theiractions. There also have been made some additions to the procedures search-for-chip and putdown-chip (E. D. Patarakin, 2017).
The interface for the modified model is shown in Figure 1. Logs_to_sociogram button switches all the
chips off the screen and only shows linkage between agents carrying one and the same chip.

Figure 1. Interface of Termites with Logs model

Termites with Log Model was implemented in NetLogo language 5.2 and its source code is available on
the Internet http://modelingcommons.org/browse/one_model/4749
This simplest model for sociograms creation based on the actions with social objects can be applied tovarious collaboration situations. In all cases when actors perform actions over shareable objects, thelog for these actions can help build sociograms as well as conduct social research.Full history can bepresented as a record of a game, consisting of many moves. Each move in a game contains threerequired elements:
Agent ID| Object ID| Type of an action
Every action of an agent towards an object leads to the formation of a link between them. If the agents
perform action over one and the same object they become agents of the collaborative activity, indirectly
linked with one another by the mutual object of activity. The collaborative activity network could bepresented as the bipartite graph combining agents with objects of collaborative activity.
Our second learning analytics application was implemented in NetLogo language 5.2 and its sourcecode is available on the Internet as Dynamic Wikigram Model
http://modelingcommons.org/browse/one_model/4769
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The use of NetLogo allowed us to present the collaboration as a dynamic diagram in which each actor
can interact with each other tens of thousands of actors. The created model used NetLogo features
such as breeds and agentset. Interface of Dynamic Wikigram Model presented in Figure 2

Figure 2. Interface of Dynamic Wikigram Model

Dynamic Wikigram Model can be used as a simple and illustrative tool of analysis. During his/herresearch, a student uses the technique of dynamic agent-based sociograms to:Trace how and based on what objects’ links between participants of collaborative productive
activity are formed.
 Identify key players and stable biggest cliques, which serve as cores that support the operation of
network communities
Key players are those elements in the network that are considered important, in regard to certain criteria.
One of the most popular criteria is the betweenness centrality of vertex. The betweenness of an edge
is the number of these paths running through it. It is clear that, when a graph is made of tightly boundclusters, loosely interconnected, all shortest paths between nodes in different clusters have to go
through the few interclusters connections, which therefore have a large betweenness value. A nodewith high betweenness centrality is responsible for connecting many pairs of nodes via the best path,and deleting that node should cause many pairs of nodes to be more distinctly. The idea behind
betweenness centrality is that being in between actors makes actor powerful because he may be able
to control the flow of information between them (Borgatti, 2006). Nodes with high betweenness centrality
are often called key-players. To calculate the betweenness centrality of a Netlogo turtle, you take every
other possible pairs of turtles and, for each pair, you calculate the proportion of shortest paths between
members of the pair that passes through the current turtle. The betweeness centrality of a turtle is thesum of these. Top ten key players are determined in NetLogo assublist reverse sort-on [norm-betweenness] users 0 9
Key players are important in themselves and as nodes that connect communities. Qualitatively, acommunity is defined as a subset of nodes within the graph such that connections between the nodes
are denser than connections with the rest of the network (Radicchi, Castellano, Cecconi, Loreto, &Parisi, 2004). The detection of the community structure in a network is generally intended as aprocedure for mapping the network into a tree. For the divisive class of algorithms one starts with thewhole graph and iteratively cuts the edges. The crucial point in a divisive algorithm is the selection of429

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the edges to be cut. Girvan and Newman have introduced an “edge betweenness algorithm” where theselection of the edges to be cut is based on the value of their edge betweenness centrality (Girvan &Newman, 2002). The single step of the edge betweenness algorithm consists in the computation of theedge betweenness for all edges in the graph and in the removal of those key players with the highest
score. The iteration of this procedure leads to the splitting of the network into disconnected subgraphs,
until the whole graph is divided in a set of isolated nodes. At each step of the edge betweenness
algorithm NetLogo model created the sociogram, which was used to discuss the role of a key players(Figure 3).

Figure 3. Wikigram with key player in the centerA clique is a subset of a network in which every node has a direct link to every other node. A maximal
clique is a clique that is not itself contained in a bigger clique (Figure 4). Cliques containing more thanN members are determined in NetLogo as
nw:maximal-cliques [if (count ?) > N

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Figure 4. Sociogram of maximal cliques

The projected system of the collaborative network activity gives extra possibilities not only for the
productive activity but also for the analysis and reflection on the processes inside the system. The tools
should open the possibility to assess from the network point of view both the position of each participant
and the degree of the whole system development as the learning network. The analysis of the activityof each participant inside the acting community combined by mutual story or mutual game, allows us
linking the act of activity and the development of one participant with the development of the wholecommunity.
We have successfully used NetLogo wikigram application to support several socio-educational projects.
Letopisi.org In Russian education MediaWiki is represented first of all by Letopisi.org project and its
regional clones in several teaching colleges and universities. Letopisi - http://Letopisi.org is the national
educational project with international participation and has continued for more than twelve years. The
leading idea of the Letopisi concept is that the collaborative network activity and the network cooperationof the learning agents are aimed at the creation of various types of learning products which in general
could be marked by a widely recognized term “digital story”. The basic scheme of the concept is thefollowing: the digital story and the constituent elements of it could be used by other participants of the431Constructionism 2018, Vilnius, Lithuania

collaborative activity in creation of new stories. In the Letopisi.org students and teachers perform thefollowing actions with wiki pages:
Read -> Create -> Edit -> Connect -> Share -> Read
Preobra.ru - collaborative environment for creation, improvement and promoting bills within public andlegislative projects (Burov, Patarakin, & Yarmakhov, 2012). Enacting a new law means that a community
devises out new rules which help it to become more efficient. In the general case, the web site contains
a complete text of the document, which chapters and items were split in small segments. The project
participants can create their own segment versions and vote for segments created by other participants.
Public construction of a document aiming at complex cloud issues has high educational value. This
collaborative practice helps not only produce a quality document and build a community of peopleinterested in its implementation, but promote the innovative document, maintain a new level of its
understanding and perception by the society. In the Preobra.ru teachers perform the following actions
with parts of document: Writing, Editing, Voting
Google Apps or G suite. Information environment of the complex of schools that share Google Appsdomain. Participants can create, view, and edit digital objects of various types. Participants vary bypositions, teaching subjects and campuses they work in. Learning analytics and diagrams methodology
help to analyze and discuss situations that develop during a network collaboration and build schoolcommunities of practice. The data, required for such a research can be extracted from a school domain.
Netlogo package contains all the features, needed for such data analysis. Our research has shownclusters of most connected teachers and staff and located collaborative documents that play the role ofboundary objects, connecting clusters of school administrators, classroom teachers and staff
membersNetLogo allows you to define different breeds of turtles and breeds of links. Once you havedefined breeds, you can go on and make the different breeds look differently and behave differently.
We have used various breeds of agents to separate the subjects and objects of activity, as well as toseparate the actors into different classes. An agentset Netlogo is exactly what its name implies, a setof agents. But what's powerful about the agentset concept is that you can construct agentsets that
contain only some turtles or some links. Due to this we are able to identify network characteristics thatare typical for certain groups of actors. For example, only administrators, only for teachers of literature,
only to employees of a particular territorial office etc (Figure 5).

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Figure 5. School domain sociogram
Scratch.mit.edu. Since all productive networking systems have common principles, we assume that asimilar apparatus may be used for building sociograms based on an analysis of the actions performedby the Scratch community members (scratchers) within studios built for their collaboration. We also
believe that the functions of Scratch studios are similar to Media Wiki categories. For experiment
purposes, we have gathered data on member activities in a number of Scratch studios and generated
collaboration sociograms based on the data. An example of such sociograms is shown in Figure 6. The
human shapes refer to members/scratchers, code sheets – projects. A solid line from a scratcher to a
project means that the scratcher is the project author. A dotted line means that the scratcher hascommented on the project. As we can see in the sociogram, the studio is characterized by average
productivity, high connectedness and cohesion, and low sustainability. The removal of the central
scratcher and his projects will entail the studio chart disintegration into a large number of unconnected
members.

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Figure 6. Sociogram of a Scratch studio

Conclusion and Discussion
We believe that socio-educational co-creative projects are not only productive environments but
developmental environments also. As John Raven wrote “in developmental environments people canthink about their organizations and their society and come to understand and perceive these institutions(and their operation) in new ways that have marked implications for their own behavior” (Raven &Stephenson, 2001). The projected system of the collaborative network activity gives extra possibilities
not only for the productive activity but also for the analysis and reflection on the processes inside thesystem. The tools should open the possibility to assess from the network point of view both the positionof each participant and the degree of the whole system development as the learning network. The
analysis of the activity of each participant inside the acting community combined by mutual story or
mutual game, allows us linking the act of activity and the development of one participant with thedevelopment of the whole community. The point of the co-creative projects is not only the createdproduct of the project, but also the social structure itself. The value is in the creation process of thissocial structure during collaborative work and the formation history of this structure. Usually the social
structure and the history of its formation are hidden for the participants. In best cases the subject forparticipants' discussion is the number of created objects and the number of comments and ratings.
Meanwhile a social structure is a significant characteristic in many respects determining the success ofcollaboration. We believe that visualization of the social structure can support the process of groupreflection. Social reflexivity is associated with the social functioning part of a team, deals withinterpersonal relation, strengthens collaboration among team members, and therefore leads to better
performance. While various studies have found that social reflexivity has significant positive relationshipwith team outcomes (Gurtner, Tschan, Semmer, & Nägele, 2007; Schippers, Den Hartog, & Koopman,2007), little is known regarding the mechanisms underlying group social reflexivity in network
collaborative projects. We start from the hypothesis that social reflection can be triggered by sociograms
and we argue that learning as `building knowledge structures' happens especially felicitously in acontext where the learner is consciously engaged in constructing a public entity, that can be discussed,
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evaluated and reused by other participants to create new entities, and data on the interactions of agentsof learning can be presented as a map.

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Reconstructing Constructionism by Construal
Nicolas Pope, nwpope@gmail.com
University of Turku, Finland

Jonathan Foss, jonathan.foss@warwick.ac.uk
University of Warwick, Coventry, CV4 7AL, UK

Meurig Beynon, wmb@dcs.warwick.ac.uk
University of Warwick, Coventry, CV4 7AL, UK

Abstract
Noss and Clayson (2015) sets out an agenda for 'reconstructing constructionism', identifying as a major
challenge for research: "... to transform constructionism from a framework for action into a set of waysof conceptualising what people do in constructionist environments that can simultaneously assist in
designing those environments." This paper relates this challenge to an approach to constructionism
based on 'making construals' within the alternative conceptual framework for computing of Empirical
Modelling (EM). Making construals is a digital skill based on three fundamental concepts: observables,
dependency and agency ("ODA"). Its application will be illustrated using the online Construit
environment. Making construals promotes an empirical and experiential perspective on computing thatis complementary to 'computational thinking'. Its epistemological roots reflect the deep appreciation of
the role of personal experience that characterises the pragmatism of William James and John Dewey.
This paper reviews and illustrates an EM approach to constructionism with respect to six topics:
provocative modelling, programming paradigms, objects-to-think-with, definitive programming, Scratch
as a 'broader constructionist methodology', and emerging empirical perspectives on computing andlearning. The progression of topics relates an EM approach to ideas, tools and practices to which current
trends in computer support for constructionism are converging.

Jonathan Foss’s construal of the Artiphon: learning music theory through construction

The concluding section considers a central problem, articulated by diSessa and Cobb (2004) and citedby Noss and Clayson, concerning constructionism as a framework for action where theories "arerelatively inexplicit, complex, and often involve multiple very diverse elements that cannot plausibly be
brought under a single umbrella." It argues that a pragmatic approach to constructionism can dissolve
this problem, bringing unity to the plurality of approaches to computing and learning it involves.

Keywords
constructionism; objects-to-think-with; making construal; Empirical Modelling; radical empiricism

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Introduction
In their paper "Reconstructing Constructionism", Noss and Clayson (2015) identify a central challenge:"... to transform constructionism from a framework for action into a set of ways of conceptualising what
people do in constructionist environments that can simultaneously assist in designing thoseenvironments." A key observation informs this challenge: the recognition that constructionism "is as
much a theory of epistemology as one of pedagogy [in that] understanding the development ofknowledge is part of and integral to the encouragement of an inclusive and powerful pedagogic theory
and practice". And if constructionism is conceived "merely as a pedagogic strategy, [it] does not offer inconcrete terms much more than a host of other worthy slogans such as 'discovery' ... 'exploratory' ...
'enquiry based' learning".
Computing has had a seminal influence over constructionism. Though constructionist practices may not
require computing technology, Papert's original vision for constructionism took inspiration from thepotentially transformative impact of the computer on education. This has inevitably meant that the
prototype conceptual frameworks for constructionism have been heavily influenced by the semantic
framework in which interaction with computers is conceived, that of computational thinking (CT) (Wing,
2006). Papert himself recognised the potential conflict between the ‘hard’ science of computing and thescope for being ‘vaguely right’ that is characteristic of construction, as he discusses in his chapter on‘Computerists’ in The Children’s Machine (cf. Beynon, 2017).
This paper is one of several publications by Beynon and others (cf. Beynon et al, 2016) that attributethe difficulty of meeting Noss and Clayson's central challenge of finding appropriate ways toconceptualise "what people do in constructionist environments" to the compromising influence of CT.
The raison d'etre of CT is to conceptualise the processes by which we construct and exploit objective
contexts in which abstract computational activities can be carried out. Computing-in-the-wild is of its
nature an activity in which 'construction' in the sense discussed by Latour (2003) plays an essential role.
We argue that a conceptualisation of construction can be based on the principles of Empirical Modelling(EM) (“EM Website”). These principles make prominent the role of appropriate personal experiences
during the construction activity which enable the formation of relevant knowledge structures, and are
general enough to apply both to computing practice in its full generality and to constructionist practices
in education. In practical terms they are associated with an activity called 'making construals' that is notnecessarily computer-based but thrives on rich technologies for interaction and perceptualisation. In
many contexts, computer use is more appropriately regarded as 'making construals' rather than'programming' as conceptualised in core computer science. This applies, in particular, where thecomputer is being used to support constructionist practices in education.
There are two aspects to the contribution that EM can make to constructionism. From a conceptual
perspective, it introduces three foundational concepts (observables, dependency and agency) which
inform both the medium and process of construction and identifies semantic principles to account forthese that are rooted in William James's philosophical stance of radical empiricism. From a practicalperspective, EM has explored a number of environments for making construals (of which the mostrecent is the online "Construit" environment) whose designs - in keeping with the terms of Noss andClayson's challenge - have been incrementally informed by the underlying concepts and principles. Theinterested reader can find more details of these two topics in papers presented at the Constructionism
2016 conference: (Harfield et al, 2016) and (Beynon et al, 2016). For convenience, we shall refer tomaking construals using an environment such as Construit as 'an EM approach' to constructionism.
The responses of experts in the field of constructionism to an EM approach reflect an interest temperedby scepticism. Such experts (cf., in particular, personal communications from James Clayson and Ken
Kahn) draw on rich experience of environments for supporting constructionism, of practices in software
construction and of paradigms for programming. In their critiques, the merits of the Construit
environment, its practical significance and what purport to be its distinctive qualities are questioned.
Their call for concrete examples of how Construit differs from other approaches reflects the manydifferent ways in which the environment can be viewed and deployed: as a programming environment
to be compared (e.g.) with Jupyter, Scratch/Snap, NetLogo Web, ToonTalk Reborn, App Inventor; asrepresenting a programming paradigm to be compared with (e.g.) prototype-based object-oriented
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programming, pure functional programming or a modern Lisp such as Clojure; as an integrated
development environment (IDE) to be compared with (e.g.) the Mathematica and iPython notebook
IDEs; or as a presentation environment in which natural language and 'code' are blended to becompared with (e.g.) Literate Programming, what Alan Kay calls Active Essays[www.playfulinvention.com] and what Bret Victor calls Explorable Explanations [worrydream.com].
In responding to the feedback from expert commentators, and their injunction to focus on concrete
comparisons, it has been important to adopt a holistic viewpoint on how environments support
constructionism. The Construit environment is just one component of the EM approach toconstructionism: its significance can only be understood in conjunction with the epistemically andexperience focused mindset that has informed its design. The contextualising framework has a radical
impact on the mode of interpretation of a construal. It is not appropriate to consider a construal simply
as a program to serve a specific functional objective. For that reason, to compare construals with purefunctional programs is to expose a chasm between two culturally distinct ways of thinking about
computer artefacts: one that highlights the concrete and experiential and one that highlights the abstract
and computational. In practice, many artefacts that have been developed as environments fordeveloping programs admit interpretations -- or partial interpretations – similar to those of construals,
though they are not explicitly conceived with such interpretations in mind and may be hybrid andincoherent as a result. Well-conceived construals highlight the epistemological merits of domain models
based on observables, dependency and agency. A faithful construal of a typical Scratch program
reveals the generally chaotic mash-up of abstract computational and concrete experiential ingredients,
and its limitations as an epistemic model of an intended behaviour.
The next, and main, section of the paper illustrates some of the above themes with reference to specificexamples: it is followed by a concluding section in which one of the key problems confronting theories
for constructionism identified in Noss and Clayson (2015) will be discussed.

Construal, construction, comparison and contrast
This section briefly reviews six topics that give useful insight into the nature and potential contributionof making construals. In three of these, construals are compared and contrasted with computer artefacts
that address a related subject. The primary emphasis is on modelling as a key "characteristic of aconstructionist agenda" identified by Noss and Clayson (2015). Our objective is twofold:

to highlight different ways in which this modelling for constructionism has been approached, andto illustrate the way in which these approaches, without explicitly spelling out a clear conceptual
framework, can be related to and distinguished from an EM approach;to assess the degree to which the Construit environment meets the criterion identified by Nossand Clayson (2015): promoting the learning of powerful ideas by enabling learners "[to create]
external building blocks by a process of building, reflecting and debugging", in such a way thatthey "can develop relevant internal knowledge structures".

The basic principles and concepts behind making construals will be sketchily and informally introduced
as appropriate: for more details of these, see Harfield et al, 2016 and the prototype online course on
making construals at http://jseden.dcs.warwick.ac.uk/construit/?load=344. Each topic will cite one ormore construals that can be further explored via the Constructionism2018 construal in the Construit
environment (“The Constructionism2018”). The six topics loosely reflect the evolution of computerbased support for constructionist practices, and serve to illustrate ways in which some of the ideas andtechniques behind making construals are, at least implicitly, represented in these practices.

Topic 1: Provocative Modelling
In Mindstorms (1980), Papert emphasises many important themes that motivate constructionism. Hisinterest was not in computer programming per se, but in how interaction with computing technology
could serve to promote ‘epistemological reflection’ in learners and teachers, and how potentially thismight help to ‘externalise intuitive expectations’ and expose ‘the non-obviousness of what we consider
obvious’. These aspirations are not well-aligned to the accepted motivations for learning to program,which are typically aimed at realising specific functional objectives rather than provoking reflection on
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the nature of human agency. For Papert, the role of Logo programming in provoking learning about asubject domain was very clear, but also limited. It may be that the almost universal use of Logo-like
programming constructs in subsequent environments to support constructionism is an aberration in thisrespect – an inappropriate attempt to generalise Papert’s principles as they apply to a specific learning
objective as if they were universally applicable.
A comparative study may be helpful in this context (see (“The Constructionism2018”)). This concerns
modelling of a room layout such as might appear on an architect's sketch. The layout is a 2d line-drawing
floorplan featuring lines to represent the walls, door, a filing cabinet and a table with a lamp upon it
connected by a cable to one of several possible sockets. The study contrasts a most elementary
application of basic Logo to draw the components of the layout with an equally unsophisticatedapplication of the Construit environment to construct an image that is visually indistinguishable.
Drawing the floorplan of the room in Logo has an educational significance that is non-obvious to theaccomplished programmer. To appreciate it, it is necessary to consider how a child might learn to makea map in a concrete situation, as when recording the findings of an archaeological dig. The relevant
epistemological ingredients are precisely concerned with the orientation and length of lines, and how
these might be exposed through projection and informed by intuitive expectations of what form thefloorplan of a buried room might take. The provocation to learn is concerned with how the child’s
experience of orienting, moving and counting paces can be connected with an appreciation of entities
and coordinates in a 2d space (cf. the Experiential Linear Algebra construal (“TheConstructionism2018”)).
It is quite apparent that such a primitive form of Logo is ill-suited to provoking reflection about the moresophisticated concerns that might arise in interpreting such an archaeological find - or in other situations
motivating the 2d sketch. This raises the question of what kind of extensions to the Logo programming
language, alternative choice of language, techniques in the program development or changes to theprogramming paradigm would be appropriate in addressing a broader agenda. It is in this connectionthat making a construal of the room layout is of interest.
The construal of the room is loosely determined in an open-ended fashion by an explicit set of current
definitions ("a script") and an implicit cloud of potential interactions. Each definition expresses thecurrent value of an observable: these correspond to entities that might be directly registered byinspecting an actual room (or conceiving an imaginary one). The north-west corner of the room, the east
wall, the door and the door status, whether open or closed are simple examples of observables in thescript. Observables may include entities that (for diverse reasons) are not directly visible, such aswhether the door is locked, whether the electricity is switched off, whether an alarm is sounding. The
criterion for observability is a personal subjective one: it differs according to who is the observer ormaker and what agency they have to interact.
A definition may attach an explicit current value to an observable, as in "the north west corner is at thelocation [100,100]" or specify it by a 'spreadsheet-style' dependency, as in "the north east corner of theroom is at the location [100+room_width, 100]". As another example: "the orientation of the hinged door
depends on whether or not it is open". Dependencies are always associated with some latent agency(cf. the 'What if?' aspect of spreadsheet interaction): as in "What if the room was wider?" or "Let's shutthe door". Though the construal of the room is visually quite primitive, as a casual sketch might be,interaction with the construal through redefinition is unconstrained, and meaningful in so far as it
respects connections being made in the experience of the maker. "At what point is a door too wide or
too narrow to be a door?" Interaction with the construal is through redefining observables; thisencompasses commonsense room actions (such as "door_open = not door_open") and more
complicated or subtle semantic changes, such as introducing a sliding door, or refining the model of thedoor so that it's orientation reflects 'how open' it is (and redefining the observable 'door_open'
accordingly).
In the simple illustrative example script, the only agency is that of the human observer, who can makeredefinitions in the role of (e.g.) a room user, room designer or expositor of Empirical Modelling
principles. With each role, there is an associated tacit context that shapes what interactions by way ofredefinition are appropriate, or plausible. This is a discretionary context pragmatically determined by440

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what connections in experience make sense in that role: a room user would not ordinarily move a wall
for instance. It is possible to introduce autonomous agency, but even where such agency is added theconstrual always conceptually corresponds to a state rather than a behaviour. As a simple example: themaker of the construal may add an automatic closing agent to the door, but still conceives the script as
representing the current state of the room, where both script and room state are dynamically changing.
This admits the possibility of intervention in automated behaviour without undermining the model ofcurrent state. In commonsense terms, this reflects the possibility that, even though the door closes
automatically, you can still obstruct it.
Juxtaposing the room construal with a primitive Logo program to draw the room layout highlights thechallenge in promoting Papert’s vision for "writing programs as promoting epistemological reflection".
The construal exposes the richness and subtlety of the experience and interpretation that surrounds
potential conversations around the room layout. In particular, it reveals the extraordinary capacity of thehuman mind to make connections in experience of diverse kinds in a flexible and fluid manner:connecting a simple line drawing with an image of a physical room, real or imagined; attributing therelocation of the NE corner of the layout to a change in the value of ‘room_width’; conceiving the current
state of the layout as part of ‘a behaviour’. The aspiration to strengthen the semantic connection
between ‘writing a program’ and ‘understanding the subject domain in which it executes’ has been aleitmotif of software development that has informed the design of PLs to support constructionism. This
helps to motivate the next topic: consideration of a declarative programming paradigm.

Topic 2: Programming Paradigms
This study examines the relationship between a model that is described in a declarative rather than aprocedural fashion and a construal to address a similar theme, viz. modelling 'playing the game ofnoughts-and-crosses'. The declarative program in this case is written in the 'pure functional' Miranda
PL, as formerly deployed in teaching first year university computer science at the University of Warwick.
As the previous study illustrates, a procedural program can generate a bewildering complex of statesthat have no semantic significance as far as the subject domain is concerned. The rationale fordeclarative PLs was to eliminate such states so as to make a more intimate connection between the
program text and the subject domain. This ambition is clearly well-aligned in principle to the aspirationsof epistemic modelling. The contrast in this case is with a construal that identifies the observables thatare involved in playing a game of noughts-and-crosses (What is the state of the grid? What are thewinning lines? How are the symbols displayed on the grid? Whose turn is to play? When is the gameover? What is a winning move? etc) and expresses the dependency relationships between thesetogether (if automated play is desired) with agents to make moves autonomously.
It is indeed the case that constructs in the Miranda program have direct counterparts in a noughts-andcrosses game. Since the declarative framework precludes modelling the explicit states of a game moveby move, there are representations for such things as "the sequence of positions that arise in the game"- but no observable to represent what can be deemed in the construal to be the current position in thegame. By contrast, the construal can readily be exercised in such a way as to simulate mistakes in play(such as a player missing a turn), cheating (such as a piece being removed from the board), changes
in the winning lines (even in the course of play). In these respects, the construal (aka 'the OXO
laboratory') has a better claim to the character that Papert attributes to "a programming language"(Mindstorms, 1980, p15): "[being] like a natural, human language in that it favors certain metaphors,
images, and ways of thinking."
As construals dating from several years ago, both the room construal and the OXO laboratory illustrate
a problematic issue in learning to make construals. In framing dependencies, it is in general necessary
to express relationships of a relatively complex form. For instance: “The player to move is X if X makes
the first move and the number of Xs on the grid is the same as the number of Os and O otherwise”. A
definition of this nature is then framed by writing a small conventional program (in a basic procedural
programming notation embedded in Construit) to compute the number of Xs and Os on the grid fromthe current position. Since each definition in the script expresses the value of an observable as a pure,and generally simple, function of other observables, the (declarative) programming task is much more441

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straightforward both technically and semantically than developing a procedural program in which there
is complex agency and state change, but the hybrid nature of the activity presents a barrier to the maker.

Topic 3: Objects-to-think-with
Towards the end of Mindstorms, Papert acknowledges the limitations of Logo and refines his position:"I see [the Turtle] as a valuable educational object, but its principal role here is to serve as a model forother objects, yet to be invented. My interest is in the process of invention of "objects-to-think-with,"
objects in which there is an intersection of cultural presence, embedded knowledge, and the possibility
for personal identification." A significant point for emphasis here is that Papert is thinking in concrete
experiential terms about what the computer offers to the learner: the Turtle is the object-to-think-with,and not the abstract Logo program. This is not a mode of thinking about computer programs that has
foundational support from theoretical computer science, though it has developed considerable
pragmatic endorsement from software practice. By way of illustration, object-oriented approaches tosoftware development, which were once regarded as a promising candidate foundational framework forsoftware development, have been undermined by a (sometimes bitter) controversy between those whochampion object-orientation as a formal approach and those who declare it to be the basis of anempirical pragmatic framework in which the emphasis is on how objects are crafted in accordance withhow they are experienced by developers (cf. (West, 2004)). To highlight the distinction that is being
made here, the term 'computer artefact' will be used to refer to what computer programs offer inexperiential terms: 'objects-to-think-with' are to be classified as artefacts, not programs.
It seems difficult to identify an iconic example of an object-to-think-with that is implemented as acomputer artefact. In Mindstorms, Papert (1980) describes such an object in connection with a Penny
Rolling conundrum posed by Martin Gardner, but no implementation is described. In any event, realisingsuch an object on the computer is a task beyond the range of a novice programmer. Microworlds canbe viewed as preconstructed environments in which learners can learn about a subject domain throughexperiment and similar claims can be made for environments to support educational robotics. Suchenvironments invite the “worthy slogans ... of ‘discovery ... exploratory ... enquiry based’ learning”, but
their constructionist credentials perhaps depend on the extent to which learners can exploit them tomake connections that are beyond the scope of what has been preconceived by their designer.
Of all the established approaches to creating objects-to-think-with, creating a spreadsheet might beseen as most promising, as it embodies the principles of ‘what if?’ modelling. Though spreadsheets
have been advocated in education (cf. the Spreadsheets in Education journal) there seems to be little
reference to their application in constructionist practices. Construals, to which spreadsheets aresemantically the closest well-known relatives, nonetheless seem well-suited to specifying objects-tothink-with. Examples include: construals of objects-to-think-with relating to the penny rolling puzzle(including Papert’s own proposal (Beynon,2017)); a construal of a purse; a number representation
construal that gives visual expression to a mental model of how integers are represented in different
bases. An important feature of these construals (all of which are available online in the Construit archive– see (“The Constructionism2018”)) is that they are constructed without the explicit use of conventional
procedural or declarative programming techniques, a theme to be elaborated in the following sections.

Topic 4: Definitive Programming
The aspiration to exploit the computer fully in constructionist practices inevitably means that muchattention has been given to how to make writing computer programs more accessible. Enabling
computing novices to experiment with prebuilt objects and environments does not offer the samepotential for learning in a constructionist idiom that enabling them to construct their own computer
artefacts does. What is more, it has long seemed that the kind of procedural thinking that is exemplified
by Papert’s Turtle is an essential component in constructing any computer artefact, and is also one towhich novices can most easily relate. This has meant that helping novices to engage with procedural
thinking has been a prominent characteristic of environments to support constructionism, whether
through introducing blocks, as in Scratch and App Inventor, or more semantically motivated animation
as in ToonTalk.

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The effect of ‘writing a program to meet a specific functional requirement’ can be emulated in Construitby crafting the agency that is to be automated and invoking this in a context where the maker’s
interaction is constrained to be that of the intended user. This activity is closely parallel to programming
behaviour in the room construal by restricting redefinitions to plausible actions (such as ‘opening thedoor’ or ‘moving the table’ by the room user, and automated ‘closing of the door’ by the door
mechanism). The discretionary context is one in which actions that redesign the room (e.g. ‘changing
the width of the room’) are suppressed, but the option to reinstate them is open at any stage – evenwhilst programmed actions are being executed. In this way, program-like behaviours expressed using
a construal can be seamlessly blended with actions that might be construed as re-programming.
By way of illustration, consider the Whack-a-Mole application that is featured as an introductory
programming exercise in App Inventor. By modelling the application in terms of observables,
dependencies and agency, a very concise script can be framed to express the way in which themechanism that moves the mole and the player’s mouse actions interact. In framing this script,
dependency plays a crucial role in ‘taming’ the procedural ingredients of the application. As the makerof the Whack-a-Mole construal, Jonathan Foss, remarks: the main feature of the development was “thedirectness of the experience. Rather than having to ‘plan’/’design’ and ‘program’, it was more a case ofdrawing some holes and a mole, then adding functionality by describing how these things are connectedto each other ... compared with App Inventor, there was a lot less abstract thought/planning required,with all statements having a direct connection to the scenario that was being modelled.” Further informal
evidence of the qualities of the construal was obtained from a Key Stage 3 pupil at a schools event. In
the space of 20 minutes, with no previous experience of Construit or making construals, and with only
the most basic indication about the nature of the script, he was able to add more holes, introduce anextra mole, and make the speed at which the mole moved dependent on the score.
A significant feature of Foss’s Whack-a-Mole construal is the absence of any explicit procedural
programming ingredients. This is a characteristic of many more recent construals, which exploit an
innovative feature of the Construit environment that has transformed the practice of making construals:
the introduction of the with-construct. Informally, the significance of with is that it makes it possible toframe succinct definitions to express new categories of observables that correspond to commonplacemodes of observation. For instance, the array of holes in Whack-a-Mole is defined by defining a
prototype hole with index i and specifying the entire as a collection of holes, based on this prototype,with indices ranging from 1 to the number of holes. More generally, whenever an existing observable isdefined by dependency in terms of other observables (its ‘dependees’), a new observable can be
specified as the counterpart of this observable with a value defined as if specific changes to the valued
to its dependees had been made. Through this enhancement, the expressive power of scripts istransformed. It becomes possible to refer to entities that routinely form part of commonsense
observation (such as ‘the height of the tallest building on the skyline”) without needing to introduce
obfuscating procedural constructs. The full implications of this are to realise a vision that has been along-standing aspiration in EM research: an observation-oriented approach to programming that isbased solely on definition and redefinition (aka “definitive programming”).

Topic 5: Scratch and a ‘broader constructionism methodology’
Noss and Clayson (2015) identifies Scratch as having "shown signs of contributing to the creation of abroader constructionist methodology", as suggested by Brennan & Resnick (2013). There is empiricalevidence for this in the millions of learners, some collaborating with each other, who have used theonline Scratch environment to create a vast archive of projects. Such a cultural phenomenon opens up
unprecedented scope for ‘construction’ leading to the development of objective insights from subjective
personal understandings. Scratch has patently been the stimulus for many people to begin to learn toprogram. What is not so clear is to what extent the Scratch programming revolution illustrates the centralprinciple of constructionist practice: understanding of the subject domain informs the construction of acomputer artefact and interaction with the computer artefact informs understanding of the subject
domain.
With its visual blocks interface, sprites and costumes, stage and backdrops, Scratch makes a significant
move towards ‘constructing a computer artefact’ rather than ‘writing an abstract program’. It also443

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incorporates several features that resonate with an EM approach to construction. Agents act
concurrently; built-in dependencies (e.g. to simplify networking and so promote collaboration) givelearners easy access to high-level agency; sophisticated examples of observables and agency (suchas enable sprites to detect whether they intersect a line of a certain colour, or to bounce at the edge ofthe stage) are built-in as primitives. This makes it easier for the learner to program and encourages
them to explore basic concepts in computational thinking.
The impact of introducing all these features on program comprehension is more problematic. The benefitof block-based programming has itself been questioned (Hermans and Aivalogou, 2016). From a
constructionist viewpoint, as shown in the room modelling exercise, procedural ingredients need to behandled with care. Without principles to guide program development, having a wide choice of possible
representations may not be advantageous: If I wish to create a vertical line from the top to the bottomof the stage, should I draw it using a Logo like command? should I ensure that it has the full height ofthe stage by using the bounce feature? or should I simply draw it on the backdrop? How do I
subsequently move the line? How can I determine the explicit coordinates of its endpoints? Building-in
instances of powerful dependency and agency to support key activities is helpful to a degree, but themerits of an EM approach stem from the automatic support for user-defined dependency to enablesimpler and more intelligible specification of agency. Such considerations make it hard to attribute clear‘epistemic modelling’ content to casually constructed Scratch programs and places the somewhat selfreferential claim that Scratch supports constructionist practices that include ‘the construction ofcomputer programs’ in a different light.
Many of the above concerns are potentially just as topical for the Construit environment. It too offers
many alternative modes of representation and techniques for development. It is quite possible to write
traditional procedural programs within Construit, and there is even provision for incorporating
JavaScript. Until the advent of the with-construct, some procedural coding element was essential increating all but the simplest construals. In order to respect the principles of ODA modelling, such coding
could then only be exploited to define the (pure – and simple) functions that expressed non-trivial
dependencies and the elementary procedures to effect the redefinition associated with automatedagents. In this respect, the absence of guiding principles to inhibit the ad hoc construction of Scratch
programs seems problematic.
The issues discussed in this section are illustrated by the Giving Change construal, developed tocomplement a Scratch program called 'Coins' developed by Phil Bagge for use in teaching programmingat primary school. For details, see http://code-it.co.uk/scratch/coins/coinsoverview andhttp://jseden.dcs.warwick.ac.uk/construit/?load=138. The construal addresses the problem of
converting a sum of money into a set of coins – a topic that is recognised to be challenging to teachyoung children because of the complexity of the skills that are involved. Bagge’s program is primarily
intended to illustrate basic techniques in Scratch programming: the construal raises issues that bear
directly on pedagogical strategies, and exemplifies the kind of exposure of epistemological questions
that Papert advocates. The most important realisation in making the construal was that in order toexpress the agency of giving change in ‘definitive programming’ terms, it was essential to consider whatmodes of observation could be assumed to be plausible for the change-giving agent. For instance, if
the agent is an automated AI, it is perfectly reasonable to presume that it can ‘inspect’ a set of coins
and select the one with largest value. By contrast, for a young child to make the same selection, they
have first to master several observational challenges, such as assessing whether the value of one coinis greater than that of another irrespective of their relative size and having regard to their colour (bronze,silver or gold). A most interesting feature of the construal is that it can be framed in such a way that thepattern of observation that serves to realise the required behaviour is exactly matched to the pattern ofobservation that might be used to teach a child to give change. In effect, the definitive program sets out
a sequence of observational challenges of the form “can you recognise from the colour of these coins
which has the greater value?”, “can you identify whether this coin is worth more than 32 pence?”, “canyou identify which coin in this set has the largest value?” Carry out these same challenges with heads
and tails reversed. etc. This intimate relationship between recipes for programming the computer andlearning strategies is precisely in tune with Papert’s intentions in advocating the use of the computer tosupport learning.
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Topic 6: Empirical perspectives on computing and learning
Over recent years, there has been strong interest in devising systematic empirical ways of using thecomputer in learning applications, to teach programming, mathematics and other subjects. Bret Victor
has been prominent in this: the Learnable Programming, Kill Maths, Exploratory Explanations projects
share a common theme: exploiting the power of the computer to offer rich interactive experiences toaddress learning goals that have generally been treated in the classroom in more abstract mathematicalterms. These projects promote computer artefacts as objects-to-think-with and to-converse-with, andVictor explicitly acknowledges the inspiration of Papert's constructionist vision.
Current trends have giving great impetus to the idea of ‘coding’, but also reflect dissatisfaction with theway in which programming is conceived. Chris Granger’s blog post: ‘Coding is not the new literacy’echoes Papert’s emphasis on the epistemological implications of using the computer: “We need theequivalent of composition, the skill that allows us to think about how things are computed. This time,
we're not recording our thoughts, but instead the models of the world that allow us to have thoughts inthe first place.” Granger goes on to identify ‘modelling wth spreadsheets’ as the most pertinent
computing skill. Novel environments that have emerged also have features in common with Construit.
In keeping with the desire to integrate construction and interaction with an artefact with learning about
the domain, these typically include support for notebook style presentations, where natural language
descriptions and code are blended. What is generally lacking in such innovations is a clear conceptual
foundation. For instance, though observables, dependencies and agency feature quite explicitly in the
Apparatus Editor, and enjoy support through a menu-driven interface, it is unclear how general thescope for defining dependencies is intended to be or to what extent the broader semantic issues
addressed in EM have been taken into account.
In comparing an EM approach to constructionism with other approaches, certain characteristics stand
out:




In developing a construal, there is semantic significance in every redefinition: if this is not the case,the principles of making construals are not being properly respected. Redefiniton by redefinition,
the thought processes of the maker are being meticulously registered by the maker, even if
subconsciously, and recorded in the script. In part, the fine detail in which thought processes arebeing traced accounts for the aura of naivety that surrounds the representation in a script.
Specification via declarative constraints or by very sophisticated pure functional expressions in an
FP idiom might appear to be much more powerful, but cannot serve the same expressive role.
Whether the maker can understand the effect of dependencies by inspection of the script, or byexperimental redefinition, are important pragmatic considerations. A formal definition may be socomplex that the maker is unable to recognise that there is a dependency.
The fact that the script invariably bears interpretation in state-based rather than exclusively
behavioural terms, and has an essential ambiguity that allows it to relate simultaneously todifferent agent roles (such as designer and user) is another crucial characteristic.

These qualities have implications for learning through both construction and interaction.
Nicolas Pope’s construal of an internal combustion engine, developed for a science festival workshop
for Key Stage 3 pupils, highlights the virtues of being able to blend automated action to a preconceived
pattern with opportunistic experimental intervention by the learner or teacher. The construal supports anuanced explanation of the principle behind the internal combustion engine where the learner canemulate the agency of the engine and develop an understanding of how it operates. It features a'drinking straw’ engine where the learner simulates the effect of internal combustion in maintaining therotation of the wheel by blowing and sucking at appropriate points as it revolves. By emulating the
behaviour of the engine in this fashion, it is possible to account for the need for a starter motor (or acrank handle!) and to illustrate the significance of issues such as timing, a blown gasket, possible
causes of misfiring, and the principles of engine braking. In effect, making such a construal opens up
the possibility of exploring the meaning of observables that are not ordinarily exposed in a conventional
non-interactive simulation. Many of these were not preconceived in the evolutionary development of theconstrual.
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Jonathan Foss’s construal of a newly developed electronic musical instrument, the Artiphon, illustrates
the manner in which Construit can support design and documentation. In his own words: "Making theArtiphon construal entailed a large musical learning experience. If I'd have built the program
conventionally, I feel (although can't necessarily prove), that traditional programming languages would
have constrained my thoughts, I'd have needed to do a lot more thinking about the musical theory before
actually doing anything with it. However, with Construit, I could just start by drawing what I could see,and then expressing how the physical observables interacted with each other. The style of the 'book'layout allowed the code building to be much more conversational than comments would normally bewhen writing code. The individual statements that were written to guide the construction process weremuch simpler than traditional programming - particularly the fact that definitions can be overridden(rather than having to change a particular line of code in place), meant that it was easier to log (e.g. a
simple form of version control) and gain an understanding of faults."
Prior to making the Artiphon construal, Foss had little knowledge of music theory. Reflecting on his
experience of learning music theory by constructing the construal, he observes:"I think the strength of EM is that things can be built without a clear idea of the subject domain. ... Inother programming languages, you'd have to already know lots about the subject domain and thentranslate it into code that is rather abstracted away from the actual subject domain. The learning process
becomes more about learning how to code a representation of the subject domain. ... Whereas with aconstrual, the directness of experience means you can start with a basic understanding of the subject
domain and fine tune it as you learn. You are thus spending much more time thinking about the subject
domain than about how to translate that subject domain knowledge into code.”
As an experienced software developer, Foss also reflected on the significance of version managementin Construit, whereby the new versions are derived from the old (and vice versa) by overriding
definitions. Contrasting this activity with “rewriting code then commiting to a version control system”, heremarks: “I suspect version control for most casual programmers is an advanced way of saving/backing
up. Whereas for construal making, overriding definitions provides a much richer experience that can beeasily ‘reviewed’.”
Other example construals illustrate qualites of making construals relevant to issues raised by Noss andClayson (2015): editing and extending (cf. the ‘Construing the Moment’ and ‘Solar System’ construals),
layering (cf. Hex Colouring), exploratory design (cf. The OXO lab) – see (“Constructionism2018”) for
more details. Further discussion is beyond the scope of this paper.

Conclusion and Discussion
The introduction to this paper refers to making construals ‘being rooted in William James's philosophicalstance of radical empiricism’. The topics discussed in the previous section make a clear justification for
considering making construals within its broader conceptual framework. The relevance of aphilosophical stance is not so easily appreciated.
In formulating the central challenge that has informed this paper, Noss and Clayson follow diSessa andCobb (2004:82), who characterise constructionism as a 'framework for action'. They also cite diSessa
and Cobb's comment to the effect that frameworks for action typically "do not cleanly separate theirscientific claims and validation from their suggested actions. That is, the theory or theories behindframeworks for action are relatively inexplicit, complex, and often involve multiple very diverse elementsthat cannot plausibly be brought under a single umbrella."
Any theory for constructionism has to confront the problem of dealing with the learner's subjective world.
To speak of the learner's subjective experience is to consider matters about which it is scarcely
appropriate to rationalise. There is no constraint on the irrationality of a learner's misconceptions, noguarantee that they will bear scrutiny even by the learner themselves, or that they will be consistentacross changing contexts. Indeed, even referring to the 'learner's misconception' betrays a perspectivethat might be defensible when learning mathematics, but is more questionable when applied (say) tomusical interpretation.
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William James's radical empiricist stance addresses the problem of discussing the subjective world ina principled way. Its focus is upon the connections that the learner directly experiences, such as comeinto play when we understand a word, recognise a person, or attribute an effect to a cause. The
semantics of a construal as a computer artefact are mediated by such connections: the principle issimilar to what Noss and Hoyles (1996) call 'situated abstraction'. In reviewing the significance ofsituated abstraction in constructionism, Mackrell and Pratt (2017) observe: 'Situated abstraction is ...
about connecting: learning is about increasingly knowing connections. However, the nature of suchconnections is left unexamined, which is problematic; it is tempting to see a situated abstraction as aninferential proposition drawn on the basis of what is known about a context and what this would entail
in action, but this is not yet warranted.'
The key phrase in the above quotation is "However, the nature of such connections is left unexamined,
which is problematic ... "; this betrays a philosophical disposition to seek a rational explanation forsituated abstraction that is at odds with a fundamental principle of radical empiricism (James, 1909):"the relations between things, conjunctive as well as disjunctive, are just as much matters of experience,neither more nor less so, than the things themselves". A similar philosophical sentiment is implicit in
diSessa and Cobb's critique of frameworks for action that "do not cleanly separate their scientific claims
and validation from their suggested actions". The EM approach to constructionism advocated in thispaper endorses a more pragmatic perspective in which we accept that the authenticity of personalconnections in experience is not amenable to validation in the strict objective sense that is expected inscience; it can only be to a degree witnessed through performance in context. What is more, it is thevery notion of trying to impose such traditional 'theories' upon constructionism that accounts for their'relatively inexplicit, complex' nature and the implausibility of bringing their 'multiple very diverse
elements ... under a single umbrella'. It is in this respect that we hope that making construals can bringunity and coherence to the plurality of approaches to computing and learning associated withconstructionism.

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Hermans, F, and Aivalogou, E, (2016) Do code smells hamper novice programming? A controlled
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Constructionist STEM Activities Using the Bridge21
Model
Brendan Tangney, tangney@tcd.ie
The Trinity Centre for Research in IT in Education, School of Computer Science & Statistics and Schoolof Education, Trinity College Dublin, the University of Dublin, Dublin 2, Ireland

Ian Boran, ian.boran@tallaghtcs.ie
Tallaght Community School, Dublin 24, Ireland

Tony Knox, tonyknox@kilkennycollege.ie
Kilkenny College, Kilkenny, Ireland

Aibhín Bray, brayai@tcd.ie
The Trinity Centre for Research in IT in Education, School of Computer Science & Statistics and Schoolof Education, Trinity College Dublin, the University of Dublin, Dublin 2, Ireland

Abstract
This paper explores how the use of a particular model of 21st Century teaching and learning (Bridge21),
which aligns easily with a constructionist approach, can be integrated into an authentic classroom
context, and the extent to which this leads to increases in students’ metacognitive problem-solving skills
as well as their engagement and confidence with STEM disciplines. The research is framed as an
exploratory case study with two embedded units: the first focused on developing problem-solving skills
in a group of 21 students (15-16 years old) in an after-school STEM club, using technologies that
included a microworld for the construction of bridges; the second focused on the teaching ofmathematical functions using a graphics calculator, with a view to improving student confidence andlevel of mathematical engagement, in a class of 24 students (15-17 year old) of mixed gender and weak
to modest mathematical ability. Statistically significant improvements in students’ metacognitive
problem solving skills are reported from Intervention A, while Intervention B reports statisticallysignificant improvements in students’ attitude to mathematics and technology.

Keywords
STEM; 21st century teaching and learning; bridge21; constructionism

Introduction
Education systems around the world face a challenge in empowering teachers to create learning
activities that foster the development of so called “21st century skills”, while at the same time helping
students master curriculum content (Danah, Punya, & Petra, 2016; Fullan & Langworthy, 2014; Voogt& Roblin, 2012). This issue is particularity pertinent in the area of STEM education, where skills suchas problem solving, communication and collaboration are fundamental to successful practice inassociated disciplines. However, internationally, the number of students considering careers in STEMrelated areas is declining (Marginson, Tytler, Freeman & Roberts, 2013). The challenges that exist in
STEM education, which are at least partially responsible for this fall off, are well documented andinclude: the use of didactic teaching styles, the restrictive nature of traditional classroom environments,
low student motivation, overloaded curriculum content, over-reliance on text books, a lack of discussionof topics of interest, and the absence of opportunity for creative expression (Henriksen, Dillon, & Ryder,2015).
In this paper, we explore how the use of a particular model of 21st Century teaching and learning(Bridge21), which aligns easily with a constructionist approach, can be integrated into an authentic
classroom context, and the extent to which this leads to increases in students’ metacognitive problemsolving skills as well as their engagement and confidence with STEM disciplines.
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In order to set this research in context, this paper first presents some background literature. This isfollowed by an overview of the study, which includes a description of the local context as well as asection that provides a rationale for the methodological choices that were made. Two separate
interventions and their findings are then discussed, and finally the concluding section discusses how
the constructionist, 21st Century approach, scaffolded by the Bridge21 activity model, has addressedsome of the challenges in STEM education through increasing student motivation, engagement andproblem-solving abilities.

Background
Innovative approaches to STEM education such as Realistic Maths Education (Gravemeijer, Rainero,& Vonk, 1994), Project-Based Learning (Capraro, Capraro, & Morgan, 2013) and Inquiry-Based
Learning (Edelson, Gordin, & Pea, 1999; Maaß & Artigue, 2013), which are promoted as ways toaddress some of the challenges in area, are also well suited to supporting the development of 21st
century skills, especially when making creative use of technology. Constructionism, with its emphasison the building of artefacts as part of the knowledge construction process, is an ideal pedagogy to use
as part of such innovative approaches to 21st century STEM education. However, neither aconstructionist approach, nor the pedagogies listed above, lend themselves well to traditionalclassrooms, which tend to embrace didactic teaching and learning methodologies (Fullan & Langworthy,
2014; Tangney, Bray, & Oldham, 2015).
In previous work Tangney et al. (2015) argued that innovative, technology-mediated learning activities
need to be embedded in an appropriate, or sympathetic, pedagogical model rather than being
accommodated and constrained within a traditional classroom approach. Activities of this type would lie
at the higher, or transformation layers, of the SAMR hierarchy for classifying technological-basedlearning interventions (Puentedura, 2012), as they are tasks that are significantly changed through theuse of the technology (modification), or that use the affordances of the technology to design new tasks
that would previously have been inconceivable (redefinition). Bridge21 is a particular model oftechnology-mediated, collaborative learning which promotes teamwork (Lawlor, Conneely, Oldham,
Marshall, & Tangney, 2018), improves student motivation (Lawlor, Marshall, & Tangney, 2015), andhelps develop key skills (Johnston, Conneely, Murchan, & Tangney, 2015). The Bridge21 approach has
been used to support innovative learning activities across a range of subject areas including
mathematics (Bray & Tangney, 2015) and computer programming (Byrne, O'Sullivan, & Sullivan, 2016).
Of particular relevance to this paper is a study that reported on the use of Bridge21 to support a
constructionist approach to teaching physics (Wickham, Girvan, & Tangney, 2016). However, theaforementioned studies were, for the most part, carried out in a learning laboratory on the university
campus and not in an authentic school context.

The Study
In order to explore whether constructionist activities that are scaffolded by the Bridge21 model can be
successfully integrated into authentic classroom contexts, and whether such activities have a positiveeffect on student confidence, engagement and problem solving, this paper reports on the experiencesof two teachers who designed and researched transformative learning activities for use in their own
classrooms. The teachers were engaged in postgraduate (masters level) professional development
which emphasized the teacher as a designer and researcher of their own innovative practice. The
activities were in the area of STEM and used a constructionist approach, aligned with the Bridge21
pedagogic model. Technology was central to the interventions and was used at the transformation
layers of the SAMR model. Both interventions are considered constructionist in nature owing to theiruse of project-based learning in which students used technology to create learning artefacts, motivatedby some real-world context, as a vehicle for collaboratively constructing understanding of the topic inquestion. Furthermore, as per the Bridge21 model, the teacher orchestrated learning rather thanengaging in direct instruction. The interventions took place in school, either during normal classes or as
part of an afterschool activity. (The latter occurred for reasons to do with the timing of the approval ofresearch ethics. Approval was quicker to obtain for a voluntary, out-of-class study. In all other ways theactivity mirrored a regular classroom experience.)
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The Local Context and Bridge21
The Irish second level education system is going through a period of reform that is emphasising thedevelopment of key competencies, or 21st century skills, along with the acquisition of subject knowledge.
The move is one in which “curriculum and assessment arrangements will promote a focus on active andcollaborative learning. In particular, learners will be enabled to use and analyse information in new andcreative ways, to investigate issues, to explore, to think for themselves, to be creative in solving
problems and to apply their learning to new challenges and situations”, p7 (DES, 2015). Bridge21 is anapproach to technology-mediated, collaborative learning which draws on a number of sources to createa model for 21st century teaching and learning that speaks to the current Irish curriculum reform process.
It uses the teamwork model from the World Scout Movement (Bénard, 2002) and follows a lesson
activity structure inspired by ideas from Design Thinking (Brown, 2008), see Figure .
Initially developed for use in workshops run on campus as part of the university’s social outreach agenda
the overarching design-based research project (which this study is a component of) is now actively
engaging with schools to adapt the Bridge21 model for use in the classroom. This overarching researchis being conducted within the context of the current reform process in order to meet the twin goals ofdelivering curriculum content while at the same time promoting the development of key skills. An
immersive model of teacher professional development is offered to interested schools (Girvan,
Conneely, & Tangney, 2016) and for the past 3 years a university accredited Postgraduate Certificate
in 21st Century Teaching and Learning has been offered, which makes extensive use of Bridge21
methodologies. One of teachers who co-authored this paper completed that certificate and bothteachers completed M.Sc. dissertations on the use of Bridge21 in the classroom.

Figure 1 : The Bridge21 Lesson Activity Model. Image adapted from (Byrne, O'Sullivan & Sullivan 2017).

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Constructionism 2018, Vilnius, Lithuania

Methodology
This research is framed as an exploratory case study with two embedded units (Yin, 2014), and uses amixed-methods approach to explore the effectiveness of the interventions, both in terms of their design
and implementation in an authentic school setting, and the degree to which learners achieved thedesired learning intentions. That is, the study is designed to explore whether a constructionist approach,aligned with the Bridge21 pedagogy and activity model, have a positive effect on learners’ engagement,
confidence and problem-solving ability in STEM subjects, within a traditional school context.
Mixed-methods research refers to studies in which the researcher synthesises ideas, techniques,
approaches, methods, and concepts from quantitative and qualitative research, within a single study(Johnson & Onwuegbuzie, 2004; Johnson, Onwuegbuzie, & Turner, 2007). In this research, each
embedded unit relates to a single intervention, in which both quantitative and qualitative data werecollected. Both types of data were collected concurrently with emphasis given to quantitative data in theanalysis.
Intervention A focused on developing problem solving skills in a group of 21 students (15-16 years old)in an after-school STEM club. The technologies used included a microworld for the construction ofbridges.
Intervention B focused on the teaching of mathematical functions. A class of 24 students (15-17 yearold) of mixed gender and weak to modest mathematical ability engaged in learning activities using agraphics calculator that focused on the teaching of functions with a view to improving their confidence
and level of mathematical engagement.
In both interventions, pre and post tests were used to collect quantitative data, which were then analysedusing paired t-tests in order to detect any changes that could be attributed to the activities. Qualitative
data were gathered through focus group interviews. Open coding techniques were used to identify
relevant codes and themes (Strauss & Corbin, 2008). These approaches will be discussed in moredetail in the following sections.

The Interventions
The sections that follow provide an in-depth exploration of each of the two interventions, the datacollection and analysis processes, and an overview of the findings of each of the teachers.

Intervention A - Problem Solving
Boran (2017) created an intervention that focused on developing students problem-solving skills. In
order to provide a relevant context for the problem solving, the intervention focused on the challenge ofbridge design using a microworld simulation. This topic requires students to use a high level of mathematics and physics as
they must analyse, compare, and contrast the fundamental forces exerted on a load-carrying bridge. The problem-solving strategy
they were encouraged to use was one devised by the teacher, based on a synthesis of ones found in
the literature, all of which build on Polya’s seminal work (Polya, 1945). A website52 was designed to scaffold thelearning experience and to provide a detailed description of the open and closed problem-solving questions that students had to answer inthe learning activities. Students worked in teams and the sessions followed the Bridge21 lesson activity model. The sessions took place ina standard classroom and students shared access to laptops. The problems they were presented with required them to construct, design,
analyse and review a series of bridge designs which had to meet specific criteria. The activity required students to use their problemsolving skills to analyse the structural integrity of their designs and assess whether the design was cost-effective. As per the Bridge21
model students had to present their designs to their peers and defend the design decisions they made.

The microworld used was Bridge Designer53, a free tool from West Point University. It was chosen as it has a
low floor and high ceiling, encouraging students’ exploration and creativity. The software is designed to provide primaryand post-primary students with a realistic and contextualised introduction to the fundamental principles
of bridge design and construction. The tool allows the student to interact and manipulate the key variables of bridge design(see Figure ) and students are presented with a realistic 3D simulation of their design (see Figure ).

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Constructionism 2018, Vilnius, Lithuania

The

Figure 2: Microworld interface

Figure 3: Bridge simulation

microworld facilitates critical analysis, e.g. the load test allows the students to investigate the fundamental forces of compression andtension that are exerted on the individual joints and components in the bridge. Students can adjust the parameters in the structure andanalyse the impact of these changes. Taken together the combination of the software and the Bridge21 methodology allow learning activities
to be created which lie in the redefinition layer of the SAMR model. The learners are actively and collaboratively engaged in artefact
construction and are thus facilitated in developing both their problem-solving skills and domain specific knowledge.

Participants & Duration
The study took place in the teacher’s school as part of an after-school STEM Club. An initial pilot was
carried out in which 20 students (15-16 years old) took part in activities of 4 hours in total duration. Thispilot helped to test and tweak the design of the website, the suitability of the microworld, the details of
the learning tasks and the appropriateness of the data collection tools. The main study involved 21(different) students of the same age in 10 hours of learning spread over 4 weeks. The students attended
the club on a voluntary basis and had a range of ability. The school is located in an area of socioeconomic disadvantage.
Data Collection
Following the mixed-methods approach described above, the data collected were primarily quantitative
but also included some qualitative data. The instrument used to collect quantitative data was the
Metacognitive Activities Inventory (MCAI) (Cooper & Sandi-Urena, 2009). This was completed before
and after the four weeks of the learning intervention, to assess any changes in student’s metacognitive
skills. The tool focuses on 4 main skills used when problem solving; planning, monitoring, reflection andgeneral problem-solving skills. Focus group interviews were carried out with two groups, 7 students in
each, pre and post intervention. Structured observation notes were recorded by the researcher, focusing
on the participation of students in the activity.
Findings
The results from the MCAI questionnaire indicate an increase in students’ metacognitive skills in eachof the subscales post intervention, and a two-tailed t-test showed the change to be significant in eachcase at the 95% confidence interval (Table ).
Table 1. Metacognitive Activities Inventory – Showing statistically significant changes, for p < 0.05, postintervention
Mean-preSD-preMean-post

SD-postt(20)𝑝Problem Solving Skills

2.3

.45

3.3

.51

-3.277.004

Planning

3

.52

3.3

.49

-3.789.001

Reflection

3

.37

3.3

.43

-2.677.015

Monitoring

2.9

.64

3.2

.57

-2.461.023

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Constructionism 2018, Vilnius, Lithuania

The qualitative data from interviews was transcribed and coded using open coding techniques. The
themes that emerged were: evidence of the use/awareness of problem-solving strategies, the use of
the problem-solving model, attitudes to problem solving, the effects of technology on learning, and theeffect of the Bridge 21 learning model. The themes supported the positive findings arising from thequantitative data as typified in the following quotes.
•“Yeah like I think the project was good at making us be creative and critically think about thedesign and structure of our bridges, sometimes in Maths we don’t get to be creative andinnovative in Maths and it’s hard to reflect on our answers because we cannot check to see ifthey are right or wrong.”(Technology - Construction of an artefact, Bridge21 - Plan-CreateReflect)•“Yeah it was cool that you could learn from your mistakes and change your design, it kind ofhelped you and you could work out by analysing the load test and testing your bridge”.
(Technology - Construction of an artefact, Bridge21 - Plan-Create-Reflect)•“This was a fun problem, we discovered that the bars were two weak for the bridge to function,so we replaced the hallow tubes with solid bars so they would be more efficient, and then we
tested our design to see what bars we could reduce the size of to be more costeffective.” (Technology - Construction of an artefact, Bridge21 - Plan-Create-Reflect)•“It was fun I enjoyed the learning experience, you gave us a lot more control and we could makeour own decisions, and you (the teacher) kind of let us work out the problems for ourselves which
was good because we learned more by doing it ourselves.” (Bridge21 – Student responsibility
for learning)

In each of the above, the students emphasised the positive impact that the Plan-Create-Reflect cycleof the Bridge21 activity, combined with the construction of an artefact in a group setting had on theirexperiences. The teacher/researcher observations aligned with the analysis of the qualitative data fromstudents in highlighting the role that the overall design of the intervention, the Bridge21 activity model,
the open-ended problems and the constructionist tool, had on its effectiveness.

Intervention B – Mathematical Functions, Engagement & Confidence
In his work, Knox (2017), explored the use of a constructionist, Bridge21 approach to improve student
engagement and confidence with mathematical functions. Within mathematics education, the topic offunctions is recognised as difficult for students to grasp, and as one in which many students develop a
number of misconceptions (Carlson, Oehrtman, & Thompson, 2005). This learning intervention was
designed according to a set of heuristics (Bray & Tangney, 2015) that promote collaborative team-basedlearning and innovative use of technology to support the Realistic Math Education pedagogicalapproach (Gravemeijer et al., 1994). The technology used was the Desmos Activity Builder 54 and anumber of challenges provided with it, including Marbleslides – see Error! Reference source notfound..
The learning activities focused on students’ engagement with construction and problem-solving
activities that required them to work with, and manipulate, different representations of functions, thus
facilitating their discovery and ‘reinvention’ of the relevant mathematical ideas. The activities lie at themodification layer of the SAMR model as the affordances of the technology have permitted significant
re-design of the task.

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www.desmos.com

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Constructionism 2018, Vilnius, Lithuania

Figure 4: Marbleslides - built using Desmos Activity Builder. In this example students must createfunctions so that the falling purple marbles touch each of the yellow stars.

Participants & Duration
The lessons were conducted in the teacher’s school with one of the teacher’s own mathematics classes.
The class was made up of 24 students (15-17 years old) of mixed gender and with weak to modest
ability in mathematics. The learning experiences took place in normal timetabled class periods, of 40 or
80 minutes, and in total 8 hours and 40 minutes were spent on the intervention. The Bridge21 approach
was used and the students worked collaboratively in teams to deal with the challenges presented. The
Bridge21 Lesson Activity Model was utilised, but the short duration of some classes meant it had to be
tailored to suit the time constraints. Once teams had been formed for the first class it was not necessary
to repeat this part of the process. Some activities spread across multiple classes with, for example, one
period devoted to the warm up/investigate steps, two periods to plan/create, and one period topresent/reflect (Figure ).
Data Collection
The Mathematics and Technology Attitudes Scale (MTAS) (Pierce, Stacey, & Barkatsas, 2007) wasused to measure student confidence and engagement pre and post the overall learning intervention.
MTAS is a validated instrument which has five subscales that measure: behavioural engagement (BE),confidence with technology (TC), mathematical confidence (MC), affective engagement (AE), andattitude to using technology for learning mathematics (MT). Qualitative data were collected throughresearcher observation, analysis of data captured by the web-based challenges, and through threesemi-structured group interviews with a total of 11 participants, each interview lasting approximately 20
minutes.
Findings
The study shows evidence that the learning experience had a positive impact on the attitudes ofparticipants. There were increases on each of the 5 MTAS sub-scales with the changes for Affective
Engagement (AE) and Attitude to using Technology for Learning Mathematics (MT) both being
statistically significant (p < 0.05) – see Table .

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Constructionism 2018, Vilnius, Lithuania

Table 2. Results of Two Sample t–tests for Means
Mean-preSD-preMean-post

SD-post

t(23)

𝑝

BE

13.5

2.0

13.6

2.2

0.267

0.792

TC

14.3

3.4

15.8

2.3

1.934

0.065

MC

10.6

3.7

11.5

3.5

1.131

0.270

AE

11.5

2.4

12.8

2.6

2.734

0.012

MT

12.7

4.0

14.6

3.9

2.187

0.039

The focus group transcripts were transcribed and an open coding process followed which yielded 77
codes, which were categorised into 9 themes including: groups, attitude, engagement, learning andtechnology. These themes, and the observations made by the teacher/researcher during class, support
the findings from the quantitative data that the students enjoyed the experience, found it engaging andsaw the benefits of working in teams and with technology. These attitudes are typified by the following
quotes.
•“I thought they were beneficial, they were like really good and kind made you think about mathin different ways.” (Attitude)•“It was more interesting and I found that I looked forward more to coming to maths.” (Attitude)•“I thought it was helpful because you got to see like every time you changed a number and like55
something would happen on the screen and you'd see something and that helped.” (Technology– Constructionism)•“Helpful because you had to work a few times at finding the numbers you needed for thegraph.” (Technology – Constructionism)

Discussion and Conclusion
Influenced by ideas to do with 21st century teaching & learning, the Irish second level education system(ages 12-18), as with others around the world, is undergoing a period of reform. A key challenge in thatprocess is to create learning activities that promote the development of key skills while at the same time
helping learners to master curriculum content, and to achieve both within the constraints of the regularschool timetable and the system level requirements of covering a curriculum and meeting national
assessment criteria. Even when the external regulatory framework, which Somekh (2008) identified asoften being a barrier to change in classroom practice, is encouraging such innovation, teacher beliefs
about teaching and learning remain what Ertmer (2005) describes as the “the final frontier in our quest
for technology integration”.
Our research team has had considerable success in using the Bridge21 model of technology-mediatedlearning in the teaching of skills and content. However much of our results to date are based on work
conducted in the learning lab on the Trinity campus, or in schools as one-off, boutique interventions thatare not constrained by timetables. To assist teachers in changing their beliefs it is necessary, among
other things, to show how innovation can be implemented within the constraints of the regularclassroom, and to produce evidence of its benefits to students.
Focusing on the area of STEM education and innovative pedagogical approaches centred on ideas ofconstructivism and constructionism, this study looked at creative learning activities in two Irish
secondary schools. We posited that the Bridge21 pedagogical approach, of team and project-basedlearning, and the associated activity model, which puts a structure on how lessons can be organized,
was a pragmatic model for use in the mainstream classroom. The two cases showed that the model
could be used in such settings, and resulted in statistically significant improvements in students’attitudes and meta-cognitive skills. The qualitative data, including the teacher-researcher observations,
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supported the view that Bridge21 provides a suitable framework for the transformative use of
constructionist technologies and practices.
There are of course limitations to a study of this scale. Within the timeframe, it was not feasible tomeasure student attainment and one of the studies took place in an after-school STEM club. However
as explained above that was due to external factors to do with the timing of receiving university ethicalapproval rather than anything to do with the intervention. The afterschool sessions ran for 1.5 hours
which is the equivalent of two consecutive class periods so it most respects it was an authentic in-school
activity.
The study was framed as an exploratory one which as (Yin, 2014) points out is a useful methodology
for the identification and refinement of research questions, hypotheses, or procedures that will be usedin further research. The findings, while not being conclusive in any way, are however sufficiently positiveto i) give encouragement to the researchers that the such research can be carried out in schools, andii) to the teachers involved, and more importantly their peers in their own and other schools, that
Bridge21 is a pragmatic model for use in schools to support the current reform process. To this end theresearch team continue to work with schools and teachers to provide the professional development
support they need to empower them to innovate in their practices for the benefit of their students.

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Exploring Girls’ Values and Perspectives in Making
for Others
Sawaros Thanapornsangsuth, st2839@tc.columbia.edu
Teachers College, Columbia University, USA

Nathan Holbert, holbert@tc.columbia.edu
Teachers College, Columbia University, USA

Abstract
This study explores a pedagogical framework on how “making for others” can influence and engage
girls in maker activities. We are particularly interested in how the relationship between builders and theirclients can influence the builders’ making process and their motivation throughout that process. Feminist
literature suggests that girls tend to locate themselves in relation to the world and describe themselves
through actions that bring them into connection with others (Gilligan, 1982, p. 35). Leveraging theschool’s Big Sister - Little sister mentorship program, the fourth-grade builders were asked to make toys
for their first-grade clients. Throughout the year, builders worked closely with their clients to iterate on
their toy designs, developing a close relationship and personalizing constructed toys to align with theclients’ requests. The interview data indicate that builders were constantly thinking about their clients’needs and that they were proud of seeing their client’s satisfaction with the handmade toys. Additionally,
we have found that encouragement and emotional support from peers and teachers are also vital foryoung female builders in completing their projects.

Keywords
making; girls; constructionism; maker movement; diversity; elementary students

Introduction
Peppler and Bender (2013) refer to the maker movement as “a growing culture of hands-on making,creating, designing, and innovating.” (p.22) Similarly, Halverson and Sheridan sees the makermovement as a group of people who are involved in “creative production of artifacts in their daily lives”(2014, p.496). As a movement, a group of “makers” often share their processes and products withothers. Makers’ distinctive identity is their do-it-yourself (or do-it-with-others) mindset. They find making,tinkering, inventing, problem-solving, discovering and sharing intrinsically rewarding (Kalil, 2013). Maker
activities provide learners with hands-on experience to design, experiment, build, and invent as they
engage in activities that involve science, technology, engineering, math (STEM), and art. It draws people
with interest in a range of activities, from textile craft, robotics, cooking, wood-crafts, electronics, digital
fabrication, or mechanical repair (Peppler & Bender, 2013). Making in education is not new.
Constructionist scholars and progressive educators have been advocating for learning through making
since the 1960s. When learners make, they construct personalized connection to the artifact thatengages their thinking, feeling, and learning (Wilensky, 1991). The construction can be anything fromprogramming to painting, to carpentry, to making a hypothesis for a scientific experiment, or even to
writing poetry (Papert, 1980). The term “maker movement” became more widely recognized in the early
2000s with the establishment of Make magazine and Maker Faire (Dougherty, 2016). Nevertheless, themaking practices that were portrayed through these popular media outlets have demonstrated a very
narrow representation of making featuring mainly male dominated practices like electronics, vehicles,
and robots (Buecheley, 2013).
The maker movement has a high potential to reach people from diverse backgrounds through handson making. However, the movement has predominantly targeted wealthy and highly educated men(Buecheley, 2013; Maker Media, 2014). In an effort to expand the space of what counts as “making” inthis maker movement, researchers and designers have worked to bridge traditionally feminine domains
such as craft and fashion to computing and engineering. For example, the LilyPad Arduino allows
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learners to sew and program electronics. Many organizations have developed coding games for girls
as well as organizing girl-only coding camps. These efforts have played an important role in increasing
girls participations in maker activities; however, there is a danger in over-generalizing or simplifying
womens’ interests. Generalizing girls’ preferences and superficially including features like fashion andbeauty into the learning environment can be problematic. The act may aggravate gender stereotypes
and community divides (Holbert, 2016). An alternative way to create a more inclusive maker-centeredlearning environment may be to directly consider women’s values and goals and then evaluate howthese values can be reflected in the design of maker activities and spaces.
This paper explores a pedagogical framework based on values cultivated by women to build
connections with others (Belenky et al., 1986; Gilligan, 1982) that can engender more girls to participate
in maker activities. It is a part of the wider two-year Bots for Tots design-based research project (Holbert,2016) where we engage young learners from diverse communities to build toys for younger kids in theirschool rather than for themselves. Using data from the second iteration of the Bots for Tots program
where all participants were girls, we investigate the relationship between fourth-grade builders (Big
Sisters) and their first-grade clients (Little Sisters) and the relationship’s impact on the builders’ making
process to explore a pedagogical framework on designing maker activities for girls. In this study. Our
research aims to answer the following questions:•

How does making personalized toys for their clients motivate and help builders to persist throughchallenges in completing their toy design•

How do builders persist through times of discouragement encountered during their making
process?

We present findings about how the relationship between builder participants and their clients motivatedthe builders during the making process.

Literature Review
Diversity in the maker movement: Engaging women in making
In the United States, the maker movement has struggled to expand its participations beyond affluent
and well educated men. An attendee survey of the 2014 Bay Area Maker Faire showed that 70% weremale, 97% attended or graduated college, and earned a median household income of $130,000 (Maker
Media, 2014). Moreover, 85% of the 41 people featured on Make magazine covers (2005-2013) weremen. Likewise, a very narrow definition of maker activities had been portrayed on the covers featuring,
53% electronics, 31% vehicles, 22% robots, 8% rockets, and 5% music (Buechley, 2013). Revisitingthese data three years later, Buechley (2016) called on the maker community to focus their efforts on
inclusion, arguing that if the program is not inclusive, it is discriminatory. Instead of narrowly defining
STEM and maker activities, Buechley argued for the inclusion of STEM-rich activities practiced bydiverse communities and cultures (2016). For example, knitting should also be considered as amathematically-rich activity as creating designs and patterns requires intricate calculation.
At the same time, we should not force learners to take on an externally imposed identity but should
support them in building their own unique one. For instance, not all learners will be drawn to the “hacker”
identity predominantly advertised by many maker communities (Worsley & Blikstein, 2016). Women inparticular may be find this framing uninviting (Fisher & Margolis, 2002). Martinez (2015) suggest
educators to be sensitive of their classroom environment as women can react to surroundings thatreflect stereotypical hacker culture by denying that they are interested in science and engineering.
Linking science and technology with learners’ values and area of interest can help create an inclusive
STEM and maker-centered learning environment for women (Rosser, 1990). Findings from Margolis
and Fisher’s (2003) longitudinal research show that context is very important for women. They suggest
bridging other disciplines such as medicine, arts, and environmental science to computer science aswell as connecting learners with local communities to sustain women’s interests in technology-focusedfields.

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Margolis and Fisher’s (2003) research also aligns with Belenky, Clinchy, Goldberger and Tarule’s (1986)
seminal work on ways of knowing cultivated by women. They examine two distinctive forms of knowing:
separate and connected knowing. Generally, the separate knowers tend to be more critical anddetached from feelings and emotions. On the other hand, connected knowers are more empathetic.
They try to understand others’ perspectives and share their own experience to foster relationship. They
are driven by the desire to connect. Gilligan (1982) used the term “separate” and “connected” to describe
two different conceptions or experiences of the self (separate from others) and in relationship(connected to others). Interviewing eleven-years-old boys and girls, Gilligan found that girls tend tolocate themselves in relation to the world and described themselves through actions that bring theminto connection with others (p.35). To examine separate and connected knowing, Belenky et al. (1986)
conducted a survey that holds statements indicating “separate knowing” and “connected knowing” andfound that their female participants had higher tendency toward connectedness. It is noted that separate
and connected knowing are not gender specific and both ways of knowing have no correlation to
intellectual capacity. Though not all women are connected knowers, this epistemological belief can help
us design an inclusive and supportive maker-centered curriculum suitable for women without relying too
much on activities or discourses related to gender stereotypes such as girls prefer pink toys or beauty
related products (Holbert, 2016).
As Belenky et al. (1986) and Gilligan (1982) have suggested, women tend to value social interactions
and a sense of community. They talk about their negative feelings (stress, anger, and disappointment)with others significantly more often than men (Simon & Nath, 2004). In a stressful situation, women are
more likely to express their feelings and cope with their emotions by seeking social support, as
compared to men (Thoits, 1995). In maker activities, women work collaboratively, provide support, andhelp their peers more often than men (Intel Corporation, 2014). Women are more motivated by thesocial service aspects of making, particularly, they want to help or give. This act can be as simple ascreating gifts for family and friends (Intel Corporation, 2014). Literature suggest women have higher
tendency than men to favor collaborative relationships to competition and enjoy helping or giving backto their community (Mosatche et al., 2013). Leveraging these findings, maker-centered environment that
support working together and working for others can leads to an increase in confidence andperformance for women in making.

Methodology
Population and Site
The data presented here is from 41 fourth grade builders (aged 9 to 11) taking an Engineering and
Design class at an all-girls private school in a suburban area in the North-Eastern United States.
Leveraging school’s Big Sister - Little Sister mentorship program, the Bots for Tots project had an explicit
goal of having the fourth grade builders or “Big Sisters” to design and build toys for their first gradeclients or “Little Sisters”. The class began with two sessions of making with a 2D to 3D objects withcardboard in order to familiarize the builders with tools and materials in the lab as well as warming themup for creative activities ahead. After the cardboard sessions, the builders interviewed their clients using
the “Client Profile” worksheet. They questioned their clients about the toy they liked and disliked. Then,the builders used the Client Profile worksheet to discuss with their classmates and brainstorm toy ideas
within a small group. After two sessions of prototyping, the builders met their clients again to show
prototypes they made and receive feedback for further iteration. The builders spent seven sessions tomake their final designs. The instructor also allowed the builders to take the toy home for the lastweekend if they could not complete the toy in the class time. The builders met their clients for “play datesession” on the last day of the semester where the girls exchanged the toys and played together. All
names used in the paper are pseudonyms chosen by the participants.

Data Collection
InterviewOf the 41 builders participating in the study, 12 were randomly selected for one-on-one interviews. We
interviewed the builders at the beginning of the year, before the Bots for Tots project had begun to462

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determine builders’ experience with technology, construction, and crafts as well as knowledge of
relevant making and engineering concepts or skills. Interviews lasted approximately 30 to 40 minutes
per builder. We interviewed the same builders again at the end of the year after the class had concluded.
In the post interview, builders were asked about their experience of making toys for their clients. Our
goal was to understand how making for their clients influenced the overall making process and howbuilders may have developed an interest in making. The interview was video recorded and transcribed.
The data was coded “bottom-up” where themes and coding categories emerged from patterns in thedata (Miles et al., 2014). For example, one code categorized instances where the participants changed
their initial designs after receiving feedback from their clients and another identified instances whereparticipants reference their clients while making their toys. A subset of the codes relevant to this analysis
were applied by coders outside of the primary coding team. Interrater reliability was computed for eachdata set with all sets achieving greater than 0.7 Cohen’s Kappa initially and improving to greater than
0.9 after discussion.
Field notes
Detailed field notes were taken during observations of the Bots for Tots project. These observations
focused on fourth-grade girls’ conversation with classmates and instructors about their clients’ toy
preferences and feedback. Particularly, we looked into the builders’ development of toy ideas, feedback
from interview with clients, changes that builders had implemented after the feedback session.
Artifacts
A variety of artifacts were produced by builders throughout the Bots for Tots project. These include inclass worksheets, photographs of builders giving toys to their clients, as well as photographs of their
toy designs throughout the construction process. Following are artifacts investigated:







My Client Profile worksheet: Builders filled out the My Client Profile worksheet, indicating theirclient’s toy likes and dislikes, during the first client interview. Here, we investigated the requests
their clients originally made.
Brainstorming worksheet: Using the data gathered from the client interview, builders discussed
and brainstorm toy ideas with their classmates. Next, they finalized their toy idea and materials
that they would use by drawing and filling in their toy design plans. We wanted to see howbuilders brought in clients’ preferences in coming up with project ideas.
Client’s feedback worksheet (Figure 2): Approximately half of the way into the project, builders
presented their prototypes to their clients to receive feedback. This form guided that process byproviding questions that included clients’ preferences and concerns about the prototype andwhether or not the prototypes met their expectation. We wanted to look into feedbacks from theclients and how that the feedback had led to the builders’ changes in their designs.
Prototypes and Final Project: Builders built prototypes of their toys as a quick and tangible way
to express their ideas and receive feedback from their clients. The final project was the last
iteration of the toy given to the clients on the play date session. We investigated the builders’prototypes and final projects to see the changes they had made in each stage of designing toys
for their clients.

Study Design
41 fourth grade builders from two classes (20-21 builders per class) participated in the study as a partof their Bots for Tots project, a bi-weekly class which ran 45 minutes per session. Throughout theacademic year, builders participated in 19 sessions in total as described on Table 1.

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Table 1. Structure of Making and Engineering className of activities# of sessions theactivity occurs

Major activities

Pre workshop
interview with twelve
builders

1

Interview 12 builders about their experienceand familiarity with tools and toys related tomaker activities

Make 2D to 3D
cardboard animals

3

Created 3D animals from cardboard

Interview with clients

1

Interviewed first-grade clients about their
dream toys

Brainstorm with small
group

2

Shared information gained from interviewing
with clients and asked classmates for inputs
about their design ideas.

Prototype2

Made prototypes

Receive feedback
from clients

1

Showed clients the prototype and asked for
feedback

Complete final toy
design7

Revisited the feedback from clients andplanned for improvements.
Finalized toy construction.

Toy delivery and playdate

1

Met clients for toy delivery

Post workshop
interview with twelve
builders

1

Interviewed the 12 builders about theirexperience in the Making and Engineeringclass.

Results

Figure 1. from left to right. 1) Amy’s prototype of orange emoji pillow inspired by her My Client Profile
worksheet. 2) Amy’s final project. 3) Amy and her client at the playdate, holding the card her client made.

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We divide the investigation of artifacts made by fourth grade builders into three phases: (1) after the
first client interview, (2) after the client feedback session, and (3) before a playdate with clients. We
found 27 out of 41 students kept their original project ideas from phase (1) to phase (3). The 14
remaining students changed their toy ideas after phase (2) but still showed alignment to their clients’requests. Out of 41 projects, 40 projects aligned with what was requested by their first grade clients.
Throughout the three phrases, builders also made changes to their design to serve their clients’preferences. For example, Amy’s client profile sheet suggested her client likes emojis: happy, hearts,and sparkly eyes. The client preferred the toy to be “kind of hard with a lot of stuffing and puffy.” Amy
made an orange emoji pillow from felt that was roughly sewn together as her prototype and showed it
to her client for feedback. Her client liked the pillow and only asked if Amy could change the color of thefabric from orange to yellow and added “sparkly eyes” using pink and yellow fabric. On the final project,
Amy took her client’s feedback and made a heart eye yellow emoji pillow. She also recorded her voice
on a recorder module to play, “have a nice day!” once the pillow was squeezed (Figure 1).
Amy was one of the 27 builders who held onto their original toy design requested by the clients since
the first client interview, making only minor changes. 14 builders completely changed their designs.
These changes were made because of several reasons including difficulties in making original ideas (8cases), feedback from clients (2 cases), materials constraint (1 case) and peer influence (3 cases).

Figure 2. Ruth (left) and Betty’s (right) responses from their Client’s feedback worksheet

Unlike Amy, whose client loved her design and only requested minor changes, 10 out of 41 builders
indicated negative feedback. For example, Ruth answered “No” on her client feedback worksheet for
the question: It is close to what [client] imagined? (Figure 2). Ruth’s client did not like her prototype atall. She made a pillow with a voice recorder that spoke “I love you, [name of the client]” when squeezed,
but her client wanted a doll house. Ruth was discouraged and worried that she would not be able to
make a doll house. She spent the next session listlessly looking at other projects and playing withmaterials. Her friends and teacher encouraged her to try to make the dollhouse. The teacher asked
Ruth to cut plywood into five rectangular pieces and showed her how to assemble the pieces into a
cube with one side open. Ruth was excited to see an initial structure of the doll house. However, thestructure collapsed when she returned for the next session. Ruth was disappointed that she had to start
all over again. Feeling discouraged, the teacher had to work closely with her and suggested she use aglue gun instead of masking tape. Once the structure was finished and had a strong foundation, Ruth
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knew exactly what to do. She picked up several colorful tubes of acrylic paint and started to paint her
doll house (Figure 3). At the end of the class, Ruth said “I’m so proud of myself today!” When asked
why, she answered “I worked so hard. I’m almost done!”
Like Ruth’s client, Betty answered “NOTHING” on her client feedback worksheet (Figure 2). Betty’sclient did not like her prototype and it was “NOTHING” like what she expected. Betty intended her
dollhouse to have two levels. But, she cut one piece of wood slightly smaller than the others so when
she assembled her house together, it was slanted. She looked unhappy with her design, so herclassmate provided encouragement by saying that it looked “like a cool modern loft house!” Anotheragreed and said Betty could decorate her dollhouse as a fun, “whimsical witch house”. However, Betty
still appeared unsatisfied and said that she would not continue her dollhouse project and would make apillow instead. Seeing that Betty already poured in a lot of time and effort for her project (cutting andgluing wood), the teacher suggested that she take the second floor out and redesign her roof. Betty
agreed and took out the top floor. She added a piece of plastic on the top to create a roof with skylight.
She also made a loft floor for a bedroom (Figure 3). During the post interview, Betty told us that her
favorite part of Bots for Tots was to see how surprised her client was to receive the toy she made. She
said, “I liked when I gave it to my little sister. I like to see how like happy and like surprised she was.”
Betty was proud of her creation, even though she almost changed her design to a pillow.

Figure 3. Ruth (left) and Betty’s (right) final designs for their doll houses on the day of Play date.

Pillows were a popular choice among builders that did not pursue the original idea. While only one
client originally requested a pillow, ten girls made pillows. For example, Panni told us during the postinterview that her client originally wanted a stuffed animal that has a “head of shark with a dolphinbody”. She tried drawing and cutting a shark’s head but she was not able to make it; she said “It was
kind of hard.” So Panni asked her client if she could make her a pillow instead. As pink was herclient’s favorite color, Panni made a pink polka dot pillow with rainbow mesh. She wrote her client’s
name a side of the pillow. Out of ten pillows, nine showed personalization features specific to eachclient. Several personalized details were requested by the clients such as the clients’ initials, soft
circuits, voice records, and sketches.

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Figure 4. Lightbulb’s pillow for her client (front and back)One of the ten builders who made a pillow, Lightbulb, described her project as not a “boring pillow”. She
decorated one side of her pillow with pompoms and an “M” for her client’s name. On the other side, shemade a soft circuit where her client could “flip the switch a little star thing would light up” (Figure 4). She
told us during the post interview that she felt like her client “wanted more than just a boring pillow”. She
added that, “I wanted to do more for her, instead of just a pillow that said, [client’s name]. I wanted tomake it special and personalized, that’s why I did the—because not a lot of people have light up pillows.”
Since Lightbulb finished her project two weeks before the playdate, she decided to make a set ofwooden blocks, a toy her client originally requested. She admitted that she made a pillow because she
initially did not know what to do, “I just didn’t know what to do so I was like, why don’t I make a pillow.”
She then realized that her pillow was not relevant to her client’s preference and recalled that her client
liked to play with Lego bricks. She said, “well these [wooden blocks] are kind of like Legos, so maybe it
would add something more to the project that [her client] would like.” Additionally, Lightbulb told us
during the post interview that not only did she like making things, but she also liked seeing the reactionof her client after receiving the toys she made. She felt really proud as she “liked watching [her client]like play and enjoy the blocks.”
Lightbulb was not the only builder who felt proud when her client enjoyed her handmade toy. Out of 12
builders interviewed, 11 also said that they felt proud or happy when they saw that their clients liked
their projects. For instance, Aditi said that she was happy when her client was hugging and playing withher bunny stuffed animal. LillyJane said that she liked seeing reactions of her client when she received
the gift. Even though the making experience was challenging, it was really fun for her. Like LillyJane,
Hailey thought that it was hard to know what her client liked but it was fun making for her client andseeing her love the personalized pillow. She said, “I really wanted to make something that she would
remember and really love.” Sarah thought that her project was “not as hard as some other people’s” but
she was still proud of herself for putting in effort and for satisfying her client’s requests.
Furthermore, the post interview data showed that the builders often talked about their clients even when
they were not asked about them. Our first seven questions on the post interview protocol aimed tounderstand the builders’ feelings toward the Bots for Tots project and their perceptions on technology
and craft. We did not ask the builders about their clients in these questions. 11 out of 12 builders talked
about their clients unprompted in the first seven questions, six girls said that making toys for their clientswas their favorite part of the class. For example, LillyJane told us that her favorite part was interviewing
her client because it allowed her to know her client better and to personalize the toy for her. Hailey
explained that her favorite part was helping her classmates with making and creating a toy that her client
would love. Hailey liked prototyping so that she could receive feedback from her client and improve.
Moreover, when asked about their feelings toward the toy they made or about things they would have
liked to do differently seven girls mentioned their clients. For example, Erica answered that she “really,
really liked” a colorful dollhouse she made. She then added, “and I think [her client] really liked it too.”
One girl, Aditi, mentioned her client on four out of seven unprompted questions. During her pre interview
Aditi was timid and quiet often answering questions with, “I don’t know” and “Mm-hmm.” However, inthe post interview, she gave lengthy responses and often referred to her client. Aditi said that467

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brainstorming session was the most challenging task because her client said “yes to everything” that it
required extra work to come up with a project idea. Aditi ended up making a pink bunny stuffed animal
with a letter "J" (her client’s initial) on its belly. She said that she enjoyed the making experience and it
was fun designing for her client. When asked Aditi if in a future Bots for Tots project she would prefer
to build for herself or for someone else, she said she would prefer making for someone else because
the receiver would be surprised and happy. She would not enjoy the making experience that much if
she were to make for herself.
Similar to Aditi, nine other builders interviewed would prefer making for someone else if they wereparticipated in the Bots for Tots project again. Panni wanted to make for others because she loved hugs
and the receivers often gave her hugs of appreciation. Another builder, Margie, explained that she
wanted to make for others as she would try hard because she did not want her receivers to feel sad.
Erica wanted to make for her parents and her good friends because she knew exactly what they liked.
Nevertheless, five of them said that they also wanted to build something for themselves. The mostcommon reason was that they felt proud of what they had made and wanted to keep it.

Discussion
A goal of this study was to explore a pedagogical framework on how “making for others” can influenceand engage girls in maker activities as well as exploring the emerging supports that the girls needthroughout their making process. We wanted to provide an alternative way to create a more inclusive
maker-centered learning environment that directly consider women’s values in cultivating personal andcommunity connections. We also suggest that providing girls opportunities for social and emotional
support is important in the design of maker activities and spaces.
Making for others: Building toy, building relationship
Belenky and her colleagues (1986) suggested that women are likely to be driven by the desire to connect
with others at a personal level. The school’s “Big Sisters-Little Sisters” program encouraged the fourth
grade builders to build a year-long mentor-like relationship with their first grade clients. This intimacy
and relationship with their first grade clients motivated the builders throughout their making practices.
Apart from making toys for their clients in Bots for Tots project, the fourth grade builders also provide
care and guidance to their client, as it was their first year of elementary school. They shared the samebuilding and often met during recess and school activities.
While the Bots for Tots project was part of a formal school experience, activities and artifacts would not
be graded. Nevertheless, the builders were motivated by their clients’ preferences, as 40 out of 41
projects showed alignment with what was requested by their clients. The builders took what they hadlearned from client interviews to come up with project ideas and more than half (27 builders) werecommitted to the original ideas throughout the year. In the 13 other instances where the builders could
not carry out their original plans, they added personalized details for their clients. For example, in making
pillows, the builders chose fabric based on their client’s choice of colors and often included clients’initials, voice recording, and sketches. This is demonstrated by Lightbulb, who incorporated a soft circuit
to her pillow because her client did not want “just a boring pillow.” She wanted to make her project
special and personalized. Lightbulb was the only builder in the class who made the soft circuit. It wasalso her first time experiencing with the materials.
In addition, the post interview data showed that builders were constantly thinking about and referring totheir clients even when they were not asked about them. They try to understand the other’s point ofview and tend to position themselves through actions that bring them into connection with others(Galligan, 1982). The clients were important to the builders’ making experience. Making the toys fortheir clients connects and tightens the relationship between the builders and the clients. Interestingly,
11 out of 12 builders talked about their clients when discussed about their favorite parts of the Bots forTots project and their feelings toward their projects. LillyJane’s favorite part of the class was interviewing
and getting to know her client better so that she could make the toy that her client would love. While
Panni regretted about the toy she made because she could not make the toy that was originally
requested by her clients. The builders empathized and connected with their clients
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The builders are particularly motivated by the social aspect in making and their desire to help or to give.
They tend to feel a sense of accomplishment in making and often depend on personal and community
connections (Intel Corporation, 2014). When asked whether the builders prefer building for themselves
or for someone else if they were to participate in the Bots for Tots project again, ten answered that theywanted to build for someone else. The most common reason was that the builders felt happy when they
saw that their clients appreciated their handmade toys. 11 girls interviewed talked about how seeing
their clients like their toys made them feel proud or happy. For instance, Aditi was happy when she saw
her client hugging and playing with the bunny stuffed animal she made. LillyJane also liked seeing herclient’s reactions when they received the gift. This finding confirmed how relationship and feelings weretied to the builders’ motives in making.
Persisting through challenges: Motivated by clients, encouraged by peers and teacher
The feedback and reactions from clients toward the toys affected builders’ feelings and confidence. The
comments-- both positive and negative--prompted the builders to improve their designs. Builders madechanges on their designs based on their clients’ feedback. While 31 clients were satisfied with theprototypes, only requesting builders to make minor changes, ten of the builders received negative anddiscouraging feedback.
Despite the tough feedback, the builders overcame challenges and finished toys that satisfied theirclients. They took comments that may be discouraging to improve their toys and ensured that theirclients were happy. Here, the builders have higher tendencies toward being connected knowers and“learn through empathy” (Belenky et al., 1986, p. 115). For example, Margie’s client told Margie that her
pillow looked like a sausage, when her intention was to a tennis ball pillow after learning that her client
liked to play tennis. Margie improved the design of tennis ball pillow and added a small tennis court
made of wood, knowing that her client loved tennis. At the end of the project, Margie was proud of what
she made and told us that her client also liked her toys. She said, “[her client] really liked the pillow.
That’s her favorite. She was like leaning on it.” Despite receiving harsh comments on their prototypes,
they tried to understand their clients’ perspectives and made personalized toys to foster theirrelationship.
Nevertheless, negative feedback can also demotivate the young female builders, especially when the
feedback came from a person who they had fostered meaningful relationship with. In a stressful
situation, young female builders need close facilitation and emotional support from peers and teachers(Thotis, 1995). Sometimes, the form of support can be as simple as giving a compliment to the builders.
Words of encouragement like: “I’m so proud of you today” or “I like the living space that you added here
in your doll house. What are you planning to do on the top floors? Good job. You’re almost done.” Asshown through the case of Ruth, who was emotionally affected by a comment which took her several
weeks to recover from and get back on track. The teacher provided her with encouragement andguidance to regain her confidence. She spent the last four weeks (and even some extra time at home)
to finish her two-story dollhouse. Ruth was proud of her work, and was also rewarded with her client’s
satisfaction and happiness. In another example, Betty was disappointed by her slanted two-level
dollhouse and wanted to make a pillow instead. She received feedback and encouragement from her
peers and teacher that the house looked “cool” and “whimsical”. After this reassurance, she decided not
to start a new project, but figured out a way to improve her existing one. The teacher supported her byreframing her project and her classmates supported her by providing encouragement, and easing her
disappointment so that she was able to progress past this mistake.
Without close support, female builders may feel demotivated and may give up on their intended project.
This was the case for Panni, who chose to make a pretty pillow instead of a disproportionate sharkdolphin stuffed animal. It was also the case for other builders who switched from their clients’ original
requests to a pillow. Though they made personalized designs for their clients, these builders lost
confidence in their ideas, became demotivated, and wanted to settle on less complex designs.

Conclusion
The intimacy and personal connection with their clients influence how the young female builders
engaged in the processes of making. This connection with their clients make the construction process
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Constructionism 2018, Vilnius, Lithuania“personally meaningful”, because the person for whom they were building was meaningful. Thoughthese young builders have not yet developed to the “connected knowers” stage as defined by Belenky
and her colleagues (1986), they have a tendency to value social interactions and a sense of community.
Making for others is an effective exemplar of how we can leverage an epistemological approach that is
cognitively and culturally unique to women in order to motivate female learners in making.
However, it is not enough to merely provide a space for girls to make. How we design the learning
experience matters. The teacher needs to provide support. At the same time, facilitation and activities
should also be designed to build a community of learners that supports one another. A better
understanding of the values of diverse learners that make up our schools and makerspaces must be
considered in order to create an inclusive maker-centered learning environment.

References
Belenky, M. F., Clinchy, B. M., Goldberger, N. R., & Tarule, J. M. (1986). Women's ways of knowing:
The development of self, voice, and mind (Vol. 15). New York: Basic books.
Buechley, L. 2013. Thinking about making. Retrieved December 6,
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Buechley, L. 2016. Inclusive Maker Education: STEM is Everywhere. Retrieved January 2, 2018 fromhttps://edstream.stanford.edu/Video/Play/a33992cc9fb2496488c1afa9b6204a571d
Dougherty, D. 2016. Free to Make: How the Maker Movement is Changing Our Schools, Our Jobs, andOur Minds. North Atlantic Books.
Fisher, A., & Margolis, J. 2002. Unlocking the clubhouse: the Carnegie Mellon experience. ACM
SIGCSE Bulletin, 34(2), 79-83.
Gilligan, C. 1982. In a different voice. Harvard University Press.
Halverson, E. R., & Sheridan, K. 2014. The maker movement in education. Harvard Educational Review,
84(4), 495-504.
Holbert, N. (2016). Leveraging cultural values and “ways of knowing” to increase diversity in maker
activities. International journal of child-computer interaction, 9, 33-39.
Intel Corporation. 2014. MakeHers Report: Engaging Girls and Women in Technology through Making,Creating, and Inventing. Retrieved November 12, 2016 from http://www.intel.com/content/www/us/en/
technology -in- education/making-her-future- report.html
Kalil, Thomas. 2013. Have fun—learn something, do something, make something. Design, make, play:
Growing the next generation of STEM innovators, 12-16.
Margolis, J., & Fisher, A. 2003. Unlocking the clubhouse: Women in computing. MIT press.
Maker Media. 2014. Attendee Study Maker Faire Bay Area 2014. Retrieved
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Miles, M. B. Huberman, A. M., & Saldana, J. (2014). Qualitative Data Analysis: A Methods Sourcebooks.
Mosatche, H. S., Matloff-Nieves, S., Kekelis, L., & Lawner, E. K. 2013. Effective STEM programs foradolescent girls: Three approaches and many lessons learned. Afterschool matters, 17, 17-25.
Papert, S. 1980. Mindstorms: Children, computers, and powerful ideas. Basic Books, Inc.
Peppler, K., & Bender, S. 2013. Maker movement spreads innovation one project at a time. Phi Delta
Kappan, 95(3), 22-27.

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Rosser, S. V. 1990. Female-friendly science: Applying women's studies methods and theories to attract
students. Pergamon.
Simon, R. W., & Nath, L. E. (2004). Gender and emotion in the United States: Do men and women differ
in self-reports of feelings and expressive behavior?. American journal of sociology, 109(5), 1137-1176.
Thoits, P. A. (1995). Stress, coping, and social support processes: Where are we? What next?. Journalof health and social behavior, 53-79.
Wilensky, U. 1991. Abstract meditations on the concrete and concrete implications for mathematics
education. Epistemology and Learning Group, MIT Media Laboratory
Worsley, M., & Blikstein, P. (2016). Children are not hackers: Building a culture of powerful ideas, deeplearning, and equity in the Maker Movement. In Makeology (pp. 78-94). Routledge.

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The Construction of Knowledge in Maker
Education: A Constructivist Perspective
José Armando Valente, jvalente@unicamp.br
State University of Campinas, UNICAMP, Brazil

Paulo Blikstein, Paulo, paulob@stanford.edu
Stanford University, USA

Abstract
The objective of this paper is to reflect on the contributions of makerspaces to the process of knowledge
construction. Initially we discuss aspects related to the theory of constructionism, and, subsequently,
using Piaget’s notions of conceptualization, we discuss how knowledge can be constructed in amakerspace. We show that in makerspaces students can develop sophisticated artifacts by using digitaltechnologies, and that besides the product, this process allows for the representation of the actions
provided to these machines, expressed as concepts and strategies used. This representation
constitutes the "window into the mind" of the learner, allowing for one to understand and to identify theknowledge used and, with that, help the learner reach a new level of knowledge construction.

Keywords
makerspaces; maker education; fabrication technologies; knowledge construction; constructionism

Introduction
Makerspaces are being introduced in K-12 education as an alternative to traditional approaches so thatstudents can have more agency, engage in project-based learning activities, and generally be more
active. In makerspaces, students learn how to produce artifacts by using traditional objects andmaterials combined with digital fabrication technologies, which are increasingly present in thecontemporary world (see Figure 1 for examples of students’ projects.) These activities are based on theconstructionist approach to learning proposed by Papert (1986) and are being inserted in education sothat learners can develop objects of their interest and, with this, explore and build knowledge about
various curricular concepts.
Seymour Papert and collaborators developed, in the late 1960s, the Logo programming language, which
aimed to allow children to “teach” the computer, an activity that, according to these researchers, would
be much more efficient than passive teaching strategies used in the traditional classroom. Papert called
the approach through which the learner constructs knowledge when s/he produces an object of her/his
interest, such as a work of art, a report, or a computer program, constructionist (Papert, 1986). Papert
emphasized the importance of learning through the “hand” on and the “head” in process: the learner isinvolved in building something of her/his interest, and in doing so, is faced with unexpected problems
for which there is no pre-established explanation.
This belief in the development of an increasingly complex and multidisciplinary problem-solving capacity
in students brings Papert’s constructionism ideas closer to the current maker movement, which seeks
to take protagonism and technological innovation to learning spaces.
A central aspect of a makerspace or digital fabrication lab is the construction of objects using different
materials such as scrap, wood, cardboard, electromechanical and electronic components, which canbe combined with computer programming activities and the use of fabrication tools such as a laser
cutters and 3D printers. The emphasis is on promoting engagement and a strong sense ofexperimentation with media and the materials, while constructing knowledge, collaborating, and building
a learning community. Making involves trying to solve a specific problem, creating a physical or digital
artifact, and sharing that product with the public. The interaction between participants and the processof knowledge sharing is often mediated by social media, as well as online repositories of objects, tools,
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and “how-to” manuals. Much of the spirit of maker labs resonates with the “do it yourself” (DIY) culture,
as Hatch states:
Making is fundamental to what it means to be human. We must make, create and express
ourselves to feel whole. There is something unique about making physical things. The things wemake are like little pieces of us and seem to embody portions of our soul. (Hatch, 2013, p.11)
Despite issues regarding equity of participation and culture mismatch (Blikstein & Worsley, 2016),
makerspaces have great potential to contribute to progressive education and to create multiple paths
for students to learn topics that are more relevant to them. Researchers have been suggesting thatmaking, associated with learning methodologies such as constructionism, can create conditions forstudent to be creative, critical, as well as able to solve problems and to work in groups (Martinez &Stager, 2013; Halverson & Kimberly, 2014; Kurti, Kurti & Flemming, 2014).
In many maker labs, the focus is on building a product, and learning how to operate different machinesand devices. However, when something is produced, multiple ideas and concepts that the learneralready has are put into action. This knowledge goes beyond technical skills and may involve
disciplinary content or can be constructed as learners interact with their objects and machines. However,through trial and error, a product can be successfully constructed without the learner necessarily being
able to understand all the concepts involved in the process.
Piaget studied the development of certain concepts, which are constructed as the result of theinteractions between the learner and everyday objects or people; process that Papert called “Piagetianlearning” or “learning without being taught” (Papert, 1980, p.7). Other researchers such as Vygotsky,
for example, understood that the construction of scientific concepts does not result from the simple
interaction between the learner and objects, or is a natural result of the development of “hands-on”
activities. The learners’ construction of knowledge goes to a certain point, and from then on, no matter
how much effort the learner makes, the content cannot be assimilated. The learner needs the help of amore experienced colleague or a specialist, who will assist in the construction of these new concepts(Vygotsky, 1989).
This article aims to understand educational makerspaces and how these contribute to learning; discuss
the theories underlying knowledge-building processes, especially regarding hands-on activities; trying
to understand how knowledge can be represented and conceptualized in makerspaces.

Makerspaces and education
From the point of view of technological diffusion, the maker concept has its roots in the Mechanics'
Institutes, created in Edinburgh, Scotland, during the beginning of the 19th century for the provision oftechnical education for craftsmen, professionals, and workers in general. These institutes have
revolutionized access to science and technology education (Holman, 2015). With the dissemination ofdigital technologies, the 1980s and 1990s saw the creation of the hacker movement, or hackerspaces,
in several cities across the United States and Europe. These were places where technology enthusiasts
could work together to invent devices, reuse and exploit new technologies such as low-cost
microcontrollers, and were inspired by the open software community (Blikstein, 2018). In this context,
the term "hacker" does not refer to the transgression of rules, but rather describes the use of existing
everyday objects to understand a phenomenon, or the production of new objects or systems. A classic
example is the disassembly of electronic devices and the reuse of their parts for the creation of new
appliances.
From the educational point of view, the interest in a student-centered or learning by doing based
education is not new either. One of the first educators to use this pedagogical approach was Dewey
during the beginning of the last century. This author criticized expository teaching as being old-fashioned
and ineffective, and proposed the implementation of hands-on based learning situations (Dewey, 1916).
Other educators and thinkers such as Freinet (1998), Montessori (1965), and Freire (2008) havedevoted special attention to the relationship between mind and artifact-production as part of theeducational process. More recently, during the first decade of this century, new trends in educational,social, economic, and technological character have contributed to the growth of these movements into
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formal and non-formal educational environments, such as schools, museums, and makerspaces incommunities.
According to Blikstein (2018), the interest in the creation, dissemination, and popularization ofmakerspaces can be attributed to five trends: the greater social acceptance of ideas and principles ofprogressive education; countries’ interest in establishing a base for an innovative economy; the growthof public awareness, in addition to the popularity of computer programming combined with the creationand production of artifacts; the sharp reduction in the cost of digital information and communication
technologies (DICT), as well as digital fabrication technologies (DFT); and the development of tools thatare more powerful and easier for students to use, along with studies and publications in academic
research focused on the effect and impact of these new technologies on learning.
Since 2005, makerspaces have gained great popularity as a result of the emergence of the broader"maker movement" (Anderson, 2012), the publication of Make Magazine, and the first Make Faire in
2006, idealized by Dale Dougherty (2013). In addition, these spaces received a great deal of attention
from educators and researchers after the former US President, Barack Obama, launched an initiative
to promote learning environments that "encourage young people to create and build and invent - to be
makers of things, not just consumers of things" (The White House, 2009).
Papert’s constructionist ideas are the rationale behind the dissemination of making in schools, since, inthese spaces, learners can learn from hands-on and “heads-in” experiences. Several researchers andresearch groups focused on this area of study have emphasized that students use different concepts
throughout the activities developed in these spaces (Martinez & Stager, 2013; Halverson & Kimberly,
2014; Kurti, Kurti & Flemming, 2014).
However, before using constructionism as a conceptual basis for the creation of the maker activities, it
is relevant to understand the context in which this concept was developed in the mid-1980s. First,
researchers believed it was important to introduce an alternative to the uses of computers in education,
which at the time were still totally focused on the idea of transmitting information through tutorials, or
exercise and practice programs, which Papert called "instructionism" (Papert, 1991, p.8). Second, for
Papert, constructionism builds on constructivist theories: “this happens especially felicitously in acontext where the learner is consciously engaged in constructing a public entity, whether it’s a sandcastle on the beach or a theory of the universe" (1991, p. 1). The emphasis was, therefore, on the factthat learning is not only the result of the learner’s interaction with objects and people around her/him,as proposed by Piaget’s constructivism, but the result of the learner’s engagement in the constructionof something of her/his interest, which can be done with or without the use of computers. Papert makes
it clear that “computers figure prominently only because they provide an especially wide range of
excellent contexts for constructionist learning” (1991, p. 8). Perhaps when these ideas were proposed
it was not as important to emphasize the presence of computers, since they were not yet widely
disseminated, and learning was not centered on the "connections between computers and real-world
artifacts" (Donaldson, 2014, p.7). Third, constructionism as a theory required further elaboration.
Although the concept is “much richer and more multifaceted, and very much deeper in its implications”(Papert, 1991, p.1), Papert even went so far as to comment on the irony that “it would be particularly
oxymoronic to convey the idea of constructionism through a definition since, after all, constructionism
boils down to demanding that everything is understood by being constructed” (p.2).
The presence of digital technologies as part of constructionism was further elaborated by Edith
Ackermann, as she differentiated constructivism from constructionism by proposing:
1. The increased role of external aids in learning and development;
2. The emphasis on digital and technological aids;
3. The learner’s hands-on initiative for a learner takes in the creation of tools, objects, or
knowledge (Ackermann, 2001, p. 5).
Digital technologies become important when they go beyond aiding in the production of a product, and,rather, make explicit the actions that one must carry out during the process of developing an object. The
ability to explain one’s actions is very different from what takes place during the production of something
using traditional objects. It is one thing to be able to produce a sand castle or a vase from a clay tusk.
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Another thing is to provide information so that a robot can produce the same sand castle or vase. In thecase of the robot, in addition to the product, one must be able to represent the actions the robot musttake so that the product can be produced. These actions are described as concepts and strategies
created by the learner. This representation can be studied and analyzed in terms of the concepts andstrategies used and be improved or debugged for production efficiency. In fact, this representation
constitutes a “window into the mind” of the learner, in the sense that it allows for one to understand andto identify the common-sense knowledge that was used during the production process and, with that,
help the learner reach a new level of scientifically-based knowledge that is a product of a growing
learning spiral (Valente, 2005).
In this sense, to create an educational makerspace, it is important to consider, in addition to traditional
objects of construction, digital information and communication technologies such as computers, digital
cameras, as well as fabrication technologies such as 3D printers, laser cutters and computerized
numerical control milling machines. These technologies should not only be part of the makerspacebecause they are innovative and part of advanced production processes, but also because of the role
they play in making the concepts and strategies learners use to develop the artifacts they produce
explicit. For these technologies to function they need to be programmed using concepts such scale,distance, geometry. Furthermore, the learner must develop different strategies to apply these concepts
in the “program.” Lastly, as noted by Riley (2015), technologies add precision, scalability, andreproducibility to the students’ work, as shown in Figure 1.

Figure 1. Examples of students’ projects (clockwise): a robot-enacted theater play (top left), a custommade guitar, a daVinci machine, and a microscope (bottom right)
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The tasks that can be performed in makerspaces, particularly using digital technologies, allow
learners the possibility of working with concepts from several knowledge areas, such as subjects instandard curricula. Riley (2015), while analyzing students’ use of fabrication technologies, identified
that students had the opportunity to develop mathematical concepts such as Cartesian coordinates forthe transposition of 2D shapes into 3D figures and vice versa, geometric shapes, units of measure,scale, Boolean operations, etc. The production of artifacts using a combination of traditional materials
and digital technologies makes it possible for learners to use concepts from other areas such as
science, engineering, and technology.
In addition to these concepts, several authors mention that makerspaces promote personal and social
development. For example, Clapp et al. (2017) identified the development of agency (a more proactiveorientation towards the world) and character building in makerspaces. The learner can take risks, copewith failures to achieve success, and develop a mindset which includes creativity, curiosity, mental
openness, persistence, social responsibility, and teamwork.
However, the lack of a deeper understanding of constructionism, of the role digital technologies play inthese environments, and of a more precise definition of what constitutes an educational makerspace,have contributed to several misunderstandings. First, makerspaces set up in schools are quite
heterogeneous, varying in terms of size, capacity, and cost. Some schools have understood that simply
having a room with tables, traditional materials, and glue guns is enough, while other schools offer
spaces with the most sophisticated digital fabrication technologies (Blikstein, 2018). It is crucial tounderstand the role that technologies play in these spaces and to seek to balance the composition of
traditional materials and digital technologies.
Second, educational makerspaces in schools should be understood as spaces for knowledge
production. In this sense, it is important that they are not seen as environments for the development ofisolated activities, but activities that are integrated with curricular disciplines. It is not enough to create
makerspaces in which learners can be creative and have agency, while curricular subjects are still
introduced in a traditional way. Third, for the learner to construct knowledge in the makerspaces, it isimportant that a series of issues are observed. The elaboration of a product is fundamental, as Papert
emphasized. However, the production process and the analysis of representations, which provide theopportunity for one to understand the concepts and strategies used by the leaner, are also important.
Thus, the fact of producing something is not enough to ensure that the learner has constructed
knowledge. The teacher’s role is fundamental to mediate processes and product development, to create
opportunities for reflection, and to develop the learner’s awareness of the concepts and strategies thatare used, as observed by Piaget and Vygotsky.

Knowledge construction in the maker activities
In his investigations, Piaget identified three types of knowledge that an individual constructs: physicalknowledge (constructed through the direct action of the individual with the object), logical-mathematicalknowledge (fruit of a reflection regarding the information collected at a practical level, generating theconceptualization), and social-arbitrary knowledge (constructed through the interaction with otherpeople in society) (Matui, 1995). However, it is the development of logical-mathematical concepts thathas received the most attention from teachers and been implemented in the learning process, sincethese concepts depend on the ability for abstraction and their development must be aided by educators.
Vygotsky makes a similar distinction regarding the construction of concepts. He distinguishes
spontaneous concepts from scientific ones. The first is developed based on the individual’s experiencein the world in which s/he lives, and with the world organization imposed by society; while as scientificconcepts are developed from spontaneous experiences, but fundamentally depend on social interaction
and on the presence of more experienced people or the school environment (Vygotsky, 1986).
Vygotsky was concerned with the study of how to provide the means for the construction of knowledge.
He makes an important distinction between development and learning. Effective or real development
can be understood as all the knowledge the learner has already constructed. Potential development is476

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what the learner can achieve during the teaching and learning process - understood here to be the literaltranslation of the Russian term obuchenie, which involves the learner, the person who teaches, and therelationship between these pairs that are subjects of the educational process (Matui, 1995). Therefore,
learning is what allows for the transition from real development to potential development. Between these
two levels is the area or zone of proximal development (ZPD) where teaching must take place, since“the only good teaching is what advances to development” (Matui, 1995, p. 121).
Papert emphasizes the importance of enriching learning environments by incorporating digitaltechnologies, so that subjects can act and construct concepts and ideas that permeate theseenvironments (Papert, 1980). The use of these technologies requires logical-mathematical concepts
and the interaction with these concepts becomes a way to stimulate "Piagetian learning". However,constructing knowledge about these concepts does not happen without the help of more experienced
people, as emphasized by Vygotsky (1986).
From this brief analysis of the ideas proposed by notable socio-interactionist authors, one can see thatthe development of spontaneous concepts, or even some kind of logical-mathematical or socialarbitrary knowledge, can be achieved through “Piagetian learning”. For learners to be able to developscientific or logical-mathematical concepts, however, the help of more experienced people who
understand the process of how to promote learning and the content being studied is necessary – there
is a need for a “true” educator, in the literal sense of the word. However, one cannot assume that simply
providing information or completing a task is sufficient for constructing knowledge.
The evaluation of teaching and learning processes is still based on the idea that the student has learned
a concept if s/he is able to successfully apply it, or is able to talk about the acquired information.
However, the fact that the learner succeeds at performing a task does not necessarily mean that s/heunderstands what was done. Piaget noted that there is a difference between doing something
successfully and understanding what was done.
In 1974, Piaget published two books: La Prise de Conscience (translated into English as “Grasp of
consciousness: action and concept in the young child”, 1976) and Réussir et Comprendre (translated
to English as “Success and Understanding”, 1978). These described the process by which children andadolescents develop what he called “conceptualized understanding” of the concepts involved in a seriesof tasks, which Piaget asked the subjects of his research to perform.
In these studies, Piaget noted that children can use complex actions to achieve premature success,which represents all the characteristics of savoir faire. The child can perform a certain task but notunderstand how it was performed, nor be mindful of the concepts involved in the task. Piaget also notedthat the passage from this practical form of knowledge to understanding is done through the grasp of
consciousness, which is not a kind of insight, but a level of conceptualization. This level of thinking isachieved thanks to a process of transforming schemes of action into notions and operations. Thus,
through the coordination of more complex concepts, the child can move from the level of premature
success to a level of conceptual understanding, which takes place in three phases. In the first, the childneglects all the elements involved in the task; in the second, s/he coordinates some elements, and in
the third, s/he coordinates all the elements involved in the task.
Besides this succession of phases, Piaget first observed that it is not the object that leads the child tothe comprehension phase. Being able to understand how to topple a sequence of dominoes does notnecessarily mean understanding how to make a castle with playing cards. For each situation, the childmust transform the action schemas into notions and operations that are involved in a given task. Piaget
also noted that understanding is the fruit of the quality of the interaction between the child and the object.
If s/he has a chance to play with objects, to reflect on the results obtained, and to be challenged by new
situations, the greater is the chance that the child will be attentive to the concepts involved, and, thus,
reach the level of conceptualized understanding.
In the case of working with digital or fabrication technologies in the makerspaces, learners can explore,
create, and reflect in a very stimulating and innovative environment. However, from the educational
point of view, it is impractical to think that they will be able to construct knowledge individually, without
being aided by others. First, it would be too costly to construct learning environments involving concepts
from all the existing domains so that an individual could act in this environment and construct her/his
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knowledge in isolation. Second, as an educational solution this model is not practical, because the timeneeded to train people with the knowledge already accumulated by humanity would be enormous. In
this sense, the idea of knowledge construction can be improved if we have teachers prepared to help
students (Piaget, 1998) or, as Vygotsky proposes, through more experienced people who can help
formalize concepts that are historically agreed upon (Vygotsky, 1986). Without the presence of an
educator it would be necessary for the learner to recreate these conventions.

Conclusion
There is a great interest in introducing maker activities in K-12 education, so the students can havemore agency, engage in project-based learning, and be generally more active in the learning process,learning to produce artifacts by using traditional and digital materials. This article argues that it ispossible to create learning environments that are based on Papert’s constructionist ideas using activities
in which the learner can develop objects of their interest and, with this, explore and construct knowledge
about various curricular concepts, especially those related to STEM.
The analysis of constructionist ideas indicates that Papert has emphasized the production of objects asa way for learners to express their ideas. However, as proposed by some researchers, production
should take place using digital technologies, which besides the product also allows for the
representation of the actions provided to these machines. These actions are registered as the concepts
and strategies the learner used, which can be analyzed and debugged. These representations
constitute a window into the learner’s mind allowing teachers or a more experienced person to help thelearner construct new knowledge.
The fact that the learner is working with digital technologies in the makerspace allows for the
representation of the action s/he is using, or her/his knowledge, in addition to the creation of the product.
This means that digital technologies play an important role in makerspaces. Furthermore, since themakerspace is created in the school, it is important to integrate activities students develop to othercurricular content.
Finally, according to what was discussed in terms of some ideas proposed by Piaget and Vygotsky, it
is important that the implementation of the makerspace consider the presence of teachers or a moreexperience person whose function is to work with the students so as to challenge them, to create
conditions for them to interact with the object being produced, and to help them understand the concepts
and strategies used. Through these interactions with the students, teachers can help students construct
new knowledge, as well reach a higher level of comprehension about what they are doing.
However, for this type of setting to take place in a school it is necessary to change the relationships
taking place in the learning environment and to determine new roles to be assumed by the different
professionals who work in the school. This means implementing changes in the relationship between
people, and the quality of the students’ interactions with the objects and activities performed. As
observed by Piaget, the fact that the learner can make an artifact or can successfully arrive at an answer
does not necessarily indicate that knowledge was constructed. The learners must also to be able toconceptualize what was produced, which allows for the transformations of their mental schemas.
The solution to education that prioritizes understanding is the use of stimulating objects and activities,
so that the student can be involved with what s/he is doing. These objects and the tasks should be richin opportunities that allow the student to explore, and must create the conditions for the teacher tochallenge the student and, thereby, increase the quality of the interaction with the product. Also, as
proposed by Vygotsky, the learner needs to get help from more experienced people. Without this typeof support the learner must recreate knowledge and conventions that are already available. However,for teachers to be able to support and help the student in the makerspace it is necessary that theyreceive training not only in terms of how to use technologies, but also regarding how to integrate theactivities the students are developing with the disciplines in the curricula. The analysis of literature on
makerspaces has shown that this integration has not fully happened yet—we still have a long way to go
in the process of creating makerspaces in schools for knowledge construction.

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Acknowledgements
This work is support by Conselho Nacional de Desenvolvimento Científico e Tecnológico, (CNPq),
Brazil, grant 306320/2015-0; by Fundação de Amparo à Pesquisa (FAPEST), Brazil, grant 2015/165280; and by the Lemann Center for Entrepreneurship and Educational Innovation in Brazil at Stanford
University, USA.

References
Ackermann, E. (2001). Piaget’s constructivism, Papert’s constructionism: What’s the difference? Futureof Learning Group Publication, 5(3), 438.
Anderson, C. (2012). Makers: The new industrial revolution. New York: Crown.
Blikstein, P. & Worsley, M. (2016). Children are not Hackers: Building a culture of powerful ideas, deeplearning, and equity in the Maker Movement. In K. Peppler, E. Halverson, & Y. Kafai (Eds.), Makeology:
Makerspaces as Learning Environments (Volume 1) (pp. 64-79), New York, NY: Routledge.
Blikstein P. (2018). Maker Movement in Education: History and Prospects. In: de Vries M. (Ed.)
Handbook of Technology Education. Springer International Handbooks of Education. Springer, Cham.
Clapp, E. P., Ross, J., Ryan, J. O. & Tishman, S. (2017). Maker-Centered Learning: empowering young
people to share their worlds. San Francisco: Jossey Bass.
Dewey, J. (1916). Democracy and Education. New York: The Free Press.
Donaldson, J. (2014). The Maker Movement and the rebirth of constructionism. Hybrid Pedagogy, 119. Retrieved from http://hybridpedagogy.org/constructionism-reborn/
Dougherty, D. (2013). The Maker Mindset. In: M. Honey & D. E. Kanter (Eds.). Design, Make, Play:Growing the Next Generation of STEM Innovators. London: Routledge.
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Hackers, and Tinkerers. New York: McGraw-Hill Education.
Holman, W. (2015). Makerspace: Towards a new civic infrastructure. Places. Retrieved from:https://placesjournal.org/article/makerspace-towards-a-new-civic-infrastructure.
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Montessori, M. (1965). Spontaneous activity in education. New York: Schocken Books.
Papert, S. (1980). Mindstorms: Children, Computers and Powerful Ideas. New York: Basic Books.
Papert, S. (1986). Constructionism: A new opportunity for elementary science education. A proposal to
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Papert, S. (1991). Situating constructionism. In S. Papert & I. Harel (Eds.) Constructionism. Norwood,
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Riley, E. (2015). What do people learn from using digital fabrication tool? In P. Blikstein, S. L. Martinez& H. A. Pang (Eds.) Meaningful Making: projects and inspirations for fab labs and makerspaces.
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Constructionism in Different Cultures: the Case of
BrazilJosé Armando Valente, jvalente@unicamp.br
State University of Campinas, UNICAMP, Brazil

Paulo Blikstein, paulob@stanford.edu
Stanford University, USA

Abstract
This paper starts with the conjecture that constructionism proposed by Papert is not used in the sameway in different countries and cultures. Based on Piaget’s ideas it is possible for a concept to be
assimilated or accommodated in terms of mental structures that were constructed based on theexperiences researchers have by living in their respective cultural contexts. Thus, it is unlikely that theconstructionist ideas proposed by Papert are used in the same way as in the USA, Brazil or Thailand.
In Brazil, the educational context has been heavily influenced by the ideas of Paulo Freire, considered
the Patron of Brazilian Education. We have several examples to show that constructionism is Brazil
was influenced by the ideas of Freire, as well other factors which are particularly related to theeducational system and the country social and economic situation.

Keywords
International education, contextualized knowledge, Brazilian education, Freire, Constructionism

Introduction
This paper starts with the conjecture that the “construction” of constructionism as an educational
philosophy by researchers in education happens according to the assimilation and accommodation
processes described by Piaget. In this case, the idea of constructionism (or any other educational idea)
might be assimilated in terms of mental structures that were constructed based on the experiences that
researchers had by living in their respective cultural contexts. It is likely, thus, that constructionism was
assimilated and accommodated according to the influences of the different cultures. The amalgam of
constructionism and knowledge constructed in these contexts may constitute a new theoretical basisthat support projects and activities related to the use of new technologies in education in different
countries or cultures. Thus, it is unlikely that the constructionist ideas proposed by Papert are used in
the same way as in the USA, Brazil or Thailand.
In this paper, we intend to examine the specific characteristics of constructionism in Brazil, given its
educational culture and academic tradition. One key element here is that in the case of Brazil,
constructionism was heavily influenced by the ideas of the well-known Brazilian educator Paulo Freire.
This can be seen in the research developed at different centers that are related to this area, such as
the Nucleus of Informatics Applied to Education (NIED) from the State University of Campinas(UNICAMP), the Teacher Training and New Technologies of the Graduate Program in Education(Catholic University of São Paulo-PUC/SP), from the Laboratory of Cognitive Studies (LEC) at theFederal University of Rio Grande do Sul (UFRGS) and others.

Freire on awarenessAccording to Freire (1980), the power of transformation is directly related to raising the subject’s
awareness, understood as a reflective attitude of the subject’s own condition and the condition of his/hersocial practices in the world. Freire believes that the subject may reach three levels of awareness: aprimitive level, in the sense of responding automatically without being aware of the situation; theawareness of the external actions when the subject starts perceiving the social context; and the selfconscious level, when the subject clearly associates the social and historical context with his/her own
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current reality. Literacy practices that manage to reach the latter level of awareness will bring about themost significant transformations that can cause impact on the cognitive, perceptive realities and on thesocial and cultural context of the subject.

Other factors
But Paulo Freire was not the only factor that influenced the adoption of constructionism in Brazil, as we
will discuss in this article. For example, there was a sense that technology was an expensive investment,
and that schools had much more urgent needs such as offering better salaries for teachers, improving
the school lunch, fixing leaking roofs or buying new chairs. A second element, which is connected to
the last point, was that the high perceived price of technology made teachers and school administrators
heavily control the access to computers, computer labs, and other technologies, creating labs that werehighly regimented and with a plethora of tacit and explicit rules for usage. A third element was the ideathat technology was associated with “imperialistic” exploitation schemes coming from foreign countries,
the US in particular.
Thus, we show that the work developed by the authors based on the constructionist approach andimplemented in different contexts in Brazil had not only principles from the Freire’s ideas, but were also
influenced by a wide variety of factors that were specific to Brazil.

Case studies and examples“Contextualized constructionism:” a concept developed by NIEDNIED was created under strong influence of Papert and Minsky’s ideas. They visited UNICAMP in two
occasions, July of 1975 and 1976. In 1975 they developed several workshops about Logo ideas. Thiswas done using only a blackboard since at UNICAMP at that time there was no program to interpretand execute the Logo commands. A group of researchers from UNICAMP visited the MIT Logo
Laboratory in February-March 1976. When this group returned to Brazil, the Logo interpreter was
deployed to the PDP 10 computer, which allowed the beginning of the first work on the use of Logo withchildren. This experiment was performed with children of UNICAMP teachers and used the only cathoderay terminal connected to the PDP 10 computer. Papert and Minsky returned to Brazil in July 1976 toteach seminars and participate in the activities of the research group on the use of Logo in educationthat had been established. These experiences and studies gave rise to Maria Cecília Calani's master
dissertation (Calani, 1981), and later this research group was consolidated with the creation of NIED in
May 1983.
Valente has been associated with NIED since its creation, was its director during the period of 1986 to
2003 and is still part of this research group. Through these years NIED research approach has gone
through basically four phases: use of Logo in activities related to public schools, use of online
technologies to promote educators’ professional development, use of mobile technology in schools, andmore recently exploring the use of ubiquitous technologies in educational settings.
After the creation of NIED, the first research project developed was related to the Educom Project, which
took place in five universities: Federal University of Pernambuco (UFPe), Federal University of Minas
Gerais (UFMG), Federal University of Rio de Janeiro (UFRJ), UFRGS and UNICAMP. This project
considered the diverse uses of the computer in different pedagogical approaches, including educational
software development and the use of the computer as resource for problems solving. The objective of
the project developed by NIED was to integrate the Logo methodology into the high school curriculumof Mathematics, Sciences and Portuguese in three public schools in the state of São PauloIn the projects and activities developed by NIED, Papert’s ideas was referred as Logo methodology,
Logo aesthetics or Logo environment. Only after the creation of the constructionism term in 1986,
activities developed by NIED incorporated the term. It was used to describe the cognitive aspects of thelearner’s knowledge construction. However, it was important to consider that the learner was also partof a social and cultural environment built locally by colleagues and teacher, and globally by parents,
friends or by the community where s/he lives. S/he could use all these social and cultural elements asa source of ideas and information or a place to find problems to be solved by the computer.
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The social aspect was included during the work that was carried out by NIED researchers as part of a
project developed by the Secretariat of Education of the Municipality of São Paulo, implementing
information technology in their school system. The secretary at that time (1989-1991) was Paulo Freire.
One of the pedagogical recommendation that was proposed by his group of educators was to relate thetheme of the project the students were developing to their cultural identity or culture context. In thiscase, the community should be the source of problems to be solved through the computer, and solutions
and knowledge constructed by the learner should return to the community in the form of some
improvement to be implemented.
Based on this experience NIED researchers created the contextualized constructionism concept inwhich the contextualized aspect is related to Freire's ideas. The rationale was that if the products thelearners are producing are related to their interest and context in which they live, the better the chanceof them understanding about the content and getting involved in the educational activities of producing
this product (Freire, 1975). The contextualized constructionism concept was used to support various
projects developed by NIED such as teacher training, training of factory workers, adult ICT literacy, andone laptop per student project (Valente, 2005).

Teacher training
The process of preparing teachers for the use of ICT in their pedagogical activities was developed by
NIED through several online courses and using the contextualized constructionist approach (Valente,2003). One of these courses was to prepare teachers from special education to be able to use ICT intheir classroom activities. One course was part of the Project of ICT in Special Education (Projeto deInformática na Educação Especial – PROINESP), sponsored by the Special Education Secretary from
the Ministry of Education Office and by the National Federation of APAEs (FENAPAEs). The objective
was to prepare special education teachers from APAE (Associação de Pais e Amigos dos Excepcionais)
a non-government organization of parents of special needs children, and other special education
institutions in different regions in Brazil to be able to use ICT in their classroom activities.
The goal of this online course was to promote the development of a reflective teacher, emphasizing
reflection-in-action and reflection-on-action and working with contextualized and decontextualized
knowledge. These different reflections were elaborated at different levels: about technical aspects of
ICT, about the use of ICT to develop their own projects, and about how to use ICT with their students.
The contextualized aspect was related to the process of knowledge construction which was groundedon each teacher’s reality in terms of previous experience, classroom students and school setting. The
decontextualized aspect had to do with each teacher capability to go beyond his/her contextual
knowledge and to be able understand and discuss with other course participants about the knowledge
they constructed based on their different contexts.
Since the courses emphasized the development of reflective teachers and provided means for them tounderstand the use of ICT activities in different learning contexts, teachers were acquiring means tointeract with their students, discussing the concepts involved in their activities as well as how knowledge
could be constructed based on different realities, whether regional or types of disability.

Training of factory workers
In 1991, NIED researchers started to interact with a local factory in the Campinas region with theobjective of using computers for the creation of learning environment for training their assembly line
workers. The first part of this project was dedicated to understanding the actions that the workers
performed in their jobs and the concepts involved in these actions. On the basis of this understanding
and with workers that the company designated to be part of the project, were developed various
software like the “Target Game,” whose objective was to explore the concept of Statistical Process
Control (Fernandes, Furquim & Baranauskas, 1996); “Enxuto,” which allowed modeling and simulationof manufacturing systems (Borges, Borges & Baranauskas, 1995); and “Jonas,” a system whosefunction was to support training in manufacturing processes (Borges, Borges & Baranauskas, 1995).
These software and systems were based on the contextualized constructionist approach. The workers
could construct models for a given problem, involving specific concepts related to production, propose

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experiments based on the knowledge that perform this experiment on the computer, and observe andanalyze the results obtained.
For example, the Target Game was used in a study to understand the effectiveness of the training
process, as part of a doctoral thesis (Schlünzen, 2000). Some workers were trained to be facilitators in
the training process since they were very knowledgeable about the concepts related to statistical quality
control process and about the difficulties their assembly line colleagues had with these concepts. They
were responsible for training their colleagues and with this methodology it was possible to work with all
employees from different production sectors. The results showed that this training process not only
contributed to improved performance of the workers in their respective workplace, as it also resulted ina modest but significant increase56 in the productivity of the company in general. During the period of
this study the company did not contract other workers or acquired more efficient equipment. The factthat this project was conducted with factory workers, and the ideas of (positive) social and hierarchicaldisruption within the workplace clearly resonate with values and ideas coming from Paulo Freire.

Adult ICT literacy
This study was associated with the investigations conducted at the Multidisciplinary Research-Action
Healthy Community Laboratory – LIPACS a joined effort with the Research Group “Culture, Society andMedia” of the Institute of Arts of the State University of Campinas – UNICAMP. The objective was toconduct studies and surveys on the use of ICT applied to the teaching and learning processes and on
the impact of these technologies on society, on the population regarded as excluded (Maia, 2011).
The study included 16 senior citizens, among whom 14 were women and 2 were men. Their schoolingranged from 3 and 7 years of Basic Education, and all of them were regarded as digitally excluded.
Practical activities were used using sites of interest for the group, emails, websites to access audiovisual
files and social network platforms. The actions were based on social and historical concepts and were
supported by the strategy of dialog with the learners. The purpose of this research was to understandhow senior citizens were empowered with technologies to build new literacies and, with that, becomeaware of their capacity to function in and to transform the context where they live so to be able to build
new realities.
For example, in one of the groups studied, gardening became the generative theme. The activity started
with a presentation on gardening delivered by one of the participants. This presentation was an integral
part of the ICT activities planned for this learner. In addition, the learner was in the process of acquiring
reading and writing skills and did not feel he fit in the group. He became the leader the moment he madethe presentation of his own professional activities as a gardener. Figure 1 shows the participants visitinga garden, being guided by this learner who is learning about the digital camera.

Figure 1. Participants visiting a garden and one of then learning about a digital camera

With his self-esteem boosted, he fascinated the whole group with the gardening topic. Mediators took
advantage of the moment to encourage debate among learners so that, based on what was observed,
discussions were raised on how gardens related to each person’s own life. Digital technologies also
contributed to the activities. They could visit websites and blogs, register their work using images, took
pictures of the garden they visited and of meaningful objects found in their own home, or objects which
are or used to be part of their lives to represent the garden itself. Messages with the images were56

The increase, as measured by the authors at the time, was 5%.

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emailed to friends and relatives. Through their work it was possible to see increased awareness of thesocial use of the ICT to the degree of even transforming the context of their own lives. These changes
were noticed both in the intellectual aspect and in relation to ICT, as well as in terms of practices in
specific social contexts.

One laptop per student project
One of the projects developed was the Inquiry Based Learning Project (Project ABInv), with the objective
to study the implementation of an inquiry-based learning approach, so that teachers and students could
be engaged in "doing science", using features of the laptop in a 1-1 situation. The project was developedin three public schools in the state of São Paulo. The methodology used in this study was action
research. NIED researchers worked with the teachers and students to define the thematic proposals forthe development of the investigations based upon the school curriculum.
The role of the researchers was, primarily, to encourage and assist the teachers’ development so thatthey could adopt a creative and pedagogic view of the appropriation of the laptops according to this new
inquiry-based approach. The teachers learned how to work with students to raise questions related tothe curriculum; how to decide together with the students what is a good question to be investigated;
how to create practical, implementable experiments; how to collect data to respond to the questions
under investigation; and how to analyze and present the results from the experiments in order to
respond to the questions they raised. The students explored the mobility offered by the laptops, working
in alternative special configurations within the classroom, in other internal spaces within the school, as
well as spaces outside the school building. They were encouraged to develop collaborative projects,
dividing up the tasks so that the experiments could be executed, and the data recorded and analyzed.
The results from the Project ABInv were recorded in the book, AbInv – Aprendizagem Baseada na
Investigação (Valente, Baranauskas & Martins, 2014). In one of the schools it was possible to involve
all the students from each of the first to fifth grades. The themes of the project developed were: 1stgrade, Indians in Brazil in which the students investigated how the Indians produced dyes to paint theirbodies; 2nd grade, Animals from the Pantanal Ecosystem, and the question investigated was why theknees of the of the Tuiuiú bird, symbol of the Pantanal region, bend backwards when he walks; 3 rdgrade, Astronomy, primarily the planets and the students’ interest was to investigate the environmental
conditions necessary to maintain an organism alive; 4th grade, Garbage, and the questions that arose
were in relation to organic and inorganic garbage; how garbage decomposes over time and if theconditions in which it is kept influences its decomposition; and 5th grade, Plant Cultivation, and the theme
studied was the growth of plants in different soil conditions, fertilized earth, normal earth, or in cotton,
as shown in Figure 2.

Figure 2a. Testing
dye fixation on skin

Figure 2b.The
Tuiuiú bird

Figure 2c.
Chrysanthemums
plant studied

Figure 2d.
Garbagedecomposition

Figure 2e.
Growth of beans

Figure 2. Students’ projects“The City that We Want” project in São PauloIn the early 2000s, a team led by the Massachusetts Institute of Technology (USA) started a projectwith the Municipal Secretary of Education in the city of São Paulo, Brazil. The goal was to show what
could be accomplished in a typical public school using technologies such as programming, robotics,
and physical computing. The project encompassed as many as 30 schools throughout São Paulo(Cavallo et al., 2004). Students were to begin by identifying generative themes that would motivate their
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projects. Due to rain shortage and lack of infrastructure investment, Brazil was experiencing a massivecrisis in the electric energy system, and the population was resorting to all sorts of creative solutions tosave energy. The crisis, being an everyday concern for all the population, appeared to be a good
generative theme, which could generate projects such as building galvanometers, timer devices, waters
heaters, energy generators, and robots to control lights. The theme eventually changed based on theinteractions with the students, and the change underscores the meaning of negotiating in real time andin locus for truly authentic Freirean generative themes. Since the majority of the households in theBrazilian favelas had illegal energy connections, and therefore neither energy meters nor bills, students
were more interested in projects about safety and raising awareness about the danger of illegal
connections, in order to train them to make safe, yet illegal, energy connections.
The second authentically Freirean moment in the project had to do with the use of materials. As students
acquainted themselves with the new resources and planned their projects, they were initially enthralledby the Lego parts, digital cameras, and video cameras. But even though students seemed excited,some were afraid to use the equipment, and these anxieties were only further stoked by some teachers.
There were historical reasons for such behavior. Access to computers in schools is often regarded as
an administrative issue, addressed with strict usage rules and constant supervision. The high cost of
the equipment and maintenance (especially in developing countries) amplifies the concern of damaging
these machines. In many schools the computer room was even more regimented than regularclassrooms. The solution was to break with all established rules. The researcher’s computers were left
on the floor unattended and available for students, cameras were made available without signup sheets.
During some of the wrap-up interviews for the project, results revealed that 70% of students mentioned“trust” as the most important element of the workshops. Students explained that they felt trusted byresearchers because they were allowed to freely use the equipment. For an attentive reader of Freire,
this should not come as a surprise: the manifestations of oppression and power are not necessarily
overt. Similarly, manifestations of trust are not always explicit. The unrestricted access to equipment
was a design decision heavily inspired by Freirean ideas.
Finally, a third element in this project was very specific to Brazil (and likely other developing countries).
As researchers started to get acquainted with the community and students’ lived experiences, by visiting
houses, small stores, and car repair garages, they identified a technological culture of repurposing andrecycling. Car mechanics would use all sorts of improvised solutions to keep cars running at a minimum
cost. In their homes, people would never discard a broken appliance without trying to fix it in all possible
ways. If fixing was impossible, they would repurpose the broken device in creative ways. The community
radio station was put together with equipment from different sources, many of which were broken or
incompatible and had to be fixed. The perception of this culture led researchers to change, again, thedesign of the project, and replace Lego materials with the repurposing of electronics, appliances andother devices.

Conclusion
The goal of this paper was to show how, in the history of constructionist implementations in Brazil, someparticular characteristics of the Brazilian context were determinant. We focused on a few of thosecharacteristics. The first and most prominent is the influence of Paulo Freire and his ideas. In particular,
there has been a considerable focus on generative themes, community-relevant projects, personal
relevance, connection with broader societal problems, and sensitivity to local culture (including
perception of cost of technology). Even though Papert and his colleagues were also very concernedwith many of those issues, the work of Freire in Brazil preceded them, so the perception was that there
was a combination of both approaches.
But there were order elements of the implementations that were not only theoretical considerations but
consequences of Brazil being a developing country, with public schools being not well resourced andmost of them being located in low-income areas. For example, computers were perceived as expensive,
foreign and threatening devices, and therefore the reaction to projects that were technology-heavy wasquite different from the reaction in the US or Europe. Relative to the salaries of teachers and the budgetsof schools, computers were so much more expensive than in the developed world, so they were more
likely to end up locked in a room with many rules and security measures. At the same time, because486Constructionism 2018, Vilnius, Lithuania

public schools in Brazil have so many shortcomings, bringing technology into those schools was seen
with much more suspicion, and seen as much less of a priority. All of those factors influenced thereception of constructionism in Brazil in ways that perhaps were very different than what took place indeveloped countries.
Seymour Papert purposely refused to define constructionism in precise ways, and advocated forresearchers and practitioners to define it by themselves through rich, contextualized learning narratives.
It seems that in the Brazilian case, that process took place with mixed results. On one hand, it waspossible to combine constructionism with existing theoretical approaches such as the work of PauloFreire. On the other hand, the difficulties stemming from the fact that technology was perceived muchdifferently in Brazil were never fully resolved in many of the projects.

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Freire, P. (1980). Conscientização. Teoria e prática da libertação: uma introdução ao pensamento de PauloFreire. São Paulo: Moraes.
Maia, I, F. (2011) No jardim dos letramentos: tomadas de consciência e poéticas em rede e na cultura daconvergência. Ph.D. thesis, State University of Campinas (Unicamp), Campinas, Brazil. Retrieved from
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National Science Foundation, Massachusetts Institute of Technology, Media Laboratory, Epistemology andLearning Group, Cambridge, Massachusetts.
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Management/Document Management – ISKM/DM2000 (pp 197 - 215), Curitiba: Pontifícia UniversidadeCatólica do Paraná, PUCPR.
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investigação. Campinas, SP: Unicamp/NIED. Retrieved from www.nied.unicamp.br/?q=livros.

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Concept-building Oriented Programming EducationJiří Vaníček, vanicek@pf.jcu.cz
University of South Bohemia, Czech Republic

Abstract
In this methodological paper we present an approach to teaching programming to 12 to 14-year-old
pupils based on concept building. The method of presenting pupils with a set of similar tasks ofincreasing difficulty with the same concept at the background is conducted through construction of
didactical environments. The world of Scratch is simplified in these environments in a way to enablepupils to focus on the given problem. A pupil is guided through various situations in which the concept
is at play and builds a generic model of the concept. The paper presents concretized principles forcreation of educational materials for teaching programming. The principles are fulfilled in teacher andpupil’s materials which were developed within the project PRIM bringing computational thinking into
compulsory primary and secondary education in Czechia. Some experience from a pilot research studyof teaching conducted is presented.

Keywords
programming education; lower secondary; concept-building; Scratch

Introduction
If Informatics is, in accordance with Gander (Gander, 2014, p. 7), regarded as a part of general
education, computational thinking becomes a substantial new skill. If it becomes a component ofcompulsory curriculum, programming, perceived as a “playground” where various components ofcomputational thinking such as abstraction, algorithmization, decomposition, evaluation or
generalization (Selby, 2014) and other cognitive functions can be developed, grows in importance. In
consequence we have to ask what approaches and methods to use when teaching programming. These
approaches and methods must respect the above stated goals as well as the decreasing age at which
the teaching of Informatics starts. Many education authorities believe that IT can help children develop
their competencies already in their early years (Kalaš, 2010, p. 9), which is in line with Bruner’s
proposition that the foundations of any subject may be taught to anybody at any age in some form
(Bruner, 1960, p. 12).
The Czech Republic, like other countries, can expect programming to become compulsory already at
primary school (see both the government programme Strategy for digital education until 2020 (MŠMT,2014) and the new edition of the Framework Educational Programme for Informatics). Yet very few
teachers of the compulsory subject Informatics have training comparable to teachers of other subjects.
A research survey conducted in the Czech Republic shows that only 18 % of respondents in a research
survey among ICT teachers at lower secondary schools show teaching qualification in informatics orsome closely related discipline (Rambousek, 2013, p. 13). The same researches show that pupils would
appreciate if they could work with computers much more and in a more interesting way (p. 159).
Currently attention in lessons of informatics is paid to the user approach to technologies. Teachers are
not ready to teach the foundations of informatics and do not realize they will need this skill. Most
teachers who teach Informatics in Czech schools cannot code a program. This means that what isneeded at this time is a curriculum and teaching materials that can be used by an insufficiently trainedteacher with little experience in the area of programming and Informatics and with a lot of fears about
not being able to cope with the new demands and content. The case is probably much the same aroundthe world.
Rather than focusing separately on educating and training professional IT specialists, it may be morepractical to focus on how teachers can learn by doing: how learning and teaching of programming candevelop teachers’ computational thinking as well as students'. Many approaches to teaching
programming favour pupils’ activity, active learning, learning by doing, and construction of knowledge
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as a result of active creative work. All this with respect to the fact that knowledge and knowing are nottransferable. We believe that knowledge is actively constructed by the learner in interaction with theworld, so we are inclined (following Ackermann) to offer opportunities for children to engage in handson explorations that fuel the constructive process. This approach is in line with Piaget‘s view that“knowledge is an experience“ and Papert’s opinion that “knowledge is formed and transformed within
specific contexts, shaped and expressed through different media“ (Ackermann, 2001, p. 3, 8).
Some might expect that this approach within the Scratch environment can guarantee that a pupil willlearn to programme in an innovative way. We doubt that this is sufficient. A very important factor in the
education process is the quality of the teachers, their experience and prior training and education as
well as their beliefs, whether they are able and willing to depart from a traditional teaching focus on
having pupils' reproduce and imitate and giving them little opportunity for creative activity.

Orienting teaching on building of concepts
It is a challenge to concretize and implement this theoretical basis into a particular way of teaching
programming. One of the possible ways is to use the framework (Explore, Explain, Envisage, Exchange,
bridgE) used in the ScratchMaths project for primary programming education in England (Benton et al,
2016, p. 29). When looking for how to conceive teaching materials for programming in Scratch on lower
secondary school level we turned our attention to theories from didactics of mathematics, which has
been interested for several decades in how concepts are built. School mathematics has sought new
ways of teaching since the 1960s. The experience from the so called New Math (Kline, 1973) mademathematics educators realize that “understanding mathematics is not given by the content but rather
by the method of teaching” (Hejný, 2012, p. 43).
The concept is the key term in procept theory (Gray, Tall, 1994, p. 117), scheme-oriented education
theory (Hejný, 2012) as well as APOS theory (Dubinsky, 2001). Block-oriented programming
environments such as Scratch allow a pupil to see all the available commands of the language but it isdifficult for pupils to see which of the commands represent important programming concepts andwhether their mastery is essential for development of programming skill. It is up to teachers to lead theirpupils through this battlefield; orientation to building concepts will help them.
Results of research analysing the frequency of the use of various commands in Scratch projects sharedby users worldwide point at this issue. The category of More blocks, essential for development of theskill to decompose, was the least frequent among 10 categories of the language; only 1 % of blocks
used fell into this category (Hudičák, 2017). This corresponds to other studies (Scratch, 2017). It seems
pupils have to be led to some concepts because their lack doesn't prevent pupils from programming but
can impede their development of computational thinking. This may occur even if teaching is oriented on
creation of products; in some cases formal knowledge is required.
Theory of generic mental models (Hejný, 1987) works with the mechanism of cognitive process andhelps to analyse pupils’ thinking processes and detect sources of pupils’ mistakes. The aim of theapproach is to decrease the emphasis on formality of knowledge without deeper understanding.
According to this theory the process of constructing a piece of knowledge starts from initial motivation,
moves to the construction of isolated models and results in creation of the so called generic models.
Having been motivated, a person first observes phenomena in which the new concept is present andcreates isolated models (Hejný, 1987, p. 59) that are tested in new situations. With enough time andopportunity to get a sufficient number of isolated models in different situations, person is able to build ageneric model, which is universal and should work in all known situations.
A concept is not a data point, a single piece of information. It is an abstraction. To grasp a concept a
pupil needs as many isolated mental models, experience with specific cases in which the concept is atplay to allow making connections in the pupil’s mind, looking for relationships, structuring theinformation, constructing a generic model of the intended concept. This implies a pupil must go througha lot of number of situations in which the concept appears through various prisms and from different
points of view. The pupil in these situations creates a number of models including what seem likemodels, but are partial and/or incomplete, as well as models that are surprising (Hejný, 2013a). The
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pupil must come across situations in which the concept behaves in a strange, specific, unexpected way
to see it plastically.
Let us illustrate the importance of understanding concepts on several examples. If we want pupils tounderstand programming, to build concepts correctly, it is not enough that they be able to develop a
programme, create an algorithm, or propose a solution to the problem. They must be able to look at the
problem from a distance; they need the practical programming skills that they will apply when solving
the situation. So called “beaver tasks” from the Bebras challenge contest (Dagienė, 2017) are situational
informatics tasks. In such situational tasks, pupils plunge into a described situation which they mustgrasp, get to understand the concepts and terms that are used, find an informatics principle the task isbased on and solve the problem using cognitive and thinking skills. Tasks of this type are unsolvable
without deep understanding of the concepts related to programming.
Hejný claims that understanding is more important than skill. A well-constructed mental model helps
one grasp a concept correctly and find relationships between this concept and others. An example ofdiametrically different behaviour of various commands in Scratch is the pen state or sprite visibility
preserving the given state and is valid until the state is changed, which is in contrast to executive
commands “stamp” or “change costume”. Pupils have problems with understanding these differences.
Other example comes from programming robots. It may happen that the “programme is standing andthe robot is moving” e.g. having carried out the command that makes the engine work the programme
waits for fulfilling the condition at the input from sensors before carrying out the command to stop theengine. Children suppose that when the programme is stopping, the robot must not move. Thesesituations are difficult for pupils if they do not understand the mechanism of the given concept well, do
not have the generic model built.

Two types of programming tasks and building of concepts
Observations of in-service teachers (well-experienced as teachers but without training in informatics)
as well as pre-service teachers (with informatics background but without experience) are a rich sourceof information about how these teachers approach their teaching. We compared approaches to teaching
programming used in Czech and other textbooks and also methodological manuals for teaching
programming available on the internet. Predominant in these textbooks and manuals are two basic
types of programming tasks.
One uses a series of several-minute long programming etudes (as in Kalaš, 2017), always targeting theacquisition of one specific skill or focused on one specific item of knowledge or one concept. Pupils caneasily check their result and it is easy to organize such lessons especially for non-well-experienced
teachers. But motivation in these activities is weaker than in larger projects.
Teaching through larger programming units – the so called “projects” (as in LEAD, 2013), e.g. creating
games or stories — often has the form of a sequence of activities organized as a tutorial or a problem
task. It is hard to combine the goal to teach a specific concept, procedure or method with open-ended
activities. These activities cannot target the development of one concept. Programing projects can resultin creation of long multiline codes in which it is harder for the pupil to orient and for the teacher to findthe pupil’s mistake.
In the two ways of selecting types of problems, two different approaches to teaching goals can beobserved: in case of shorter programming etudes the approach is intensive, focused on competencesin the area of concepts, and the other is holistic, more open, emphasising creativity, focusing on product(Vaníček, 2015).

Didactical environments
One of the disadvantages of block oriented programming environments is that users can see all theblocks of the language. However, pupils cannot master all these commands, especially if they are tounderstand them in depth. Then it is difficult to give pupils a problem because if they don’t immediately
know how to solve the task they may, instead of thinking, waste time by looking for an appropriate toolor command that would solve the problem. A tutorial is not a solution because following fully specified
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procedures does not help a student learn to generalize. Especially in introductory activities for teaching
programming, we find it most efficient to create didactical environments. A didactical mathematics
environment as understood by Wittmann (2001) is a set of linked situations that provide problems that
let a pupil discover important ideas. We add to this concept three more conditions: the motivating power,
long-term commitment, and flexibility in the level of difficulty. (Hejný et al, 2013b).
Mathematics educators use ways of creating learning environments based on one task. Wollring
mentions the relation “Task  Task format  Learning environment” (Wollring, 2008). In his schemeoriented approach to mathematics education Hejný (2011, 2013b) uses learning environments that areclose to children’s everyday experience (e.g. Spider web, Snakes, Staircases, Stepping, Seat in thebus) to create learning environments, microworlds in which it is possible to grasp the rules quickly andto solve a given set of similar or progressive tasks. Similar environments have been used inprogramming education for years (e.g., turtle graphics, robot Karel). Scratch allows one to create
learning environments through sprites, their costumes and scripts, by setting the backdrop of a stage,
and by preparing blocks and scripts on the script building area. In fact it enables one to create
environments didactically as strong as Hejný’s which depart from the world of mathematics and are
viable as Scratch projects that a pupil opens and works in.
Paraphrasing Hejný’s characteristics of effective teaching mathematics (Hejný, 2012), we characterise
effective teaching of programming by three cognitive goals:

pupils understand programming, their knowledge is not mechanical;
pupils are intrinsically motivated for work, they are not frustrated by programming;
pupils develop intellectually, which primarily means development of the ability to: 1. communicate
both orally and in writing, 2. cooperate in a group or even lead a group to solve problems, 3.
analyse a problem situation, 4. effectively solve problems and 5. correct one’s own mistakes.
(Hejný, 2012).

Properties of curriculum based on building concepts
The theoretical background described above allows us to state principles for creating a concept building
programming education curriculum.








It is made of a lot of very short activities, which are related to the same concept. The aim is for
pupils to get an idea of the behaviour of the given concept in various situations. The activities help
the teacher; unlike broader activities in which a teacher can get lost, in these short activities, theteacher is able to find a pupil’s mistake in a script, and there is less chance that the pupil will
programme a situation which the teacher is not able to test, fails to correct or cannot guide thepupil to discovery.
Several didactical environments must be used for teaching one concept. These environments must
be based on different activities of sprites, e.g. drawing lines, stamping shapes, moving on thescene, changing costumes. In these environments the intended concept is shown in various
thought-out situations, allowing the pupil to abstract away the incidental or irrelevant properties
and attach to the concept properties that are substantial and independent of a particularenvironment. The pieces of knowledge gained by the pupil do not remain isolated.
A mistake is not unwelcome; on the contrary it is one of the main sources of knowledge. A pupilmust be presented with various possible mistakes that can be the result of misunderstanding andthat can help the pupil grasp the concept more deeply. Pupils find it motivating assignments ofsuch activities are worded as if somebody has made a mistake in their programme and is facing asituation in which they desperately need help. The pupil’s task is to figure out what situations
expose the mistake, what it is caused by, if it really is a mistake (some solutions may be erroneous
only from certain points of view) and whether the mistake is covert (e.g. the solution is not generalenough or the mistakes shows only after change of input parameters or when run again).
Activities should aim at various programming competences. Pupils are asked not only to create
and run a script but also to read it, interpret it, predict the output, test its correctness, set thesituation in a way that would make the mistake manifest or to adapt a working script to solve asimilar problem. Then the pupil’s pieces of knowledge do not remain isolated.
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







The curriculum should be built on concepts of in order of increasing difficulty. According to our
long-term experience and after studying other textbooks, we set the order command (block),programme (script), sequence (arrangement of blocks), repetition, procedure (new block), event,object (sprite), message, condition, decision making, arrangement and embedding of structures,parameter, variable.
Individual work alone or group work alone when creating a script is not enough. Activities should
be varied with respect to the form of pupils’ work. Pupils also need to experiment with various
blocks, with changes of order or different arrangement of blocks, with different inputs. Other good
activities are individual creation, discussion with justification and reasoning, commenting on otherpeople’s work or unplugged activity in which the pupil in the role of a sprite acts out what the script
demands.
The curriculum should be graded from basic to more difficult tasks (and to really difficult ones formore advanced or faster pupils). The curriculum should have a spiral structure. The masteredconcepts appear again in tasks in later chapters, in new relations and environments. Pupils learn
by revisiting.
Each activity, however short, must have some effect, and must provide a new experience to thelearner. The pupil gets used to the fact that feedback is provided by the computer, not the teacher
and expects the reaction of the computer. The curriculum respects that the goal of programmingfrom the pupil’s point of view is to create something that works with “one click”, whether it is a story,game, drawing or piece of music. Pupils’ interest and success results in the teacher’s change ofbeliefs and willingness to invest in changes in their teaching style.

Illustration

Figure 1.

Strategic project PRIM – Support for Development of Computational Thinking, is supported by the
Ministry of Education and being conducted in the period 2017 – 2020 at all Faculties of Education in the
Czech Republic. Within the frame of this project, teaching and learning materials for pupils and lower
secondary school teachers working in Scratch are developed. These materials try to follow the principles
listed above. The target concepts for teaching are taken from the new Framework Education
Programme for Informatics and are provided by one of the project partners, National Institute for
Education. Although the project PRIM is simultaneously preparing teaching materials for programmingat primary school level, the materials our team is working on are designed even for beginners of any
school age because even lower secondary pupils have had no programming lessons so far.
We will present here some didactical environments and tasks for illustration.
Figure 1 shows the didactical environment Digital Numbers. The pupil creates scripts from blocks
provided by the authors in which the sprite moves to the given point. The task from the introductory492

Constructionism 2018, Vilnius, Lithuania

chapter about assembling blocks poses the question “Which script did the cat use to draw letter U?” toteach pupils to read scenarios.

Figure 2.

Figure 2 shows a task in which students are to find a mistake in the script. Students know that, pen
down sets the sprite’s state to stay down through subsequent moves. For students who imagine thatstamp acts in the same way, setting a sprite-state that makes it continue stamping through subsequentmoves, the sprite behaves contrary to expectations. Pupils are meant to reason and test hypotheses. If
more tasks of this type with different kinds of mistakes with different sources are used, pupils are guided
to deeper understanding of the given concept.
Figure 3 shows the didactical environment Words and Letters. Letters are “printed” by stamping a spriteof the appropriate costume. This is an advanced task where pupil creates a procedure with a text
parameter.

Figure 3.

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Figure 4.

Figure 4 shows the didactical environment Coordinates for understanding conditions, conditionals andcoordinates. A randomly jumping sprite makes points whose colour depends on whether the given
condition is fulfilled. In the bottom part, conditions of growing complexity are shown. By experimenting
with various conditions a pupil may get to understand coordinates.

Conclusion
The first version of the textbook is finished and is being piloted at schools. Preliminary research was
conducted with experienced teachers. In later stages, the textbook will be tried out with teachers wholack education in informatics and had no prior experience teaching programming. So far the piloting has
shown thateven experienced teachers find it difficult to adapt teaching in which they do not talk too much topupils and let them work independently;teachers skip those tasks they find too similar to previous tasks and fail to see a different teachinggoal in them, to see a different situation that offers the pupils another isolated model;teachers tend to evaluate each pupil’s answer, to state whether it is right or wrong instead ofallowing the computer to provide the feedback, allowing the computer to show whether the pupil’s
idea was correct or not;

if a pupil asks for help when they make a mistake, teachers immediately show the mistake insteadof giving encouragement or advice on how to look for the mistake;494

Constructionism 2018, Vilnius, Lithuania

but teachers find pupils’ attitudes to programming positive, which is motivating for them asteachers.

We believe that teaching programming can contribute significantly to development of each pupil. To
achieve this goal, it is important that students not simply adopt ready-made knowledge but learn toargue, discuss and evaluate. Critical thinking as a way to work out what is disinformation is an importantskill of a citizen of a democratic society. Especially in our geographical area we believe the school
should put emphasis in education on pupils’ ability not to allow anybody to manipulate them. This willallow us all to fulfil the thesis of the famous Czech educator Comenius, who believed education would
help to solve problems of the whole world.

Acknowledgement
We would like to thank the project “PRIM” – “Support for Development of Computational Thinking”(CZ.02.3.68/0.0/0.0/16_036/0005322) for funding this work.

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Practice papers

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Constructive and Collaborative Digital Storytelling
for Enhancing Creativity and Cooperation In andOut of School
Francesca Agatolio, francesca.agatolio@phd.unipd.it
University of Padua, Dpt. of Psychology, Italy

Alfredo Asiain, alfredo.asiain@unavarra.es
Public University of Navarra, Dpt. of Philology & Language didactics, Spain

Alfredo Pina, pina@unavarra.es
Public University of Navarra, Dpt. of Math & Computer Engineering, Spain

Gabriel Rubio, gabrielmaria.rubio@unavarra.es
Public University of Navarra, Dpt. of Philology & Language didactics, Spain

Michele Moro, michele.moro@dei.unipd.it
University of Padua, Dpt. of Information Engineering, Italy

Abstract
An effective integration of technologies in the didactical context means much more than providing
students with an easy access to computers. Technological tools can be used to provide students a
personalised and active learning environment in accordance with the constructionist principles but, forthis purpose, teachers should be aware of which type of knowledge and skill they aim to transmit andwhich technologies can help with that. In this paper we describe two digital storytelling experiences,held respectively in Spain and in Italy using Scratch, which provide an example of how teachers canoffer an engaging learning experience without much effort. In both cases, Scratch is seen as a catalyst
for different knowledge, skills and disciplines.

Figure. Two digital storytelling experiences using Scratch

The activity made in Spain, in particular, focused on the multidisciplinary aspect by the creation of stories
related to the intangible cultural heritage. The Italian experience, on the other hand, was aimed at
supporting creative and collaborative learning for gifted children.

Keywords
constructionism; digital storytelling; personalised learning; multidisciplinary learning; gifted children

498Constructionism 2018, Vilnius, Lithuania

Introduction
In the interesting paper “Digital Storytelling: A Powerful Technology Tool for the 21st Century
Classroom” (Robin, 2008), Robin started his reflection about the potential of digital storytelling reporting
the results emerged in 2007 by the survey of the U.S. Department of Education. The report states thatno significant differences are detected in the performance of students that use technologies at school
compared to others. In 2015, the results of the PISA survey (OECD, 2015), not only confirm this fact
but also suggest that the students’ performance seems to be inversely related to the quantity of timespent using computer in school. Indeed, students from countries where schools provide easier accessto computers, show worse performance in ‘digital reading’ and mathematics (fig. 1). The reportconcludes that “On average, in the past 10 years there has been no appreciable improvement in student
achievement in reading, mathematics or science in the countries that have invested heavily in
information and communication technologies for education”. This should not come as a surprise. The
report states that the main activities mentioned by the students are: chatting on line; sending e-mails;
browsing the Internet; downloading, uploading or browsing material from the school’s website; posting
work on the school’s website; playing simulations at school; practicing and repeating lessons; doing
individual homework on a school computer; using school computers for group works and tocommunicate with other students. If we consider the usage of computers during mathematics classes,
things are not much better. More frequently tasks are: drawing the graph of a function; calculating withnumbers; constructing geometric figures; entering data in a spreadsheet; rewriting algebraic
expressions and solving equations; drawing histograms; finding out how the graph of a functionchanges. All this has little to do with the computer’s role described by Papert; all this is still the “computerthat programming the children” (Papert, 1980).

Figure 1. Trends in mathematics performance and increase in computers in schools (OECD source)

We are among those educators and researchers that think that “Integrating technology is much, much
more than putting a piece of software into a classroom” as Robin states citing Mary Ann Wolf, executive
director of the State Educational Technology Directors Association. To invest in technologies foreducation does not only mean ensuring that schools must have computers and that students have to
use them a certain number of hours per week; and neither does only mean to provide internet
connection. This is only a step - maybe not the most urgent – in the process of integrating technologies
in education. Technologies are not a panacea for all the problems of education, but some technologies
lend themselves to help teachers in “engaging students in experiences liable to encourage their own
personal construction of something in some sense like it” (Papert et al., 1991). Integrating technologies
in education means 1) to identify the added value provided by using a specific technological tool toconvey a certain knowledge and/or skill (Mishra et al., 2006), and 2) to use this tool to deliver a
personalized “discovery experience” to the students. It is a “magic” that requires many interconnecteddifferent kinds of knowledge, among them the technological knowledge is not the main one. However,499

Constructionism 2018, Vilnius, Lithuania

teachers should not get discouraged because it is a gradual path and there is no need for them to have
all the answers. In this paper, we present two experiences of digital storytelling held in Spain and in
Italy: both of them are easily replicable, do not require complex technological knowledge and provide
an example of how technologies can be readily used to prepare lessons where different disciplines andskills converge and students are actively engaged. Though the two experiences were organizedseparately,thepastcollaborationsoftheauthors
inspiredbycommonpedagogical principles render these experience comparable and deserving a unique presentationand discussion.

Digital storytelling as a catalyst for different knowledge, skills
and disciplines
Digital storytelling concerns the storytelling process supported by the use of digital tools such as Scratch
or Alice. The digital component allows integrating text with animations, sounds, narrative voices andalso interactive elements. The possibility to combine this variety of resources contributes to making
storytelling a problem solving experience, particularly engaging for the students (Sadik, 2008) and able
to bring out their creativity (Malita et al., 2010). During a digital storytelling activity, students are involvedin many different processes such as the research on a topic, the organization of information, theconstruction of meaningful narratives and often team working (Robin, 2008). The use of digitalstorytelling goes back to the 1980s with the creation of the Center for Digital Storytelling in California
and now it is worldwide diffuse, in part due to the spreading of low-cost technologies (Meadows, 2008).
In (Yuksel et al., 2011) the authors report the results of an investigation about the use of this tool aroundthe world. It emerges that the main reasons for teachers to choose digital storytelling are: to allow
students to construct their own understanding in a content area, to facilitate team working, to promote
discussion, to introduce students to new contents, to help them in facing problem solving issues, criticalthinking and complex ideas. Moreover, since the software involved in this context are in most cases
programming environments, digital storytelling is often used to introduce the basis of programming andcomputational thinking (Kelleher et al., 2007). On the contrary, the use of digital storytelling focused on
the improvement of writing skills is less frequent (Burke et al., 2010). The two digital storytelling
experiences that we are going to describe have been carried out separately in Spain and in Italy:
activities in the Spanish case as regular classroom activities while in the Italian case they weredeveloped as extra-curricular. The main aspect they have in common is the use of the technologicaltool (Scratch) seen as a catalyst for diverse skills, knowledge and disciplines. During the activities,
children are not asked to solve a little isolated task but they are actively involved in the construction of
a complex product (a story), acting like in a laboratory experience, applying contents from many
subjects, sharing ideas and competences. Moreover, they have time to shape and refine their product.
In defining the principles of constructionism, Papert was inspired by students who were working on soap
sculptures (Papert et al., 1991). He reported that what had struck his imagination was the fact that “theproject was not done and dropped but continued for many weeks.” During these weeks, students have“time to think, to dream, to gaze, to get a new idea and try it and drop it or persist, time to talk, to see
other people's work and their reaction to yours”. We think “time” is one of the key point that teachers
should take into account: if we want that our students become really involved in the learning experience,
we should allowed them the time to embrace their project. Another aspect that should be considered isthe difference of each student from the other and they should be allowed to express themselves andtheir own talent. Most technological tools (Peirce et al., 2008) are particularly suited to personalize thelearning process: the two experiences we report in the paper, for instance, show how Scratch can beused both with standard groups of children and students with special needs. In all this, the technologicaltool never steals the ‘center of stage’ but it is only an instrument at the service of the students, just as
a ruler or a protractor. In every class of each school there is at least a ruler, and teachers know when it
can be useful for students and to what end; we’d like to be the same with a computer.

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Digital storytelling to enhance creativity and to bring secondary
students closer to our intangible cultural heritage: a Spanish
experience
The intangible Cultural Inheritage of Navarra has several graphical, textual or sound elements as amultimedia database for the research and knowledge of the cultural richness of Navarre and Basse
Navarre. However, this database is not known neither used by some of his target users: secondary andprimary level students. Nevertheless, its contents are really suitable for exploring, handling and evengaming.

The choice of the task
The article 2 of the Convention for the safeguarding of the intangible cultural heritage (UNESCO 2003)
says:
The “intangible cultural heritage” means the practices, representations, expressions, knowledge,
skills – as well as the instruments, objects, artefacts and cultural spaces associated therewith – that
communities, groups and, in some cases, individuals recognize as part of their cultural heritage. Thisintangible cultural heritage, transmitted from generation to generation, is constantly recreated bycommunities and groups in response to their environment, their interaction with nature and their
history, and provides them with a sense of identity and continuity, thus promoting respect for
cultural diversity and human creativity.
Resnick (Resnick 2007) says:“To succeed in today’s Creative Society, students must learn to think creatively, plan systematically,
analyze critically, work collaboratively, communicate clearly, design iteratively, and learn continuously.
Unfortunately, most uses of technologies in schools today do not support these 21st-century learning
skills. In many cases, new technologies are simply reinforcing old ways of teaching and learning”
Our hypothesis is that if we work with the contents of the intangible heritage in the right way using digital
objects and an adapted narrative for young people, this could provide an attractive and creative engine
to access and recreate intangible heritage. For thus we define a didactic strategy and appropriate tools
suitable to our scholar context and able to deal with digital objects and to create digital stories.

Description of the activity
We used 4 narrative engines based on 4 Navarre legends (Mito de Mari, Mielotxin, Sanchicorrota andthe Virgen of Uxue). The text of all of them has been adapted with a didactic simplification and theintangible heritage produced some digital images. The teacher provided to the students with 4 Scratch
projects as possible examples of how to tell digital stories based on the 4 legends. Their task was to telltheir own story of one of the legends, working in 3-4 persons teams. This has been done with 46
students (25 boys and 21 girls) of 2 classes of the first year of the secondary school (13 years old). The
Stages/Sessions were the following and combined regular lectures of language literacy with computing
sessions of technology/digital competency:
1. Pre Questionnaire. Reading and Analysis of the narratives
2. Examples that show how to transform a story into s videogame/digital story
3. Choosing the story and storyboards design
4. Storyboards presentations
6. Programming the game/interactive digital story (2 sessions)
7. Results presentation and post questionnaire
501Constructionism 2018, Vilnius, Lithuania

Figure 2. Digital objects for the activity (https://scratch.mit.edu/studios/3737471/projects/)

Results and considerations
We used two questionnaires in order to check the knowledge on programming and on the Navarre
legends before and after the activity.

Figure 3. Programming skills knowledge before/after the activity.

Figure 4. Legends and Myths knowledge before/after the activity.

Figures 3 & 4 show the improvement after the activity; other feedback from the students is that they
liked creating videogames, the work team was fine and all of them enjoyed the activity. Mixing
programming and language literacy as we did, gives lots of possibilities both for technology and forliterature.

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Digital storytelling to enhance cooperation between giftedchildren: an Italian experience
From 2016 to 2017, we taught a programming course dedicated to 7-10-years-old gifted children. The
course was based on Scratch; it lasted nine months and took place outside of school. The lessons wasboth face-to-face (three hours, once per month on Saturday) and online (one hour per week). In the lastpart of the course, we decided to introduce a digital storytelling activity. At that point, the children
programming skills had reached a good level and we sought to focus their attention on the cooperative
aspect through a digital storytelling experience.

The choice of the task
When we started to design the mentioned activity, we had to take into account some limitations related
to the course’s organization. Indeed, the course aimed at offering mathematically talented students thepossibility to explore challenging contents. Because children came from different parts of the region,
many classes were held online, through an e-learning platform. Of course, this fact restricts the occasion
to have team working experiences but it does not prevent the possibility to make a reflection on theimportance of collaboration in some situations. Gifted children are often loath to work cooperatively withpeers (Robinson, 2007). They appear frustrated when arguing their ideas and giving explanations,ending up as lonely dominators or silent onlookers (Diezmann et al., 2001). Moreover, we noticed they
are not prepared to listen to each other and to modify their own ideas in relation to those of peers. This
prompted us to propose a digital storytelling activity in which students, after defining the plot of the story
as a group, make separately their scene while taking care that it was coherent with the ones made by
the workmates. What we expected was that, after an initial “failure”, they would understand the necessity
to improve communication among their group to have a better result. Regarding the choice of the topic,
we sought students to create their stories starting from the images of “Dixit” (fig.5), the famous board
game created by the paediatric psychiatrist Jean-Louis Roubira. The peculiarity of the “Dixit” images isthat they are metaphorical and open to different and creative interpretations. The frequent use of
metaphors and their comprehension is a typical trait of talented students to the point that it is cancontribute to the identification of giftedness (Tan et al., 2013).

Figure 5. Some images from the “Dixit” board gameDuring our teaching experience with gifted children we often confirm this fact; we like to report here theexample of a 10-years-old pupil that, about teamwork, said us he has learnt that teamwork is likedrinking a hot drink: membership has to happen gently, otherwise you afford to burn your tongue. These
considerations lead us to structure the activity as follows.

Description of the activity
In the face-to-face lesson, we introduced the activity giving children the “Dixit” cards and proposed
them to play a simple game: we gave them four cards each and asked them to create a simple story
using the images. Taking turns, they had to invent the beginning of the tale taking inspiration from a
card; then, the next student had to continue the tale using one of his cards, and so on. In this way they
explored the metaphorical potential of the images. After that, we divided the 12 students in 4 groups of
3 members each and we gave every group six cards. We asked students to select three cards, one for503

Constructionism 2018, Vilnius, Lithuania

each member of each group and to draft a storyboard drawing inspiration from them. During the next
week, they made their sketches using Scratch and uploaded them in the e-learning platform; workmates
could communicate through mail. In the first online lesson, we commented together with the students
their works, reflecting on how could be improved. The next week children worked to fix and refine thesketches and, finally, they joined them to create the final story. In this way, we aimed at giving them
the time both to express their creativity individually and to refine their work taking into account theideas of the others.
Results and considerations
The groups of children approached the activity in different ways: only the members of one group felt
spontaneously the need to coordinate from the beginning using email. The other children made the firstversion of their scenes without facing with the workmates, but almost all recognized the necessity totake in account the others’ work when we asked them to join the sequences of the story. In general,they have proved to be reluctant to communicate with the others, probably also because using email is
not suitable for children of this age. Despite this, they were willing to modify their own project adjusting
the timing, changing the images and adding explanation to accommodate the workmates. Regarding
this, we think that using a technological tool like Scratch makes the difference: indeed, it allows making
changes in a quick way so that children can easily refine their work. Moreover, Scratch enhance thestudents’ creativity providing different ways to express themselves: many students integrated theprogram with their personal drawings and voices inspiring the other children. The final products resulted
quite different but they have in common the sense of humor and some surreal traits (fig. 6), suggesting
the importance for a teacher to provide tasks open as much as possible so that also students withspecial needs can express themselves. Finally, even if the online mode proved to be a barrier tocommunication, it enables students to take the time they need following their inspiration.
Figure 6. Frames of a story. In quotes the children’s voiceover

Conclusion and Discussion
An effective integration of technologies in the didactical context means much more than providing
students with an easy access to computers: it requires an informed choice of the tools by which it ispossible to create an active learning environment. Tools like Scratch make it easier to offer anexperience in which different skills, contents and disciplines converge. In the paper we reported two
examples of how, without much effort, teachers can use the digital storytelling as a multidisciplinary
engaging tool (the Spanish experience) or as an instrument to promote personalised and collaborative
learning without limiting creativity (the Italian experience). More specifically, the two experiences showthateitherdisciplinary-orientedormotivationally-oriented projects are different faces of the same medal and that they can coexist
when carefully adapted to the context, to the group of kids involved, to the expected outcomes.
On the other hand, it must be observed again that the Spanish experience was designed and evaluatedinaformalsetting,
whereas the Italian project was necessarily adapted to an informal setting, particularly504Constructionism 2018, Vilnius, Lithuania

challenging for the exploitation of communication means. In taking inspiration from thetwo experiences, these characteristics should be taken into account.

References
Burke, Q., & Kafai, Y. B. (2010, June). Programming & storytelling: opportunities for learning about
coding & composition. In Proceedings of the 9th international conference on interaction design andchildren (pp. 348-351). ACM.
Diezmann, C. M., & Watters, J. J. (2001). The collaboration of mathematically gifted students onchallenging tasks. Journal for the Education of the Gifted, 25(1), 7-31.
Kelleher, C., & Pausch, R. (2007). Using storytelling to motivate programming. Communications of theACM, 50(7), 58-64.
Malita, L., & Martin, C. (2010). Digital storytelling as web passport to success in the 21st century.
Procedia-Social and Behavioral Sciences, 2(2), 3060-3064.
Meadows, D. (2003). Digital storytelling: Research-based practice in new media. Visual
Communication, 2(2), 189-193.
Mishra, P., & Koehler, M. J. (2006). Technological pedagogical content knowledge: A framework forteacher knowledge. Teachers college record, 108(6), 1017.
OECD (2015), Students, Computers and Learning: Making the Connection. OECD Publishing, Paris.
http://dx.doi.org/10.1787/9789264239555-en

Papert, S. (1980). Mindstorms: Children, computers, and powerful ideas. Basic Books, Inc..
Papert, S., & Harel, I. (1991). Situating constructionism. Constructionism, 36(2), 1-11.
Peirce, N., Conlan, O., & Wade, V. (2008, November). Adaptive educational games: Providing noninvasive personalised learning experiences. In Digital Games and Intelligent Toys Based Education,2008 Second IEEE International Conference on (pp. 28-35). IEEE.
Resnick, M. (2007): «Sowing the Seeds for a More Creative Society», Learning and leadingwith technology, 35(4), pp. 18-22
Robin, B. R. (2008). Digital storytelling: A powerful technology tool for the 21st century classroom.
Theory into practice, 47(3), 220-228.
Robinson, A. (1997). Cooperative learning for talented students: Emergent issues and implications.
Handbook of gifted education, 243-252.
Sadik, A. (2008). Digital storytelling: A meaningful technology-integrated approach for engaged student
learning. Educational technology research and development, 56(4), 487-506.
Tan, M., Barbot, B., Mourgues, C., & Grigorenko, E. L. (2013). Measuring metaphors: Concretenessand similarity in metaphor comprehension and gifted identification. Educational & Child Psychology,
30(2), 89-100.
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2003.

Convention

for

the

safeguarding

of

the

intangible

culturalheritage

http://portal.unesco.org/en/ev.php-URL_ID=17716&URL_DO=DO_TOPIC&URL_SECTION=201.html

Yuksel, P., Robin, B., & McNeil, S. (2011, March). Educational uses of digital storytelling all around theworld. In Society for Information Technology & Teacher Education International Conference (pp. 12641271). Association for the Advancement of Computing in Education (AACE).

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Towards a Framework for Educational Robotics
Julian M. Angel-Fernandez , Angel-Fernandez@acin.tuwien.ac.at
Vienna University of Technology, Vienna, Austria

Nikoleta Yiannoutsou, nyiannoutsou@ppp.uoa.gr
University of Athens, Athens, Greece

Chronis Kynigos, kynigos@ppp.uoa.gr
University of Athens, Athens, Greece

Carina Girvan, girvanc@cardiff.ac.uk
Cardiff University, Cardiff, Wales, UK

Markus Vincze, Vincze@acin.tuwien.ac.at
Vienna University Technology, Vienna, Austria

Abstract
Educational robotics has been considered as a field with a good potential to teach difficult concepts(e.g. friction) in appealing way. As a consequence, the interest in educational robotics has grown in thelast decade, which is reflected in increasing number of robotic platforms, kits, and programming
interfaces now available. Nevertheless, researches still fail on describe activities that could be used byteachers and other people with no technological fluency, who are scared by the overwhelm amount of
information that made them avoid the use of robotics to teach. Moreover, most of the activities
developed until now do not consider pedagogical methodologies to inform the design andimplementation of them. As a direct consequence of the misinformation about the correct use of
pedagogical methodologies and robotics' multidisciplinary, the number of people who master the use ofrobotics in education is still scant. This paper presents ongoing work on the development of a frameworkin the European project Educational Robotics for Science, Technology, Engineer, and Mathematics(ER4STEM). The framework aims to make evident the connection between 21st century skills, robotics
and pedagogical methodologies to support the creation of pedagogical activities, which is defined in
ER4STEM as an activity that has clear learning outcomes and evidence of learning, use of one or morepedagogic methodologies during the activity, and detail description of the activity. This is achievethrough the critical use of tools and examples of activities developed ER4STEM.

Keywords
educational robotics; framework for educational robotics; pedagogical activities; educational activities;
educational robotics for STEM; constructionism

Introduction
Robotics is a field where mechanical, electronic and computer engineering converge but it also involves
other fields such as mathematics, psychology (e.g. human behaviour and attitudes), biology, arts, andsciences. Therefore, it has been recognized as a technology that could highly impact education (Papert,
1980). Nevertheless, the broad connection with different fields and the constant evolution of technology
can make people to focus on the technology aspect without fully consider how pedagogicalmethodologies should be included, such as best approach or specifying learning outcomes clearly.
Despite all these, robotics has been already used to teach diverse topics, such as Geography (Serholt,
et al., 2014), Geometry (Walker & Burleson, 2012), Maths (Hussain, Lindh, & Shukur, 2006), Physics(Church, Ford, & Rogers, 2010) among others, with a high predominance in physics and programming.
Despite all of these works, most of them are not well documented, which reduce their availability andtherefore replication in other educational context.
This does not mean that researchers are not aware of were not aware of these and other weaknesses.
For example, the project TERECoP (Alimisis, et al., 2012) presented a constructivist methodology for
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teacher training in the use of robotics in education. Several training sessions were available acrossEurope. Nevertheless, this approach focuses on face-to-face training to teachers and it is linked with
Lego Mindstorms. Others have come with frameworks to establish precise procedures that have to been
followed to create and activity with robotics. This is the case of Roberta initiative (Bredenfel & Leimbach,2010), which established specific criteria for the activities that could have the brand Roberta and more
important the teachers. Although these approaches are beneficial in the long term, it is still requiredmaterials that could increase the use of robotics in a critical way that considers benefits of the technology
and pedagogical methodologies.
Educational Robotics for Science, Technology, Engineer and Education (ER4STEM) is a European
project that aims to realize a creative and critical use of Educational Robotics (ER) to maintain children’s
curiosity in the world. ER4STEM has adopted constructionism as a foundational approach to designingworkshops, robotic solutions and in the development of an integrated framework for inclusive learningand engagement with STEM. The project partners have found fundamental value in designing a variety
of approaches, thus each workshop implements activities that foster students to discuss, argue andcommunicate their ideas about STEM concepts in a meaningful context for them. Consequently, theframework created in ER4STEM aims to make the explicit connection among pedagogicalmethodologies, knowledge in robotics, and 21st century skills.

Frameworks in Educational Robotics (ER)There is a limited number of works that offer a clear guideline on the correct use of robotics in education,
especially on the connection between technology and pedagogy. Roberta initiative (Bredenfel &Leimbach, 2010) aims to create a gender-balance didactic material and course concept. It specifies
several characteristics that teachers and activities must have to be considered as Roberta teacher andactivity, respectively. These characteristics could be cluster in four main areas: activity and teacher
characteristics, design ideas, and quality criteria. The design ideas for an activity are: selection ofinteresting topics, provide examples, allow rapid achievements, and strength participants’ selfconfidence. Once the activity is created, it has to fulfil the following requirements: last from 2 to morethan 40 hours, be suitable for mixed groups, be connected to real problems, and be certified by the
initiative.
Another framework is created by (Chiou, Lye, Lai, & Wong, 2011), called EARLY. Their framework isbased on the work done by (Carroll, 2002), that identifies four critical components in activities thatinvolve technology. These components are: people, activities, context and technology. As aconsequence, the EARLY framework describes three basic components: participants (i.e. teachers,
learners, developers and experimenters), environment (i.e. computer, material, software and robot) andarena (e.g. problem based arena and soccer). A final element called scope embraces all of them todescribe a specific situation or activity. Although the authors present five different case studies, theframework lacks literature support and formal evaluation.
The Educational Robotic Applications (ERA) is a framework created by (Catlin & Blamires, 2010) that
postulates ten principles for the correct use of robotics in education. They grouped these principles inthree categories. (1) The Technology category where the principles are intelligence, interaction andembodiment. They are related to expect features that robots have and could improve the educational
experience. (2) The Student aspect that focuses on engagement, sustainable learning andpersonalisation. (3) The Teacher category that covers pedagogy, curriculum and assessment, equity
and practical. Although these are important aspects to be considered in any educational activity, theauthors neither offer information in how they should be implemented nor consider difficulties that may
arise in their use. Nevertheless, there is a clear direction on how to use these principles and, as theauthors suggest, require supportive testing and evaluation.

ER4STEM Framework
The manner that ER is being presented lacks that guidance that can help people design, develop andimplement activities in ER that uses pedagogical methodologies to inform any decision. However, ER
involves a huge group of stakeholders. Therefore, the first task in ER4STEM was to determine who the507Constructionism 2018, Vilnius, Lithuania

stakeholders in ER are. The stakeholders identified were (Angel-Fernandez, y otros, 2016): young
people, young people parents, teachers, school boards, organizations offering educational robotics,
educational researchers, robotics researchers, human computer interaction researchers and industry.
This group of stakeholders is still too big, if it is to consider that each one of them has a different needs,
requirements and objectives. This variety makes it difficult to address all at once. Therefore, it was
decided to focus on those stakeholders who have a direct impact on the quality of the activities. Thiswas decided because those stakeholders would provide information that could inform other interested
parties to implement ER. Teachers, researchers, organizers of educational activities and industry havebeen identified as those stakeholders (Angel-Fernandez, y otros, 2017).
Based on their requirements and needs, and ER4STEM’s aims, ER4STEM's researchers suggested
that workshops and lessons must be treated as similar because the place where the activity isimplemented should be transparent for the final users. In order to achieve this, any activity should havea clear learning outcomes and evidence of learning, which could be formal or informal. This has several
benefits: (1) the activities designed and implemented as a workshop are easily implemented as lessons.
The description of objectives and proof of learning makes it easier for teachers to link the activity withany school’s curriculum. (2) The evidence of learning allows people to verify if the activity is reaching
the expected results or not. Also it could be used to measure the real impact of ER, which has not been
quantified yet (Fabiane & Barreto, 2012) and it would generate arguments towards the use of ER informal settings. As a consequence all activities done under ER4STEM, and hopefully in all ER, must be
pedagogical activities, which have the following characteristics: (1) Clear learning outcomes andevidence of learning, which could be formal (e.g. assessment) or informal (e.g. write to a friend about
what you have done today). (2) Use of one or more pedagogical methodologies during the activity,
which has to be thought during the design of the activity and refine after the implementation of it. (3)Description of the activity using the activity template created in the project (Yiannoutsou, Nikitopoulou,
Kynigos, Gueorguiev, & Angel-Fernandez, 2016). This will help other stakeholders to have a clear ideaof all considerations taken into account and the assumptions done by the designer.
As a consequence of these all elements already presented, weaknesses of current approaches andindustry requirements, ER4STEM’s framework is a work on progress that aims to guide any ER’s
stakeholder on the design or adaptation, implementation and evaluation of pedagogical activities. Thisis achieved through the explicit connection among pedagogical methodologies, knowledge in robotics,
and 21st century skills. To achieve this, the ER4STEM’s framework provides four components, such as
it is depicted in Figure 1. (1) An ontology of ER. The concept ontology in this case must be understoodas it is done in Computer Science. This ontology provides specific definition of each word used in thefield and the connection between them. (2) Tools created specifically to be used in ER, such as a webrepository, activity template and activity blocks. The last is a piece of activities that have been provento be useful to foster specific skills and could be connected with other blocks to create a pedagogicalactivity. (3) Values or pillar of ER4STEM were selected from the industrial’s needs, literature review andproject’s objectives. These values are: creativity, collaboration, communication, critical thinking,evidence of learning, mixed gender teams, multiple entry points, changing and sustaining attitudes to
STEM, and differentiation. (4) Processes for workshops and conferences for young people.

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Figure 1. Graphical representation of the elements that compound ER4STEM’s framework. The dash arrows
represent the connections between elements that constitute the framework. The other lines come from different
elements to the framework because they constitute the framework.

Values of ER4STEM
The values of ER4STEM were selected after doing a literature review, analyzing current industry’s
needs and project objectives. From the literature review, several weaknesses on how works in ER wereidentified (Angel-Fernandez, y otros, 2016). Thus: (1) There is not a clear evidence how pedagogicaltheories were considered during the design of the activity. (2) Activities reported in many cases are notfully described and therefore limiting their replication. (3) Some of the studies lack rigorous andsystematic analysis of the data, which would make it become anecdotal. On the other hand, the analysisof the industry revelled that there is a common agreement that STEM is critical to the future economic
growth. However, there are different views on whether the supply of STEM-skilled labour will be
sufficient or not in the near future. According to Business Europe the lack of STEM-skilled labour will be
one of the main obstacles to economic growth in the coming years (Europe, 2011). Therefore, theproject objectives are four. (1) The provision of multiple entry-points to ER and STEAM. (2) Empoweringchildren to solve real world problem and address all young children. (3) Provide a continuous STEM
schedule. (4) Develop an open and conceptual framework. As a result, the values are: creativity,
collaboration, communication, critical thinking, evidence of learning, mixed gender teams, multiple entry
points, changing and sustaining attitudes to STEM, and differentiation. For each one of these values aliterature review is been done to provide stakeholders with suggestions that have been already studied
by other researchers.

An example of a value: Creativity
This is one of the skills that most of the people talk but it is difficult to explain in words. An important
aspect to foster creativity is to avoid tell children that they are no creative just because the person does
not consider that they are doing something new or innovative. Regarding this, it is important toremember that there are diverse level of creativity, for example (Kaufman & Beghetto, 2009) proposedfour types of creativity: little-c, big-c, mini-c and pro-c. Little-c is the creative that involves novelty beyond
individuals. Pro-c could be positioned between little-c and big-c, and it embedded ideas that areconsidered with significant valuable in their field but their contribution has not been recognized as bigc. Little-c, which occurs when individuals comes ideas that are new for them and for others but withouta significant relevance to their field; and big-c, which occurs when individuals come with ideas thatrevolutionize their fields. Other important facts to remember are:509

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The creation of environments, that promotes creativity, is also possible byoDefining clear goals in the activity (Csikszemtmihalyi, 1996)oBalancing knowledge and challenge (Lewis, 2015) (Csikszemtmihalyi, 1996). Too
difficult or easy will not contribute in the development of creativity.

oCreate a climate where students are not concerned that they may fail (Lewis, 2015)(Csikszemtmihalyi, 1996) (Sefertzi, 2000) (Vassileva, et al., 2012)o

No creating competitions or providing rewards after finishing the activity (Lewis, 2015)oMotivating students to be creative (DeHan, 2009)

Elements proposed by (Nelson, 2012) to foster creativity in robotics are:o

Ability to visualize solutions, for example sketching or building prototypes of robots.

oThorough knowledge base in the domain, for example building on previous robotic
projectsoAbility to decompose and manipulate partial solutionsoAbility to take informed risks, which include tasks with no right or wrong answersoFlexibility to try alternative techniquesoCreativity friendly environmentoPracticeFailure most not be penalized (Sefertzi, 2000) (Lewis, 2015)

Use of diverse tools to motivate creativity (Sefertzi, 2000), such us brainstorming, story boarding,lotus blossom, checklist, morphological analysis, and excursion technique.

Educational Robotics Ontology
An ontology as is presented by (Grimm, Abecker, Volker, & Studer, 2011) is a formal explicit
specification of a domain of interest that could be executed by a machine and understand by humans.
This representation is helpful in two ways. (1) It provides a specific definition of the concepts in thedomain of interest. This will avoid misinterpretation of a concept that has different meaning dependingof the field. For example, in ER4STEM when the idea of creating an ontology came, there were amisunderstanding between engineers and educational researchers because each one had a different
definition of it. Also it will help stakeholders without knowing the concept to understand it. (2) It is thebase of a semantic search on the repository, which would let it to provide better results to a query.
Thus in context, the ER4STEM’s ontology was created in two steps. (1) Determining requirements andpossible queries that should be answers and (2) Describing and formalizing the ontology. During thefirst step, it was decided to use the activity template as a base to determine concepts that must be in
the ontology. On the other hand, the queries were created from diverse meetings between all partners
in ER4STEM. This allow the discussion between researchers, practitioners and industry, which
contribute to have different perspectives. The final questions are:o

What kind of activity I can use to for participants between x and y?

oWhich activity I can use to improve an X skill?

oWhich activities I can implement with an X robotic platform?

oWhat platforms I can use with Y programming language?

o

What type of activities I uses an X pedagogical methodology?

oWhich activities I can use for participants with X, Y and Z characteristics?

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Based on requirement analysis, it was firstly decided to focus on concepts that are intrinsicallyembedded in ER, and avoid concepts and terms that unequivocally do be described in other ontologies
that could not add any additional value to the base of knowledge. For example robotics or technology
ontologies. The second step was initiated with the creation of a beta version of the ontology. This version
was discussed with educational experts from University of Athens and Cardiff University, who provided
corrections to the educational concepts. Taking into account their comments, a new version of the
ontology was created and shared with all partners to have a feedback from them. This feedback lead tothe first stable version of the ontology. The taxonomy and its relations are presented in the webrepository.

Tools
In ER4STEM three tools have been created to support stakeholders in ER. (1) The activity template isa generic design instrument that identifies critical elements of teaching and learning with robotics basedin theory and practice (Yiannoutsou, Nikitopoulou, Kynigos, Gueorguiev, & Angel-Fernandez, 2016). It
was designed to be a mediating artefact between pedagogical experts and the ER4STEM partners
interested in design activity plans for ER. The template addresses the following aspects: a) the
description of the activity, with explicit reference to the domains involved, objectives, duration andnecessary materials; b) a level of detail that will demonstrate the influence of a specific approach. (2)Activity blocks were designed the outcomes of the first year of the project. They focus on the practicalaspect of the activity plan. The activity blocks are adjustable short activities that were selected as good
activities that could be used to foster one of the ER4STEM’s values. (3) Repository is the digital
representation of activity template, activity blocks and ontology. The repository’s main objective is tosupport other people in the creation of new activities and inspire them ideas that other users haveshared. Figure 2 presents the front page of the repository, which has the option to login in case the user
wants to share their activity. Also there is the possibility to visualize diverse activities that already exist
in the repository. Also it is also possible to search for specific key words or features, such as age.

Figure 2. Front page of the ER4STEM’s repository.

Processes
A macro process was created base on research cycle and the professional teaching and learning cycle(Laboratory, 2008). The main aim was to conceive a suitable structure that could be used in activities
that involves the use of robots. The final result is depicted in Figure 3. As it could be seen this process
is composed of four main macro phases: design or adaptation of an activity plan, implementation in real
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settings, activity’s evaluation or assessment, and improvement of the activity plan. The first macro phaseis divided in two possible steps, which represents the possibility to design an activity from scratch or
adapt one from other existing activities. The second macro phase is implementation, which mainly
focuses on considerations involving the settings and the context in which the activity is going to takeplace. The third phase provides instruments and procedures for evaluating the implementation. The
fourth and last macro phase focuses on possible improvements of the activity plan based on information
derived from the implementation in real settings, on reflections from the teachers, the students and thedesigners. Once the activity has been improved, there is the possibility to being implemented again as
an activity for future groups.

Figure 3. Framework’s macro process definition

Using this macro process as reference there has been identified two processes can be created from the
project experience: conferences and competitions, and pedagogical activities.

Acknowledgements
This work is funded by the European Commission through the Horizon 2020 Program (H2020, Grant
agreement no: 665972). Project Educational Robotics for STEM: ER4STEM. Also the authors would
like to thank all researchers, who have participated in all conversations that allow the specification of
the framework.

References
Alimisis, D., Arlegui, J., Fava, N., Frangou, S., Lonita, S., Menegatti, E., . . . Pina, A. (2012). Introducing
Robotics to Teachers School: experiences from the TERECop project. Constructionism. Paris.
Angel-Fernandez, J. M., Kynigos, C., Lepuschitz, W., Pullicino, J., Grizioti, M., Girvan, C., & Todorova,
C. (2017). Towards an Extended Definition of ER4STEM Framework.
Angel-Fernandez, J. M., Lammer, L., Kynigos, C., Gueorguiev, I., Varbanov, P., Lepuschitz, W., . . .
Vrba, P. (2016). Best Practice and Requirements.
Bredenfel, A., & Leimbach, T. (2010). The Roberta Initiative. Conference on SIMULATION, MODELING
and PROGRAMMING for AUTONOMOUS ROBOTS (SIMPAR 2010). Darmstadt.
Carroll, J. M. (2002). Human-Computer Interaction in the New Millennium. Addison-Wesley
Professional.
Catlin, D., & Blamires, M. (2010). The Principles of Educational Robotic Applications (ERA).
Constructionism, (pp. 1-17). Paris.

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Chiou, A., Lye, N. C., Lai, R., & Wong, K. W. (2011). Framework for robotics in education: Someexperiences and case studies in test arena based projects. IEEE International Conference on eLearning in Industrial Electronics (ICELIE). Melbourne.
Church, W. J., Ford, T., & Rogers, C. (2010). Physics with robotics Using LEGE MINDSTORM in HighSchool Education. Educational Robotics and Beyond. Stanford.
Csikszemtmihalyi, M. (1996). Flow and the psychology of discovery and invention. In Creativity (pp.
107-126). New York: Harper/Collins.
DeHan, R. L. (2009). Teaching Creativity and Inventive Problem Solving in Science. CBE Life Science
Education, 172-181.
Europe, B. (2011). Plugging the Skills Gap – The clock is ticking (science, technology and maths).
Brussels: Business Europe.
Fabiane, B., & Barreto, V. (2012). Exploring the Educational Potential of Robotics in Schools.
Computers and Education, 58(3), 978-988.
Grimm, S., Abecker, A., Volker, J., & Studer, R. (2011). Ontologies and the Semantic Web. In Handbook
of Semantic Web Technologies (pp. 509-579). Berlin: Springer.
Hussain, S., Lindh, J., & Shukur, G. (2006). The Effect of LEGO Training on Pupils' School Performancein Mathematics, Problem Solving Ability and Attitude: Swedish Data. Educational Technology & Society,9(3), 182-194.
Kaufman, J. C., & Beghetto, R. A. (2009). Beyond Big and Little: The Four C model of Creativity. Reviewof General Psychoogy, 1-12.
Laboratory, S. E. (2008). The Professional Teaching and Learning Cycle. Austin: Southwest
Educational Development Laboratory.
Lewis, T. (2015). Creativity - A Framework for the Design/Problem Solving Discourse in Technology
Education. Journal of Technology Education, 35-52.
Nelson, C. A. (2012). Generating Transferable Skills in STEM through Educational Robotics Robots. In
K-12 Education: A New Technology for Learning (pp. 54-65). Hershez: IGI Global.
Papert, S. (1980). MINDSTORMS: Children, Computers, and Powerful Ideas. New York: Basic Books,
Inc.
Sefertzi, E. (2000). Creativity. Retrieved from http://www.adi.pt/docs/innoregio_creativity-en.pdf
Serholt, S., Barendregt, W., Leite, L., Hastie, H., Jones, A., Paiva, A., . . . Castellano, G. (2014).
Teachers’ Views on the Use of Empathic Robotic Tutors in the Classroom. IEEE International
Symposium on Robot and Human Interactive Communication. Edinburgh.
Vassileva, M., Sharkova, A., Laister, J., Zörwer, B., Arrizabalaga, E., Uriarte, X., . . . Beatty, E. (2012).
Creativity Development and innovation: Hadbook for SMEs.
Walker, E., & Burleson, W. (2012). User-Centered Design of a Teachable Robot. International
Conference on Intelligent Tutoring Systems. Crete.
Yiannoutsou, N., Nikitopoulou, S., Kynigos, C., Gueorguiev, I., & Angel-Fernandez, J. (2016). Activity
Plan Template: a mediating tool for supporting learning desig with robotics. International Conference on
Robotics in Education 2016. Vienna, Austria.

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Think, Create and Program: Evolving to a K-9
Nationwide Computational Thinking Curriculum in
Costa Rica
Carol Angulo, carol.angulo@fod.ac.cr
Alberto J. Cañas, acanas@gmail.com
Ana Gabriela Castro, ana.castro@fod.ac.cr
Leda Muñoz, leda.munoz@fod.ac.cr
Natalia Zamora, natalia.zamora@fod.ac.cr
Omar Dengo Foundation, Costa Rica

Abstract
As early as 1988, the National Program of Educational Informatics (PRONIE MEP-FOD) led by theOmar Dengo Foundation in partnership with the Ministry of Public Education of Costa Rica, has
implemented Papert´s constructionism ideas and computer programming as part of the public schools
curriculum, as a means for students to learn through constructing. This large scale program currently
benefits 87.6% of the country’s K-9 students, who receive two weekly lessons.
In an effort to update and revise the Program to respond to the challenges brought about by the Fourth
Industrial Revolution, and to stimulate the development of the skills our students need in order to fulfill
future jobs that we cannot imagine today, we are enhancing the Program into a richer curriculum that
includes the understanding of computational thinking where computer programming is a fundamental
methodology to achieve and exercise higher-order thinking skills, while at the same time students learn
and comprehend key concepts in ‘computing’, all within a constructionist learning environment.
Computational thinking is addressed from a broad but also deep perspective, based on the definition of
key concepts, skills and attitudes from preschool to ninth grade. After the definition of the learning
outcomes and the designing processes, we have been piloting this new curriculum in 107 schools andhave started extending it to 1300 in 2018.

Sixth grade student using Arduino

In this paper we present advances, including methodological and conceptual foundations of the updated
educational proposal and the implementation of pilot studies.

Keywords
computational thinking; programming skills; computing concepts;
competences; problem solving; higher order thinking skills; K-9 curriculum

educational

informatics;

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Introduction
The National Program of Educational Informatics of the Omar Dengo Foundation in partnership with the
Ministry of Public Education of Costa Rica (PRONIE MEP-FOD), constitutes a public-private
collaborative effort which has been designated since 1988 with the task of introducing technology in the
education system of the country with an emphasis in promoting higher order thinking skills. With apathway of thirty years, the Program has been established as an important component of theeducational policy of the country, driving the national public education forward, offering innovativeeducation models that are based on technology, contributing to solve the digital gap, and preparing
future generations to actively participate in the new digital society.
PRONIE MEP-FOD departs from the constructivist epistemological framework proposed by Jean
Piaget, and the constructionism of Seymour Papert, which guides the pedagogical practices to use
technology as a way to facilitate learning, and to promote cognitive development in children, teenagers,
and teachers all over the country. The Program considers that the main potential of computers is theiruse as tools to increase people´s capabilities to think, create and share. It aims to facilitate and promotestudents to become active users of technology and responsible creators.
As of December 2017, the Program had reached a total coverage of 87.6% of the K-9 students enrolled
nationwide, benefiting 610,883 students from 3,174 schools. From these, 1,199 have a computerlaboratory where Papert’s pedagogical ideas are implemented, benefiting 476,565 students annually(356,617 elementary and 119,948 high school students). The work in the labs consists of a projectbased strategy that guides the programming tasks, under the facilitation of an educational informatics
educator that works with groups of 30 students approximately. The PRONIE MEP-FOD aims to reach
100% of schools in the next few years.
The incremental presence of digital technologies in all activities of the new society, opens the challenge
of introducing these resources and especially their logic, foundations, and full appropriation to the newgenerations. This implies transforming programming, coding, and all skills associated with programming
into a new literacy, a literacy as important as learning how to read and write. “To reading, writing, andarithmetic, we should add computational thinking to every child’s analytical ability.” (Wing, 2006, p.33).
According to Costa Rica´s Chamber of Information and Communication Technologies (CAMTIC) thevisionary idea of the Program founders 30 years ago has been an important element favoring theevolution of the country´s economy to one with a stronger and dynamic sector associated withtechnology. As a consequence, there is an estimated lack of 8000 computer scientists nationwide (ElFinanciero, 2017). Between June and August 2016, there was a supply of 5000 new jobs in companies
related to technology in Costa Rica (La República, 2016), a condition similar to what is found in othercountries around the world where the labor demand in informatics is wide and in constant growth.
Besides looking for human talent in technological areas, companies also want their employees to havedifferentiated skills for new jobs, according to OECD (2016).
The accumulated experience of the PRONIE MEP-FOD in promoting skills like programming, problem
solving, critical thinking and collaboration, has provided a solid basis from which to build a revised
curriculum to address this new context. In this framework, PRONIE MEP-FOD designed didactic
practices according to new demands and technologies and their incorporation to Costa Rican society
through the public education system, ensuring equity and mutual benefits. Consequently, since 2014
the Program has evaluated and updated the educational informatics computer lab proposal, in order toencourage students to develop the skills required to become 21st century citizens.

Educational Informatics Laboratory: Think, create, program
After studying international trends and the evolution of educational proposals that incorporate
technology to solve problems with programming, and revising the experience gained by the PRONIE
MEP-FOD itself through three decades, four major competences linked to the expected learning
outcomes were established. These are:515

Constructionism 2018, Vilnius, Lithuania•

Problem-solving with programming: to understand and solve problematic situations (forwhich the solutions are not immediately obvious) through strategies that imply computer
programming.

•

Modeling and representation of data: abstraction, modeling, and representation of data as
the basis for communication and information-building through programming.

•

Understanding of concepts, operations, and components of computational and computer
systems: to understand (and manipulate) the main components of computer systems in an
effective and safe way, through the understanding of its most basic theoretical foundations, vital
aspects of the surroundings, and ethical and social implications.

•

Manufacturing of physical devices and robots: to design and create tangible and interactive
objects, made with different materials and technological resources, which can be programmed
and controlled from a computer or mobile device.
Inspired by the constructionist didactics of Creative Computing (ScratchEd team at the Harvard
Graduate School of Education, 2014), each of the activities designed for the ten grades consists of adesigning process, personalizing process, sharing and collaborating process and reflection process(Bujanda, 2016), aiming at promoting meaningful learning, the development of computational thinking,and learning of computer science concepts, and following a competencies-based methodology. These
competences became evident after the construction of a computational thinking concept map (Figure1) that identified the computing concepts, programming skills and attitudinal elements (tolerance tofrustration, preference for precision, learn from mistakes) aimed to pursue.

Figure 1. On the left, a high-level summary concept map on Computational Thinking; on the right, a purposefully
unreadable image of the (work in progress) concept map used to identify the big-ideas that would guide the K-9
curriculum and the concepts associated with them gives an idea of the scope of the number of concepts
considered.

As the competencies went beyond simple coding to include big ideas such as data processing, in-depth
programming, abstractions and models, machines and programs, in addition to the attitudes andpractices of the computational thinker, we found ourselves that our 30 years of experience provided usa good guideline in terms of the progression in programming we could expect from students through K9, but with little to rely upon in terms of the identification of big ideas and associated concepts incomputing, let alone the progression of the understanding of those big ideas and concepts throughout
10 years. To identify the big ideas and associated concepts with constructed the concept map shownin Figure 1. These conceptual big ideas enable the understanding of how current technologies workfrom their foundations, and it creates the basis to comprehend future technologies.

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The big ideas and the associated concepts provide a framework that facilitates the work of theeducational informatics teacher in guiding an integrated and coherent learning process; they provide away for the teacher to help the student better understand what is behind the programming that thestudent is realizing These ideas influenced the activities and units organized, identifying not only which
key concepts should be learned but also their connection with others elements of the curriculum. For
each big idea, a progression of that idea was built from kindergarten to 9th grade, and together conform,
as a result, a progression for Computational Thinking, as defined by the proposal.
The concepts are to be made evident by the teacher as the student encounters them throughprogramming, or as is appropriate through the advancement in programming skills, instead of being
associated with a fixed curriculum. For this, the progression of concepts through each big idea is carriedout through a block of years (grades) we called levels 57, generating a more flexible and fluid learning
process, giving students opportunities to create links among the concepts covered in different levels,and to teachers and students as well, to easily detect the progress in the achievement of expected
results. Teachers are made aware that certain level of understanding of a concept is expected at aparticular block, but it is up to the teacher to determine when to make the concept evident to thestudent(s) or class.
We established a group of procedural, conceptual, and attitudinal indicators to obtain a description of
the aimed competence and how it will be reflected in the students. These indicators guided theconstruction of didactic designs with two or three units and several activities for each year.
Additionally, a plan to train and update educational informatics teachers has been developed, and it ismainly delivered through virtual courses for scalability reasons.
The design of this new curriculum takes specific initiatives into consideration, such as the Maker
Movement, which have a close relationship with the way we believe students build knowledge. In thisregard, it promotes that people in the new culture should: make, share, give, learn, be equipped, play,
participate, support, and change (Hatch, 2014). Accordingly, three grades in elementary school havebeen selected to incorporate resources associated with physical computing, such Makey Makey,
PicoBoard, Arduino Uno, and Circuit Playground, and preschool and eighth grade were selected toincorporate robotics. so that students can develop computational thinking while creating andprogramming robots to solve problems. In grades that do not incorporate physical computing, projects
that involve game design or computer networking are proposed.
Starting with 4th, 5th and 6th grades in 400 schools in 2018, the project will be gradually implemented
until all Educational Informatics labs in the country are covered by 2021.

Implementation and monitoring of pilots
We have been piloting this project since 2015, reaching 57 elementary schools and 50 high schools
around the country. The pilot plan has been accompanied by a monitoring process that registers thereactions, constraints, likes and dislikes from students and teachers, which has been used to improveand adjust the designs.
The distribution of designs, monitoring and piloting per year, is detailed below:
2014: Design of the first and fourth grade proposals
2015: First and fourth grade proposals were piloted and monitored with 20 teachers. Secondand fifth grade proposals were designed.
2016: Second and fifth grade proposal were piloted and monitored with 50 teachers. Third, sixthand seventh grade proposals were designed.

57
Level 1: kindergarden, first, and second grades.
Level 2: third and fourth grades.
Level 3: fifth and sixth grades.
Level 4: seventh, eighth and ninth grades.

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2017: Third, sixth and seventh grade proposals were piloted and monitored with 50 third andsixth grade teachers, and 20 seventh grade teachers. Preschool, eighth and ninth grade
proposals were designed.
2018: Preschool, eighth and ninth grade proposals are being piloted and monitored, with 20
teachers in preschool and 40 teachers in eighth and ninth grades.
For each of the monitoring stages, we defined the main indicators to be evaluated, methods of datacollection, and people in charge of the process to comply with the following monitoring objectives:
 General objective: Compile inputs that allow critical and timely evaluation of the updated
proposal, for decision making and adjustments to the designs.
 Specific objectives:o To identify the relationship between the activities carried out in classroom and therecommended curricular proposal.
o To review teachers' understanding of the main components of the proposal after training
is received.
o To assess didactic resources of proposal by teachers.
o To identify the main constraints that affect the implementation of the proposal.
o To assess likes, dislikes and understanding of the proposed activities by the students.
Data collection has been done through three specific channels: teacher survey, student survey and nonparticipatory observation.

Preliminary conclusions
Both the piloting phase and the recently initiated scaling up to the national level, will allow us to continue
to learn about the strengths and weaknesses of the proposal, specially to evaluate how big ideas
associated with computational thinking evolve in students, and their understanding of concepts andstrengthening of programming skills for problem solving, in order to continue the necessary adjustments.
However the preliminary results are stimulating so far, and signal of a robust and pertinent proposal toaddress the new digital society with all its challenges and opportunities.
After 30 years of learning through programming based on Papert's ideas and facing the fourth revolution,
we evolved towards a new curriculum that further deepens programming, and is enriched with elementsof physical computing and computational thinking concepts, all within a constructionist framework.
Papert’s fundamental ideas are as pertinent and provocative, if not more, than 30 years ago, and theeducational systems need to explore new ways, even new pedagogies such as this, to strengthen theleading role that they are called to have in this knowledge society.

References
Angeli, C., Voogt, J., Webb, M., Cox, M., Malyn-Smith, J., and Zagami, J. (2016) A K-6 Computational
Thinking Curriculum Framework: Implications for Teacher Knowledge. Educational Technology &Society, 19 (3), 47-45.
Angulo, C. (2017) Design of the monitoring of the Update of the Educational Computing proposal.
PRONIE MEP-FOD. San José, Costa Rica.
Brennan, K., Balch, C. & Chung, M. (2011) Creative Computing. Harvard Graduate School of Education.
Bujanda, M. E. (2016). Framework of expected learning results in students. PRONIE MEP-FOD. San
José, Costa Rica.
Bujanda, M. E., Pérez, Ó., Otárola, A., López, E., Matarrita, D., Angulo, C., Hernández, A. V. (2016)
Review learning objectives. Update of the LIE proposal. PRONIE MEP-FOD. San José, Costa Rica.
Cañas, A., Castro, A. G., Acuña, A. L., Rodríguez, A., Angulo, C., Matarrita, D., Zamora, N. (2017)
Progression of powerful ideas associated with computational thinking. PRONIE MEP-FOD. San José,
Costa Rica.
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Castro, A., & J. Lopez, F. O. (2017) Technology review report for use in eighth grade. PRONIE MEPFOD. San José, Costa Rica.
El Financiero (2017) Increase offer of computer scientists will require an additional effort in the academy.
Retrieved from: http://www.elfinancierocr.com/tecnologia/Camtic-UCR-UNA-ITCR-Cenfotec-Ulacitempleo_0_893310680.html.
Fonseca, E. (2017) Observation report and surveys to Secondary Teachers, in training of the New
Proposal LIE 7th level. PRONIE MEP-FOD. San José, Costa Rica.
Fundación Omar Dengo. (2016) Digital technologies and capacities to build the future: contributions of
the Program. PRONIE MEP-FOD. San José, Costa Rica.
Fundación Omar Dengo (2016) Contribution of PRONIE MEP - FOD to the learning of students who
graduate from II and III cycles of Basic General Education. PRONIE MEP-FOD. San José, Costa Rica.
Hatch, M. (2014) The Maker Movement Manifesto. McGraw-Hill Education.
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(2016)
SkillsandWork.
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Rica: Programa Nacional de Informática Educativa MEP-FOD.
Otárola, A., Castro, A. G., & Pérez, Ó. (2017) Seventh Year Educational Proposal: "Imagine and design
worlds in 3D". San José, Costa Rica: Programa Nacional de Informática Educativa MEP-FOD.
Papert, S. (1980) MINDSTORMS: Children, Computers & Powerful ideas. Basic Books, Gálapago.
Buenos Aires.
Papert, S. (1995) The children´s machine: Rethinking school in the age of the computer. Paidós,
Barcelona.
Quesada Solano, M. E., Cedeño Suárez, M. A., & Zamora Calvo, J. M. (2001) The Curricular design inthe curricula: theoretical aspects and methodological guide. Heredia, C.R.: EUNA.
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Madrid.
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River, Prentice Hall, New Jersey.
Schwab, K. (2017) The Fourth Industrial Revolution. Crown Business. World Economic Forum. USA.
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Shannon, R., & Johannes, J. (1976) Systems simulation: the art and science. IEEE Transactions on
Systems, Man and Cybernetics, 6(10), 723-724.
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Computational Thinking and Music Learning
Judith Bell, jbell@chisnallwood.school.nz
Chisnallwood Intermediate School, Christchurch, New Zealand

Tim Bell, tim.bell@canterbury.ac.nz
University of Canterbury, New Zealand

Abstract
Computational thinking (CT) can be integrated with subjects outside computer science, and music is no
exception. CT ideas such as decomposition, patterns, abstraction and algorithms can all be exercisedin a meaningful way while at the same time engaging students with key concepts from music. This paper
explores connections between computational thinking and music, and gives vignettes of creative ways
to connect the two subjects in a manner that explores each subject in an authentic way.
The first example is sorting musical values (such as pitches and note lengths), which explores the ideas
around sorting while exercising music reading and aural skills. We then explore the idea of giving
commands to robots to position them on a giant stave to represent music; and data representation is
explored by coding binary values using high and low pitches. Finally, students write computer programs
to play scales and tunes, which forces them to think about both the rules around these musical concepts,
as well has having to exercise programming discipline to make it sound correct and be adaptable toplay in different keys.
These ideas provide students with ways to genuinely engage with both computational thinking andmusic. As well as the practical benefit of teaching two topics at once, it shows students how school
subjects don’t exist in isolation, and how there are aspects of thinking in common between the subjects.

Keywords
computational thinking; music theory; curriculum integration

Introduction
Computational Thinking (CT) is becoming part of school curricula in many countries under titles suchas “Computing”, “Digital Technologies”, and “Computer Science” (Hubwieser, Giannakos, Berges, et al.
2015), which brings the challenge of fitting it into what is usually an already full school curriculum. This
raises the concern that STEM subjects will overshadow traditional subjects that are important for arounded education. However, the arts have an important role in STEM subjects because they encourage
creativity, communication and teamwork.
In this paper we provide several vignettes of how these issues can be addressed in music education by
integrating aspects of music education with Computational Thinking. Integration means that bothsubjects are being taught at the same time, which not only provides efficiency in the use of class time,
but also helps students to avoid seeing subjects in isolation, and can potentially engage students who
are more attracted to one of the elements than the other. The contrast may seem particularly strong
when music and computer science are juxtaposed, particularly based on stereotypes of music as acreative art and computer science appearing to be a machine-centric science. In fact, they have morein common than might be expected from the stereotypes, both in terms of skills needed to be effective(creativity, teamwork, communication, working with notation), and also the opportunities to use one
subject to engage and even enhance learning in the other. Furthermore, in practical situations bothmusic and computer science involve constructing something that is intended for an audience; incomputing the audience might be the users of an app or web page, or an organisation collecting data,
while in music the audience might be at a concert, watching a film, or listening to an advertisement.
Because both involve thinking about the needs of an audience and constructing something to meetthose needs, when teaching them there are strong constructionist opportunities for student projects
developing something with a real purpose.
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The challenge is to design activities that include genuine music learning as well as genuine
computational thinking. Simply using computers in a music class (such as making digital recordings orusing online resources) is unlikely to help students engage at a deep level with computational thinking.
Similarly, using music in a computer class (such as playing background music in a game or learning
how to compress audio files) is unlikely to be teaching many key elements of music. There is a wellestablished body of work that achieves a strong link between CT and music by using computer
programming to generate music, such as “Media computation” (e.g. Guzdial, 2003; Greher & Heines,2014) and using systems such as EarSketch (Engelman, Magerko, McKlin, et al., 2017) and Sonic Pi(Aaron, Blackwell, & Burnard 2016), but we will explore other approaches here.

The elements of CT and music
In order to be sure that an exercise is teaching both CT and music, we need to be aware of the kinds ofconcepts that are covered by these two areas of learning.
Curricula based on CT generally include learning to create new artefacts on digital devices, as well as
using the devices. This often includes working with and for others, which has been argued to be an
important part of this discipline (Kafai, 2016). There are many definitions of CT, and even some debate
about what should be included (Tedre and Denning, 2016), but there are common elements that appear
in most definitions. For example, Selby and Woollard (2013) use the following list: algorithmic thinking;abstraction; decomposition; generalization and evaluation. Computer programming isn't listed explicitly
here, although learning to program does cover these concepts well, and some would argue that it
defines the scope of CT (Denning, 2017). In this paper we evaluate the relevance of each activity against
these CT criteria, including programming, since this gives concrete evidence that they are likely togenuinely support CT in a computing curriculum. Our examples also provide broader contexts in which
students can engage with CT.
A music curriculum, at a high level, will typically cover creating, performing, responding and connecting(Kaschub and Smith, 2016), and the elements of music that define the scope of curriculum are often
articulated as a list such as pitch, timbre, texture, dynamics, duration, tempo, and structure (Burton,
2015). As with CT, the elements do not provide a curriculum, but they help us to identify if something
belongs in a music curriculum. For a general classroom course these are likely to be made accessible
through the use of basic notation, accessible instruments, and meaningful contexts such as popular
music, film music, and local culturally relevant music.
Again, we will evaluate activities against these criteria to ensure they match the concepts that are likely
to appear in school music curricula. We note that these are very atomic components of music, and arelikely to be covered by broader descriptions. For example, pitch might come up in relation tounderstanding the range of a musical instrument, which in turn might be in the context of the instrumentsof an orchestra or other ensemble.
There are common elements between music and CT. In practical situations, both rely on notations informal languages (for music that could include Common Music Notation on 5-line staves, tablature,solfège notation or graphic notation; for computing it includes programming languages as well as
markup languages and protocols); and both use concepts around sequence (the order in which notes
appear in time; and the order of statements in a computer program) and repetition (in music this includes
repeats, as well as forms such as rondo or the structure of popular songs; in computing loops andrecursion provide this).
However, we are not advocating that these related ideas should be used directly as common examples,
as sometimes analogies and differences might add to confusion, but we do note that there are alreadyrelated forms of thinking in both subjects.
In the remainder of this paper we give some vignettes of ways that music and CT can be combined ina way that genuinely engages students with both subjects. The first three exercises are built on activities
from CS Unplugged (csunplugged.org), and the fourth is based on programming in a simple block-based
language. The exercises mainly cover music theory, but also some composition.
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Parallel sorting network
A parallel sorting network is an abstract concept for parallelising the task of ordering data into asequence, usually in increasing order of value. It is a quintessential activity from the CS Unplugged
activities (Bell, Rosamond, & Casey, 2012), where the network is drawn on the ground (or floor), andstudents traverse the network and come out in sorted order.
Figure 1 shows the layout of a 6-way sorting network. Six students enter the network at the left, eachholding a card with a numeric value on it. When they meet at a node, they compare numbers, with thesmaller value leaving to the left, and the larger one to the right. This simple rule is repeated each timethey meet, and at the end the students emerge with their cards in ascending order.
Since sorting can be applied to any values that have a binary relation that is a total order, sorting
networks can be used for keys other than numbers, including putting words or names into alphabeticalorder, or putting elements of a well-known story into the sequence in which they should occur. This
opens up a number of possibilities in music.
One valuable approach is to have students compare written notes in Common Music Notation (Figure
2, left). Initially this can be simple notes that are on the same clef and only use lines and spaces (so thesimple rule is that the note that is further up the stave should go to the right). This can be extended byadding accidentals, so that two notes on the same line might be distinguished by being sharp, natural,or flat. If the students have mixed levels of understanding, the "harder" notes (with accidentals) can begiven to a more advanced student, so that if such a comparison is needed, the student holding the card
will be able to explain it to the student that they meet each time. The next step up is to add ledger linesand different clefs (bass, alto, tenor). This approach could be used with a variety of notations, such as
solfège, tablature or numbered music notation, and even mixing up different notations!

- Sort ing Net w ork - csunplugged.org

Figure 1. A 6-way parallel sorting network

The next extension is to compare note and rest lengths (students are given quavers/crotchets/minims
and so on, with tied and dotted notes used as an extension). It can also apply to dynamics (ff, f, mf, mpetc.) and tempo indications (Allegro, Andante, Presto etc.). Aural skills can be exercised if the students
are given instruments that make a sound; we have found tuned bells to be particularly effective, as it ispossible to get bells that are the same size but have different pitches (Figure 2, right). This forces
students to make pitch comparisons over and over, and success is measured if the bells come out inascending order.
While a fixed sorting network itself is fairly easy to master, we have been surprised that students enjoy
using it regularly. They can try out different things to compare, and reason about the meaning of theorder for different musical elements. The activity has a built in gamification in that students want tocomplete the task as quickly as possible, but if they are inaccurate because of speed then the team
doesn’t achieve a successful outcome.

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Figure 2. Comparing musical notes (left) and bell pitches (right) in a sorting network

From a computational point of view, students can encounter the factorial number of orders that the input
can start in, and the idea that two identical values end up being sorted together. It touches on every
aspect of CT, but is particularly strong in abstraction (the algorithm works regardless of what is being
sorted), decomposition (a sophisticated outcome is broken into very simple comparison steps), andlogic (trying to explain, for example, why the smallest value will always find its way to the correct node).
It is also an effective music education tool - students are repeatedly gaining experience with the notation
for pitch and rhythm, and exercising their aural skills in a motivating context; and as they become
comfortable with a notation, it can be extended by adding more difficult variations. When using this wehave repeatedly found that students are able to meaningfully grasp music concepts beyond their current
music theory level, which results in much deeper understanding of their current knowledge.

Positioning robots
An activity based on simple turtle-style robots is to draw a large stave on the ground (Figure 2), andhave students program simple robots to go to the location of a specified note on the stave. The robots
in Figure 3 are “BeeBots”, a simple device that can be programmed using only four commands (forward,
back, left, right) that can be entered as a sequence. These commands are then followed when the "go"button is pressed, which is equivalent to running a program. The BeeBot moves 15cm for eachforward/back command, so with the stavelines 30cm apart, each movement corresponds to one spaceor line on the stave.

Figure 3. Students using BeeBots to show a musical interval

Initially the BeeBot only needs to move forward to the given note (e.g. “get the BeeBot to go to D”), but
this can be extended into notating a short tune either with the BeeBot pausing on each note of the tune
and spinning 360 degrees, or having multiple BeeBots. This now requires programming the position in
two dimensions based on the simple movements.
In our experience, students initially make mistakes with the programming, but are determined to get to
the right note. After a while they become adept at the “language” of the BeeBots, and some have eventaken the risk of sending the BeeBot on longer paths than needed to introduce some humour to theexercise. This approach is easiest using tunes based on a diatonic C major scale, although representing
sharps and flats can be done by the direction the BeeBot is pointing (left for flat, right for sharp).
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Another variation is to use two BeeBots, and work with intervals, either programming them to show aninterval (possibly given aurally), or having other students name the interval that has been displayed.
This can be extended further by having one group of students program a chord with three or four
BeeBots either vertically aligned (harmonic) or in a horizontal pattern (melodic), which also provides thechallenge of avoiding collisions. We then have another group play the chord on a piano, and/or namethe chord (starting with simple triads, and extending to other types of chords, including inversions, 7ths,
sus chords and so on). A further extension is to change the clef.
A challenge that exercises evaluation from CT is to try to make all the BeeBots arrive at their finaldestination at the same time. This would require students to accurately determine the number of steps
in the “program” before it is run, and pad out the steps for shorter routes to match the longest one. This
exposes students to the idea that we could analyse the running time of an algorithm without running it.
This exercise motivates students to repeatedly use the fundamental idea in programming of sequences,with the concomitant CT elements, particularly algorithmic thinking, and decomposition of the path into
steps. Musically, students are working with rudiments of music theory, particularly pitch notation,
accidentals, clefs, intervals, and triads/chords. They also need to demonstrate teamwork to create“chords”.
Another variation of this is to create ukulele or guitar diagram grids for students to program the BeeBots
to land on chord shapes. In this case the grid lines are 15cm apart, representing the strings and frets.

Codes based on soundThe idea that all data on computers (text, sound, images, numbers etc.) is represented as binary
numbers is fundamental to the idea of a digital device. The CS Unplugged activities include a popular
way to introduce binary numbers with minimal mathematical background required (in fact, the mainprerequisite is being able to count up to 31). This is done by having students work with 5 cards, with 16,
8, 4, 2 and 1 dots on one side of them respectively. The Unplugged website gives a lesson plan for aconstructivist approach to show students the relationship between decimal numbers and binary
representation by flipping over the cards as a binary representation.
This segues to representing letters of the alphabet using these numbers; for the 26-letter English
alphabet students will usually suggest using 1 for A, 2 for B, 3 for C, and so on. For example, the binary
number 11010 represents the number 26, or the letter Z. Since the 0 and 1 digits are abstract
representations, this transitions easily to the idea of using other representations that have two values,
including sound. A variety of alternatives can be explored (loud/soft, long/short, timbre, melodic patterns,
low/high), with the latter (low/high) being effectively how a telephone modem works. Once students
realise that letters of the alphabet can be transmitted using only high and low notes, a tune can be used
to represent some text. As a simple example, the first exercise in the CS Unplugged “Modems” activity
has a recording of a jazz singer singing this sequence, which students can decode (https://www.youtube.com/watch?v=MOMXxRbpkjM ).
The concept of encoding messages into an apparently unrelated format is called “steganography”. The
idea that this is even possible is an important opportunity to show students how innovative algorithms
can achieve something that might not have been thought possible, and can motivate students to create
their own hidden messages.
This leads to a constrained exercise in composition for students - they should first work out the binary
representation that they would like to hide in a composition, and then write a meaningful piece of musicthat encodes the message. This is typically done using notes that are clearly high and low, but any othertwo-state representation could be used, such as whether each note is higher or lower than the previous
one, a percussion part that has two sounds (such as a kick and snare drum), or a backing part that is
ascending or descending. The composition could be performed live on acoustic instruments, enteredinto recording or notation software, or played using websites that allow students to create music usinga range of graphical input formats.
As an intriguing variation, the hidden message could be a melody itself, represented with diatonic music
numbers or MIDI! If MIDI is being used, the notes can be represented in 7 bits, and this could map on524

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to rhythms with 8 quavers (eighth notes) in a 4/4 bar (notes or rests for 1 or 0 respectively), with either
the first or last quaver being chosen by the composer, and the other 7 based on the bits in the MIDI
note. In this case, a melody is being represented by a rhythm.
This activity exercises a variety of CT concepts, including algorithms for converting between decimaland binary, abstraction where even representations may have different representations(!), andevaluation of the number of bits needed to represent a range of symbols (e.g. for alphabets of morethan 26 characters).
Musically, it provides motivation for composition as well as some constraints. Students listening tosteganographic recordings will be exercising their aural skills to recognise the binary digits that makeup the message.

Programming note sequences
Music theory contains a lot of rules around sequences of notes, particularly scales, which are
fundamental to many genres of music. These scales are based on repeating patterns, so they areamenable to being programmed. Most programming languages are able to play MIDI notes, which
opens up the possibility of writing programs to play scales. Parameters could be added to determine
aspects like the key, range and speed.
For example, Figure 4 shows a simple one-octave chromatic scale programmed in the Scratch
language, starting on a C (midi note 60). Each note is 1 semitone higher than the previous one (the“change degree by 1” command). Students can be given this as an example, and asked to identify thescale. They can then “remix” the program (edit it) to make the degree change by 2 each time, creating
a whole-tone scale, which has a distinct sound to it. Other step sizes produce various arpeggios (forexample, steps of 3 produce diminished 7th arpeggios), and students could be challenged to make thenotes ascend in octaves (12 steps, as there are 12 semitones in an octave), to write descending scales(negative steps), and identify them aurally.

Figure 4. A first attempt at a Scratch program for a scale

Figure 4 shows how the choice of variable names provides a valuable form of integrated learning. The
terminology (tonic and degree) for how the note is calculated articulates how the logic of the program
works. In this example, the term “degree” is not quite musically accurate as it is the number of semitones
above the tonic, rather than the degree. While such discussions might seem pedantic, they give theopportunity to work with the precise terminology in music, and to recognise the importance of accuratelynamed variables to make a program understandable.
The next challenge is to have students adapt the program for other scales (such as major, minor, andmodes). These will involve a mixture of intervals between notes for each octave (for example, a major
scale increases the pitch of each note by a tone, tone, semitone, tone, tone, tone, semitone respectively)
to give 8 notes. Programming this forces students to think about each degree of the scale, and they can
debug their program by listening to it and checking that the scale sounds like a conventional major
scale. They could also write a program to play a sequence of scales (e.g. C major, then C# major, Dmajor, and so on), which provides opportunities to work with nested loops. For more advanced
programmers, this also raises the possibility of storing the degrees of the scale in a list, and looking up
the degree in the list, which makes it easy to move up and down a scale.

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This exercise engages students with loops and variables in a way that there are precise desired
outcomes (such as playing a particular scale). The need for accuracy playing scales can transfer frommusical training to the need for the program to do precisely what is specified. As well as working withalgorithms, students are using abstract representations of notes, and are also using decomposition tobreak scales into their elements.
Another valuable activity is to have students write programs to play a tune, and use musical phrases
that can be represented as functions (“More Blocks” in Scratch), so that they are both aware of thestructure of the music and using the CT concept of decomposition.
Musically, students are developing their knowledge of music theory (such as the intervals between notes
in each scale and use of musical phrases) as well as their aural skills.

Conclusion
In addition to the natural connections between music and CT, we have given four examples of exercisesthat can be used to engage students meaningfully with both subjects in an integrated manner. This
provides ways to involve students who might be more interested in one aspect than the other, and alsomeans that teaching time is being used to cover two subjects at once. They may also help teachers
who feel confident in one subject to explore the other subject with students. In practice, it is likely that
this would benefit from a collaboration between a computing teacher and a music teacher if each subject
is to be explored in depth.
These examples only touch on aspects of each curriculum. We recognise there are other ways that CT
and music can be combined, particularly using programming languages to create compositions, either
offline or as a live performance. Computational thinking is a relatively new subject in schools, and welook forward to new ideas appearing as teaching collaborations and teachers with cross-curricula
interests create new activities that meaningfully combine different subjects.

References
Aaron, S., Blackwell, A. F., & Burnard, P. (2016). The development of Sonic Pi and its use in educational
partnerships: Co-creating pedagogies for learning computer programming. Journal of Music,
Technology & Education, 9(1), 75-94.
Bell, T., Rosamond, F., & Casey, N. (2012). Computer Science Unplugged and related projects in mathand computer science popularization. In H. L. Bodlaender, R. Downey, F. V Fomin, & D. Marx (Eds.),
The Multivariate Algorithmic Revolution and Beyond: Essays Dedicated to Michael R. Fellows on theoccasion of his 60th birthday (Vol. LNCS 7370, pp. 398–456). Heidelberg: Springer-Verlag, Berlin,
Heidelberg.
Burton, R. L. (2015). The elements of music: what are they, and who cares? In The Australian Society
for Music Education National XXth Conference Proceedings (pp. 22–28).
Denning, P. J. (2017). Remaining trouble spots with computational thinking. Communications of theACM, 60(6), 33–39.
Engelman, S., Magerko, B., McKlin, T., Miller, M., Edwards, D., & Freeman, J. (2017, March). Creativity
in Authentic STEAM Education with EarSketch. In Proceedings of the 2017 ACM SIGCSE TechnicalSymposium on Computer Science Education (pp. 183-188). ACM.
Greher, G. R., & Heines, J. M. (2014). Computational thinking in sound: Teaching the art and scienceof music and technology. Oxford University Press.
Guzdial, M. (2003, June). A media computation course for non-majors. In ACM SIGCSE Bulletin (Vol.
35, No. 3, pp. 104-108). ACM.
Hubwieser, Peter, Michail N. Giannakos, Marc Berges, Torsten Brinda, Ira Diethelm, Johannes
Magenheim, Yogendra Pal, Jana Jackova, and Egle Jasute.

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Hubwieser, P., Giannakos, M. N., Berges, M., Brinda, T., Diethelm, I., Magenheim, J., Pal, Y., Jackova,J., & Jasute, E. (2015). A Global Snapshot of Computer Science Education in K-12 Schools. In
Proceedings of the 2015 ITiCSE on Working Group Reports - ITICSE-WGR ’15 (pp. 65–83). New York,New York, USA: ACM Press.
Kafai, Y. B. (2016). From Computational Thinking to Computational Participation in K–12 Education.
Commun. ACM, 59(8), 26–27.
Kaschub, M., & Smith, J. P. (2016). The Big Picture: Developing Musical Capacities. Music Educators
Journal, 102(3), 33-40.
Selby, C. & Woollard, J. (2013). Computational thinking: the developing definition. Available fromhttp://eprints.soton.ac.uk/356481.
Tedre, M., & Denning, P. J. (2016). The Long Quest for Computational Thinking. In Proceedings of the
16th Koli Calling Conference on Computing Education Research (pp. 120–129).

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Teaching in a Sustained Post-Secondary
Constructionist Implementation of Computational
Thinking for Mathematics
Chantal Buteau, cbuteau@brocku.ca
Brock University, Canada

Ana Isabel Sacristán, asacrist@cinvestav.mx
Cinvestav, Mexico

Eric Muller, emuller@brocku.ca
Brock University, Canada

Abstract
In this practice report, we reflect and discuss the roles of, and demands on university instructors in three
undergraduate mathematics computer-based courses implemented since 2001 at Brock University,
Canada. These are the Mathematics Integrated with Computers and Applications (MICA) I, II, III
courses, in which instructors create an environment that supports students’ constructionist learning
experiences as they design, program, and use interactive environments (i.e., microworlds) to learn anddo mathematics. Using Ruthven’s (2009) model on the professional adaptation of classroom practicewith technology, we feature constructionist characteristics of the course design highlighting the shift
from traditional, instructionist pedagogy towards one of empowering students. Since there seem to berelatively few sustained implementations of microworlds in mathematics instruction (Healy & Kynigos,
2010), this report, grounded on a continuous practice of over 15 years, contributes to our understandingof roles and demands of “ordinary” instructors in the “real” classroom, who have aimed at creating anenvironment for supporting students’ constructionist learning experiences. In particular, this reporthighlights the instructor’s demanding role in these student-centred courses, more so since students
select their own topics for their last project in lieu of final exam, thereby having the opportunity of it being
meaningful to them.

Keywords
computational thinking; constructionism; mathematics; programming; microworlds; university; teachers

Introduction
In this practice report, we reflect on the roles of, and demands on university instructors in undergraduate
mathematics computer-based courses – the Mathematics Integrated with Computers and Applications(MICA) I-II-III courses – carried out at Brock University, Canada (Ralph, 2001; Buteau, Muller, & Ralph,2015). These courses follow the constructionist paradigm (Papert & Harel, 1991), requiring students todesign and program computational objects for mathematical learning, and have had sustained
implementation for over 15 years (Buteau, Muller & Marshall, 2015).
More specifically, at Brock University, mathematics majors and future mathematics teachers learn todesign, program, and use interactive environments –called Exploratory Objects– for the investigation ofmathematical concepts, conjectures, and theorems or real-world situations (Muller, Buteau, Ralph, &Mgombelo, 2009). During their first undergraduate years, students may enrol in a sequence of three
MICA courses and create in total 14 Exploratory Objects as part of their course load (Buteau, Muller,
Marshall, Sacristán, & Mgombelo, 2016). At the end of each term, students, individually or in groups oftwo or three, create an original Exploratory Object for which they select the topic (see MICA, 2018). As
a result of a literature review study (Marshall & Buteau, 2014), we classified the development of
Exploratory Objects as mathematical microworlds: they constitute open-ended exploratory computer
activities (Edwards, 1995) where MICA students engage in computational thinking for mathematics(Buteau et al., 2016).
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This paper provides a discussion, based on insightful reflections, on the “real” MICA classroom byparticularly focusing on how the “ordinary” MICA instructors have created an environment that supports
the students’ constructionist learning experiences. We are interested in discussing the constructionist
experiences involved in the teaching of MICA courses. In the following, we first briefly lay out aspectsof Constructionism, Computational Thinking and Microworlds that frame the pedagogical approach in
the MICA classroom. Using Ruthven’s (2009) model of five key structuring features of school classroom
practice, we then discuss demands on, and roles of instructors in these courses. This discussion
focuses, first, on the overall courses and, second, on students’ final individual projects which are usedin lieu of final exams. We end with a few concluding remarks.

Constructionism, Computational Thinking and Microworlds: A
Theoretical Framework for MICA’s Pedagogical Approach in the
Mathematics Classroom
As stated above, our MICA courses follow the constructionist paradigm; constructionism is defined byPapert and Harel (1991, p.1) in the following way:Constructionism--the N word as opposed to the V word--shares constructivism's connotation oflearning as ´building knowledge structures´ irrespective of the circumstances of the learning. It
then adds the idea that this happens especially felicitously in a context where the learner isconsciously engaged in constructing a public entity, whether it's a sand castle on the beach or atheory of the universe.
Thus, the basic principles of the constructionist paradigm involve learning situations or environmentsthat are student-centred, where students build or construct shareable objects that are somehow“tangible”. These situations usually involve open projects, often computer-based ones, in which learners
engage with meaningful “powerful ideas” (Papert, 1980) within a social context of collaboration,
discussion or interaction among peers.
One concept that is linked to constructionism, is that of exploratory computational environments known
as microworlds. A microworld is defined by diSessa (2000) as
a type of computational document aimed at embedding important ideas in a form that students
can readily explore. The best microworlds have an easy-to-understand set of operations thatstudents can use to engage tasks of value to them, and in doing so, they come to understanding
powerful underlying principles. (p. 47)
For Papert (1980), microworlds involve objects “to think with” and “allow a human learner to exercise
particular powerful ideas or intellectual skills” (p. 204) through exploration and discovery in a knowledge
domain.
Two ways of describing microworlds are given by Edwards (1995): a “structural” definition which focuseson the design elements (e.g. collections of computational objects to model mathematical or physicalproperties of a domain; representations; and activities or challenges for students to explore in thedomain); and a “functional” definition which highlights how students learn with microworlds, such as
through the interaction between the student, the software, and the setting in which it is used.
Related to the latter, an important aspect for us is that related to the role of the teacher inconstructionism: Papert (1980) stated that in a microworld situation “the relationship of the teacher tolearner is very different: the teacher introduces the learner to the microworld in which discoveries will
be made, rather than to the discovery itself” (p. 209); that is, the situation presented by the teacher is
one to facilitate students to think like mathematicians rather than to teach them about mathematics.
Weintrop et al. (2016) remind us that “Papert (1996) was the first to use the term computational thinking
to refer to the affordances of computational representations for expressing powerful ideas.” (p.130).
These authors discuss extensively on how mathematicians and scientists have come to engage incomputational thinking in their professional (including research) work, and argue that “the varied and529

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applied use of computational thinking by experts in the field provides a roadmap for what computational
thinking instruction should include in the classroom” (p. 128).
In the next sections we present the structure and functioning of our MICA courses, highlighting theconstructionist aspects and role of the instructors.

University Instructors in MICA Courses: Roles & Demands
To provide a structure for our reflection and discussion on demands on, and roles of instructors in MICA
courses, we use Ruthven’s (2009) model of five key structuring features of school classroom practice
to “illuminate the professional adaptation which technology integration into classroom practice depends”(p. 131).

Working environment
For MICA courses, instructors teach in regular lecture rooms where they mostly elaborate themathematical content. They lead mathematics programming-based activities in computer laboratories(one computer per student). The course format is two hours per week for lectures and two hours per
week for lab sessions. The class size is capped at 35 students per course section.

Resource system
Ruthven (2009) describes: “[t]he concept of ‘resource system’ focuses… on the combined operation of
the mathematical tools and curriculum materials in classroom use, particularly on their compatibility andcoherence of use, and on factors influencing this” (p.136). For their mathematics lectures, MICA I
instructors use lecture notes which to date have been shared among instructors. In MICA II, instructors
use lecture notes and possibly a mathematical modelling textbook depending on the instructor. For thelab sessions, programming software is used. Since MICA I students need to learn programming, theinstructor uses a friendly textbook for the introduction of this technology (currently VB.NET in Visual
Studio environment), in addition to lab activity guidelines (Ralph, 2017) that emphasize the connectionof mathematics and programming (Buteau & Muller, 2014). In MICA II-III, instructors use only lab activity
guidelines since students are then knowledgeable programmers. MICA instructors actually use
programming in their own mathematical research, and therefore consider programming as an integral
part in doing mathematics (i.e. they are engaging in computational-thinking-based mathematics
research as described by Weintrop et al., 2016). In other words, because of their research practice,
instructors naturally merge the mathematics and programming resources as a system. Individual
instructors might need to learn the specific programming language. However, this is a not overwhelming
particularly since within the lab sessions they can count on knowledgeable teaching assistants.

Activity format
Ruthven (2009) introduces the key structuring feature of activity format where “Classroom activity isorganised around formats for action and interaction which frame the contributions of teacher andstudents to particular lesson segments (Burns & Anderson, 1987; Burns & Lash, 1986).” (p. 137).
Overall the MICA instructor needs to provide a learning environment in which students:•

design and program mathematics experiments/modeling/simulations with an appropriate
interface in order to conduct an investigation;•reflect on mathematical results and data in a written report.
(Buteau & Muller, 2010)

This constitutes what we call in this paper ‘a microworld approach’ to learn and do mathematics. In thefirst-year MICA I course, students learn the microworld approach, and in the upper-year MICA II-III
courses, they apply it to broader and more sophisticated mathematics contexts (Buteau et al., 2016).
The instructor in any MICA I – II - III provides an environment for students to experience constructionist
learning: students learn by making and using these microworld projects. This includes a pedagogicalapproach where instructors guide students to carefully develop their visual interfaces to support theirinvestigations (the construction is shareable). In fact, this overall aim for students to learn and apply the530Constructionism 2018, Vilnius, Lithuania

microworld approach grounds the MICA course design and pedagogy (Buteau et al., 2015), and leads
to empowering students to work as mathematicians (Broley, Buteau, & Muller, 2017; Buteau et al.,2016).
In MICA I lectures, the instructor elaborates on the mathematical content, although it is not presented
quite in a traditional manner. For example, the instructor thinks out loud as a ‘working mathematician’to make transparent the messy development of mathematical ideas. Or the content may be presented
through selected examples in order to prompt students to ask mathematics questions and state
conjectures. This is a shift from a traditional, instructionist approach towards one of empowering
students. For the instructor, it requires acceptance of a slower pace in terms of mathematics content
covered and surrendering some control (i.e., s/he acts as facilitator, not lecturer). In MICA II-III, theinstructor follows a more regular lecture format. However, due to the smaller size of classes and theinquiry component of the course, lectures are often more interactive including aspects of an inquirybased mathematics classroom (Rasmussen & Kwon, 2007); e.g. with interaction between instructorstudents or among students, at structured times.
In MICA lab sessions, the instructor usually gives overall guidelines to the whole class for a short periodof time (2-15 minutes), then leads students to their individual work, and when necessary, provides
opportunities for the whole class to discuss a situation encountered by one or more students. Teachingassistants assist the instructor with students’ individual work. This stresses the student-centred activity
format of the lab sessions. Overall, and particularly in MICA I, instructors need sensitivity to individual
students’ development of their instrumental genesis (Trouche, 2004) during their programming and intheir involvement in the mathematics microworlds (Buteau & Muller, 2014), stressing again the studentcentred characteristic of MICA courses.

Curriculum script
When preparing or teaching lessons on a topic, instructors use their professional knowledge that
includes, in particular, “a loosely ordered model of relevant goals and actions which serves to guidetheir teaching of the topic” (Ruthven, 2009, p.138).
MICA courses are not mathematics content-driven: the microworld approach to learn and domathematics defines the courses (Buteau et al., 2015). The overall content is not ‘traditional
mathematics’, and the overall script had to be rethought since the technology impacts ‘what’
mathematics is covered in MICA courses (Buteau et al., 2016). In MICA I, the course content is driven
by students needing to learn programming and the microworld approach: the mathematics content
provides context in which students learn those (Buteau & Muller, 2014). In particular, the learning of
programming technology for mathematics follows a back-and-forth instrumental orchestration (Trouche,
2004) model (Buteau & Muller, 2014). The instructors’ script also includes the component of debugging
one’s own program and for students to appreciate the value of debugging. In MICA II-III, each instructor
involves mathematics content relevant to the microworld approach (i.e., a computational thinking
approach) according to his/her own, evolving mathematics interests. As such, the instructors integrateauthentic programming-based mathematics tasks (Buteau, Mgombelo, Muller, Rafiepour & Sacristán,submitted) which, in particular, includes inquiry practices (i.e., involving using the microworld). We
associate this with Wagh, Cook-Whitt and Wilensky’s (2017) position that “the constructionist approachof interacting with and manipulating program code of computational models can facilitate productive
forms of [students’] engagement with inquiry-based science” (p.615).

Time economy
Because of the student-centered characteristic of the microworld approach defining the MICA courses,
individual student guidance is required, and this means ‘time’. To respond to this characteristic, thedepartment limits enrolment to 35 students per section and provides two teaching assistants for the lab
sessions to assist the instructor.
As mentioned before, the instructors need to accept and adapt, due to the nature of the MICA courses,
to a slower pace in terms of mathematics content covered in lectures. Also, instructors use programmingin their own research and do not require additional time to learn the technology (except if there is achange in the programming language). However, when teaching the MICA courses for the first time,531

Constructionism 2018, Vilnius, Lithuania

instructors need to prepare in terms of the change in the pedagogical paradigm required for the MICA
implementation of the microworld approach (Papert, 1980; Muller et al., 2009). This is a shift inphilosophy that is new to university mathematics instructors who normally mainly, if not strictly,
concentrate on content. This shift requires time.
University instructors have a dual role for their departments as teachers and policy makers to create,delete, and modify courses in their curriculum (Barker et al., 2004). Because of the innovative nature at
the time of the proposed course design, a professor in the department was awarded a course releaseto dedicate his time to design what became MICA courses (Buteau et al., 2015).
In the next section, we elaborate on the roles of and demands on the instructors required in the last partof the MICA courses for which the course format is significantly different.

Instructors Creating an Environment for Individual Projects
Meaningful to Students: Roles & Demands
Each of the three MICA course terms culminates to a complete constructionist learning experience
opportunity for every student. This is a very demanding and engaging exercise for both students andinstructors.
Students, individually or in groups of two or three, create original microworld open projects for which
they select their own topics. The MICA instructor encourages and insists that students select a topic ofinterest to them, i.e., something related to them, either an application of mathematics or a mathematical
topic of interest. In other words, the instructor motivates students to engage in a project that ismeaningful to them. For example, Matthew and Kylie wondered if it is better to walk or run in the rain
(Figure 1, left) while Adam investigated the bounded area, as the exponent increases, of the iterativecomplex function defining the Mandelbrot set (Figure 1, right). See MICA (2018) for these and otherexamples. Since these original microworlds are meaningful to students and shareable constructions,
we have observed that students show pride of, engagement in, and ownership of their own projects(Muller et al., 2009).

Figure 1. To the left, Matthew and Kylie’s real-world situation microworld: “is it better to walk or run in the rain?”;to the right, Adam’s pure mathematics microworld about the bounded area of the iterative complex function
defining the Mandelbrot set as the exponent increases.

This individual project-based activity requires a completely different teaching format. Because students
select the topic of their individual microworld projects, this is a demanding, ‘risky’ approach for the
instructor, in particular in regards to the instructor’s curriculum script, resource system, and time
economy features (Ruthven, 2009). The change in the instructor’s activity format and working
environment is not challenging: the instructor acts ‘on-demand’ for students, and this individual guidancetakes place in labs or at unscheduled time.
During the original project period, details of the instructor’s curriculum script could not have been
elaborated ahead of time as it is driven by the students’ selection of topics. The instructor may need to532

Constructionism 2018, Vilnius, Lithuania

engage in research for some student projects: mostly to evaluate the feasibility of the project, which
possibly involves a simplification of a proposed topic or problem and to guide students to adequately
research the topic. The instructor may also be called upon to guide and to assist in the designing andrunning of the investigation. This is demanding as it is different and possibly new for each project andfor each time the course is offered, and is part of the “at risk” aspect for the instructor. In terms ofresource system, the instructor mostly relies on his/her research skills. This involves for each student
project to help identify ‘on the spot’ good resources at the right level for students. As for instructor’s time
economy, this part of the MICA courses is very much time demanding because of the individual
guidance and research component required by the instructor for each student project (one of thereasons why MICA course sections are capped to 35 students). The evaluation of these projects is noless demanding.

Concluding Remarks
Throughout this practice report paper, we described how the teaching at Brock University of the MICA
courses embraces a constructionist paradigm based on using and developing computational thinking
for mathematics. By course design, MICA students learn by making through individual projects in theform of designing, programming, and using a mathematics microworld (i.e., a tangible computer object
that is shareable) to learn and do mathematics. This is what we have called the ‘microworld approach’in this paper. The student-centred constructionist learning experience in MICA courses culminate toindividual original projects for which students select their own topics, thereby have the opportunity of it
being meaningful to them. As such, the design aims to empower students to work as mathematicians.
In terms of mathematics content, the microworld approach outlining the MICA courses has impactedthe ‘what’ of mathematics (Buteau et al., 2016), a key characteristic distinguishing constructivism fromconstructionism (Noss & Clayson, 2015).
In order to reflect the design of the course, instructors have to change their pedagogy in a significant
way, which highlights many characteristics of constructionist teaching. For example, MICA instructors
tend to act more as facilitators than lecturers, and to use a slower pace to cover mathematics content.
When appropriate, they make transparent the messy development of mathematical ideas. This is a shift
from a traditional instructionist approach towards one of empowering students, thereby aligning with theaims of the courses (Buteau et al., 2015). In Buteau et al. (2016), we examined the learning experienceof a student, named Ramona, through her three MICA courses. We concluded that “students such as
Ramona engage in constructionist experiences of mathematics learning…,” adding that “[s]tudents also
progressively develop proficiency in the third pillar of scientific inquiry mentioned by the European
Mathematical Society (2011),” namely: “Together with theory and experimentation, a third pillar ofscientific inquiry of complex systems has emerged in the form of a combination of modeling, simulation,
optimization and visualization” (European Mathematical Society, 2011, p.2).
The present paper discussed the roles of and demands on the university instructors teaching the MICA
courses. The application of Ruthven’s (2009) model on the professional adaptation of school classroom
practice with technology proved to be very insightful as to identify key components of instructors’ rolesand demands specific to the constructionist approach in the MICA courses. However, we suggest that,at the university level, a ‘Teaching Assistant’ structuring feature be added to Ruthven’s (2009) model.
There seem to be relatively few sustained implementations of microworlds in mathematics instruction(Healy & Kynigos, 2010). In this paper, we identified in the mathematics MICA courses, implemented at
Brock University since 2001, that the ‘meaningful to students’ constructionist principle impacted, in asignificant and challenging way, three of the five key structuring features of the instructor’s classroom
practice as given in Ruthven’s (2009) model, namely curriculum script, resource system, and time
economy. This could highlight why the implementation of a constructionist approach by (university)
instructors is so challenging.

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Design Curriculum for Educational Robotics:
Constructionist Pedagogical Experience in Formal
Education
Flavio Campos, flavio.rcampos@sp.senac.br
University of Sao Paulo – USP, Brazil

Abstract
This paper presents a pedagogical constructionist experience in designing curriculum for educational
robotics for k-12 formal education. The discussion is based on 15 years’ experience of a school in Brazil,
detailing a timeline of an educational robotics curriculum project development. Beginning as anafterschool program back in 2003, running as a technological resource used by math and scienceteachers and later integrated into curriculum from kindergarten through high school. The analyses
emphasize the historical scale of a constructionist curriculum project with robotics, describing the
aspects of each step and the challenges faced by the school, teachers and the administration.
Therefore, the paper intends to address a curriculum design approach that drives educational robotics
activities, highlighting the constructionism perspective. Pedagogy and its dimensions such as
methodology, teaching and learning related to educational robotics are presented within theoretical andeducational foundations. Beyond the use of robotics, deepens the discussion of curriculum design,
engineering design process during robotics activities and how the integration of robotics education canboost constructionist approach in formal education.

Keywords
design curriculum; educational robotics; constructionism; engineering design process.

Chapter 1
Introduction
In the last decade, robotics has sharpened the interest of teachers and researchers as an importantresource for cognitive development and social skills of students from kindergarten to high school and in
the learning background of science, mathematics, technology, computing and other knowledge.
As a technological resource used by educators, robotics is one of the most upgraded and integrated
into school’s activities around the world. From STEM projects through political pedagogical standards,
formal education is allowing computational thinking and constructionist approach to emerged in teaching
and learning process using robotics and its technology. Many countries, including Brazil, have been
benefited by a growing number of educational robotics technology such as LEGO Mindstorms,
fischertechnick, Kibo, K’nex, GoGo board and others.
Robotics projects in k-12 education in many schools represent as an isolated practice in different
development projects, because these projects have been identified as a specific subject in the
curriculum, which means it has been using in professional education in high school or College. Robotics
have been seen by educators and the population as a sophisticated toy, in which people that love
robotics find themselves in championships and conferences around the world.
The research in robotics has been reaching the university context – engineering and mechanics – andindustries. The interest for the subject is growing, and we can see the investments from the government
in educational technology. Even with all the investments, only a few k12 schools in Brazil integrate
educational technology subjects (such as robotics) in curriculum. Projects that are more significant arelimited in professional education and College.
Despite all that, It´s not unusual to find educators interested in exploring robotics, STEM curriculum andconstructionist concepts in their practice. Influenced by researchers and projects using robotics in536Constructionism 2018, Vilnius, Lithuania

schools, by cinema and media, or by simply amused by technology, teachers and students mobilize
themselves to create and conduct their projects. Making the design, build, program and analyse theresults of robotics become a motivated activity in learning process and STEM curriculum, helping
cognitive process, as well as provides creative activities.

Phase 1 – Afterschool program and science fairs
Beginning in 2003, the school acquired initially knex and LEGO Mindstorms(RCX) educational sets tostart its afterschool program. The goal was to develop a program to give students an opportunity tolearn and engage in engineering and robotics activities.
The activities were based on engineering and robotics concepts and were developed considering
students from 6th grade to high school (11 to 17 years old). Once a week for three hours, students
interested in those activities participated in this program offered only after school and they could design
artifacts with either knex or mindstorms.
Usually, the projects were presented by the teacher, with challenges prepared based on science
concepts and mathematical problems. One aspect of this approach was that only students with apersonal interest in robotics and engineering looked for to participate, which represented less than 5%of total students at the school.
Although a small percentage of the students participated at the beginning, it was significant to create a
motivational atmosphere and to initiate a discussion about robotics integration into curriculum activities.
During the afterschool program phase, students started to talk more about different possibilities withthis technology in the classroom, asking teachers and the administration about using robotics sets tolearn different types of subjects such as science, math and even humanities.
After only two years of afterschool programs, the administration decided to add another step into
educational robotics and constructionism perspective allowing science and math teachers to integrate
educational robotics in science fairs projects.
The 2005 science fair integrated robotics with 9th grade and high school students. Teachers decided touse robotics with a group of 35 students, divided into small groups of 5 and let them with the choice of
their own projects. Therefore, students worked for two months in their projects, designing, building andprogramming devices to present at the science fair.

Figure 2. Knex Eiffel Tower project and RCX spider project – (Science fair 2005)

First, students gathered to decide a theme to work on, and then they structured a schedule to develop
the project. Research and prototype design were the main tasks during the project development, andstudents had time between school program and afterschool program to finish the project.
The teachers were mentors during the process, helping students in everything they need, from scientificconcepts to engineering design. At the science fair, students had to present their project to thecommunity, demonstrating not only the “product” but all the process.
By that time, robotics were only used at the afterschool program and during the science fair schedule.
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Phase 2 – Mathematics and Science curriculum with educational robotics
The first science fair with robotics sparked a desire to use educational robotics beyond afterschool
programs and science fairs. Teachers and school administration decided to use robotics in math andscience classes.
In 2005, science and math 9th grade teachers specifically started to plan their classes and projects. The
expectations at the beginning were to create room for more and more activities with robotics, letting
teachers of science and math experience the learning process with robotics.
One of the math classroom projects designed was a car created by the students to study equations,which students engineered a door to calculate the data. The teacher planed the activity and students
had the chance to create the object, although it had to be a car.

Figure 3. Knex car project equations – (Math classroom activity 2006)

The scenario in 2005 was different from what the administration expected. While students were excited
to use robotics to learn school content such as equations, simple machines and so forth, teachers
actually only used educational robotics in six or seven classroom activities during the school year.
Teachers had a professional development to comprehend theory and practice of educational robotics,
as well as time to prepare and discuss possibilities of curriculum projects with robotics. Unfortunately,that seemed not to be enough, as students had just a few opportunities to experience a constructionist
approach to robotics.

Phase 3 – Participation First LEGO League competition
After two years of educational robotics, the school administration invited some of the students that had
been participating in the afterschool program to be at the First Lego League competition. They had
group meetings every week (2 hours) and dedicated time to deep understanding of programing andengineering, using at first Lego mindstorms RCX.
The project was not only to participate in the competition but also to present the challenges to otherstudents at the school and even promote a “school competition” in order to engage students witheducational robotics. Therefore, during 2006 competition, they had a waiting list of students wanting toparticipate, and after three seasons, the school usually takes 3-4 to teams to the competition.
This experience was significant because, with only two years, teachers and administration motivatedwith students participation decided to think about integrating educational robotics and constructionist
approach into curriculum.

Phase 4 – Educational robotics integrated into curriculum
The year of 2008 represented an important change in educational robotics at the school. Teachers andadministrators decided to include a subject in the curriculum called “Engineering and Robotics”, withclasses once a week for an hour and forty minutes. Such as math, science, history, geography and soforth, this subject was integrated in the curriculum from 1st grade to high school students.
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Constructionism 2018, Vilnius, Lithuania

Since 2008, educational robotics and constructionist approach have been part of the curriculum, withweekly activities using different resources (Lego mindstorms, knex, GoGo board, Arduino), programingwith a diverse type of programing languages (robolab, nxt, Labview, scratch).
At the beginning, teachers organized classroom activities based on science and math concepts andused educational robotics to achieve the goals, as an example, an activity was to calculate distance,time and speed using a car built with Lego mindstorms.
For two years, teachers were planning activities using educational robotics to understand some subject
concepts, but not too deeply engage into technology and robotics itself. The initial experience was touse robotics as a resource, helping teachers to present complex concepts.

Figure 4. Engineering and Robotics curriculum development with NXT and makey makey board– (from left toright: classroom activity 2011 and 2016)

Combining the experience with the desire to expand robotics activities, teachers decided to integrate
robotics and technology with complex concepts in different subjects such as science, math andhumanities.
Thereby, educational robotics classroom activities started to be planed based on computing, robotics
and subjects content. At the same time, teachers prepared a storytelling approach with 1 st to 5th grade
to integrate educational robotics.

Figure 5. Storytelling, from left to right – (Story with a challenge; an engineering design and Keywords to study
during the project)

After reading a story, which contains a challenge, students always in groups of three or four start to plan
the solution, picking up an engineering design prototype to build. Actually, students begin the activity aweek before, investigating the problem and keywords teachers give them in order to maximize the time
between every project.
An example of this storytelling approach is the activity called “submarine” with students in 5 th grade in
2017. After students had read the story with two characters talking about submarines, they did research
about it; then, students designed their prototypes and built the submarine based on engineering design539

Constructionism 2018, Vilnius, Lithuania

process and programmed it to do what the students planned. At the end, students showed their projects
to the class and shared ideas and choices, registering the most important parts of the physicalconstruction and how the mechanism created worked. A quote from the students about the mostimportant parts of the submarine created and how it works:“The motor, engineering basis, gears and propellers. The motor makes the propellers spin and the submarine
moves”. Student (2017)

Figure 6. Submarine activity (novel, design´s prototype and the submarine model built – 5th grade)

Every project/activity has keywords related to four dimensions: technology; science; vocabulary andengineering to guide the teaching and learning process. For example, in the submarine activity these
are the keywords:
Technology

Science

Vocabulary

Engineering

Relation
betweenstructures and motors

Experiment and relation
to motors, design andengineering;

Motors,structures,design, machines;

Assemblingcomponents

Simple machines;

Test and evaluation;

Evaluation;

Scientific investigation;

Engineering design;

Description
explanation
construction;

andofTable 1. Keywords dimensions for educational robotics project/activity

Chapter 2
Design curriculum for educational robotics – A curriculum model approach
We consider the perspective of robotics in the curriculum in a broader way and its integration into thecurriculum permeates both the curricular framework itself and afterschool projects. However, weprioritize the integration of robotics in the curriculum, as the afterschool projects have specificcharacteristics, allowing greater flexibility in the development of the projects.
Relevant element of this aspect lies in the fact that schools, in general, do not have the “clear” direction
about what to teach related to robotics, for instance, they usually have difficult to choose related any
robotics content and thus take different paths in relation to the choice of didactic materials and contents
for this component.

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Indeed, this is fundamental when we discuss about integrating robotics into the curriculum. Differently
from subjects such as mathematics, science, geography, historically constituted as the school corecurriculum with structured contents for each school year(educational policies), which contribute to adiversity of didactic-pedagogical contents.
For instance, we can think of schools that are planning curriculum of robotics based on the concepts oftechnology linked directly with the materials of this resource, such as learning programming and the useof sensors and motors. In this case, schools demonstrate difficulties in aggregating contents fromdifferent subjects that constitute the school core curriculum.
Another example are schools that privilege content linked to subjects such as science, mathematics,
physics, which limits the concepts of technology learning.
Therefore, to think of robotics integration in the curriculum is indispensable to considerate theperspective of three axes: science, technology and subjects, according to the model we propose below:

Figure 7. Educational Robotics curriculum model

These aspects provide guidance to the design of robotics curriculum in formal education environment
and their integration in a meaningful way, having as reference the construction of knowledge and theautonomy of students in the teaching-learning process.
In science, elements such as investigation process, research, hypothesis, scientific method, among
others are central. This axis contributes to a curriculum guided by the immersion of the student in aprocess of investigation of the studied phenomena, in research and tests of hypotheses.
About technology, we need to consider the knowledge of functioning parts such as sensors, motors,
electronics, programming language, the field of computation itself, computational thinking, andadvances in technology (as a technological artifact).
Finally, subjects, which contemplate the school core curriculum (Physics, Language, History, Sciences,and Mathematics), those referring to robotics, engineering, artificial intelligence, creativity, as well assoft skills such as teamwork, collaborative learning, among others.
The organization of these three axes is what we call the “DNA” of interdisciplinarity. In this sense, it
refers to the whole process of knowledge production, constituting itself not as the simple inter-relation
between knowledge, but the concrete constitution of all knowledge produced in the activity or project.
Interdisciplinarity is the production of meaning in the whole process, and not only the "mixing" of areasof knowledge around a theme/project, in other words, it enlarges the interconnections and produces

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knowledge that did not exist before, involving aspects of philosophy, anthropology and sociology.
(Fazenda, 2010)
Therefore, we propose this model for educational robotics curriculum design, in which for every projector activity the three axes must be present as a core curriculum, with interdisciplinarity as a DNA
considering a link to the three axes and balancing the importance of content knowledge related to theteaching and learning process.
Considering this context, robotics curriculum can contribute to an emancipatory teaching-learning
process for both student and teacher. These aspects usually encounter challenges in order to actuallymaterialize during day-to-day of school education, given the relation time/space, available resources,
teacher training, among others.
Integrating robotics is fundamental, because it goes beyond a technological resource that allows theactive participation of students in constructing knowledge. It has the potential to contribute to thedevelopment of projects that aim the emancipation of students in learning complex concepts and skills
development of the 21st century.
In addition, it contributes not only to the construction of a multi-referenced curriculum, which considers
both the historically constituted core contents and the particular contexts of each school for thedevelopment of pedagogical projects, but also for the strengthening of a culture of technology use ineducation that has as fundamentals the autonomy and emancipation of students in the teachinglearning process.
It is not, therefore, simply to add robotics in the curriculum framework because it is interesting, to"conquer" new students, nor to use this technological resource at a few times during the school year.
Creativity in the context of integrating robotics in the curriculum stands out as another fundamental
element. Integration projects of such technology that contemplate activities that do not allow students
to create in all steps described are limited to only superficially incorporate robotics.
Thus, during the stages of a robotic activity, students need to exert creativity, for example, they cannot
receive ready-made assembly model and instead they need to build the device based on the challenge
proposed at the beginning of the activity. They need to create the device's programming and not receive
it ready to just test it.
In this sense, creativity must permeate the learner´s action during all stages of a robotic activity, in orderto maximize the reach of this technological resource in the teaching-learning process and, therefore,
ensure the integration of robotics in the curriculum significantly.

References
Blikstein, P. (2013) Digital fabrication and ‟making‟ in education: The democratization of invention. In J.
Walter- Herrmann & C. Bόching (eds.). FabLabs: Of Machines, Makers and Inventors (pp. 1-21).
Bielefeld: Transcript Publishers, 2013.
Fazenda, I. C. A. (2010) Interdisciplinaridade. São Paulo: Papirus.
Papanikolaou, K, Frangou, S., Alimisis, D. (2008) Teachers as designers of robotics-enhanced projects:
the TERECoP course in Greece. In SIMPAR. 1, 2008, Itália. Veneza, volume 6472, 556 p.
Papert, S. (1980) Mindstorms: Computers, Children and Powerful Ideas. NY: Basic Books.
Papert, S. (1987) Computer criticism vs. technocentric thinking. Educational Researcher, 16(1), 22-30.
Piaget, J. (1974) To understand is to invent. N.Y.: Basic Books.
Resnick, M., Berg, R.; Eisenberg, M. (2000) Beyond black boxes: Bringing transparency and aesthetics
back to scientific investigation. Journal of the Learning Sciences, 9(1), 7-30.
Resnick, M. (2007) Sowing the seeds for a more creative society. Learning & Leading with Technology,
35(4), 18-22.
Ortiz, J., Bustos, R., Rios, A. (2011) System of indicators and methodology of evaluation for the robotics
in classroom. Proceedings of the 2nd International Conference on Robotics in Education (RiE 2011)(pp. 63-70). Vienna, Austria: Austrian Society for Innovative Computer Sciences.
http://www.innoc.at/fileadmin/user_upload/_temp_/RiE/Proceedings/37.pdf, 2011.
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Forming Concepts for Programming Conditional
Statements in the Primary School
Miroslava Černochová, miroslava.cernochova@pedf.cuni.cz
Radek Čuma, radek.cuma@seznam.cz
Hasan Selcuk, hasan.selcuk@pedf.cuni.cz
Faculty of Education, Charles University, Prague, Czech Republic

Abstract
The Ministry of Education, Youth and Sport of the Czech Republic is currently preparing an update ofcurriculum documents for primary, lower and upper secondary school education in which two majorchanges to be made: (1) A concept of digital literacy will be incorporated into all school subject acrossthe curriculum in accordance with DigComp 2.0 defined by JRC EC; (2) Instead of the existing
compulsory subject ICT, a new compulsory subject of Informatics focused on computational thinking
development, will be introduced in all levels of education. In the context of the forthcoming curricularchanges, not only educational activities, but also research on the way in which pupils of different ages
acquire basic information concepts has great importance.
The authors conducted a case study focussed on discovering how pupils of primary school (especiallyYear 3, 4 and 5) acquire, use and understand some programming conditional statements and loops (IFTHEN, IF-THEN-ELSE; REPEAT/ REPEAT-UNTIL). Programming conditional statements areundoubtedly one of the fundamental algorithmic concepts that pupils will need to understand and use
in programming and algorithmic thinking development. How can pupils apply them in programming?
Does it make sense to introduce these programming conditional statements into programming activities
in primary education?
The research was carried out in 2017/18 at a small village school among 31 pupils (17 girls and 14boys) of Year 3, 4 and Year 5 during 16 lessons of a compulsory subject “Work with a computer”. Pupils
usually worked in groups of three or four. The activities were designed in accordance with a proposalof requirements for algorithm skills and programming development in primary school education. The
research was organised into four phases: (i) Preparatory phase (out of school), (ii) CSunplugged
activities with a special set of paper cards and LEGO toys, (iii) Activities in a virtual environment
Code.org, and (iv) Testing acquired skills and knowledge.
Findings showed that firstly, primary school pupils are able to use programming conditional statements
and loops, nevertheless the Year 3 pupils can lose motivation and willigness to work if something iswrong or if they are not successful, and also they can have some linguistic barriers how to describe
more details verbally their algorithmic schemes. Secondly, it has a sense to introduce these conditional
statements and loops into primary education if we create for pupils conditions to link their concrete ideas
based on manual operations (such as with a Lego toy) to their experinces gained in a virtual
programming environment (such as Code.org).

Keywords
conditional statements; loops; algorithm; programming; computational thinking; unplugged activities;
code.org

Introduction
The Ministry of Education, Youth and Sport of the Czech Republic is currently preparing an update of
the curriculum document Framework Educational Programme (FEP), see (MoYES, 2013). In the new
FEP for elementary/primary schools (for pupils aged 6-15), two major changes relating to the digital
education strategy (MoEYS, 2014) will be made. (1) Pupil’s digital literacy, which was formed throughcompulsory ICT, will be now developed in all subjects. The concept of digital literacy will be incorporated
into the curriculum in accordance with its form defined in DigComp 2.0 described in R. Vuorikari et al.
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Constructionism 2018, Vilnius, Lithuania(2016). (2) In the curriculum for primary schools, instead of ICT a new compulsory subject ofInformatics, which will focus on the development of information thinking, will be introduced.
Informatics will be included in both primary and lower secondary education.
The changes awaiting primary schools are relatively radical and should be put into practice by 2021.

Research aim
Since 2017, nine Czech faculties of education have been working closely together to develop andvalidate teaching materials, methodological guidelines for teaching a new subject of Informatics and forvalidating these at several selected schools (starting with pre-school centres and kindergartens andending with secondary schools). At the same time, courses and subjects for teachers of kindergartens,
primary and secondary schools are being prepared to be ready for the planned curricular changes. All
nine faculties of education innovate study programmes for student teachers of all subjects including ICT
and Computer Science.
In the context of the forthcoming curricular changes, not only educational activities, but also researchon the way in which pupils of different ages acquire basic information concepts has a great importance.
The research, we have done, is focussed on discovering how pupils of primary school (especially Year3, 4 and 5) acquire, use and understand when designing programs involving selected commands andfunctions associated with some programming conditional statements (IF-THEN, IF-THEN-ELSE;REPEAT/ REPEAT-UNTIL). Programming conditional statements are undoubtedly one of thefundamental algorithmic concepts that pupils will need to learn and use in programming and algorithmic
thinking development. How do they understand these concepts? How can they apply them inprogramming? Does it make sense to introduce these programming conditional statements into
programming activities in primary education?
In our case study research, we decided to design and test a pedagogical experiment with primary school
pupils a methodical approach to teaching programming with programming conditional statement like IFTHEN, IF-THEN-ELSE, REPEAT, REPEAT-UNTIL which can lead to easier understanding andacquiring algorithms of this type of task and learning activity.

Theoretical framework
The activities for pupils were prepared in accordance with a draft document of requirements for skills
and knowledge for a computational thinking (NÚV, 2017), especially for algorithm and programming
development in primary school education.
The content here (Table 1) was drawn upon in preparation for the research.
Table 1. A proposal of standards and continuity in algorithm and programming development (NÚV, 2017)
A pupil is able to read and interpret text or symbolic scripts of the algorithm and to
explain their individual steps.
A pupil is able to describe a simple problem, to design and to explain individual steps of its
solution.
Algorithm
development
andprogramming

A pupil is able to adjust and modify a ready procedure for a similar problem. S/he isable to verify a correctness of a proposed procedure and to find and debug errors.
A pupil is able to recognise if two different algorithms can solve the same problem.
In a block-oriented programming language, a pupil is able to design a computer program.
S/he is able to test it and debug errors in it.

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A pupil is able to recognize recurring patterns, to use cycles for repeating and to apply subprograms (sub-routines). S/he is able to use events to run sub-routines and scripts.

Methodological design
The research was carried out in 2017/18 at a small village school among pupils of years 3, 4 and 5
during 16 lessons of a compulsory subject “Work with a computer”. In the research there were N1 = 15
pupils (8 girls and 7 boys) of Year 3 and Year 5 and N2 = 16 pupils (9 girls and 7 boys) from Year 4.
Pupils usually worked in groups of four.
Tasks were designed to develop the skills to compile algorithm development with using hand-made andready-made teaching tools for CS unplugged activities.
The case study was organised in these phases (Table 2):
Table 2. Phases of the case study
Preparatory
phase

weekend playing and games with several pupils (outside school) managed by ateacher - researcherCS
unplugged
activities

Activities without a computer in (8) groups with 3-4 pupils. The two groups consistedof Year 3 pupils, four groups were Year 4 pupils and two groups of pupils from Year
5.
Pupils were divided into mixed groups so that in each group there were girls and boys
and pupils with different study success (marks, grades).
For Csunplugged activities there were developed a set of cards from which pupils
could compile algorithms and learn to read procedures and interpret and performthem (using a vehicle constructed in Lego).

Activities in a Tasks in a virtual Code-Oriented Programming Environment Code.org were solved
virtualby pupils individually in a 1: 1 model, each pupil worked on one device.
environment The pupils' results were recorded in the online environment and further analyzed.
Testing
Control tasks for pupils were assigned with the aim to find out to what extent pupils
acquiredunderstood the meaning and principle of using commands with conditional statement
skillsand (Repeat X-times, Repeat Until, IF - THEN, IF – THEN – ELSE)
knowledge

Research data were collected using video and audio records by means of: (1) observation of pupils'
behaviour and activities; (2) records of processes how pupils solved problems and tasks; (3) testing
how pupils understand selected algorithmic concepts. Pupils were very often asked (i) to describe andinterpret how they proceeded in solving a problem, (ii) to read the codes, and (iii) to interpret meaningof the assembled algorithms (using, for example, a vehicle made in Lego). Collected data were added
to data gained using the environment Code.org with the aim to analyse a progress in pupils' thinking.

Expected conclusions/findings
The data are currently being analysed. The results will be available in June 2018. The analysis of thecollected data should contribute to understanding how students comprehend their programming, how
and if they are able to find errors in their work.
Experience shows that primary school pupils are quick to lose motivation and willingness to work if
something is wrong or if they are not successful. The same goes for programming. If teaching
programming is not responsive to pupils’ capability, a lot of pupils can be discouraged. By creating abridge between a real and virtual world of programming using unplugged activities, we can achieve workin a virtual environment which will not be difficult for pupils.
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In our case study, the pupils demonstrated that they correctly understand the meaning and principle ofusing an algorithm when programming conditional statements. The pupils also showed that they cancomprehend what events occur at each step of the program, to which the said unplugged activities witha set of cards greatly contributed, especially in activities where the students manually demonstrated an
assembled algorithm using vehicles made from Lego blocks.
The pupils subsequently transferred these learned skills into tasks in Code.org which led to the desired
link between unplugged activities and activities in a virtual programming environment.

Resources
Futchek, G., Moschitz, J. (2011) Learning Algorithmic Thinking with Tangible Objects Eases Transition
to Computer Programming. In: International Conference on Informatics in Schools: Situation, Evolution,
and Perspectives. ISSEP 2011: Informatics in Schools. Contributing to 21st Century Education.
Sprinnger, 2011, pp 155-164. Available at https://publik.tuwien.ac.at/files/PubDat_199953.pdf
MoEYS (2013) Rámcový vzdělávací program pro základní vzdělávání. Praha: Výzkumný ústav
pedagogický v Praze, 2013. Available at: http://www.msmt.cz/file/43792/
MoEYS(2014)
Strategie
digitálního
http://www.msmt.cz/uploads/DigiStrategie.pdf

vzdělávání

do

roku

2020.

Availableat:NÚV (2017) Informatika - rámec očekávaných výstupů (prosinec 2017). Tabulka pro posouzenínávaznosti.
Availableat:https://docs.google.com/spreadsheets/d/1op92O_ZFNcLRbKxm6FcanUmPjSdP_Dd2JAznIfG5YDU/e
dit#gid=1456952308
Vuorikari, R., Pune, Y., Crretero, S., Van Den Brade, L. (2016) DigComp 2.0: The Digital Competence
Framework for Citizens. Update Phase 1: The Conceptual Reference Model. European Union, 2016.
ISBN 978-92-79-58876-1.

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Designing Constructionist Learning Environmentswith Computational Design and Digital Fabrication
Christos Chytas, christos.chytas@uni-oldenburg.de
Computing Education Research Group, University of Oldenburg, Germany

Ira Diethelm, ira.diethelm@uni-oldenburg.de
Computing Education Research Group, University of Oldenburg, Germany

Abstract
Makerspaces like fab labs (digital fabrication laboratories) are open workshops that promise to
democratize the means of production and technical knowledge. Even though such laboratories are often
seen as innovation spaces for small business they are receiving increasing attention as informal learning
environments for STEAM (Science, Technology, Engineering, Art, Mathematics) subjects. ‘’Making’’ asa set of learning activities roots in learning theories of educators like Seymour Papert. Inspired byPapert’s constructionist learning theory, we designed, implemented and evaluated workshops on digital
fabrication and computational design for children and youth. The workshops’ goal was to introduce
computational concepts and programming as means of personal expression through the creation ofcomputational design models that could be fabricated in our labs.

Figure 1. Generated 3D parametric model of a flower which was 3D printed to make an interactive artistic project

The workshops’ concept provided opportunities for creativity, personal expression, collaboration andcontent rich learning activities to create artistic, practical or entertaining artifacts. We identify theelements of maker tools and culture that enhanced the learning experience in our workshops. We
discuss implications and challenges of these elements for educators who wish to use digital fabrication
for programming learning activities.

Keywords
constructionism; computational design; digital fabrication; the maker movement; computing education;
Fab LabIntroduction and Theoretical Background
The Maker Movement is on the rise and what started as a hobbyist community for tinkering and crafts
has now expanded to reach millions of people in a growing number of physical and online spaces.
Making activities often include crafts and the use of sophisticated technologies like digital fabrication
machines (3D printers, laser and vinyl cutters, CNC routers etc.), CAD (Computer Aided Design)547

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software and microcontrollers. These tools are becoming increasingly accessible for everyone to use(Anderson, 2012). The expiration of patents in 3D printing and the decreasing price of other digital
fabricators like laser cutters have triggered the rise of open-source software for 3D modelling (e.g.
OpenSCAD), vector graphics (e.g. Inkscape) and image processing (e.g. GIMP) among others.
Furthermore, online makerspaces dedicated to open-source hardware like Thingiverse.com provide
digital models that can be shared under the creative commons licenses. Such models can also becreated through computational/parametric design tools and be customized by users of diverse
experience in design.
By focusing on design and construction, making provides exciting opportunities to explore STEAM
subjects, including engineering design (Blikstein, 2013a), programming through physical computing withthe use of microcontrollers and microcomputers to make interactive artifacts (Blikstein, 2013b) orcomputational design (Jacobs and Buechley, 2013; Dittert et al., 2014; Chytas et al., 2018). Such
activities attract increasing attention from educators who strive to support the development ofengineering and computing skills in a meaningful and interesting way to foster 21st century skills.
According to Vossoughi and Bevan (2015), making settings ‘’are generally to inspire interest, foster
engagement, develop understanding of the processes and concepts at the center of making activities,
and support students’ identities as thinkers, creators and producers of knowledge’’.
Literature review on the maker movement in education (Blikstein, 2013a; Vossoughi and Bevan, 2015)shows that making inherits ideas of educators like Dewey, Froebel, Montessori, Vygotsky and especiallythose of Piaget and Papert. Seymour Papert inspired by Piaget’s Constructivism developed
Constructionism, a learning theory that emphasizes on learning by constructing mental models and thatthe learning experience can be further enhanced by creating something tangible that can be sharedwith others. ‘’Constructionism--the N word as opposed to the V word—shares constructivism's
connotation of learning as "building knowledge structures "irrespective of the circumstances of thelearning. It then adds the idea that this happens especially felicitously in a context where the learner isconsciously engaged in constructing a public entity, whether it's a sandcastle on the beach or a theoryof the universe’’ (Papert and Harel, 1991).
Few decades ago, Papert and his colleagues developed Logo (Papert, 1980), a programming
environment to introduce aspects of mathematics and computer science to children. In computing
education programming is a major activity that is important for everyone to learn. Even more essential
is the development of computational thinking skills and digital literacy which also require at least basic
knowledge of algorithms and computational concepts. Logo was aligned with the theory of
constructionism and provided exciting opportunities for children and youth to engage in programming(and math) activities.
Advancements in technology and the accessibility of digital fabrication tools allow us to create digital
designs and turn them into physical artifacts faster and cheaper than ever. Eisenberg suggests that amaker-centred CSE would ‘’situate computers as elements in a creative technological landscape that
includes 3D printers and scanners, a growing selection of sensors and actuators". According to him,combining elements of CSE with making culture would expand its focus, not only on software and digital
realities but physical artifacts and hardware as well (Eisenberg, 2017).
Moreover, computation has expanded to reach industrial design and architecture, opening new
possibilities for creative computing in the physical realm. Computational design has a long history incomputing education since the popularization of Logo (Papert, 1980) and other educational tools like
Turtle Art. Even though there is a plethora of promising tools that promise more creativity in computing
education, we still believe that there is a gap in current practices with digital fabrication andcomputational design in constructionist learning environments.
Meanwhile, educators and academics have called for best practices to take advantage of the tools of
making in the classroom. Even though digital fabrication in the realm of computing education is receiving
increasing attention from the constructionist community, the findings on computational design have notprogressed as much as in physical computing with the use of microcontrollers. Furthermore, there is an
ongoing trend on the use of digital fabrication and especially 3D printing in education but the potentialof such technologies for programming learning activities is still something new for most educators.
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Chytas et al. (2018) focused on the use of computational design and digital fabrication forcomputationally rich learning activities and their potential to develop a programmer’s mindset by creating
personally meaningful artifacts. Dittert et al. (2014) and Jacobs and Buechley (2013) focus on the useof the Processing programming language to generate 2D shapes that can be manufactured by laser
cutters and its impact on the empowerment of workshop participants to use programming for creative
purposes. Kastl et al. (2017) focused on the use of 3D modelling through turtle graphics to create 3Dmodels that could be fabricated by 3D printers, as motivation to support programming and mathematics
actions in educational settings.

Methodology
To investigate best practices for coding learning activities with digital fabrication, we follow a mixed
methods approach. Similar with the method of Katterfeld et al. (2014), our approach includes iterative
circles of design and research on digital fabrication in educational context in order to improve our current
workshops. The evaluation of our workshops is based on before and after surveys, observations,evaluations of artifacts and interviews with the participants. This evaluation helps us to better
understand their expectations, profound experience, self-efficacy on the use of technology, their wishes
as well as what they liked or did not like during the workshops.
From 2016 until now, we evaluated workshops with more than 50 participants aged from ten to 17 years
old in formal and informal learning spaces. All these spaces were equipped with 3D printers and had
close access to a laser cutter as well. The number of participants in our workshops varied from small
groups of two to a class of 16 students. The workshops’ duration is usually between three to five days
so that the students have enough time to explore design and programming practices in depth and build
physical artifacts of their designs through digital fabrication technologies. Our educational setting follows
a constructionist approach, meaning that the participants are encouraged to familiarize with thetechnology by themselves and learn-by-doing. The workshops are usually supported by two to fourtutors and researchers who act as facilitators. The implementation of the workshops included 1)
demonstration of digital and physical artifacts, 2) description of the values and ideas behind the makermovement and making, 3) a short introduction of computational/parametric design (CAD andprogramming features), 4) the “making” phase, 5) the demonstration of the produced artifacts and 6)
the reflection on the experiences from the workshop.
To introduce programming under the lens of computational design we use two parametric design tools:a) BlocksCAD (an online platform which exploits block-based parametric design tools) and b)
OpenSCAD (an open-source parametric design software which includes a syntax similar with the C
programming language). An example of code from both tools and the generated 3D models areillustrated in Figure 2 and 3 respectively. These tools share two important similarities which were thedetermining factor to use this combination in our workshops. Both tools are free to use and share thesame essential commands for parametric design which fall under four main categories: 1) Shapes, 2)
Transformations (e.g. commands to move or scale the generated models), 3) Constructive Solid
Geometry Operations (e.g. commands to merge two models together or subtract one set of existing 3Dsolids from another) and 4) Programming Features (e.g. iterations, conditional statements, functions
and the use of parameters to change the properties of a model). The first three categories of commands
are broadly used in most hobbyist and professional CAD tools while the last one adds computational
elements like iterations and customization features to the design.

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Figure 2. Generated 3D parametric model of a flower through blocks. The code of the workshop’s participant
includes modules (which group parts of code for future reuse), iterations, conditionals and core CAD elements(shapes, transformations and constructive solid geometry operations).

Figure 3. Generated 3D parametric model of a building through a text editor which includes programming
features and CAD elements.

In the following sections we provide elements of computational design and maker culture that we foundto be suitable to enhance personal expression in programming activities.

Computational Design and Constructionist Learning
In maker culture, technical skills like coding are often used for personal expression through the creationof physical projects that are linked with social, educational and entertaining activities. Within theconstructionist community, there is a rich discussion on the benefits of making tangible media that canbe enjoyed and shared with others (Blikstein, 2013). The tools of making like open-source 3D printersand programming CAD tools (e.g. parametric design software) promise to bring creative computational
activities in the physical realm. However, according to Katterfeld et al. (2015), current research on digital
fabrication in educational context should focus not only in developing computing skills andcompetences, but also nurturing deep and sustainable learning about the medium through personal
development. After ten years of implementing digital fabrication workshops, the authors highlight threeideas as crucial in achieving deep sustainable learning with digital technologies. These are: begreifbarkeit (as the ability to deeply understand (grasp) but also grab something tangible), Imagineering(as ‘’means to invent and create yet unknown products that relate to personal life worlds’’) and selfefficacy (Katterfeld et al., 2015). Their work focuses on constructionist learning environments for digital
fabrication with the use of physical computing construction kits and provides implications of theirresearch to extend the educational concept to also include fabricators like 3D printers and laser cutters.
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Inspired by their research, we aim to extend this concept with computational design and digital
fabrication machines in the spotlight. We find computational design to be a very promising design asset
that can greatly contribute to constructionist learning through the creation of tangible projects that canbe personally meaningful and enhance the share-ability and customization of the designs (Chytas etal., 2017).
To this point, we evaluated eight workshops that took place in formal and informal learning settings. The
first three workshops took place at a university fab lab and were evaluated in-depth using qualitative
methods, either by taking part in individual or group semi-structured interviews (regarding theirexperience and impression of the workshops, the difficulties they met and the things that they would
like to be different), observations and evaluation of artifacts. The low number of participants in all threeworkshops (two participants per workshop) gave us the opportunity to capture with higher precision thereactions and attitudes of participants towards computational design. With some participants we also
had the opportunity to introduce non-programming 3D modelling tools to compare with computational
design and investigate which features the participants preferred to use. Two workshops took place at aschool makerspace and were evaluated with quantitative methods using questionnaires and statisticalanalysis of their code, while three workshops took place at our informatics learning lab and wereanalysed with triangulations of the previous methods.
Based on the evaluations of the workshops that were described above, we identify four more elementsthat showed the potential to enhance the learning activities and personal expression with digital
fabrication and computational design. Summarizing our findings, we concluded that when designing alearning environment that exploits digital fabrication and computational design, the following should be
kept in mind:
Connecting computational design with our lifeworld
Our workshops aimed at empowering youth and children to use design and programming as means forexpression and artistic creation. The benefits of computation could be reflected through designing
geometries that included recursive elements and complex design patterns. Designing such patterns
without computation requires longer times that tend to be frustrating. Before engaging children andyouth in computational practices with digital fabrication they need to understand the role of programmingand design in their daily life. Computational design enables the creation of designs so complex that
could not be created handmade or with non-programming CAD tools. The participants from ourworkshops created artistic, practical or entertaining artifacts that were intended for diverse uses ranging
from household objects to figures of popular animations and items that were intended for social good.
The evaluation from the interviews showed that the participants were more motivated to engage incomputational design activities and put more effort into creating something tangible that can be used intheir daily life by them or others. Participants that were engaged in personally meaningful projects were
intrinsically motivated to ask themselves about the benefits of computing science in their lifeworld andhow it can connect with entertainment, personal expression or real-world problem solving in both thedigital and physical realm.
Encouraging exploration
We found the demonstration of digitally fabricated artifacts essential to trigger imagination forcomputational design and digital fabrication. Demonstrating digital and physical artifacts in fab labs,
maker faire, makerspaces and websites is a fundamental feature of Maker culture (Posch et al.,2010;
Eisenberg, 2017). Novices cannot fully explore the possibilities of digital fabrication technology in thelimited time of the workshops. Even people with significant experience in programming and design, canlearn about new properties of innovative materials and production or assembly techniques. For example,
cutting wooden surfaces with laser cutters in specific patterns can give them the ability to bend wooden
surfaces and produce curved objects as illustrated in Figure 4a. When it comes to production of big
objects a puzzle-like (snap-fit) modelling like the one illustrated in Figure 4b., can result in an easy andcheap assembly, reducing considerably the production time in comparison with alternative
manufacturing methods like 3D printing. Learning about these possibilities of digital fabrication
technology requires a lot of time to explore autonomously but the demonstration of physical and digital
artifacts provides the basics to trigger the design process.
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Figure 4. Bending wood and snap-fit patterns.

Encouraging purposeful design
Computational design tools for hobbyists and professionals provide opportunities to explore interesting
patterns, concepts and geometries in design though computational practices. However, there weretimes when participants generated models that were aesthetically appealing but not intended (Chytas
et al., 2018). Creating artistic shapes and geometries that impress by combining programming elementswith CAD involves complex thinking processes that could lead to the exploration of new concepts andschemata which cannot be easily calculated and visualized in our minds. Even though such situations
could also be fruitful opportunities to explore computation under the lens of design, we need to ensurethat the activities are aligned with deep sustainable learning. We found that the creation of objects thatare intended to be combined with other technologies (e.g. the creation of a model that is meant toinclude a microcontroller with LED lights) or serve a specific purpose like replacing a broken part or fit
perfect on a surface can overcome this situation in artistic projects as well.
Computational design as a social experience
Let the learners engage in collaborative activities and create a culture of sharing to explore the social
dimension of computation and design. Maker culture is aligned with collaboration, sharing and helping
others in physical and online spaces and communities (Martin, 2015; Kostakis et al., 2015). The
workshops’ concept provided opportunities for brainstorming about different ideas, solutions for designs,
reflection on the artifacts that were created, working collaboratively on projects, helping or getting
feedback from others and getting involved in discussions about personally meaningful projects.
Furthermore, the use of computational/parametric design and the rise of the open-source hardware
models on the internet enhance the share-ability of the designs. Designers can reach everyone to use
their design, modify it by changing the parameters and code or further improve it. In his book Makers,
Anderson (2012) states that ‘’The ability to easily “remix” digital files is the engine that drives community.
What it offers is an invitation to participate. You don’t need to invent something from scratch or have an
original idea. Instead, you can participate in a collaborative improvement of existing ideas or designs’’(p. 74). This ability enhances collaboration and sharing by providing customization possibilities that areoften missing from traditional non-programming CAD tools.

Conclusion
Computational design and digital fabrication have not been widely used for programming learning
activities by youth and children. In this work, we emphasize on computational design as a powerful tool
to support design and coding learning activities in constructionist learning settings. We combine
computational design with digital fabrication technologies to highlight programming as means forcreation in the digital and physical realm. Inspired by the work of Katterfeld et al. (2015) on designinglearning environments with digital fabrication, we further focus on elements from computational designthat are often missing from the digital-physical swift. These elements can greatly contribute toconstructionist learning through the creation of tangible projects that can be personally meaningful andenhance the share-ability and customization of the designs. After evaluating our workshops oncomputational design, we report best practices on learning with digital fabrication by exploiting thepossibilities of computation in modern design tools. We provide examples of embracing opportunities
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and ideas behind the maker movement to take advantage of the increasing accessibility of the new tools
and foster 21st century skills that are not intended only for industry but personal development andexpression as well.

References
Blikstein, P. (2013). Digital fabrication and ‘making’ in education: The democratization of invention.
FabLabs: Of machines, makers and inventors, 4, 1-21.
Blikstein, P. (2013, June). Gears of our childhood: constructionist toolkits, robotics, and physicalcomputing, past and future. In Proceedings of the 12th international conference on interaction designand children (pp. 173-182). ACM.
BlocksCAD. https://www.blockscad3d.com/
Chris, A. (2012). Makers: The new industrial revolution. New York: Crown Business.
Chytas, C., Tsilingiris, A., & Diethelm, I. (2018, April). Learning programming through design: An
analysis of parametric design projects in digital fabrication labs and an online makerspace. In Global
Engineering Education Conference (EDUCON), 2018 IEEE (pp. 1978-1987). IEEE.
Chytas, C., Diethelm, I., & Lund, M. (2017) Parametric Design and Digital Fabrication in Computer
Science Education.
Dittert, N., Katterfeldt, E.-S. & Wilske, S., (2014). Programming Jewelry: Revealing Models behind
Digital Fabrication. Short paper at FabLearn Europe: Digital Fabrication in Education Conference. June
2014. Aarhus, Denmark.
Eisenberg, M. (2017). Approaching Computer Science Education Through Making. In New Directions
for Computing Education (pp. 35-44). Springer, Cham.
GIMP. https://www.gimp.org/
Inkscape. https://inkscape.org/
Jacobs, J., & Buechley, L. (2013, April). Codeable objects: computational design and digital fabrication
for novice programmers. In Proceedings of the SIGCHI Conference on Human Factors in Computing
Systems (pp. 1589-1598). ACM.
Kastl, P., Krisch, O., & Romeike, R. (2017, November). 3D Printing as Medium for Motivation andCreativity in Computer Science Lessons. In International Conference on Informatics in Schools:
Situation, Evolution, and Perspectives (pp. 27-36). Springer, Cham.
Katterfeldt, E. S., Dittert, N., & Schelhowe, H. (2015). Designing digital fabrication learning environments
for Bildung: Implications from ten years of physical computing workshops. International Journal of ChildComputer Interaction, 5, 3-10.
Kostakis, V., Niaros, V., & Giotitsas, C. (2015). Open source 3D printing as a means of learning: An
educational experiment in two high schools in Greece. Telematics and informatics, 32 (1), 118-128.
Martin, L. (2015). The promise of the maker movement for education. Journal of Pre-College
Engineering Education Research (J-PEER), 5 (1), 4.
OpenSCAD. www.openscad.org/
Papert, S. (1980). Mindstorms: Children, computers, and powerful ideas. Basic Books, Inc..
Papert, S., & Harel, I. (1991). Situating constructionism. Constructionism, 36 (2), 1-11.
Posch, I., Ogawa, H., Lindinger, C., Haring, R., & Hörtner, H. (2010, June). Introducing the FabLab asinteractive exhibition space. In Proceedings of the 9th International Conference on Interaction Design
and Children (pp. 254-257). ACM.
TurtleArt. https://turtleart.org/
Vossoughi, S., & Bevan, B. (2014). Making and tinkering: A review of the literature. National ResearchCouncil Committee on Out of School Time STEM, 1-55.

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Developing Mathetic Content Knowledge using an
Emergent Systems Microworld
Sugat Dabholkar, sugat@u.northwestern.edu
Learning Sciences and Center for Connected Learning, Northwestern University, USA

Gabriella Anton, gabby.anton@gmail.com
Learning Sciences and Center for Connected Learning, Northwestern University, USA

Uri Wilensky, uri@northwestern.edu
Learning Sciences, Computer Science and Center for Connected Learning, Northwestern University,
USA

Abstract
In this paper, we define and develop a theoretical construct Mathetic Content Knowledge (MCK) in thecontext of children’s learning. We discuss acquiring MCK using a special type of constructionist learning
environment, which we call Emergent Systems Microworlds (ESMs). ESMs allow students to engage
with emergent phenomena in an exploratory way. We argue that the students who participated in our
ESM-based curricular unit, GenEvo about Genetics and Evolution not only learned disciplinary coreideas, but also possibly developed mathetic insights into how to learn by engaging in the scientificinquiry process. The GenEvo curriculum incorporates a series of computational models designed using
NetLogo that follow the agent-based modeling approach to emergent systems. In this curriculum,
students design and conduct computational experiments in the ESM learning environment to figure out
the answers to the guiding questions collectively build towards the ideas about emergent properties inthe ESM.
We argue for the importance of fostering Mathetic Content Knowledge, knowledge of how to learn byengaging in discipline specific inquiry practices, in general, and specifically for science education. We
also discuss how systematic exploration of computational models using ESM learning environments
could be an effective way to develop science MCK.

Keywords
Mathetic content knowledge;
constructionism; design

emergent

systems

microworlds;

scienceinquirypractices;

Introduction
Papert, in his book Mindstorms, introduced the term ‘mathetics’ to talk about the guiding principles thatgovern learning (Papert, 1980). Papert made an argument that just like pedagogy is a word for art ofteaching, there should be a word for the art of learning. His proposed candidate for that word is‘mathetics’. This word has the same Greek root as mathematics; mathamein is a verb which means tolearn. Mathetics has much broader connotation as the same root, math has in case of the word
‘polymath’, which means a person of many learnings. Papert later elaborated on this idea in his book
‘The Children’s Machine’, where he talked about mathetics as the art of learning (Papert, 1993). We
argue that as learning scientists, when we design learning environments to support different kinds oflearning, it is also critical to evaluate whether and how the design supports this ‘art of learning’.
In a somewhat related context Shulman introduced the concept of pedagogical content knowledge(PCK), when he argued for the importance of research questions dealing with the content of the lessons
taught in the field of teaching and teacher education (Shulman, 1986). Pedagogical content knowledge
is about teachers’ interpretations and transformations of subject-matter knowledge in the context offacilitating student learning which also incorporates understanding of common learning difficulties andpreconceptions of students (Magnusson, Krajcik, & Borko 1999). This has argued to be central part ofteacher training programs and argued to be a central area for research in the field of education (Van554

Constructionism 2018, Vilnius, Lithuania

Driel, Verloop, & de Vos, 1998). With more incorporation of technology in education, the idea of PCK
has been extended to Technology-enhanced PCK (TPCK) (Niess, 2005). Niess (2005) mentions thatwith technology becoming an integral component or tool for learning, science and mathematics teachers
must develop an overarching conception of their subject matter and what it means to teach withtechnology. All these constructs focus on role of teachers and on the art of teaching. In this paper, we
combine these two strands the art of teaching in the context of subject-specific knowledge andtechnology, and the art of learning, which we call Mathetic Content Knowledge.
Each discipline has a discipline specific practices of engaging in inquiry and construction of knowledge.
MCK is strongly connected to these discipline specific practices. The art of learning and being good atthis art is different for mathematics, languages, history or sciences. We argue that Emergent Systems
Sandbox (ESM), a special kind of constructionist learning environment, allows students to develop MCK
in the context of the inquiry practices they engage in. ESMs are a specifically designed to support
students in creating, exploring, and sharing virtual models and model-based artifacts of dynamic
systems that exhibit emergent phenomena. In this paper, we discuss how students develop MCK whenlearn genetics and evolution using an ESM-based curriculum called GenEvo.
Research in mathematics and science education over the past few decades has investigated and shown
the effectiveness of model-based inquiry in classrooms, both in fostering students’ thinking skills and inlearning of mathematical and scientific concepts. Specifically, researchers have demonstrated thatlearning based on investigations of models leads to development of competence in disciplinary inquiry
practices such as constructing argumentation based on evidence and communicating it effectively toothers (Passmore & Svoboda, 2012; Windschitl et al., 2008; Schwarz et al. 2009) as well as can support
content mastery (Stewart et al. 2005). The Next Generation Science Standards (NGSS) stipulate
developing and using models as one of the eight core scientific practices (NGSS Lead States, 2013).
In particular, these standards suggest that models should be developed ‘‘to predict and show
relationships among variables between systems and their components in the natural and designed
worlds’’. We demonstrate that model-based inquiry using with an ESM goes beyond merely engaging
students this specific scientific inquiry practices; in another paper, we have argued elsewhere that thestudents that participated in our ESM-based curriculum meaningfully engaged students in several otherinquiry science practices recommended by the NGSS (Dabholkar et al., 2018). Engaging meaningfullyin scientific inquiry practices is critical to develop Mathetic Content Knowledge for science. As students
construct their own knowledge in a microworld by engaging in scientific inquiry practices, such as
constructing explanations and engaging in arguments using evidence, they also understand thescientific process of knowledge construction. This understanding is critical for developing MCK for
science.

GenEvo: An ESM-based curriculum about genetics and evolution
The GenEvo curriculum incorporates a series of computational models designed using NetLogo(Dabholkar et al., 2016). NetLogo is an agent-based modeling software that has been used for research
work regarding emergent systems as well as to design educational curricular units (Wilensky, 1999).
The design of computational models in the GenEvo follows the agent-based modeling approach to
emergent systems that has been demonstrated to be effective for fostering deep understanding of
disciplinary core ideas (e.g., electricity, the particulate nature of matter) as well as crosscutting ideas
such as complex systems thinking and computational thinking (Blikstein & Wilensky, 2004; Levy &Wilensky, 2006; Wilkerson-Jerde & Wilensky, 2010).
In this curriculum, students are first presented with a computational model of a bacterial cell with agenetic circuit in which certain components interact in specific manner (See Figure 1) (Dabholkar et al.,2016).
The students explore and play with the model to figure out these interactions and engineer the genetic
circuit to make their cells ‘fitter’ to reproduce. In the next two subunits, students explore and tinker withthe models of genetic drift and natural selection. Finally, the cells where genetic circuits are designedby the students will ‘compete for survival’ in a limited resource environment. These computationalmodels are intestinally designed specifically from the agent-based perspective of modeling emergent
systems. In each model, the agents and their behaviors at the micro-level are computationally coded.
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The interactions between the agents and their interactions with the environment result in emergence ofpatterns at macro-level (Wilensky & Resnick, 1999; Wilensky, 1999b). In this curricular unit, theemergent properties of biological systems that students investigate include, genetic regulation, carrying
capacity, genetic drift and natural selection. Students design and conduct computational experimentsin the ESM learning environment to figure out the answers to the guiding questions in the curriculum.
These answers collectively build towards the ideas about emergent properties in the ESM.

Learning using ESM-based curricula
There are two ideas that are central to the learning using ESMs and ESM-based curricula, which wecall, ‘big-M’ Models and ‘little-m’ models. This theoretical framework has been developed and discussed
in detail in the context of Emergent Systems Sandboxes, which is a specific kind of ESM (Brady et. al,2015). Big-M models are fundamental scientific paradigms (Kuhn, 2012) that form the fundamental basis
for design of ESMs. Every entity in the microworld follows the rules that are specified by the Big-M
model. Incorporation of these rules involves heavy simplifications of the existing scientific paradigm.
However, an ESM is designed in a way that it captures Big-M principles in sufficient details for students
to engage meaningfully with those. In contrast, when students participate in an ESM-based curriculum
they construct little-m models. Little-m models can be thought of as personal hypotheses or theories
about how a system functions. As students construct their little-m models, the consequence of theserules become salient to them. Since, in the most cases, these rules are not available for editing, aconstruction in an ESM will always be faithful to the Big-M model. It may not produce the aggregatelevel behaviors that a student intends, but the outcomes will always be logically determined by the rulesof the Big-M Model. Exploring such ESMs and learning to construct little-m models in them would
gradually nudge the learner’s intuitions into alignment with the Big-M model. Several studies that haveused ESMs indicate that it’s not a smooth trajectory for students to move from little-m to Big-M in termsof their conceptual understanding of the system. The actual trajectories of these transitions are different
for each student. Such open-ended scaffolded explorations along different trajectories have been
demonstrated to be effective for students to develop deep understanding of ideas central to a Big-M
model.

Data collection and analysis
The data used in this paper is from a Computational Modeling in Biology course based on the GenEvo
curriculum. The first author of this paper was the lead-designer of the ESM and the curricular unit andthe lead-teacher of these implementations. We conducted this course twice during a weekend extraschool program for middle school students conducted by a talent-development center in a mid-western
university in the United States; and in a residential summer camp in a western city in India wherestudents from all over the India participated. The students participated in both these programs were ofage 11 to 14 and intellectually advanced based on their academic performance. There were 6 female
and 8 male students of mixed racial and ethnic backgrounds; the break-up of self-reported racial andethnic backgrounds was, 6 White non- Hispanics, 4 Asians, 1 White Hispanic, 1 American Indian or
Alaskan Native, 2 Others. In the summer residential program in India, 15 students participated of which
8 were females and 7 were males. All the students were of Asian Indian origin. We collected data invarious forms, namely videos of student discussions, screen-capture videos to capture students’investigations of computer models, workbooks in which students wrote their observations andexplanations, and the computational artifacts (models and screenshots) that students created.
We use mixed-methods analysis to investigate whether students learned disciplinary core ideas throughtheir participation in ESM-based curricula and how they engaged with science inquiry practices. Using
quantitative approach, we have demonstrated elsewhere that the students learned disciplinary coreideas about genetics and evolution using pre- and post- tests (Dabholkar et al., 2018). In order tocharacterize students’ engagement in inquiry science practices used both bottom-up and top-down
process coding approach. The bottom-up approach involved process coding to describe student
engagement and teacher strategies, whereas the top-down codes are from NGSS recommended
science and engineering practices (Miles, M. B., Huberman, A. M., & Saldaña, J., 2014; NGSS Lead
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States, 2013). The analysis that we present in this paper is case-based analysis of student learning
Mathetic Content Knowledge in this ESM-based computational learning environment.

Developing MCK through scaffolded exploration of GenEvo curriculum
In the course based on the GenEvo curriculum, students work in groups, conduct model investigationsindividually within a group, and then present, discuss and debate their observations, claims andtheories. In this part, we present data of students-teacher discourse, where the students discuss theirown theories about an emergent concept, a big-M idea, ‘carrying capacity’ and how it affected the growthof a population in a computational model. All the students had performed their experiments using thecomputational model in the GenEvo curriculum (based on the screen recording data) before this
discussion started.
Students used their prior knowledge as well as the knowledge they constructed through their
explorations of computational models. Owen and Randi58 presented their arguments based on theirprior knowledge. Owen had mentioned that ‘carrying capacity’ is the amount of food an organism carries
and that led him to an incorrect inference of his observation.
Owen: “….., if you have more carrying capacity, that means the cells can carry a lot more food
which means they can split faster …..”
In the above sentence, Owen attributes carrying capacity as a property of a cell, whereas in fact it isproperty of the environment. This is an example of level-slippage where a learner attributes patterns orproperties of macro-level to micro-level or vice-versa (Wilensky & Resnick, 1999; Levy & Wilensky,2008). In the computational model, students can change carrying capacity settings to conduct different
computational experiments in order to figure out what it means and how it influences the population
growth. Such playful explorations resulted in Hasan constructing the knowledge about carrying capacity
through his own explorations. When asked about who he knew that answer, Hasan mentioned, ‘I wasjust playing with it and I noticed…. the amount of cells….”
The following is the complete except as an example of students arguing about what ‘carrying capacity’means based on experimental evidences or their prior knowledge.
Teacher – “Oh! Do you? I don’t. Can you explain it (what carrying capacity means) again to me?”
Owen – “So pretty much, if you have more carrying capacity, that means the cells can carry a lot
more food which means they can split faster like in the last thing, which means they will be bigger andeat a lot more and need a lot more food.”
Teacher – “Do you all agree to that?”
Hasan – “It seems like he is saying that carrying capacity is how much food they sustain but carrying
capacity is how much the map…the square can hold.”
Randi – “Ya, because carrying capacity is how many …”
Hasan – “cells”
Randi – “Ya, and he is talking about food.”(Transcript from video data, March-19, 2017)
In this conversation, Owen attributed ‘carrying capacity’ as a property of a cell, which is based on his
prior incorrect conception which he tries to connect with his experimental observation of cells splitting
faster. The teacher then directed the question to the rest of the class. This is an important teacher
strategy of not correcting Owen’s answer, rather than trying to correct Owen’s conception andreasoning. As a response to the teacher’s question, Hasan and Randi argued differently. Hasan referredto the map or the square which represents the environment in the computational model to demonstrate
his little-m idea, which is his contextual understanding of the big-M concept, carrying capacity. Randi
supported him in the argument. This analysis demonstrates that these students used different reasoning
58

All the names used in this paper at pseudonyms.

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approaches to explain their answer, some of which were based on the prior knowledge whereas somewere based on the experimental investigations. It also shows that the student, Hasan, who did not haveany prior knowledge, mentioned that he was just playing around with it and noticed the amount of cells.
It is also important to notice that Hasan is referring to his explorations of computational model as playing
around. In this class, as the students learn the concept of carrying capacity, they also investigate how
to figure out what carrying capacity means and how it affects the system they are studying. This
understanding that knowledge can be constructed and verified through systematic investigations, andthat’s how the scientific knowledge is generated is critical for developing science MCK.
This kind of development of MCK is also evident in another part of our analysis of students’ reflectionsof what they learned when they participated in systematic exploration of the computational models in
the curriculum. In the weekend course at a Midwestern suburb in the United States, by the end of thesecond day the teacher asked what they learned in the two days and how. After Alex mentioned all thedisciplinary ideas they learned like genetic regulation, cell producing energy and that effecting their
growth rates, genetic drift and natural selection, the teacher asked about how they learned these ideas,
if they worked like scientists, if they figured out stuff like scientists. Alex continued, “Yeah, because wecame up with some kind of theory and we built off of it.” Tanya, another student in the class added, “LikeAlex said, we came up with theories and we worked to either prove them right or prove them wrong.”(Italicized part is from the transcript from video data, April-01, 2017). In the further discussion, thestudents talked about which theory they proved to be right and which theory they proved to be wrong.
Tanya, who had strongly supported a theory (her little-m idea) that lactose is bad for cells and blue color
for death, was quick to point out that it was the theory that was proved to be wrong. Throughout thediscussions over the two days the teacher never mentioned that a theory is right or wrong, until thestudents presented evidence in support of it and against it, and argued about it. When the teacher asked
if this experience was different from the other learning experiences, Alex said, “Yeah, it was a lot
different. Because we have done this kind of stuff before with the models that are interactive. But,
normally it’s just like follow these steps and combine those to find answer.” He was possibly referring tothe open-ended nature of the scaffolded explorations where there was no ‘standard procedure’ to arrive
at a ‘correct answer’, rather the investigative approach for gathering evidence in support of arguments
was emphasized in such kind of ESM learning environments.
In the previous part, we have discussed how students engage in learning inquiry science practices withan ESM-based curriculum and what they think about that engagement in scientific practices. In this part,
we discuss how students expressed what they learned the course and how they learned it. These are
the quotes from the students in who participated in the Computational Modeling of Genetics andEvolution course in India. The first quote is from Amita, who did not participate much in the discussions
and when she did, she made some very sharp points. Amita wrote that “In the past few days, I lived mylife like a scientist. I made observations and presented those in front of others. I learnt a lot of stuff which
I believed till now was impossible.” Amita referred to something new that she learned which was beyond
her imagination. Dinesh wrote that “The most important thing I learnt is to observe and learn from others.
I also learned that it is important to give credit to other people for their contributions….” Two important
things reflected in Dinesh’s writing are science being a collective enterprise of constructing knowledge
and citing others to give credit for their work. Dipti mentioned that “… I got to know a new way oflearning.” Akshay mentioned that “For me, the most important thing to learn was how to learn.” Dipti and
Akshay’s quotes are indicative of the fact that the students thought about the process of learning.
Vasumitra’s quote captures how he thought he learned; he wrote “The thing I learnt was designing myown experiments, collect the proof and make observations. I also learnt that it is fine to be wrong and it
ultimately leads to our betterment.”
Reflections of these students on the process of learning in this learning environment indicate that how
they understood the process of constructing knowledge by the scientific community as well as how they
perceived their role in this science classroom. Vasumitra’s reflection is an indication of how he thoughtthat science MCK, which is about constructing knowledge by conducting experiment, collecting proofs
and making observations, and more importantly making mistakes is critical for one’s learning in ascience class.

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Conclusions and Implications
In this paper, we argued for the importance of fostering Mathetic Content Knowledge, knowledge of how
to learn by engaging in discipline specific inquiry practices, in general, and specifically for scienceeducation. We also discussed how an ESM-based curriculum can be effective in fostering learning of
MCK. With increased emphasis on the scientific inquiry practices in science curricula and use of modelbased inquiry learning for the same, characterization of inquiry learning with environments like ESM andunderstanding design principles for such environments is critical. We have presented two kinds ofevidences using case-based analysis in this paper. First, we analyzed student participation in an ESMbased curriculum and demonstrated how students use various reasoning strategies in engagement inan argument, how they can use evidence from ESM to support their arguments, and to constructknowledge. We also presented data about how students view their own engagement in inquiry science
practices and how they view their learning with an ESM-based curriculum. We argue that systematic
exploration of computational models using ESM learning environments are effective in developing
science MCK.

Acknowledgements
We thank Connor Bain and Ümit Aslan who helped us in designing the models and curricular units. We
also thank Aniruddh Sastry who has helped in implementation of the curricular unit in India. We are
grateful to Educational Initiative’s ASP for helping us conduct the research in India. Authors 1 and 2
gratefully acknowledge Learning Sciences program at Northwestern University for the funding support.

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Dabholkar, S., Bain, C. and Wilensky, U. (2016). NetLogo GenEvo 1 Genetic Switch model.
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for model-based scientific inquiry in the context of genetics and evolution. Proceedings of theInternational Conference for the Learning Sciences.
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Making Together: Cultivating Community of Practicein an All-Girl Constructionist Learning Environment
Caitlin Davey, csd2126@tc.columbia.edu
Teachers College, Columbia University USA

Sawaros Thanapornsangsuth, st2839@tc.columbia.edu
Teachers College, Columbia University USA

Nathan Holbert, holbert@tc.columbia.edu
Teachers College, Columbia University USA

Abstract
This practice paper investigates instructional practices that support relationships among young female
makers. It summarizes a design-based research study where a group of all-female makers constructedtoys for younger students in their immediate school community. Makers supported one another throughcontributing ideas, building together, sharing expertise, and providing helpful encouragement. The
finding shows that the classroom practices extended beyond physical construction to include playfulinteractions amongst makers such as: switching projects, working together outside class time, and amyriad of other activities both related and unrelated to making. Through the process of making, they
developed individual expertise that contributed to the sharing of knowledge within their classroom
community. Drawing on literature from constructionist design paradigms and community of practices,
data from this Making and Engineering class describes an emergent community of makers. Additionally,
this paper highlights the value of makers creating personally and socially meaningful projects incollaboration with others. Finally, we describe the flexible and playful environment of the Making andEngineering classroom that contributed to how makers shape their shared practices.

Keywords
constructionism; community of practice; maker education; girls in making

Introduction
Makerspaces are venues where individuals gather around a shared interest in making to learn, create,and share expertise. Sharing of expertise has been identified as a key feature of these spaces thathelps to induct new members and expand the making community (Halverson & Sheridan, 2014). This
practice paper aims to illustrate the importance of near-peer communities of practice (Lave, 1991) within
an all-female makerspace as research on collaboration in makerspaces has thus far focused on intergenerational or adult learners who develop their expertise to teach or mentor younger makers (Blikstein,
2013; Halverson & Sheridan, 2014; Resnick & Rusk, 1999; Holbert, 2016). Our study investigates thecollaboration between elementary-aged makers in a Making and Engineering class. This paper
examines how young makers who are newly introduced to Making and Engineering, support one
another during the making process and gain individual expertise through their collaborations. In
examining this community of young makers our paper aims to answer the following questions:

How does working alongside peers influence the process of making?

How do young female makers adopt and develop specialties when assisting one another during
making activities?

How did the Making and Engineering classroom structure influence the practices developed by acommunity of young female makers?

In making artifacts alongside their peers, an emergent set of practices developed among the makers.
Several cases will be analyzed to examine evidence of peer-support indicative of a community ofpractice within the constructionist classroom project. The values of this community of practice uniquely561

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supported the peer-to-peer and peers-to-client relationships of this instructional design. Sharing newly
formed expertise, collaborating, and providing supportive feedback were facets of their making practice.
Through assisting others, makers reinforced their learning of essential engineering and design skills.
This paper presents cases of students that exemplify the community of practice formed within the allgirl maker classroom. These cases emerged from field note data analysis and one-on-one interviews.

Literature Review
Constructionism: “In the world” tangible and sharable knowledge
The activities employed in the Making and Engineering class draw on the constructionist design
paradigm. Papert’s constructionism extends Piaget’s constructivism by proposing that the constructionof knowledge “in your head” happens best when constructing tangible and shareable objects “in theworld” (Papert & Harel, 1991; Papert, 1993). Constructionism is a “framework for action” (DiSessa &Cobb, 2004,) situated and pragmatic (Ackermann, 2001; Noss, 2010). In constructionist design learners
build public artifacts that are personally and socially meaningful. This construction process leverages
diverse ways of thinking, knowing, and practicing (Turkle & Papert, 1990) and situated learning so it isabout developing concrete relationships with objects and ideas (Ackermann, 2001; Wilensky, 1991).
The creation of an artifact, which could be either physical or virtual, allows learners to externalize theirmental models and iterate on their thinking throughout the making process. Additionally, it enables
learners to see and critique one another’s work (Papert & Harel, 1991). Resnick (2004) highlights thevalue of learners playfully creating personally-meaningful projects in collaboration with their peers. He
believes that learning should be a social activity where learners share ideas, collaborate on projects,
and build on others’ work. In their “Instructional Software Design Project” research, Kafai and Harel(1991) found that by ensuring all students in a class worked towards the same goal, in this case creating
math games to teach younger students about fractions, the students were able to support each otheras they faced similar problems, shared ideas, helped others and discussed technical problems. Somestudents in this study choose to work together by discussing problems with a partner or working together
as a team. Even those who chose to work alone, sought help or inspiration from others’ for their work.
Working on the same project goal is an engine to drive constructionism and communities of practice.
However, the environment needs to be designed to encourage and support learners’ collaboration andsharing. Learners learn better when they are immersed in an environment and community that supports
their interests (Papert, 1980). Access to other creators can be especially important for deepening
learner’s expertise through receiving feedback, brainstorming ideas, working on projects together, andfinding encouragement (Ito et al., 2009).
Community of practiceLearning in a community of practice is inherently social (Lave & Wenger, 1991). “Communities ofpractice sprout everywhere, in the classroom as well as on the playground, officially or in the cracks”(Wenger, 1998, p.6). Instead of didactic instruction from a teacher to a learner, this viewpoint describes
a diverse set of essential actors and forms of participation. Lave (1991) explains that structure andexperience reinforce each other such that they shape the relations among persons acting, settings,
situations, and systems of activity. Near-peers, members of a community matched in ability, areidentified by Lave and Wenger (1991) as important in the circulation of knowledgeable skill.
Just as there is a rich field of actors, there may be many different ways of participating in a communityof practice. As members develop mastery through peripheral participation, they move into more central
modes of participation within the community. However, there may be no such thing as a central role.
The notion of legitimate peripheral participation outlined by Lave and Wenger (1991) is used to suggestthat there are “multiple, varied, more- or less-engaged and inclusive ways of being located in the fields
of participation” defined by a community (p. 36). Developing an identity gives meaning to shared skills
which, are then incorporated into identities. Forming an identity as a member of a community anddeveloping skills are mutually reinforcing.
Participation is central to learning in a community of practice. Learners absorb and become absorbed
in the culture of practice, which increasingly provides them with occasions to make the culture of practice562

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their own (Lave & Wenger, 1991). As described by Halverson and Sheridan, “communities of practice
emerge around makerspaces as members co-participate in a range of activities” that go beyond making
to unrelated socializing (Halverson & Sheridan, 2014, p. 502). Makerspaces can be understood ascommunities of practice where making activities are a part of a larger in-person, or online, community.

Methodology
Population and Site
This practice paper is part of a larger design-based research project called “Bots for Tots” which aims
to increase diversity in maker and engineering design activities (Holbert, 2016). In investigating girls’
participation in maker activities, we draw on data from the second iteration of the project, where thepopulation is solely female participants. This iteration of Bots for Tots took place at an all-girls private
school in a suburban area in the North-Eastern United States. 41 fourth-grade makers (aged 9-11) fromtwo classes (20-21 students per class) participated in the study as a part of their Making and Engineeringclass, a bi-weekly class which ran 45-minutes per session. Throughout the academic year, students
participated in 17 sessions in total as described in Table 1.
The school has a long tradition of pairing fourth grade “Big Sisters” with first grade “Little Sisters” under
its Big Sister/Little Sister mentorship program. For the first time, as part of the Bots for Tots project, thefourth grade Making and Engineering class had the explicit goal of designing and building “dream toys”
for their first grade Little Sisters. The class began with two sessions of making with a 2D- to 3D-objects
with cardboard in order to familiarize them with tools and materials in the lab as well as preparing themup for creative activities ahead. After the two cardboard sessions, the fourth-grade makers interviewed
their Little Sisters using the “My Client Profile” worksheet. During this interview makers questioned their
Little Sisters about toys by asking: What kind of toys do you like? If you could imagine any toy, what
would it look like and how would you play with it? Makers then used the completed My Client Profile todiscuss and brainstorm toy ideas with their classmates. After three sessions of prototyping, makers met
their Little Sisters again to receive initial feedback on their prototypes before they began working on
their final toy construction. The makers then spent seven sessions building and completing their final
designs. Finally, the makers delivered their newly constructed toys to their Little Sisters and had aplaydate where the girls discussed the toy’s design and construction while playing together.
Table 1. Structure of the Making and Engineering class.
Name of activity# of sessions the activity
occursMajor activities

Make 2D to 3D cardboard
animals3

Makers drew animals they selected on a piece of paper and usedcardboard to make it 3-D.

Interview with Little Sister

1

Interviewed first-graders about their dream toys

Brainstorm with small group2

Shared information. Each maker gained from their Little Sisterand asked for classmates’ input about their design ideas.

Prototype2

Made prototypes

Meet Little Sister for prototype
feedback

1

Showed Little Sister their prototype and asked for feedback

Complete final toy design

7

Revisited the feedback from Little Sister and planned for
improvements.
Finalized toy construction

Toy delivery and play date

1

Met Little Sister for toy delivery, explained the design of the toy,and played together.

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Data Collection
All names used in this paper are pseudonyms chosen by the makers.
Interview: While all 41 makers participated in the study, 12 were randomly selected for one-on-one
interviews. We interviewed the makers at the beginning of the year, before the Making and Engineeringclass had begun to determine makers’ experience with technology, construction, and crafts as well as
knowledge of relevant Making and Engineering concepts or skills. Interviews lasted approximately 40
minutes per participant. We interviewed the same makers again at the end of the year after the class
had concluded. In the post interview, makers were asked about their experience working with theirfriends and making toys for their Little Sister. Our goal was to understand how making with others
influenced the overall making process and how makers may have developed and adopted specialties
when collaborating. All interviews were initially video-recorded then, transcribed first by both the authors
and then by an independent service.
Field notes: Detailed field notes were taken during observations of the Making and Engineering class.
These observations focused on how the fourth-grade girls interacted with their classmates in the Making
and Engineering learning environment (offering help, sharing materials, developing the sense ofexpertise, providing each other feedback and support).
Artifacts: A variety of maker artifacts were produced throughout this project. These include worksheets(My Client Profile worksheet, a worksheet to record feedback from their Little Sisters, and others),
photographs of participants working on their projects, as well as photographs of their toy designs
throughout the construction process. These artifacts provided a broad picture of each participant’s work,
such as whether they worked independently or alone, as well as their level of expertise both in techniqueand constructed toy. All artifacts were de-identified.

Results
Idea Generation
Idea Sharing
After conducting the initial client interviews with their Little Sisters, the teacher had makers gather insmall groups to brainstorm ideas. Linda noted under “Concerns” on her “My Client Profile” worksheetthat her Little Sister was, “hesitant about answering making me think that toys are not her favorite andshe said that she likes toys that draw.” Three of her peers provided suggestions about how to design a
toy that would address these concerns. One girl tried to convince Linda to make a huge stuffed crayon
so it would be, “soft like a fuzz ball.” Another, added it could be a double-sided marker. Building on thisidea, another maker told Linda the marker should have four colors. While Linda wasn’t initially interested
in a multi-sided marker, at the end of the class session on her post-it note summary she wrote, “Double
sided marker. Put a squishy thing on top. Four sided marker/crayon and put squishy things on thehandle.”Co-constructing
As there were more fourth-grade Big Sisters than first-grade Little Sisters, four makers had to share a
Little Sister with another maker. Fourth-grade makers Susie and Paula decided to make a toy together
for their Little Sister, while Betty and Chloe made two separate toys.
Susie and Paula interviewed their Little Sister together and wrote similar findings on both of their “My
Client Profile” worksheets. For example, under the heading “Likes” they wrote, “yellow, blue, medium,
real animals, panda, cheetah, hard” and under “Concerns,” they wrote, “don’t break, no spider, andcolorful”. She also made the observation that their Little Sister “likes dolls with clothes.” In order to assist
the makers with planning their projects, the instructor made a “Brainstorming” worksheet as a helpful
guide. The makers were asked to answer the following questions: “I want to explore:” and “My sketchfor (Little Sister’s name)” Susie and Paula both crossed out the word “my” and “I” on their worksheets
and changed those words to read, “we” and, “our” as shown in Figure 1. They summarized their shareddesign plan as making a “wooden or plastic doll with movable parts and brown hair.” They worked
together for the duration of the school year and consistently divided their making efforts.
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Figure 1: Susie and Paula’s Brainstorming worksheet.

In contrast, Betty and Chloe did not work together. In the post-interview Betty reflected back on her
making process. She described how she didn’t make the toy her Little Sister asked for. Betty said, “Ishared my Little Sister with my friend Chloe. I was trying to make her a stuffed bunny…I feel like weshould have worked together. It would have turned out a lot better...yeah.” Betty described that as themaking process progressed she realized, “that it was kinda dumb,” not working together.
Making Process
Overcoming challenges
Betty intended her dollhouse to have two levels. But, she cut one piece of wood slightly smaller thanthe others so when she glued her house together it was slanted. She looked unhappy with her design
so her classmate provided encouragement by saying that it looked “like a cool modern loft house!”
Another agreed and said, Betty could decorate her dollhouse as a fun, whimsical witch house. However,Betty still appeared unsatisfied. She decided to take the second floor out and redesign her roof. Though
this process delayed her work, Betty offered continued help to her friend Maya.
Maya’s project was a stuffed animal and Betty said she “helped Maya a couple of times because [Maya]
just didn’t know how to sew.” Betty taught Maya to hand sew because there was a queue to use thesewing machine. Betty humorously talked about her experience supporting Maya, “I just kind of showed
her how to like sew, and she’s like, ‘Oh.’ And then she tried to use the sewing machine and it just got
all bunched up. I’m like, ‘Okay Maya, you go hot glue this onto my house and I will cut all this off.” Maya
and Betty worked on each other’s projects to finish their toys in time.
Sharing expertise
In the post-interview Hailey said, “I asked my mom to teach me the sewing machine at my house sothat helped me a bit when I sat down at the sewing machine there [referring to the one in the classroom],
I certainly know how to use it, to thread it and stuff so that’s a big help.” Hailey described the best partof the class as helping others saying, “you got to help other people….like...oh can you help me thread
the needle?,” referring back to the sewing machine needle. She added that “[helping others] felt really
good.” Hailey helped others sew by providing procedural cues such as, “you gotta pin it. You’re goingto start here...leave a place open to stuff,” and to another maker, “pick the guide up. Pick the needle up.
Rotate.” In one instance, Madison wanted to begin sewing her project. She asked Hailey, “can I sew it
now?” Hailey responded, “can I just get you started?” Madison replied, “can I do it? I’m kind of a control
freak.” Hailey then said, “trust me, I do it too.” After more prompting, Hailey stepped back from themachine. For Hailey’s own project she sewed a pillow for her Little Sister. To test it, Hailey placed thepillow on the ground and rested her head on it. She then solicited feedback on her pillow design byinviting a friend over to also rest their head.
Wrapping up
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Facing time constraints
Towards the end of the school year, Margie had finished her toy early, a purple stuffed tennis ball. In
the post-interview Margie said that her friend, Cathy suggested that she make a tennis court to go withthe tennis ball. While Margie used her extra time to supplement her toy, Ellie walked around the classand shouted, “who needs help?”. LillyJane mentioned Ellie in her interview. She said that she received
help from Ellie and that if she had extra time, she would have offered similar assistance to her peers, “If
I was finished with mine, people were coming around and helping people like Ellie. She helped me. But
like I didn’t get, because I was doing my stuff, and I didn’t have enough time to help other people.”
Hailey also mentioned Ellie as a person she went to get confirmation from on her ideas.
Erica had her friends help her by finding materials and painting her doll house because it “was prettybig”. Similarly, Betty was making a dollhouse and needed help making items to furnish it. She told us
during the interview that she saw Chloe “just wandering around doing nothing, so it was like, ‘Can you
help me make the chairs?’” Holly was also behind in finishing her toy doll during the final class period
and enlisted her classmate Anna to help her make a skirt and hair for the doll. When the instructor told
the class to clean up, Holly asked Anna if she could visit her house over the weekend to help her withthe project.

Discussion
Making with others
Working with others influenced the makers process of constructing toys for their Little Sisters. The
Making and Engineering class created a community that involved making, brainstorming, designing,helping, valuing others’ ideas, and sharing their projects. When Linda was struggling to generate anidea that responded to her Little Sister’s toy preferences, she asked for input from her peers. Linda
incorporated all of their ideas to create her soft three-sided marker project. Valuing the ideas of others
emerged as a characteristic of this community. Makers reified their practices through the toys theycreated for their Little Sisters (Wenger, 1998). Though Linda was responsible for her own project, herwork reflects the contributions of her four fellow makers.
Making tangible objects acted as a medium for the makers to communicate their abstract ideas to eachother. It became an external representation of the makers’ ideas that enabled them to play and to gaina better understanding of possibilities and limitations (Resnick, 2013). Sharing the same Little Sister,Susie and Paula needed a common vision to construct together effectively. From the beginning of theprocess, they compared their “My Client Profile” worksheets to generate toy ideas. On their“Brainstorming” worksheet, they both sketched out a picture of a girl wearing an apron with a paintpalette as shown in Figure 2. This sketch can be understood as a mutual concrete representation of
their project. While they individually sawed each wooden piece, they would frequently piece them
together to align their designs. The artifact they shared allowed Susie and Paula to communicate moreclearly about their progress towards their shared vision.

Figure 2: Susie and Paula were sawing wood together to make different parts of their doll.

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Creating an object allowed learners to explore and expand their ideas but also to see their thinking andprovide feedback on a visible artifact (Peppler & Hall, 2016). When Betty was disappointed by her
slanted two-level dollhouse and wanted to quickly make a pillow instead. She received feedback andencouragement from her peers, that the house looked “cool” and “whimsical,” after this reassurance
she decided not to start a new project, but figured out a way to improve her existing one. Just as “having
a tool to perform an activity changes the nature of the activity,” so did having objects to share their ideas
around change the makers’ process of making and helping one another (Wenger, 1998, p. 59). Betty
was able to refer to her project and how it differed from her intended design. But also, her classmates
supported her by reframing her project, providing encouragement, and easing her disappointment sothat she was able to progress past this mistake her project.
Developing expertise
As the makers progressed in their projects they began to develop specialized areas of expertise. In
doing so, some members such as Hailey can be understood as moving into more central roles within
their community of practice (Lave & Wenger, 1991). Though makers were not assigned stations tooversee, Hailey took it upon herself to improve her sewing skills by asking her mother to teach her athome. She used her expertise to provide support to her peers and assist them in developing their own
abilities. Hailey is shown assisting her peers in Figure 3. The makers can be understood as displaying
different learning trajectories or modes of participation, as not all makers developed expertise in one
domain. Hailey’s proficiency was developed to support a core practice that emerged within thiscommunity, supporting others. As expressed by Hailey, “[helping others] felt really good.” Herknowledge of sewing allowed her to support others and made her feel proud.

Figure 3: Hailey (first from the left) helping her friends at the sewing machine station.

Similarly, Ellie positioned herself as a creative contributor to the classroom community by walking
around the room and providing suggestions to her classmates. Ellie’s project, a giant stuffed purple
snake, was highly praised by her friends and teachers. Ellie’s skills at making mutually reinforced her
identity as a proficient maker that she adopted within the Making and Engineering classroom (Lave &Wenger, 1991). She expressed her expertise, by going around the classroom and providing help to her
friends who were struggling with their own projects. For Ellie, being an expert wasn’t simply about having
specific skill, soft toys, but being able to be a generalist who could assist anyone in the classroom.
Making and Engineering classroom
Though the makers were designing for others, by working alongside each other their construction
became more personally meaningful. An important instructional decision within the Making andEngineering class was to allow makers the freedom to move around and express themselves. The
program also ran throughout the academic year. This allowed makers to have enough time toexperiment and tinker with multiple new ideas and iterate on their designs (Resnick, 2013). The makers
practice within this space involved activities that extended beyond making. Halverson and Sheridan(2014) described communities of practice in makerspaces as involving activities that moved beyond
making to include playing board games, caring for resident pets, and taking walks together. Similarly,567

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the much younger all-female makers in this Making and Engineering school-based class were able (andallowed) to talk, sing, and move around the classroom as they worked. The makers expressed theirenjoyment of making together. When Hailey wanted to test the pillow she had created she lied downon the floor and invited a friend to rest their head next to her to try out the pillow as well. The classroom
environment allowed makers to more freely interact with their peers, whether it was inviting a friend torest their head on your pillow or by singing a song together.
Despite the open-ended instructional approach, many students experienced time constraints that
impacted their making. As Lave (1991) explains structures and practices reinforce each other to shape
the relations among actors, settings, and systems of activity. Structures contributing to the formation ofthis community can be understood as the single gender school, the “Little Sister” program, and the tighttime restrictions of each classroom period. When Maya couldn’t complete her stuffed animal because
she didn’t know how to use the sewing machine she sought help from a friend. Her friend, Betty tried toteach her but she still had difficulties operating the machine in Figure 4. Betty hadn’t finished her own
project yet despite wanting to assist her friend. They switched their projects so their abilities could matchthe remaining tasks and they both could work more efficiently. Another set of makers, Anna and Holly,
indicated that they would continue working together outside the classroom. Holly invited Anna to herhouse on the weekend to help her finish her toy. Despite time-constraints, the flexible working
environment allowed these makers to creatively address their problems whether by continuing to work
together outside of class or by dividing up tasks across projects.

Figure 4: Betty teaching Maya how to use the sewing machine.

Conclusion
Through our analysis of young female makers who formed an informal community of practice, we
provide insights into what learning looks like in a school-based constructionist learning environment.
Working with others influenced the makers processes of constructing toys for their Little Sisters. As
clearly demonstrated in the collaborative efforts of Susie and Paula, making changed from being an “I”
and “my” process to a “we” and an “our” one. As the makers created their projects, they developed
different areas of expertise. Whether specific knowledge or general making skills, makers used theirabilities to complete their own projects as well as assist others. The classroom practices extended
beyond physical construction to include playful interactions amongst makers such as: singing, restingon the floor, switching projects, working together outside class time, and a myriad of other activities both
related and unrelated to making. By looking into how young girls make and interact in a constructionist
learning environment, we can better facilitate and cultivate the learning community where learners help
one another, share their knowledge, and create meaningful projects together.

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Assessment of Modeling Projects in Informatics
Class
Natasa Grgurina, n.grgurina@rug.nl
University of Groningen, Netherlands

Erik Barendsen, e.barendsen@cs.ru.nl
Radboud and Open University, Netherlands

Cor Suhre, c.j.m.suhre@rug.nl
University of Groningen, Netherlands

Klaas van Veen, klaas.van.veen@rug.nl
University of Groningen, Netherlands

Bert Zwaneveld, zwane013@planet.nl
Open University, Netherlands

Abstract
The introduction of the new Informatics curriculum in the Netherlands in 2019 raises the need for newteaching material that includes practical assignments and guidelines for their assessment. As a part ofour research project on teaching Computational Science (modeling and simulation), we participate inthese efforts and developed a curriculum intervention and an assessment instrument consisting of apractical assignment and grading rubrics to assess student’s level of understanding. The rubrics wedeveloped can be used both for formative and summative assessment. In this paper we describe thedesign of this assessment instrument and indicate further research directions focusing on validation ofthis instrument.

Keywords
modeling and simulation; NetLogo; assessment; SOLO-taxonomy

Introduction
In the Netherlands, where informatics is an elective subject in grades 10 and 11 of the senior generalsecondary education spanning grades 7 through 11 (in Dutch: HAVO) and in grades 10 through 12 of
the pre-university education spanning grades 7 through 12 (in Dutch: VWO), the new 2019 informatics
curriculum recognizes the importance of modeling and includes an elective theme comprised ofmodeling and simulation, together called Computational Science. It is described by the high-level
learning objectives: “Modeling: The candidate is able to model aspects of a different scientific discipline
in computational terms” and “Simulation: The candidate is able to construct models and simulations,and use these for the research of phenomena in that other science field.” (Barendsen & Tolboom, 2016).
The curriculum does not provide further details about these objectives, instruction or assessment. Inline with the Dutch tradition, this is left to educators and authors of teaching materials. The elaborationof these learning objectives, the development of teaching materials, assessment tools and teacher
training courses are already taking place and we both participate in these endeavors and monitor thedevelopments.
This study is a part of a larger research project on teaching Computational Science in the context of
informatics in Dutch secondary education, investigating pedagogical aspects and teachers’ pedagogicalcontent knowledge (PCK) about modeling. (For clarity, in this paper the terms modeling, simulation
modeling and computational science all refer to the learning objective computational science.) Following
Magnusson et al. (Magnusson, Krajcik, & Borko, 1999), we distinguish four elements of content-specificpedagogy: (M1) goals and objectives, (M2) students’ understanding and difficulties, (M3) instructional
strategies, and (M4) assessment. Previously, we refined the CSTA definition of computational thinking
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Constructionism 2018, Vilnius, Lithuania(CT) (Grgurina, Barendsen, Zwaneveld, van de Grift, & Stoker, 2013), made initial explorations ofteachers’ PCK (Grgurina, Barendsen, Zwaneveld, van Veen, & Stoker, 2014a; Grgurina, Barendsen,
Zwaneveld, van Veen, & Stoker, 2014b) and of the computational modeling process (Grgurina,
Barendsen, van Veen, Suhre, & Zwaneveld, 2015), obtained an operational description of the intended
learning outcomes (ILO) of the learning objective Computational science — thus focusing on
Magnusson’s element M1, observed students working on modeling tasks — focusing on Magnusson’s
element M2, and established what data sources were suitable for assessment — Magnusson’s element
M4 (Grgurina, Barendsen, Zwaneveld, van Veen, & Suhre, 2016), and finally, investigated teachers’
initial pedagogical content knowledge on modeling and simulation (Grgurina, Barendsen, Suhre, vanVeen, & Zwaneveld, 2017). In our subsequent study, we focus on monitoring the levels of understanding
in the learning outcomes of students engaging in modeling projects - Magnusson’s element M4 - andaddress the following research question: What are the characteristics of the assessment instrument forassessment of the intended learning outcome for computational science? In this paper, we describe thedesign of this assessment instrument. The results of the entire study will be reported elsewhere.

Background and Related Work
Computational Thinking: Modeling
Formulating problems in a way that enables us to use a computer to solve them and representing data
through abstractions such as models and simulations are integral parts of computational thinking (CT)(CSTA Computational Thinking Task Force, 2011). With the arrival of computers into schools, newvenues are created to aid students’ learning in various disciplines through the use of computer models(Blikstein & Wilensky, 2009; Van Overveld, Borghuis, & van Berkum, 2015). Wilensky argues,“Computational modeling has the potential to give students means of expressing and testing
explanations of phenomena both in the natural and social worlds” (2014), as do Caspersen and Nowack(2013). Indeed, modeling plays a significant role in the development and learning of science (Justi &Gilbert, 2002) and informatics equips the students to actively engage in learning science by providing
tools and techniques to engage in modeling, thus enabling them to provide meaning to the learning bothof the discipline at hand (Gilbert, 2006) and informatics. In the Informatics curriculum, for the intended
learning outcomes of the learning objective Computational science, in one of our previous studies wedeveloped an operational description that describes the modeling cycle for simulation modeling throughits elements purpose, research, abstraction, formulation, requirements/specification, implementation,verification/validation, experiment, analysis, and reflection (Grgurina et al., 2016).

Assessment
Brennan and Resnick focused on assessment of the development of CT during learning in informal
settings and developed a CT framework distinguishing three dimensions: computational concepts
describing the concepts designers employ as they program, namely “sequences, loops, parallelism,
events, conditionals, operators, and data”; computational practices describing the practices designers
develop as they program, namely “being incremental and iterative, testing and debugging, reusing andremixing, and abstracting and modularizing”, and computational perspectives describing theperspectives designers form about the world around them and about themselves, namely “expressing,connecting and questioning”. (Brennan & Resnick, 2012). Zhong et al. brought these three dimensionsof CT into the classroom when designing an assessment framework for elementary school students andthey redefined them as follows: computational concepts as ”objects, instructions, sequences, loops,
parallelism, events, conditionals, operators, and data”; computational practices as “planning anddesigning, abstracting and modeling, modularizing and reusing, iterative and optimizing, and testing anddebugging”, and computational perspectives as “creative and expressing, communicating andcollaborating, and understanding and questioning” (Zhong, Wang, Chen, & Li, 2016). Using thisframework, Lye and Koh analyzed 27 intervention studies in K-12 aimed at the development ofcomputational thinking and found that the majority focuses on computational concepts and only six oncomputational practices. In order to promote focus on computational practices and computational
perspectives in a K-12 classroom, they suggest an instructional approach providing “a constructionismbased problem-solving learning environment, with information processing, scaffolding and reflection
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activities.” (Lye & Koh, 2014) Since assessments can provide learning opportunities, Brennan andResnick offer six suggestions for assessing computational thinking via programming, among others tomake assessment useful to learners, to incorporate creating and examining artifacts, and to have thedesigner illuminate the whole process. (Brennan & Resnick, 2012).
These views are corroborated by the findings in our prior study on informatics teachers’ pedagogicalcontent knowledge (PCK) of modeling and simulation, where we learned that the interviewed teachers
mostly suggest hands-on approach to learning and that the preferred assessment form for most of themwould be a practical assignment lasting several weeks, where student groups would construct models
and use them to run simulations and conduct research while extensively documenting the wholeprocess. At the same time, we observed a great diversity in the assessment criteria teachers mentioned,
yet very few corresponding quality indicators used to judge to what extent these criteria are met (Grgurina
et al., 2017).
In the eyes of the students, the assessment defines the actual curriculum, according to Biggs and Tang,
who advocate a criterion-referenced system where the objectives are imbedded in the assessment
tasks. In their constructive alignment network, the curriculum is stated in the form of clear intended
learning objectives (ILO) specifying the required level of understanding, the teaching methods engage
students in doing things nominated by the ILO’s and the assessment tasks address these ILO’s. The
learning outcomes can be classified using the Structure of the Observed Learning Outcome (SOLO)
which describe the learning progress through five levels of understanding. The first three levels —prestructural, unistructural and multistructural — are considered to be quantitative in the sense that
prestructural indicates missing the point, unistructural means meeting only a part of the task andmultistructural shows a further quantitative increase in what is grasped: “knowing more”. Relational, on
the other hand, indicates a qualitative change indicating conceptual restructuring of the components —“deepening understanding”, and extended abstract takes the argument into a new dimension: (Biggs &Tang, 2011). Meerbaum-Salant et al. interpreted SOLO as five ordered categories:






Prestructural: Mentioning or using unconnected and unorganized bits of information which make no sense.
Unistructural: A local perspective – mainly one item or aspect is used or emphasized. Others are missed, andno significant connections are made.
Multistructural: A multi-point perspective – several relevant items or aspects are used or acknowledged,
but significant connections are missed and a whole picture is not yet formed.
Relational: A holistic perspective – meta-connections are grasped. The significance of parts with respect tothe whole is demonstrated and appreciated.
Extended abstract: Generalization and transfer – the context is seen as one instance of a general case.

According to them, while the strength of the SOLO taxonomy lies in the fact that it offers a holistic, rather
than a local perspective, “using [it] for various types of activities, simultaneously, is not straightforward”,
so they combined the Bloom’s taxonomy and the three intermediate categories of the SOLO taxonomy
in order to assess how novice programmers learned programming with Scratch (Meerbaum-Salant,
Armoni, & Ben-Ari, 2013). Whalley et al. noted that previous research had indicated difficulties inmapping from student code to the SOLO taxonomy “since the mapping process seems very context
bound and question specific”. To alleviate this problem, they developed a mapping framework where
first, the salient elements are identified at syntactic level of the code; subsequently, basic replicable anddiscernible features such as redundancy, efficiency, generalizability and integration are abstracted fromthe code itself, and finally, SOLO mapping takes place to the five SOLO categories they suggest forcode writing solutions (Whalley, Clear, Robbins, & Thompson, 2011).
The issue of assessing the learning of the students engaged in larger programming projects attracts attention as
well. Casto and Fisler explored how to track program design skills through the entire CS1 course and suggest amulti-strand SOLO taxonomy, thus corroborating the idea that using SOLO taxonomy simultaneously for various
types of activities is not straightforward. They suggest a multi-strand SOLO-taxonomy without the extended
abstract level, since none of the students in their study reached that level (Castro & Fisler, 2017). A multi-strand
SOLO taxonomy is in line with the idea that one assessment task might address several ILOs and vice versa, one
ILO might be addressed by several assessment tasks (Biggs & Tang, 2011). Assignments for complex tasks
encompassing diverse ILOs — such as going through a modeling cycle by formulating a problem, pinpointing the572Constructionism 2018, Vilnius, Lithuania

research question, designing a model and using it to answer the research question — warrant the elaboration of
criteria defining performance for each of the ILOs involved.

Assessment instrument
Based on these findings, we developed constructionist teaching material about agent-based modelingwith NetLogo, meant for the informatics students in the 11th and 12th grades who are preferably nonovice programmers but rather somewhat experienced, probably in other programming languages. The
teaching material covers all the aspects of the ILO’s of Computational science we identified earlier(Grgurina et al., 2016), and focuses not only on computational concepts such as programming to
implement the model, but also on computational practices such as the validation of the model andcomputational perspectives such as formulating the research question to be answered through the useof the model. Together with this teaching material, we also developed an assessment instrument on
which we focus here.
Following suggestions for the rubrics construction by Wolf and Stevens (2007), from the modeling cycle
we first identified the criteria that defined performance as: stating the case and the research question,
designing the model, implementation, validation, experiment, analysis, answering the researchquestion, reflection, and additionally, logbooks. Subsequently, we designed an assessment instrument
consisting of a practical assignment that provides several cases and research questions for students tochoose from, a detailed description of the modeling process they need to engage in, and acorresponding rubric based on SOLO taxonomy with unequally weighted criteria defining performance.
The description of SOLO categories was based on the interpretation by Meerbaum-Salant et al.,stressing the progression from the local to the global perspective.
An example of the cases provided is the question whether sustainable human life is possible on Mars.
The students are pointed to the websites of NASA and SpaceX to learn about the current state of affairs
and subsequently have to explore whether, after the initial supplies and shelter were delivered, it would
be possible to produce sufficient water, air and food to survive and thus whether it would be possible tofound a sustainable human colony on Mars. Among other cases are the questions, what is better for
traffic flow on a junction: a roundabout or traffic lights, and to investigate the optimal number and task
division of bank counters as to minimize the waiting time of the customers with various needs. In linewith our dedication to stimulate student engagement, the students are allowed to come up with theirown research questions instead.

Assignment
The assignment consists of a number of questions the students need to answer in writing while
designing their model and using it to answer their research question. After forming groups and choosing
a case to model, the students answer the following questions:
Case and research question. Describe what you are going to model and with what purpose: (1) Whatdo you know about this phenomenon? If need be, carry out the necessary research. (2) What part ofyour phenomenon would you like to build a model of? (3) What do you hope to observe from this model?
(Questions 2 and 3 suggested by Wilensky & Rand (2015).)
Design the model. Design a model following the questions listed here. Describe the considerations and choices
you make. (E.g., “The sheep can reproduce. If two sheep meet, there is a chance of 20% that a new sheep will be
breed. We decided not to take into account the gender of the sheep because that is not relevant in this case.”) (1)What are the principal types of agents involved in this phenomenon? (2) In what kind of environment do theseagents operate? Are there environmental agents? (3) What properties do these agents have (describe by agenttype)? (4) What actions (or behaviors) can these agents take (describe by agent type)? (5) How do these agents
interact with this environment or each other? (6) If you had to define the phenomenon as discrete time steps, what
events would occur in each time step, and in what order? (All questions suggested by Wilensky & Rand (2015).)
Implement the model. Implement the model in NetLogo. Write your code in small chunks and keep
testing!

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Validate the model. (1) Microvalidation: to what extent does the agents’ behavior resemble theobservations of the phenomenon in reality? If the behaviors are (somewhat) dissimilar, is this variation
relevant to your research question? (2) Macrovalidation: to what extent does the behavior of the system
as a whole resemble the observations of the phenomenon in reality? If the behavior is (somewhat)
dissimilar, is this variation relevant to your research question?
Experiment, analysis and conclusion. Use the model to answer your research question: (1) Describe
the experiment in detail. If you use Behavior Space, report the number of experiments conducted andthe parameters used. (2) Report the findings in an appropriate manner (e.g., a narrative, a table, agraph, etc.) (3) Analyze the results. (4) Answer the research question.
Reflection. Reflect on your modeling process: (1) What went well and what could be better? (2) Did
you make any assumptions which, in retrospect, you would like to reconsider? (3) Are there any aspectsof your model which you would like to change? Are there any aspects of your model (agents or behavior)
you decided not to include in you model while now you believe they do need to be included? Make awish list of aspect of your model that need to be added, removed or changed in the next version of themodel.
In addition, the students were asked to keep a logbook recording all their activities, problems, successes
and dead ends they encountered; possible explanations for problems and successes, and finally,lessons learned.

Grading Rubrics
After we identified the criteria that defined performance, we created performance descriptions (Wolf &Stevens, 2007) to describe the appropriate level of understanding for intended learning outcomes (Biggs& Tang, 2011). Here we quote some of these descriptions:

Case and research question







Prestructural: (1) Nothing or simplistic idea of the phenomenon. Performed no research. (2)Nothing, or a few non-specific remarks but missing the point (3) Research question not clear
Unistructural: (1) Some general description. Performed no research or only limited to isolated
aspects of the phenomenon (2) Few isolated aspects of the phenomenon identified. (3) Identified
the question from a local perspective.
Multistructural: (1) Performed some research. Able to name more relevant aspects of thephenomenon, but mentions no relations among these aspects (2) Described what (part of the)phenomenon is being modeled. (3) Described the question from a multi-point perspective.
Relational: (1) Performed research. Complete idea of the phenomenon. Able to name relevant
aspects of the phenomenon, have insight into relations among these aspects(2) Described what (part of the) phenomenon is being modeled. (3) The research question clear andpredicts possible outcomes.
Extended abstract: (1) Additionally, described the relation of this phenomenon to other phenomenain the world and/or conceptualized this phenomenon so as to be able to use it other contexts
restricted and its relevance explained. Stated its relevance for other phenomena. (2) Additionally,
theorize about possible generalization of the model or transfer into a different context. (3)Additionally, theorize about possible generalization or transfer into a different context.

Design the model and implement it






Prestructural: No agents mentioned.
Unistructural: A few agents and actions identified.
Multistructural: Several agents and actions described.
Relational: Agents, actions and interactions correct and substantiated. Their contribution to thewhole acknowledged.
Extended abstract: Additionally, generalize or hypothesize about similar models in different
contexts or extend the model beyond the minimal requirements.

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Validate the model






Prestructural: Nothing. No working program.
Unistructural: Identified some resemblances and differences between the model and reality.
Relevance for the research question not clear.
Multistructural: Described resemblances and differences between the model and reality.
Relevance of the differences for the research question not clear.
Relational: Resemblances and differences between the model and reality described. Analyzed andexplained their relevance for the research question.
Extended abstract: Additionally, hypothesized over model adjustments to improve its validity for amore general purpose.

Results and Further Research Direction
In this paper, we described the design of our assessment instrument for the assessment of the intended
learning outcomes for Computational Science consisting of a practical assignment covering the ILO’s
defining Computational Science and an accompanying rubric based on SOLO taxonomy that describes
the levels of understanding. During the design process, we faced many challenges due to the fact thatsome ILO’s of modeling are at the core of informatics (e.g. implementation of the model), while others
are not often seen in an informatics classroom (e.g. experiment). Even for implementation, which comes
down to programming, it was not easy to find related work addressing assessment of programming at
just the right level of granularity. The same holds true for validation: while there is plentiful literature on
validation of computational models, to our best knowledge there is none focusing on the assessment ofvalidation in a formal learning setting.
So far, several teachers used our teaching material and assessment instrument to teach Computational
Science in the Informatics class of the 11th and 12th grade of pre-university education (VWO) in the
Netherlands. We are collecting and analyzing feedback from them and their students in order to aid theon-going project of development of teaching materials and assessment instruments. Specifically, thecurrent version of the assessment instrument will be analyzed to establish its reliability, validity andobjectivity, and in particular, it will be scrutinized in relation to the descriptions and attainability ofcurrently proposed levels of understanding as specified in the rubrics.

Acknowledgments
This work is supported by the The Netherlands Organisation for Scientific Research grant nr.
023.002.138.

References
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secondary education. Enschede: SLO.
Biggs, J., & Tang, C. (2011). Teaching for quality learning at university. McGraw-Hill International.
Blikstein, P., & Wilensky, U. (2009). An atom is known by the company it keeps: Content, representationand pedagogy within the epistemic revolution of the complexity sciences.
Brennan, K., & Resnick, M. (2012). New frameworks for studying and assessing the development ofcomputational thinking. Proceedings of the 2012 Annual Meeting of the American Educational Research
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Caspersen, M. E., & Nowack, P. (2013). Model-Based thinking & practice.
Castro, F. E. V., & Fisler, K. (2017). Designing a multi-faceted SOLO taxonomy to track program design
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Education Research, pp. 10-19.
CSTA Computational Thinking Task Force. (2011). Operational definition of computational Thinking for
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Education, 28(9), 957-976.
Grgurina, N., Barendsen, E., Suhre, C., van Veen, K., & Zwaneveld, B. (2017). Investigating informatics
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Grgurina, N., Barendsen, E., van Veen, K., Suhre, C., & Zwaneveld, B. (2015). Exploring students'
computational thinking skills in modeling and simulation projects: A pilot study. Proceedings of theWorkshop in Primary and Secondary Computing Education, pp. 65-68.
Grgurina, N., Barendsen, E., Zwaneveld, B., van de Grift, W., & Stoker, I. (2013). Computational thinking
skills in Dutch secondary education. Proceedings of the 8th Workshop in Primary and SecondaryComputing Education, pp. 31-32.
Grgurina, N., Barendsen, E., Zwaneveld, B., van Veen, K., & Stoker, I. (2014a). Computational thinking
skills in Dutch secondary education: Exploring pedagogical content knowledge. Proceedings of the 14th
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Grgurina, N., Barendsen, E., Zwaneveld, B., van Veen, K., & Stoker, I. (2014b). Computational thinking
skills in Dutch secondary education: Exploring teacher's perspective. Proceedings of the 9th Workshop
in Primary and Secondary Computing Education, pp. 124-125.
Grgurina, N., Barendsen, E., Zwaneveld, B., van Veen, K., & Suhre, C. (2016). Defining and observing
modeling and simulation in informatics. International Conference on Informatics in Schools: Situation,
Evolution, and Perspectives, pp. 130-141.
Justi, R. S., & Gilbert, J. K. (2002). Science teachers' knowledge about and attitudes towards the useof models and modelling in learning science. International Journal of Science Education, 24(12), 12731292.
Lye, S. Y., & Koh, J. H. L. (2014). Review on teaching and learning of computational thinking throughprogramming: What is next for K-12? Computers in Human Behavior, 41, 51-61.
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Constructionist Experiences in Teacher
Professional Development: A Tale of Five Years
Daniel Hickmott, daniel.hickmott@uon.edu.au
School of Education, The University of Newcastle, Australia

Elena Prieto-Rodriguez, elena.prieto@newcastle.edu.au
School of Education, The University of Newcastle, Australia

Abstract
Computational thinking and coding have recently become compulsory elements in the Australian K-8
curriculum that should be taught using ‘authentic learning challenges’ (ACARA, 2018a). However, very
few teachers, particularly in the primary school setting, have been schooled on computational thinking
or coding and rarely possess pedagogies to teach them authentically. A range of professional
development opportunities are currently being offered to impart this knowledge, both for content andpedagogy. In this paper, we provide an account of the evolution we have experienced when designingand improving professional development workshops for teachers in coding and computational thinking.
We reflect on our challenges and successes, and attest that it was only after ‘discovering’
Constructionism in late 2015 that we have been able to prepare activities that truly emulate the authentic
learning experiences that teachers are required to use in their classrooms.

Learning from pioneers: the influence of Seymour Papert on our professional development

Keywords
teacher professional development; constructionism; computing; programming

Introduction
Teacher preparation for new digital technologies curricula across the world has become a topic ofinterest in recent years. Globally, coding and computational thinking appear almost daily in the news,
and there seems to be a strong sense within industry that they are skills essential for the workforce ofthe future (Chalmers & Quigley, 2017; Pappano, 2017).
In September 2015, the national Australian Curriculum was officially endorsed. This curriculum includes
the new Digital Technologies (DT) subject, within the Technologies learning area, which is focussed on
the teaching of “computational thinking and information systems to define, design and implement digitalsolutions.” (ACARA, 2018b). This subject will be mandatory for all Australian students from Kindergarten
to Year 8 and available as an elective for Year 9 and 10 students.

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Introducing a subject that has a focus on computing reflects a current global trend in K-12 Information
and Communications Technology (ICT) education. Several countries have begun to change ICT
curricula to focus less on the learning of particular software packages and to focus more on the teachingof skills core to computing, such as computational thinking and programming (Webb et al., 2016). It isargued that teaching students these skills will allow them to become creators, rather than justconsumers of, digital technologies (Bower & Falkner, 2015).
Australian educational stakeholders have concerns about the feasibility of implementing the new DT
subject. Falkner, Vivian, and Falkner (2014) revealed that “a consultation with Industry, Community and
Education stakeholders in Australia showed that 55% of respondents had concerns about manageabilityof the implementation of the curriculum, while 45% of respondents did not think that its learning
objectives were realistic” (p. 6). One of the stakeholders’ main concerns is whether teachers areadequately prepared to teach computational thinking and programming because, unless they havequalifications in computing, they are unlikely to have had formal experience learning these skills. Thisis particularly true for primary school teachers, who don’t usually have the option to complete atechnology major and who are often generalist teachers (Vivian, Falkner, & Falkner, 2014).
Several teacher Professional Development (PD) initiatives have been developed by universities andprivate organisations to address concerns related to teacher preparation for the Australian DT subject(Commonwealth of Australia, 2016). These initiatives include Massive Online Open Courses (MOOCs),
such as those run by the University of Adelaide’s Computer Science Education Research (CSER) group(Vivian et al., 2014), and face-to-face workshops (Prieto-Rodriguez & Berretta, 2014).
Similar PD initiatives have also been developed in other parts of the world. In Europe, for example, atransnational initiative, TACCLE, combines learning through a website, news related to coding inschools, and the sharing of resource reviews by teachers (García-Peñalvo, 2016). In England,
researchers, who were working with the Computing At School (CAS) organisation, developed a modelof PD that is holistic and sustained for teachers implementing the Computing curriculum (Sentance,
Humphreys, & Dorling, 2014). In the United States (US), computing PD research has mainly been
conducted by computer science academics (Menekse, 2015). Menekse (2015) systematically reviewed
US articles on computing PD published between 2004 and 2014. One of the aims of this review was toevaluate the effectiveness of these programs, according to five factors that the author determined to be
indicators of effective PD from a literature review. The results of the review indicated that many of theface-to-face PD opportunities did not have these factors of effective PD present in them. Meneksesuggested that one of the reasons why these factors were not present could be that these PD
opportunities are often conducted by computer science faculty academics, who may have limited
collaboration with other education researchers and practitioners.
In this paper, we reflect on our experience designing and improving PD for teachers in computing. We
describe the learning process that we followed, as computer science academics new to computing
education, trying to implement relevant approaches to PD that are valued by Australian teachers anduseful in preparing them for the DT curriculum. Looking back, it is clear to us that it was after we started
looking at our work through the lens of Constructionism, that we were able to make meaningful changes
to our PD. The learning process we underwent to identify the changes to be made, highlighted the needto explore certain forms of PD that are not prevalent in recent computing education literature. We believethese forms of PD are not only useful for preparing existing teachers, but also for assisting in thedevelopment of pre-service teaching courses in computing. These courses should be designed so thatthey are relevant to current practice, particularly for primary school teachers.

Our Journey
Our team has been conducting computing PD workshops over the last five years. These workshops
have been held at our university, conducted face-to-face, and have ran over two or three days. In 2013
and 2014, these were only available for high school teachers but in 2015 we began to include primaryschool teachers as well. Since their inception, the three overarching aims of the workshops have been
to: 1) communicate the applicability and importance of computing to a wide range of research areasand careers, 2) provide examples of activities that address DT concepts and 3) provide resources for578

Constructionism 2018, Vilnius, Lithuania

teachers to use in their classrooms. These workshops have involved computational thinking andprogramming exercises with step-by-step instructions, collaborative problem-solving exercises, andpresentations by academics and industry representatives.
The design and implementation of the workshops has evolved each year as a result of participants’feedback, which has been collected through validated surveys (Prieto-Rodriguez & Berretta, 2014),
review of the literature, and reflection. Initially, the changes were a direct consequence of the feedback
received through the surveys, particularly within the open-ended questions. Later, since the 2016
workshops, changes were made to incorporate more constructionist activities.
When comparing the responses to the items common to all our surveys, it became clear that, as we
included Constructionist approaches to the design of the workshops, the satisfaction with the content
presented and its applicability to classroom practices increased. In particular the major changes
introduced in 2016, as seen in Figure 1, seemed to increase the ‘sense of community’, ‘inspiration to
improve teaching’ and ‘use of applications of computer science’, as we included more collaborative andhands-on activities (note: the asterisk next to the year indicates a primary school focus, all others aresecondary school).
I felt a sense ofcommunity among
participants of thisworkshop.
This workshop
inspired me toimprove my teachingskills.
I will use what I havelearned about
applications ofcomputer science
with my students.

4
3,5
3
2,5
2
1,5
1

Figure 1. Mean responses to selected survey questions in the workshops delivered from 2013 to 2016. Items in
a 4-point Likert scale with Strongly Disagree coded as 1 and Strongly Agree coded as 4.

Before Constructionism
The first PD workshop took place in 2013 and ran over three days. This workshop included a day oflectures presented by researchers from a variety of disciplines who incorporate computing in their work.
The other two days involved a combination of presentations and some hands-on activities. Analysis ofsurvey data of participants in 2013 showed that there were certain misconceptions with regards to thenature of computer science that we were able to unmask (XXX, 2014). In terms of the pedagogicalapproach utilised, feedback from the participants suggested that to improve future workshops we should
include “…more hands on with Robotics…” and “more hands on activities that we can take back anduse in school”. It was clear that many of the teachers preferred to build their own knowledge rather thanhave it delivered to them in an instructionist manner.
In 2014, we increased the number of hands-on activities as a result of this feedback and reduced theworkshop length to two days. Feedback from 2014 indicated that teachers wanted more ideas for
projects that they could use with their students. One teacher suggested that we include “Realistic
projects for school students e.g. project ideas when using C++” in future workshops. Reflecting back onthese comments, we see now that teachers were looking for us to help them provide context andpurpose to their students.
In 2015, we took a more systematic approach to the design of the PD and explored the use of studentcentred learning approaches in the two two-day workshops that we ran that year. The hands-on
activities included in the two previous years were written as step-by-step guides with explicit instruction
on how to complete the activity. We adopted a framework for instruction based on constructivist
principles to help us design and run collaborative problem-solving exercises. The Five Cs (5Cs)579

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framework (Tom, 2015) is built on 5 constructs identified in the literature: Consistency, Collaboration,
Cognition, Conception, and Creativity. The sessions in the two workshops were thus purposely designed
to investigate participant responses to different pedagogies.
We used self-reported measures to compare perceptions about the activities designed with the 5Cs
Framework against those that were step-by-step activities. Preliminary results were presented at theConstructionism 2016 conference (Prieto-Rodriguez & Hickmott, 2016). Our analysis showed that while
concepts were understood consistently for all constructs, the Scratch session was the one were theteaching methods were preferred by participants and higher order thinking was more present. Thissession was the only one not involving collaboration and it was not designed using the 5Cs Framework.
The open-ended responses to the survey prompted us to reconsider what was valued by teachers andmade us come to the realisation that higher order thinking and time to ‘tinker’ should be a focus of ourPD. This can be illustrated with one of the participant’s survey responses stating that time to ‘tinker’
would have been desirable: “It would have been fantastic to have time to do a visual graphingexperiment similar to the social media graphing presented on Tuesday morning. Use responses fromthe group to carry out an activity that could be done in the classroom”.

After Constructionism
In 2016, inspired by our attendance to the Constructionism in Action conference in Bangkok, we
conducted two two-day workshops that incorporated Constructionist principles. The results from the2016 surveys seemed to indicate that the inclusion of Constructionist approaches enhanced thesatisfaction with all aspects of the workshop (see Figure ). The workshops from that point thereafter,
not only aimed to be more student-centred and include more hands-on experiences, but were also
inspired by Constructionism.
As suggested by participants in the surveys, we had begun including an increasing number of handson activities in the workshops. However, it became apparent to us that it was important to incorporate
hands-on activities that involved open-ended problem solving, in which teachers construct theirknowledge in context, rather than just having hands-on activities that involved explicit step-by-step
instruction.
To incorporate these Constructionist activities, we considered suggestions outlined by several
Constructionist PD researchers and practitioners. For example, Martinez and Stager (2013) argue that
PD is often “too meta” (p. 200), and suggest that teacher educators should provide PD where teachers
experience learning from a student’s perspective. Martinez and Stager designed and implemented
Constructing Modern Knowledge, enabling teachers to spend four days of uninterrupted time working
collaboratively on a project of their own creation. Brennan provides a similar argument concerning PD
learning environments, stating, “teachers should have learning experiences that are comparable to theirstudents’ learning experiences, situated within a supportive community of fellow teachers” (p.293).
Brennan (2015) developed and implemented ScratchEd, a form of PD for computing that incorporated
Constructionist principles. The main intent of ScratchEd is to support the use of technology for creating
meaningful projects rather than focussing on the use of specific technologies. Like Martinez andStager’s Constructing Modern Knowledge workshops, ScratchEd is designed to involve experiences forteachers that comparable to their students’ classroom experiences. Brennan, however, recognises thatproviding fully Constructionist learning environments can be at odds with K-12 mainstream education.
She calls the difficulty of providing PD aligned with “the lived reality of K-12 education” (p.295) while
preserving the ideal of a Constructionist environment, the “tension between the actual and theaspirational” (p.295). She also identifies four other tensions, which have guided and given focus to ourreflections about the PD we provide.
We agreed with the sentiment that PD is often “too meta” (Martinez & Stager, 2013), and upon reflection,
recognised that our previous PD opportunities had suffered from being too instructionist at times.
However, like Brenan, when we introduced Constructionist learning into our PD, we had to negotiate
some tensions.

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Constructionism 2018, Vilnius, Lithuania

In 2016, we ran two two-day workshops: a workshop for primary school (K-6) teachers and a workshop
for high school (Years 7-12) teachers. These workshops involved activities and presentations that werealigned with the curriculum outcomes for the relevant school level. For example, in the primary school
workshop, teachers took part in an activity where they used Scratch to learn about visual programming,
which is a concept in the K-6 Australian Digital Technologies curriculum. In the 2016 workshops, in an
attempt to move towards more Constructionist learning, we began to include some activities where
teachers could choose their own direction for learning. In the high school workshop, for example,
teachers could choose between learning about teaching Data Science with R or being introduced togeneral-purpose programming with Sonic Pi. Also, in the primary school workshop, we included extra
activities for teachers who were already proficient at visual programming. Additionally, in the primaryschool workshop, we included a collaborative lesson planning activity, in which teachers worked
together to construct a plan for introducing computing into their classes. However, we could not include
longer self-directed learning, and had to negotiate with pressures such as the limited time available toteachers to attend the workshops, or the number of PD staff offering support to teachers working on
individual projects.
In 2017, we revisited Brennan’s tensions (2015) as a lens to improve the PD. The analysis of thesetensions and our own reflections on how we negotiated them, were instrumental to envisioning thechanges we made to the workshops in 2017. One of the main changes introduced that year, which
responded to our reflection on the tension between the actual and the aspirational identified by Brennan(2015), was the introduction of workshops addressing specific curriculum outcomes.
In light of this, we ran two new specific workshops, one for primary teachers and one for high schoolmathematics teachers. These two workshops aligned entirely with areas of the curriculum and were
designed to use computing as an exploratory tool for teachers (and subsequently their students) withmathematics. The first of these workshops utilised one of the modules produced for the ScratchMaths
project (Benton, Hoyles, Kalas, & Noss, 2017). We mapped the content of the module to the Australiancurriculum. The second workshop targeted a new area of the Year 12 curriculum, Minimum Spanning
Tree algorithms. In this workshop, teachers learned and used Edgy, an adaptation of Snap! for graphtheoretical explorations (Cox, Bird, & Meyer, 2017). Teachers coded Kruskal and Prim’s algorithms withvarying degrees of guidance after reviewing other content, such as graph theory and data structures,
necessary for the completion of the task.
We also ran two more two-day workshops, similar to the ones in 2016 but more general in scope, anddeliberately targeting outcomes, such as critical and creative thinking, from the General Capabilities of
the Australian National Curriculum (ACARA, 2018b). These two workshops specifically addressedissues of differentiation of learning and learners - the tension between novice and expert identified by
Brennan (2015).
In workshops prior to 2017, we had assumed that the teachers attending were novice to computing, andwe felt that most of the PD content had to be heavily scaffolded. However, teachers with expertise incomputing also attended the workshops. To better support these expert teachers, thus helping us
negotiate this last tension, we interviewed a local teacher who had participated in our workshops in2013, and had been invited to present in subsequent years’ workshops. This teacher is a high school
DT teacher with an academic and professional background in computing. She is experienced inteaching computing, regularly presents at conferences and assists in training her colleagues and thus
fits in the category of Master Teacher in the classification devised by Sentance et al. (2014). Master
Teachers are experienced educators chosen by CAS in England to organise and run face-to-faceworkshops with teachers in their local community. We asked questions to ascertain the extent to which
her approach to teaching and learning was a constructionist one:
Question: “Do you learn using YouTube, read instructions, or tinker?”
Answer: “Yes! First place I go is YouTube and tinkering is the best part of learning and teaching.”
Question: “Do you use learning resources created specifically for teachers? If so, what were the mosthelpful aspects of these sort of experiences?”581

Constructionism 2018, Vilnius, Lithuania

Answer: “Sometimes but these resources often lack room for creativity or make teachers into
dependant zombies instead of content creators. However these can be great for networking and idea
generation.”
We also asked the teacher to provide insights into their learning for the classroom. We found thattinkering is also a part of the process to develop their pedagogy.
Question: “Do you find that the activities you find need to be modified to suit your needs? If so, which
type of activities and how do you modify them?”
Answer: “Most often, yes. Usually tech PD or resources are aimed at beginners so I often extend or
find additional bits to meet my needs or particular learning requirements for my students.”
Her responses to these questions quite clearly indicate that her approach to both learning and teachingis a Constructionist one, and that she is at a level were step by step instruction is no longer necessary.
One of the foci of our future PD endeavours will be to incorporate more differentiation, which would
allow expert teachers to learn during our PD while respecting that other teachers need more guidance.

Discussion and Future WorkThe recently endorsed Australian National Curriculum includes General Capabilities (such as criticaland creative thinking), which teachers are expected to assess and report across all subject areas. In
addition, this curriculum includes computational thinking and programming as part of the new DT subject
that should be taught using ‘authentic learning challenges’ (ACARA, 2018a). These changes in theAustralian curriculum provide an ideal opportunity for teachers to include Constructionist activities in K12, which involve computing and open-ended problem solving. As argued by Angeli et al. (2016), there
are many opportunities for K-12 educators to include authentic learning challenges that incorporate
computing and also address outcomes from diverse content areas. In Australia, these authentic learning
challenges could be particularly beneficial to K-6 teachers, who often teach multiple content areas, as
they could target outcomes from a variety of subjects and also address the General Capabilities.
Due to the large number of teachers that need to be upskilled in computing, there has been a focus on
creating computing PD that is scalable. An example of PD designed to be scalable that has been very
successful, are the MOOCs designed by CSER. These MOOCs have reached many teachers, bothnationally and internationally (Falkner, Vivian, Falkner, & Williams, 2017; Vivian et al., 2014), and CSER
received government funding for a national rollout of their initiative, focussing on teachers in remote anddisadvantaged areas (Birmingham, 2016).
There are many advantages of providing PD through online courses. Online courses such as CSER’s
MOOCs, are useful scalable solutions, as teachers can access them on their own time and at their own
pace, and they are generally free of cost. However, as Brennan (2015) argues, it can be difficult toprovide Constructionist learning experiences solely through an online course. As PD providers who
have been influenced by Constructionist ideas, we believe that the inclusion of authentic learning
challenges in K-12 should be encouraged. Thus, we reflect that our PD should include face-to-facecomponents and authentic learning challenges to prepare teachers effectively. This reflection has led
us to make further changes to our PD which we plan to implement in 2018.
In 2018 we will run three PD programs. The first program will be a two-day workshop for ‘novices’, witha strong emphasis on the Australian General Capabilities and will incorporate authentic tasks that wewill develop with the teachers. Our second PD program will focus on expert teachers who are interested
in running professional learning events for other teachers at their school or in their professional learning
communities. Like the first workshop, it will involve hands-on activities where teachers will familiarise
themselves with the content to be delivered, but they will also learn to run workshops for colleagues.
We will facilitate a session with the aim of teaching about the logistics of running this type of event. We
believe that this scalable model can complement online learning platforms whilst providing opportunities
for Constructionist learning.
Our third PD program is focussed on sustained engagement and will run over six months with primaryschool teachers. This program will incorporate a greater depth of Constructionist experiences for582

Constructionism 2018, Vilnius, Lithuania

teachers. Finding ways to provide such PD is the main aim of our future research, and constitutes thecore research of Author 1’s PhD studies. As well as improving PD, these studies could inform the designof learning experiences for pre-service teachers in computing, which could help address pipeline issues(Yadav, Sands, Good, & Lishinki, 2018).
We believe that the insight gained from designing, reflecting on, and researching PD has not only
improved our PD programs, but also has the potential to create Constructionist learning opportunities
for pre-service teachers that will ensure that they are able to include authentic learning challenges intheir future teaching.

Acknowledgements
The authors would like to thank Google Inc. for funding the CS4HS workshops held at our institution.
We would also like to thank the university academics who presented talks and gave laboratory tours
during the workshops, as well as the administrative staff of our institution.

References
ACARA.
(2018a).
DigitalTechnologies
curriculum
rationale.
Retrievedfromhttps://www.australiancurriculum.edu.au/f-10-curriculum/technologies/digital-technologies/rationale/
ACARA.
(2018b).
F-10Curriculum
GeneralCapabilities.
https://www.australiancurriculum.edu.au/f-10-curriculum/general-capabilities/

Retrieved

fromAngeli, C., Voogt, J., Fluck, A., Webb, M., Cox, M., Malyn-Smith, J., & Zagami, J. (2016). A K-6
computational thinking curriculum framework: implications for teacher knowledge. Journal of
Educational Technology & Society, 19(3), 47.
Benton, L., Hoyles, C., Kalas, I., & Noss, R. (2017). Bridging Primary Programming and Mathematics:
some findings of design research in England. Digital Experiences in Mathematics Education.
Birmingham, S. S. (2016). Closing the ‘digital divide’ for disadvantaged students [Press release].
Retrieved from http://www.senatorbirmingham.com.au/Latest-News/ID/2928/Closing-the-digital-dividefor-disadvantaged-students
Bower, M., & Falkner, K. (2015). Computational Thinking , the Notional Machine , Pre-service Teachers, and Research Opportunities. Paper presented at the Australasian Computing Education Conference,
Sydney, Australia.
Brennan, K. (2015). Beyond Technocentrism: Supporting Constructionism in the Classroom.
Constructivist Foundations, 10(3), 289-296.
Chalmers, J., & Quigley, M. (2017, 13 October). Twenty ideas for our schools and politicians in the newmachine age. The Sydney Morning Herald. Retrieved from https://www.smh.com.au/technology/twentyideas-for-our-schools-and-politicians-in-the-new-machine-age-20171012-gyzfm7.html
CommonwealthofAustralia.
(2016).
STEM
Programme
Index.
Retrieved
http://www.chiefscientist.gov.au/2016/01/spi-2016-stem-programme-index-2016-2/

from

Cox, R., Bird, S., & Meyer, B. (2017). Teaching Computer Science in the Victorian Certificate of
Education: A Pilot Study. Paper presented at the Proceedings of the 2017 ACM SIGCSE TechnicalSymposium on Computer Science Education.
Falkner, K., Vivian, R., & Falkner, N. (2014). The Australian Digital Technologies Curriculum: Challenge
and Opportunity. Paper presented at the Australasian Computing Education, Auckland, New Zealand.
Falkner, K., Vivian, R., Falkner, N., & Williams, S.-A. (2017). Reflecting on Three Offerings of a
Community-Centric MOOC for K-6 Computer Science Teachers. Paper presented at the Proceedingsof the 2017 ACM SIGCSE Technical Symposium on Computer Science Education, Seattle, Washington,
USA.

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García-Peñalvo, F. J. (2016). A brief introduction to TACCLE 3—coding European project. Paper
presented at the International Symposium on Computers in Education (SIIE), Salamanca, Spain.
Martinez, S. L., & Stager, G. (2013). Invent to learn: Making, tinkering, and engineering in the classroom:
Constructing modern knowledge press Torrance, CA.
Menekse, M. (2015). Computer science teacher professional development in the United States: a review
of studies published between 2004 and 2014. Computer Science Education, 25(4), 325-350.
Pappano, L. (2017, 4 April). Learning to Think Like a Computer. The New York Times. Retrieved fromhttps://www.nytimes.com/2017/04/04/education/edlife/teaching-students-computer-code.html
Prieto-Rodriguez, E., & Berretta, R. (2014). Digital technology teachers’ perceptions of computerscience: It is not all about programming. Paper presented at the Frontiers in Education Conference(FIE2014), Madrid, Spain.
Prieto-Rodriguez, E., & Hickmott, D. (2016). Preparing teachers for the Digital Technologies curriculum:
preliminary results of a pilot study. Paper presented at the Constructionism 2016 Conference, Bangkok,
Thailand.
Sentance, S., Humphreys, S., & Dorling, M. (2014). The network of teaching excellence in computerscience and master teachers. Paper presented at the Proceedings of the 9th Workshop in Primary andSecondary Computing Education, Berlin, Germany.
Tom, M. (2015). Five C Framework : A student-centered approach for teaching programming courses
to students with diverse disciplinary background. Journal of Learning Design, 8, 21-37.
Vivian, R., Falkner, K., & Falkner, N. (2014). Addressing the challenges of a new digital technologies
curriculum: MOOCs as a scalable solution for teacher professional development. Research in Learning
Technology, 22.
Webb, M., Davis, N., Bell, T., Katz, Y. J., Reynolds, N., Chambers, D. P., & Sysło, M. M. (2016).
Computer science in K-12 school curricula of the 2lst century: Why, what and when? Education andInformation Technologies, 1-24.
Yadav, A., Sands, P., Good, J., & Lishinki, A. (2018). Computer Science and Computational Thinking
in the Curriculum: Research and Practice. In J. Voogt, G. Knezek, R. Christensen, & K.-W. Lai (Eds.),
Handbook of Information Technology in Primary and Secondary Education (pp. 1-18). Cham: Springer
International Publishing.

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Pictogramming: Learning Environment Using
Human Pictograms Based on Constructionism
Kazunari Ito, kaz@si.aoyama.ac.jp
Aoyama Gakuin University, Japan

Abstract
We have been developing a pictogram authoring tool “Pictogramming.” Pictogramming is based on two
words: “pictogram” and “programming.” The basis of this application is the use of a human-shaped
pictogram (i.e., a “human pictogram”). Pictograms are an abstract representation of a person or an
object. Creating a posture of a human pictogram is strongly associated with the embodies knowledge.
And the works with free themes are created with one's ego syntonic and are firmly and positively basedon ones' culture. A useful pictogram is made by strongly connecting with one's culture. That is,constructing human pictograms are very familiar with syntonic learning insisted on by Papert. We can
expand this concept beyond the limits of the proposed application (we call this the "narrow meaning of
Pictogramming") to any activities that correspond to human pictograms, which is termed the "broad
meaning of Pictogramming."

Narrow and broad meaning of Pictogramming

In this paper, we first explain the function of the application and discuss the broad meaning of
Pictogramming from the viewpoint of constructionism.

Keywords
Logo; human pictogram; syntonic learning; duplication of viewpoint, educational environment

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Constructionism 2018, Vilnius, Lithuania

Introduction
A pictogram is a graphical symbol that is used to understand a semantic concept based on the meaningof its shape [Ota 1987]. Pictograms have been studied and used in various fields such as counseling,safety, and facilities management. Especially there is large amount of research related to the use
pictograms as alternative and augmentative communication (AAC) [Martínez-Santiago 2016].
Pictograms are designed to provide information regarding a human’s action or status. And especiallyhuman shaped pictograms are widely shown in the various international standard pictograms set. So,
the appendix of ISO 3864 provides guidelines for the depiction of a human-shaped pictogram itself (i.e.,a “human pictogram”).
Various programming languages have been developed for learning purposes. Logo is multi purpose
programming language, for example, for AI and logical programming. And Logo is widely used forchildren to help them learn various mathematical concepts by operating a turtle robot/character on thescreen [Seymour 1977]. Papert developed Logo and called this method syntonic learning; he argued infavor of its importance as a learning method [Seymour 1980]. Numerous extensions based on Logo
have been developed. Laubomir developed EasyLogo [Salanci 2010], which adopts a grid forsimplification and has an easy-to-use environment. StarLogo [Resnick 1996] can handle more than one
turtle and can simulate a multi-agent system.
Scratch is a visual programming environment that was developed by the MIT Media Laboratory [Resnick2009]. Users program in Scratch by dropping blocks of code. Scratch is suitable for use as an
introductory programming course because no specific knowledge of syntax is needed and syntax errors
cannot occur. In addition, Alice allows novice programmers to generate virtual worlds by controlling
three-dimensional (3D) objects [Cooper 2003]. The users operate a cat in Scratch, a turtle in Logo(sometimes represented as a simple triangle), or some 3D objects in Alice; thus, the working style ofnovice students may influence their learning techniques. The main character shown in the initial stageof the programming language is a highly important parameter with which one can understand theconcept of the programming.
So, unprecedented effectiveness may be achieved if a human pictogram is used as the main characterin the programming learning environment. Indeed, virtual humans are used to enhance the learning
environment [Halan 2012].
In this study, we developed a prototype programming learning environment called “Pictogramming” [Ito2018]. Pictogramming is based on two words: “pictogram” and “programming.” We evaluated andanalyzed the effects and characteristics of Pictogramming from the viewpoint of constructionism.

Pictogramming—Learning Programming Application using a
Human Pictogram (Narrow meaning of Pictogramming)
Overview
The application was implemented using HTML5, CSS, and JavaScript. The JavaScript library“processing.js” was used to execute the Processing format program, which is to display the humanpictogram. The application is compatible with typical browsers and other applications do not need to beinstalled. Figure 1 shows a screenshot of when the application is accessed using a PC or smartphonebrowser. This application can be accessed at http://pictogramming.org/.

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Constructionism 2018, Vilnius, Lithuania

Figure 1. Screenshot of PictogrammingA large human-shaped pictogram is displayed in the human pictogram display panel. The panel candisplay either the front or side views of the human pictogram, as defined by ISO 3864 Appendix, where
both comprise nine parts: body and head (considered as a single part), two upper arms, two lower arms,
two upper legs, and two lower legs. The size of each part conforms to ISO 3864 (see Figure 2). A
programming panel indicating parts of the body in the human pictogram faces front, but it is assumed
that the human pictogram is user yourself of reflected in a mirror. So, “left upper arm” and “left lowerarm” are displayed on the left-hand side and users associate it with their left arm.

Figure 2. Front and side views of a human pictogram587

Constructionism 2018, Vilnius, Lithuania

Sample code

01: SC 0.3

Operations on the human pictogram are
input and defined in the program code
description area. The input string to change
states (positions) follows a format59 that
separates opcode and arguments withblanks, as follows.

02: PEN DOWN

opcode07: END

arg1 arg2 …

Figure 3 shows a sample code. Line 1changes the scale of the human pictogram.
Lines 2 to 8 are in accordance with the
LOGO programmer; this draws a rectangle
represents the victor's platform. The"M(ove)" command shown in line 10 means
move 50 px in the positive x-axis direction
200 px in the negative y-axis to get up on
box. The "R(otate)W(ait)" commandmeans rotate a part of the body over someseconds and the next command is not
executed until the movement is complete.
example, "RW LUA -120 1" shown in line
means "rotate the Left Upper Arm (LUA)
120° clockwise for 1 s."

03: // Draw victor's platform
04: REPEAT 4
05: FD 100
06: RT 90

08: PEN UP
09: // Get up on the victor's platform
10: M 50 -200
11: // Raise the left arm diagonallythat

12: RW LUA -120 1
13: // Wave hands to left and right 3 times
14: IF [rand(1,6) >= 4]

and
the

15: REPEAT 3
16: RW LLA -60 0.3
17: RW LLA 60 0.3
18: END

For
12

19: END

Lines 13 to 19 represent waving the left
hand three times at a probability of 50%.
Figure 3. Sample source and output

The commands are classified into two
types. First, the command that changes the shape of the human pictogram is the “Pictogram Animation
Command.” The second type of command is almost equivalent to turtle graphics, i.e., the “Pictogram
Graphics Command.” Command names, arguments, and each process can be easily predicted frompersonal experiences or existing knowledge. Moreover, the commands and movements of the humanpictogram are associated with fine granularity.
Thus, pictogram graphics focus on the movement of the human body, and pictogram animation focuseson the rotation of human body parts. The combination of these two types is a unique feature ofPictogramming.
Figure 4 shows an example of pictogram animation, pictogram graphics, and a combination of bothtypes.

Figure 4. Examples of outputs from pictogram animation (left), pictogram graphics (center), and a combination ofboth types (right)

59

The command list can be seen at http://pictogramming.org/?page_id=475588

Constructionism 2018, Vilnius, Lithuania

Syntonic Learning
Papert developed Logo and noted that it is very important that children can execute Logo commands
by pretending to be a turtle using their own bodies; this is called “syntonic learning” [Papert 1977][Papert
1980]. Papert also noted the following:
1. Body syntonic learning: Strongly associated with the senses of children and knowledge of theirbodies.
2. Ego syntonic learning: Consistent with the self-consciousness of children as humans with intention,
purpose, desires, likes, and dislikes.
3. Cultural syntonic learning: Linked to personal activities that are firmly and positively rooted in one’s
culture.
The human pictogram resembles a body that represents the ego and the illustrated pictograms
represent one’s culture. Thus, the human pictogram conforms to these three types of syntonic learning.
Figure 5 shows works that were all created by the application. Figure 5(A)–(B) shows typical human
movement, created mainly based on body syntonic learning. Figure 5(C)–(D) represents artistic, humor,
some major person's famous action, and so on. These pictograms are difficult to categorize, but all are
based on ego syntonic. Figure 5(E)–(F) shows the pictograms linked to personal activities based on
rules, morals, and culture, and some of these are widely used in one's culture. Of course, these typesof syntonic learning cannot be separated clearly, but these works are based on a combination of various
kinds of syntonic learning.

(A) Running(B) Waving
hand(C) Cupid(D) Famous
Japanese TV
character'spose(E) Do not use
smartphone
while walking(F) Please knock
when you enter aroom

Figure 5. Example of works influenced by various type of syntonic learning

Pictogramming —Enhance constructionism using Human
Pictogram (Broad meaning of Pictogramming)
Shift from instructionism to constructionism by reconstructing a humanrelationshipNow, we consider a mass classroom. Figure 6(A) represents a typical relationship between membersof the classroom. The teacher instructs the students unilaterally. The human pictogram is introduced
and the student starts to command or operate it (Figure 6(B)), and then begins to communicate with
one's human pictogram (Figure 6(C)). Figure 6(D) is a scene in a classroom using the Pictogramming
application. One student talks to another student about his/her pictograms and mimics the movement.
As the pictogram is highly visible, the student happens to see the pictograms in the monitors naturally
and acts with them. This means that one student communicates with other students’ human pictograms(Figure 6(E)), and communicates with other students on human pictograms (Figure 6(F)). The students
program and create numerous works via this process using the philosophy of constructionism.
589

Constructionism 2018, Vilnius, Lithuania

Figure 6. Shifting relationship between the student and the teacher by interference of human pictograms

Experimental cognition and reflective cognition
A cognitive science researcher mentioned that the cognition could be classified into two types [Norman
1993]. One is experiential cognition, which is data-driven information processing with reactivation of
information patterns in human memory. The other one is reflective cognition, which is concept-driven
processing
indeepreasoningsuchasdecision-making
and planning. Table 1 shows an example of activities of experimental cognition and reflective cognition.
Table 1. Example of activities of experimental cognition and reflective cognition
Experimental cognitionParticipating in participatory activities.

Reflective cognition


Learning how to play.



Watching the state of activities and thinking
instead of participating.

Okamoto associated a duplex viewpoint with these two types of cognitive processes [Okamoto 2005].
Figure 7 shows an image of a duplex viewpoint realized by a human pictogram.
One is an “object-level view,” and the other is a “meta-level view.” These two viewpoints are tightly
linked through the self-identification of “ego” (reflective subject) and “self” (experiential subject). In
observing the interaction of others from a distance, such duality is generally not established. However,if the observer can emphasize his viewpoint with one participant in the interaction, he can also acquire
the virtual object-level view so that one can experience the interaction as if it were one's own. This
duplex viewpoint via empathy shows how first-person engagement can be achieved.

590

Constructionism 2018, Vilnius, Lithuania(A) Part of Figure 6(E)–(F)(B) Involvement as a participant(C) Involvement as an observer

Figure 7. Empathy channel: (B) involvement as a participant in an interaction using a human pictogram, (C)involvement as an observer of the interaction (panels (B) and (C) are based on [Okamoto 2005])

Conclusion
This paper presents an overview of a new programming learning application called Pictogramming andindicates the effectiveness of this application. Moreover, we proposed the broad meaning of
Pictogramming, which is a learning environment based on the adoption of a human pictogram. We
illustrated that this environment enhances the learning process with the philosophy of constructionism;
then, we discussed the duplex viewpoint. In the future, we aim to demonstrate the effectiveness of using
human pictograms by performing experiments and in practice.

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Salanci, L. (2010) EasyLogo – discovering basic programming concepts in a constructive manner. In
Proceedings: Constructionism 2010.
Resnick, M. (1996) StarLogo: an environment for decentralized modeling and decentralized thinking. In
Conference Companion on Human Factors in Computing Systems (CHI '96), Michael J. Tauber (Ed.).
ACM, New York, NY, USA, p. 11-12.

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Resnick, M., Maloney, J., Monroy-Hernández, A., Rusk, N., Eastmond, E., Brennan, K., Millner, A.,
Rosenbaum, E., Silver, J., Silverman, B. and Kafai, Y. (2009) Scratch: programming for all. Commun.
ACM, 52, 11, p. 60–67.
Seymour, S. (1977) A learning environment for children. Computers and Communication: Implications
for Education, New York: Academic Press, p. 271-278.
Seymour, S. (1980) Mindstorms, Children, computers, and powerful ideas. Basic Books, Inc.

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Human Pictogram Unplugged: Unified Learning
Environment of Computer Science Unplugged
Using Human Pictograms
Kazunari Ito, kaz@si.aoyama.ac.jp
Aoyama Gakuin University, Japan

Aoi Yoshida, aoi@si.aoyama.ac.jp
Aoyama Gakuin University, Japan

Takashi Yoneda, yoneda@port.kobe-u.ac.jp
Kobe University Secondary School, Japan

Yuichi Oie, c8116002@aoyama.jp
Aoyama Gakuin University, Japan

Abstract
We have been developing a unified learning environment for computer science unplugged (CSU) using
human pictograms called “Human Pictogram Unplugged” (HPU). CSU is a methodology of teachingcomputer science without the use of personal computers. One of HPU’s characteristics allows students
to conduct activities with a set of unified human-shaped pictograms (i.e., a “human pictogram”).
Educators usually must prepare and maintain teaching materials for each CSU activity: a balance for"sorting" activity, worksheets for "image representation," and special cards for "binary numbers," forexample. To decrease this economic and temporal burden is an important and notable issue ineducation. Human pictograms are abstract representations of a people. Activity themes using humanpictograms would at once be more strongly connected to one's experience and knowledge and also
firmly and positively based on one’s culture. Therefore, HPU will promote syntonic learning.
Activity examples of Human Pictogram Unplugged

Binary Number

Image Representation

Routing and Deadlock

In thispaper, we explain the concept of HPU, show example activities of HPU, and discuss HPU’s effects andcharacteristics from a constructionist viewpoint.

Keywords
human pictogram; computer science unplugged; syntonic learning; duplication of viewpoint; educational
environment

Introduction
Computer Science Unplugged (CSU) is a methodology for teaching the principles of computer science
without the use of personal computers. CSU was developed by Dr. Tim Bell and Mike Fellows[2009][Bell2012] and has since been adopted by many countries as a method of teaching computer science. The
main approach of CSU is learning through indoor or outdoor physical activities. This is to say, the subject
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is person. So, we implemented an idea that engages physical activities to unify all teaching materials
using human pictograms.
A pictogram is a graphical symbol used to understand semantic concepts based on the meaning of its
shape [Ota 1987]. Pictograms have been studied and used in fields such as counseling, safety, facility
management, cross-cultural communication, and semiotics [Mori 2009, Hassan 2015]. Pictograms arealso designed to provide information regarding a human’s action or status. So, if human pictograms areused as main characters in computer science education, we may expect very high levels ofeffectiveness since virtual humans are often used to enhance learning environments [Halan 2012].
The next Japanese national curriculum will begin in 2020, and it aims to expand informatics in K-12. Insuch situations, Japan increasingly emphasizes computational thinking [Wing 2008].
Further, unplugged is the main activity used by teachers for ordinary school subjects, so teachers may
come to think of unplugged as a more attractive method to teach computers. However, most elementaryand secondary school teachers are not familiar with computer science. Indeed, the phrase “computerscience” is often met with fear and disgust in the minds of teachers. Thus, using term “human pictogram”
is one strategy to ease teacher anxiety about computer science activities.
Educators usually must prepare and maintain teaching materials for each CSU activity. For example,
balances are used for "sorting" activities, worksheets are used for "image representation," and cards
are used for "binary numbers." To lessen these economic and temporal burdens, we can adopt unified
learning materials that can be used for multiple activities. This is a very important, notable issue foreducation.
For this study we developed "Human Pictogram Unplugged” (HPU), a unified computer science
unplugged learning environment that uses human pictograms. We then discuss the effects andcharacteristics of HPU from a constructionist perspective.

Learning Material
We designed two types of pictograms: one made from wood, and one made from paper. Of course,
human pictogram construction is not limited to these materials.

Wooden Based Human Pictogram
One type of pictogram is wooden and upright (Figure 1) . The
material is a 5.5mm-thick MDF (Medium Density Fiberboard) cut
by a laser. One side is painted black to distinguish it from the
opposite side. A seal-type, 1cm width by 2cm height white sheet isaffixed to the chest areas of both sides so that one can write orerase symbols such as numbers and characters with a marker
or an eraser.
Figure 1. Wooden based pictogram
Paper Based Human Pictogram
The other pictogram is made from 55mm x 91mm name card
paper (Figure 2). This paper is made of special material, so one
can write and erase symbols such as numbers and charactersusing a marker or an eraser.

A Set of Learning Material
The learner then uses the human pictograms, a white board
marker, and an eraser (Figure 3).

Figure 2. Paper based pictogram

We introduce color information (black and white) to human pictogram, and permit to write somecharacters on one's chest. The pictogram has been used colors to inform specific information, andnormally used for visually or cognitive impaired person. And the representation style that somecharacters are attached to pictogram is also widely adopted in the public used pictograms. That's key594

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point that these human pictogram materials can be adopted to lots of unplugged activities. We also aim
to help users imagine real humans who illustrate something to audience by paper or who play in someevents with attaching bibs by drawing a rectangle shape on the chest area for writing some characters.
He/she also uses restriction sheets to limit the human pictogram's movement for some activities.

Figure 3. A set of learning material

Activity List of Human Pictogram Unplugged
Overview
This section shows how we have taken Bell’s activities [Bell 2015] and redesigned them to incorporate
human pictograms. These activities are listed in Table 1.
Table 1. Activity list of HPU
Binary numbers

Data
representation

Image
representation

Information theory

Error detection anderror correction

Search algorithms

Sorting algorithms

Sorting networks(Parallel processing)

Finite state
automata

Routingand deadlock

Binary numbers
The original activity is called “Count the Dots”
and uses binary or other notations to represent
numbers. Five kinds of cards (labelled 16, 8, 4,
2, and 1 dots respectively on one side) aredisplayed by five children. Here, Figure 4 on theleft is real activity, and Figure 4 on the right issimulated version using human pictograms.

Figure 4. Real and simulation

Data representation
This is one of additional activities for binary numbers. The original activity’s name is“Sending Secret Messages.” Data Representation here uses binary number
representations of characters. For example, assign 0-25 to alphabet A-Z, andrepresent one character with five human pictograms. Figure 5 shows, as an
example: “L (11: first row) O (14: second row) V (21: third row) E (4: fourth row).”
Figure 5.

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Image representation
The original activity’s name is “Colour by Numbers.” Students learn
image representation of pixel images and data compaction by
expressing an array of numbers. The activity remixes "Forming human
letter" by corresponding one pictogram to each pixel. Figure 6 shows
arrangements, which can represent continuous number of same color's
human pictograms “0131, 131, 5, 131, and 131.”
Figure 6. Example of character A

Information theory
The original activity’s name is “Twenty Guesses.” The “answerer” guesses correct answers
by asking a maximum of 20 questions, to which the “question master” can reply “Yes” or“No” clearly. The question master sets a correct answer and writes it on the back side inadvance. Figure 7 shows that question master sets a number "42."Error detection and error correction

Figure 7.

The original activity’s name is “Card Flip Magic.” Square magnet sheets are used,with different colors on each side arranged in a grid structure. First, a magician
arranges an even number of same colored sheets in rows and columns. An
audience member turns over one of sheets while the magician looks away. The
magician later guesses the sheet. The HPU version of this activity uses humanpictograms instead of magnet sheets. Figure 8 is an example.

Search algorithms
The original activity’s name is “Battleships.” An illustrated sheet with numbered
warships is provided, and two players each guess the location of the other’s target
warship. In the HPU version, one human pictogram is the equivalent of one warship(Figure 9). Players write a comparative sign (like a number or a friend’s familyname) on a human pictogram’s chest area.

Figure 8.

Figure 10. Activity example of sorting algorithm

Sorting algorithms

Figure 9. Activity example

The original activity’s name is “Lightest and Heaviest.” A balance and some weights are provided, andplayer arranges the weights in order of weight. In the HPU version, each human pictogram’s weight issame. Player writes a comparative sign (like a number or a friend’s family name) on the chest area of ahuman pictogram, and sets each human pictogram’s value. Figure 10 on the left is the initial state(Japanese family name is written on one's chest). Figure 10 on the right is the final state, where humanpictograms are arranged in alphabetical order.

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Sorting networks (Parallel processing)
The original activity’s name is “Beat the Clock” and the goal of this activity is
to teach parallel processing. Each pair of human pictograms in stored in oneof the decks of first row (Figure 11). Each human pictogram is then compared
to another pictogram in the same deck, and it moves to the next row deck as
a result of comparison. The comparison and moving to next row level are
conducted simultaneously. All human pictograms are sorted upon arriving at
the final row level.

Finite state automata

Figure 11. Example

The original activity’s name is “Treasure Hunt.” The transition source, input, and indications of whether
it is an initial status are described on the front of a pictogram, and the transition target is described on
the back. Figure 12 on the left shows an example of finite state automaton, and Figure 12 on the rightshows the same structure as the state is constructed by several human pictograms. The player turns
over a pictogram with a status number written on the chest. The next status is then illustrated on theback of the selected pictogram. The player attempts
to catch the target pictogram (Number 5 circled in
the Figure 12(right)).

Figure 12. Example of finite state automaton (left) and representation by human pictograms (right)

Routing and deadlock
The original activity’s name is “The Orange Game.” In this activity, the learner can easily experience
first and third person viewpoints. Figure 13 on the left is an activity with first-person point of you. Each
player holds a pictogram in their hand, and passes pictograms from hand to hand to satisfy thearrangement conditions. Figure 13 on the right demonstrates a third-person viewpoint. Flesh-coloredhuman pictograms, equivalent to real people, are arranged in a circle. Black pictograms located on theboth sides of the flesh-colored pictograms are equal to pictograms in a player's hand in first-person
point of view.

Figure 13. Activity with first-person point of you(left) and third-person viewpoint (right)

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Enhance constructionism using Human Pictogram Unplugged
Shift from instructionism to constructionism by reconstructing humanrelationships
We now consider a large classroom. Figure 14(A) represents typical relationships between classmembers. The teacher instructs students unilaterally. Human pictogram unplugged is introduced, andthe students handle (Figure 14(B)) and begin to communicate and reflect using their human pictograms(Figure 14(C)). Figure 14(D) portrays a multiplayer activity classroom scene. One student talks to theothers about the human pictograms on the table. This means that one student communicates with otherstudents using human pictograms (Figure 14(E)), and communicates with other students about humanpictogram unplugged activities (Figure 14(F)). The students use the philosophy of constructionism inthis process.

Figure 14. Shifting relationships between students and teachers by human pictogram interference

Experimental cognition and reflective cognition
A cognitive science researcher mentioned that cognition can be classified into two types [Norman 1993].
One is experiential cognition, which is data-driven information processing with reactivation of
information patterns in human memory. The other is reflective cognition, which is concept-driven
processinginthecontextofdeepreasoningsuchasdecision-making
and planning. Table 2 shows examples of experimental cognition and reflective cognition activities.
Table 2. Example of activities of experimental cognition and reflective cognition
Experimental cognition
Participating in activities.

Reflective cognition


Learning how to perform the activities.



Watching the activities and thinking ofparticipating.

Okamoto associates a duplex viewpoint with these two types of cognitive processes [Okamoto 2005].
Figure 15 shows an image of a duplex viewpoint as realized by a human pictogram.
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One is an “object-level view,” and the other is a “meta-level view.” These two viewpoints are tightly
linked through the self-identification of “ego” (reflective subject) and “self” (experiential subject). In
observing the interaction of others from a distance, such duality is not generally established. However,if the observer can emphasize his viewpoint with another participant in the interaction, he can alsoacquire the virtual object-level view of another participant so that one can experience as his own
perspective. This duplex viewpoint via empathy shows how first-person engagement can be achieved.

(a) Part of Figure 14(E)-(F)(b) Involvement as a participant(c) Involvement as an observer

Figure 15. Empathy channel: (b) involvement as a participant in an interaction using a human pictogram,(c) involvement as an observer of the interaction (panels (b) and (c) are based on [Okamoto 2005])

Activities design conscious of syntonic learning, and scaffold foror simulation of real daily life
Papert, who developed Logo, insists that children can execute Logo commands by using their bodies
to pretend to be turtles; this is called “syntonic learning” [Papert 1977, 1980]. Papert also notes thefollowing:
1. Body syntonic learning: Strongly associated with children’s senses and knowledge of their bodies.
2. Ego syntonic learning: Consistent with children’s self-consciousness as humans with intention,
purpose, desires, likes, and dislikes.
3. Cultural syntonic learning: Linked to personal activities that are firmly and positively rooted in one’s
culture.
Unplugged encourages learners to design and create new activities. When designing and performing
unplugged activities, it is very important to be aware of syntonic learning. The human pictogram is akind of human body projection that represents ego, and it becomes increasingly easy to create new,culturally friendly activities since the human pictogram conforms to these three types of syntonic
learning. The final goal of unplugged activities is to identify with real daily life. Human pictogram
unplugged activities are human-centric, so they scaffold real daily life or a simulation of it.

Conclusion
This paper proposes and presents an outline of a computer science unplugged unified learning
environment using a human pictogram called "Human Pictogram Unplugged." We illustrated that thisenvironment enhances learning processes using constructionist philosophy. We have already used thismethod in several schools, and we shall soon analyze the effectiveness of using human pictograms by
experiments.

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References
Bell, T. et al. (2009) Computer Science Unplugged: School students doing real computing without
computers. The New Zealand Journal of Applied Computing and Information Technology, Vol. 13, No
1, pp. 20-29.
Bell, T., Rosamond, F., & Casey, N. (2012). Computer Science Unplugged and Related Projects in Mathand Computer Science Popularization. In H. L. Bodlaender, R. Downey, F. V. Fomin, & D. Marx (Eds.),
The Multivariate Algorithmic Revolution and Beyond (pp. 398-456). (Lecture Notes in Computer
Science; Vol. 7370). Online: Springer. DOI: 10.1007/978-3-642-30891-8_18
Bell, T. et al. (2015) The English-language CS Unplugged activities in book form as a free download.
https://classic.csunplugged.org/wp-content/uploads/2015/03/CSUnplugged_OS_2015_v3.1.pdf (Last
visited: March, 30th 2018)
Hassan, M. M. E. (2015) The semiotics of pictogram in the Signage Systems. International Design
Journal, Vol. 5, No 2, p. 301-315.
Halan, S., Rossen, B., Crary, M. and Lok, B. (2012) Constructionism of virtual humans to improve
perceptions of conversational partners. In CHI '12 Extended Abstracts on Human Factors in Computing
Systems (CHI EA '12), ACM, New York, NY. p. 2387-2392.
Mori, Y. Takasaki, T. and Ishida, T. (2009) Patterns in pictogram communication. In Proceedings of the2009 international workshop on Intercultural collaboration (IWIC '09), ACM, p. 277-280.
Norman, D. A. (1993). Things That Make Us Smart: Defending Human Attributes in the Age of theMachine. Addison, Inc.
Okamoto, M., Nakano, Y. I. and Nishida, T. (2005) Toward enhancing user involvement via empathy
channel in human-computer interface design. Bold. L., et al. (Ed.), Lecture Notes in Computer ScienceVol. 3490, Intelligent Media Technology for Communicative Intelligence, p. 111-121, Springer.
Ota, Y. (1987) Pictogram Design. Kashiwashobo, Inc.
Papert, S.(1977) A Learning Environment for Children. Computers and Communication: Implications for
Education. New York, Academic Press, p. 271-278.
Papert, S. (1980) Mindstorms, Children, Computers, and Powerful Ideas. Basic Books, Inc.
Wing, J. M. (2008) Computational thinking and thinking about computing. Philosophical Transactions of
the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 366(1881), pp.
3717–3725.

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Some Reflections on Designing Constructionist
Activities for Classrooms
Yasmin B. Kafai, kafai@upenn.edu
University of Pennsylvania, Philadelphia, PA, USA

Deborah A. Fields, deborah.fields@usu.edu
Utah State University, Logan, UT, USA

Abstract
In this paper, we present our guiding principles for designing a constructionist curricular unit withelectronic textiles called “Stitching the Loop,” which introduces high school students to key concepts inin crafting, circuit design and computing. Our principles were to design for (1) engagement, (2)expression, (3) depth, (4) experiences, (5) audience, (6) collaboration, (7) reflection, (8) failure, (9)practicalities, and (10) iteration. Over three years, we worked together with dozens of high school
teachers and hundreds of students in designing, implementing and revising classroom activities wherestudents create and craft a series of individual and collaborative electronic textiles. Situated within thelarger framework of the Exploring Computer Science curriculum, we illustrate how these guiding
principles fostered an equity- and inquiry-oriented pedagogy through which teachers can support
students’ learning.

Keywords
curriculum; coding; computer science education; electronic textiles, constructionism, maker movement

Introduction
In constructionist approaches to learning and teaching, much emphasis has been placed on the designand development of construction tool kits (Resnick & Silverman, 2005). Hundreds, if not thousands, ofdigital, physical and hybrid construction kits have been developed to help students engage with STEM
topics and express their ideas and personal interests (e.g., Blikstein, 2012). Far less attention has been
given to the design and development of curricula where constructionism connects to academic content
within a classroom. While curriculum design is often seen as contradictory to constructionist pedagogy
because it constrains and directs student activities, we argue that it also can broaden access to bothmaking and coding, deepen learning in those fields, and promote better diversity in what is being made.
This is particularly important in coding and maker activities which have a longstanding history of
inaccessibility to non-White, non-male students from working-class communities (Margolis, Estrella,
Goode, Holme, & Nao, 2017).
In this paper, we report on the design of an eight-week long formal curricular unit, called “Stitching theLoop,” which facilitated students’ interest-driven projects, supported peer collaboration, and applied
equity-minded teaching. Our electronic textiles (e-textiles) unit was situated within Exploring Computer
Science (ECS), an equity-focused and inquiry-based year-long introductory computer science course
taught in public high school classrooms all over the country (Goode, Chapman & Margolis, 2012). We
concentrated on bringing creative making in the form of e-textiles into computer science classrooms.
E-textiles are hybrid designs, using conductive thread to sew LEDs, sewable microcontrollers (e.g.,
LilyPad Arduino, Adafruit Circuit Playground), sensors and other actuators into fabric or similarly soft
media (Buechley & Eisenberg, 2008). The unit consists of a series of four open-ended projects (seeFigure 1) with creative constraints that help students learn challenging concepts in computing,electronics, and crafting three-dimensional designs while also supporting personal expression andcreativity. In the following sections we review different approaches to constructionist curriculum design
and articulate the guiding principles we developed in designing the e-textile activities.

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BackgroundArguably the first constructionist curriculum was published in 1971 as a memo titled “Twenty Things To
Do With A Computer” in which Papert and Solomon suggested a variety of activities that could engage
children in programming, among them: making a turtle draw images on paper by programming a pen to
lift up and down; programming behaviors such as the turtle following along walls in a room; writing
programs to draw geometric spirals, making an online movie by programming a change in petals on aflower, programming sounds to play a song, playing spacewar games, and many more. The memo
concludes its list with the last recommendation asking the reader to come up with twenty more things
to do with a computer.
This curriculum consisted of a collection of different projects, not necessarily organized in a sequence.
These projects promoted what Papert (1980) called “powerful ideas” about computing such as recursion
or repetition by situating them in visible realizations such as drawing flowers or circles. They also
connected to other academic subjects providing an alternative way for children to experience geometry
or mathematics. Furthermore, they sought out compelling applications such as making music or movies
or playing and designing games that would resonate with students’ interest. Many of these features
have become guiding principles for other constructionist efforts, for instance, the development of therecent guide to Creative Computing (Brennan, Balch, & Chung, 2014) for Scratch activities.
A different approach to constructionist curriculum has been software design for learning, where one
project becomes the central focus, such as designing instructional software (Harel & Papert,1990). Herestudents work on designing software such as learning tools or educational games that teach academic
content like mathematics or science. In this context, the learning of programming is connected with thelearning of academic content by teaching subject areas to others through designed software. For
instance, software games designed by students (Kafai, 1995) included multiple problems in words,
graphics, and often provide stories and animations. Furthermore, students created a whole product by
including package design and advertisements for their games. A key distinction to the previous efforts
is that in designing software applications, students work on one complex, long term project rather thana collection of several smaller projects. Another dimension is that students journal about their ongoing
design process in notebooks outlining project ideas and reflecting on their challenges.
More recent efforts have combined elements from these two approaches by providing an explicit
sequence of projects, such as game design as in Repenning and colleagues’ (2015) “Scalable Game
Design,” which engaged students in a series of game design projects with Agentsheets. Likewise, the“Globaloria” platform (Caperton & Reynolds, 2011) included a year-long game design curriculum with asupporting social network where students can post and comment on games. Building on studio design
pedagogy (Hetland, Winner, Veeenema, & Sheridan, 2013), Fields and colleagues (2016) also createda weeklong “Scratch Camp” where students engaged in an intentional series of projects with creative
constraints interjected with mini-lessons, gallery walks, and a final interactive event sharing with families
and friends. Our curricular e-textile unit combines the collection of projects and the one-projectapproaches by creating an intentional series of projects with creative constraints, culminating in a final
project with a reflective portfolio. Below we share the guiding principles for designing the e-textile
curricular unit.

Guiding Principles for Stitching the Loop
The Stitching the Loop curriculum contains big ideas, recommended lesson plans, and sample rubrics,
with much room for students and teachers to interpret and bring in their own style, evidenced in the
successful and nuanced ways it has been implemented by teachers over the past three years. It is
accompanied by a 60+ page technical guide with fine-grained tutorials about crafting, circuit design,coding, and troubleshooting. By consciously combining traditionally masculine activities such asengineering and computing with traditionally feminine activities such as crafting and sewing, e-textiles
can disrupt preconceptions about who can do computing, engineering, and crafting (Kafai, Fields &Searle, 2014). With this background, we brought together experts in e-textiles educational activities
and the ECS development and implementation team. The curriculum was co-developed to combine
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best practices of teaching and creating e-textiles based on a constructionist philosophy alongside ECS
principles (inquiry, equity, and computing) and style.
Over the past three years, we have written and piloted the curriculum with two teachers (Year 1—
Spring 2016), four teachers (Year 2—Spring 2017), and now 15 teachers (Year 3—Spring 2018) in oneof the largest and most diverse school districts in the United States. All teachers participated in 3-4
days of professional development, focusing on creating the core projects (see Table 1 and also Figure1) and reflecting on principles of the unit like valuing aesthetics, personalization, mistakes, andaudience. Between implementations we used analysis of observations and interviews with teachersand students to revise the unit. Our experiences in creating, revising, and implementing this unit
highlight the possibilities in introducing making to computer science in ways that promote equity,
imagination, and personalization in classrooms.
Table 1. Overview of projects in Stitching the Loop curricular unit

Project

Content

Description#1 PaperCircuit(~1-2 hrs)

Single circuit project design: Create a
simple paper circuit greeting card that
includes one LED. Introduce the conceptof aesthetic design and personalization.
Simple wearable project: Create awristband with three LEDs in parallel anda switch that turns on the project when theends of the wristband are snapped
together.

· Simple circuit· Polarity· Materials: LEDs, copper tape (wire), paper

Collaborative project: As a class create amural, with each panel made by two
students. Each panel must have fiveindependently programmable LEDs andtwo switches, allowing for four blinking
light patterns.
Capstone project: Create a project with
two aluminium foil patches that act as asensor when both are touched by aperson. Program four+ lighting patterns
based on different sensor readings.

· Programming: Sequences, conditionals,
embedded conditionals or Boolean
statements· Collaborative work & division of labor· Materials: Conductive thread, LEDs, fabric#2
Wristband(~5-6 hrs)

#3
Collaborative
Mural Project(~10 hrs)#4
Human
SensorProject(~10-14 hrs)

· Parallel circuit, switch· Reading circuit diagrams· Three-dimensional project· Deconstruction· Materials: Conductive thread, LEDs, fabric· Sensor design (handcrafted)· Programming: operators, sensor range,
Boolean statements· Materials: Conductive aluminium foil,
human body, LEDs, fabricWe made several changes as we developed the unit. One of the most important ones involved a change
in assessment as we shifted from pre/post-tests to reflective portfolios where students summarized
their final projects, shared challenges that came up, and wrote about their progression during the etextiles unit (Lui et al, 2018). The portfolio served to support student meta-reflection on their learningand to emphasize the process of making as much as the final product of making. The leadership modelof the PD also changed as the two teachers from the first year of implementation took over nearly all
training activities in the third year, bringing their hands-on expertise about managing students’ creative
making, teaching students how to sew, organizing materials, and handling classroom management in
the e-textiles unit.

Designing for Engagement
We designed the e-textiles unit for engagement by keeping all projects open-ended, allowing students
to express their interests, hobbies, and personal relationships in their artifacts. This was demonstrated
by the vast diversity of students’ projects and in students’ own consistent expressions of creative
freedom in the projects. As one student related, “I was able to make something that I wanted, anything,and I just created that and I liked it. It was fun.” In some instances, students also displayed their
engagement and relationships by designing their projects for others such as making a touch-sensitive
soft toy for a little sister or making a blanket throw for a brother's birthday.
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Designing for Expression
The unit emphasized “aesthetics first” through a personal design or sketch at the beginning of every
project. In prior studies we found that starting with instruction (i.e., ways to design circuits) instead ofwith design resulted in poor engagement by students (Kafai et al., 2014). In contrast, by having students
sketch out what they wanted their projects to look like, even if ideas were technically or practicallyinfeasible, encouraged personal ownership from the beginning and set up students up to persevere
through challenges (Kafai, Lee, et al., 2014).

Designing for Depth
We also wanted students to learn deeply through providing increasingly complex projects. Too often
constructionist projects stay at what Blikstein and Worsley (2016) call the “keychain phenomenon”
where students enjoy “low floors” to design but do not continue onward to the “high ceilings” possible
with more advanced ideas and skills (Resnick & Silverman, 2005). To this end, the unit had introductoryand complex e-textiles projects (Figure 1 and Table 1) that built on design, crafting, circuitry, and coding
skills, each increasing in both difficulty and open-endedness.

Designing for Multiple Experiences
The design of the e-textile unit also provided multiple experiences in learning about design, crafting,circuitry, and coding skills through having students conceptualize and then implement more than justone complex project. Initially we had six projects but worked with teachers to provide repeated
opportunities for students to engage in practices such as debugging, revising, testing, collaborating, anddesigning for other users in just four projects.

Designing for Audience
We further supported project designs with authentic audiences by having teachers display their own
and students’ projects at the beginning of the unit, during the unit as a way to share peer knowledge(see next section), and at the end of the unit as a form of collaborative show and tell. Some teachers
went further to encourage students to show their projects to other teachers at the school or put projects
on display in school hallways and display areas. This made students’ projects transparent to each otherfor idea generation and also provided authentic audiences for students, a common principle in studiodesign education (Hetland et al., 2013).
Designing for Collaboration
Peer pedagogy, or students teaching each other, was another design principle of the unit in bothintentional and emergent ways. We deliberately used pair programming during coding instruction
moments and chose to make one project (the mural) collaborative at both a classroom and partner level.
But we were also surprised to find from our research how often students’ helped each other in
unstructured ways. When reporting on challenges on their individual final projects in their Year 2
portfolios, nearly one-fourth of students explicitly mentioned peer help as key to resolving bugs (seeJayathirtha, Fields & Kafai, 2018). Observations show even more frequent peer-to-peer help,
encouragement, and support. Two things support this unstructured peer pedagogy in the unit. First, thephysical structure of the classroom with students at small tables with shared supplies (scissors, thread,alligator clips, etc.) encouraged unstructured student collaboration. Student work (including errors) is
visible and frustration is audible by sheer proximity. Second, teachers developed practices that support
peer pedagogy, such as providing help to one student so that student could help others, connecting
students to others who have expertise, and allowing student mobility in the classrooms, permitting themto get up and down and move around the room.

Designing for Productive Failure
Another goal of the unit was to support students in valuing the process of making projects, not just the
final product. This meant finding ways to highlight mistakes and make them into learning opportunities
rather than learning barriers. The teachers themselves developed several practices in this regard that
we now explicitly model and name in professional development workshops (Fields, Kafai, Nakajima,
Goode, & Margolis, in press). For instance, the teachers highlighted their own iterative practices of604

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creation, including their own mistakes, errors, and less-than-perfect projects in front of the classroom.
This allowed the teachers to self-deprecatingly model practices of revision and iteration and coachstudents on tips for dealing with this process. The teachers encouraged students to think that it wasokay not to be perfect the first (or the second, third, fourth) time.
The teachers also showcased students’ challenges, mistakes, and in-process projects. They did this in
multiple ways. First, teachers would highlight mistakes for the entire class during project time. One
teacher created a tradition of saying, “This is my favorite mistake of the day!” and then would show the
mistake and ask the rest of the class for help in identifying what was wrong and why. Second, teachers
had students highlight mistakes through personal journal entries, some of which we adopted formally inlater versions of the curriculum. For instance, one teacher added a journal question after the completionof the wristband project that solicited challenges that students had faced: “Think about this week’s
project, what was the biggest challenge?” Students wrote their own reflections before sharing out ideas.
These methods made students’ mistakes into a form of shared classroom knowledge, foregrounding
students as experts in the classroom, a key practice of equity-based and constructionist teaching
principles that situates knowledge in the hands of learners and not just teachers.
Designing for Reflection
Honouring the role of reflection in constructionist learning settings, another key element of thecurriculum involves supporting students in consciously thinking about their processes of learning andhonouring mistakes and challenges that occur during that process. Drawing on practices of reflection
already present in the larger ECS curriculum (namely short journal entries and class discussion), we
intentionally expanded these by including design notebooks and portfolios. Students responded very
positively to the portfolio, saying that it helped them to see how much they had learned and appreciatingthat they were graded not just on the final product but also on their process of learning (Lui et al., 2018).
In Year 3 we increased the supports for the portfolio by encouraging more frequent practices of
documenting and reflecting on mistakes throughout the entire unit and not just on the final project. We
included taking regular photographs of unfinished projects as “exit tickets” on crafting days, and mademore “design notebook” entries where students suggested tips for others or noted changes that theymade.

Designing for Practicalities
We also designed for the practicalities of managing materials and students, improving this each year.
Teachers of Stitching the Loop taught in regular computer science classrooms not set up for themessiness of crafting. Early on the teachers developed practices to manage materials and set-up
including 1) Using lidded boxes to contain table supplies; 2) Initiating set-up and clean-up practices
where one student per table would pick up a craft box and later take it back; and 3) Organizing student
work in individual Ziploc bags and storing these within the craft boxes. We also worked with
manufacturers to make it easier to purchase materials for the e-textiles unit. This involved simplifying
the number of merchants to order from (to satisfy school administrations) and lowering costs. The
development of new microcontrollers like the Adafruit Circuit Playground that already had multiple
switches and sensors onboard further allowed us to steeply lower the per-student cost of the unit toabout $40/student (instead of $60+/student). We also negotiated with the manufacturer to create student
and classroom kits that were easy to order and organize, with the added benefit of offering teachers of
the unit bulk pricing for additional classes they needed supplies for.

Design for Iterations
Finally, as with any constructionist venture be it tools or activities, we iterated through the various
aspects listed above. For instance, in the first year of the unit we had six projects, but this became
overwhelming to fit into the limited 8-week window available as an ECS unit. We identified overlapping
skills between projects and cut two projects that did not significantly add to students’ skills. In Year 2
implementation we found that the four projects were sufficient for students developing the knowledge
needed to carry out the final project and the time required was much more manageable. Furthermore,
we shifted from a test-based assessment to a portfolio-based assessment where students shared605

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summaries of their projects, challenges and revisions that happened as they made them, and reflections
on their learning overall.

Conclusion and Discussion
In Mindstorms Papert (1980) outlined a bold vision of how computers could help children learn,launching the development of numerous programming languages for learners, the design of various
computational construction kits, and the creation of learning communities. Nearly forty years later thisvision is making a comeback around the globe, promoting coding and making inside and outside ofschools. Success stories of the Scratch platform and community and the Maker Movement havedemonstrated that millions of kids can be interested in programming and in making electronics together
in afterschool and online spaces. What does it mean for these activities to move back into the classroom
with its focus on standards, curricula, and assessments within limited time periods and limited staffing?
Our guiding principles for designing the e-textile curriculum unit embrace constructionist ideas andapproaches in creating anew the conditions where personal projects can flourish, students can support
each other, teachers can become members of the learning community, and failure is seen as part of theprocess. In many ways, constructionist-oriented teachers and researchers have adopted theseprinciples for a long time. In designing “Stitching the Loop,” we hope we have made them explicit sothat other teachers and designers can adopt them for bringing constructionist activities to classrooms
and promoting more equitable teaching and learning opportunities for students. Though afterschool,out-of-school, and online constructionist experiences have much to offer, we believe that classrooms
provide unique opportunities to reach out to broader numbers of children and youth who may not takethe initiative to step into those more informal experiences. Further, classrooms furnish circumstancesthat can support greater rigor and depth because of consistent attendance and dedicated time toprojects.
In return we must of course consider the constraints of classrooms themselves, with the need to promotecertain academic content and practices, limited staff, and physical and material constraints. Teachersreport that “Stitching the Loop” has been a tremendous success with student engagement andpreparation for more advanced computing courses. It provides a proven example that one teacher canwork to support personalized project-creation with 25, 35 and even 40+ students. Projects with creative
constraints, peer pedagogy, and process-based reflection all support depth of learning while legitimizing
learners’ expertise and supporting interest-driven engagement. Robust professional development,
building on the ECS model and educating in the way we hope teachers will educate their own students,
is a key factor in ensuring the design principles are implemented fully.
Of course, challenges remain. Supply costs for the unit have become more reasonable but not all
schools can afford them consistently. Inevitably, not all teachers will embrace the principles of the unit
equally, resulting in inconsistent implementation. This means we need to support teachers beyond thefirst year or two of implementation now that the curriculum is ready for national release. In developing“Stitching the Loop,” we illustrated how guiding principles need to apply to the design of construction
tools and kits as well as to the constructionist projects and activities in which they are employed. Only
then can we provide personally meaningful and equity-minded experiences to all learners.

Acknowledgment
This work was supported by a grant #1509245 from the National Science Foundation to Yasmin Kafai,
Jane Margolis, and Joanna Goode. Any opinions, findings, and conclusions or recommendations
expressed in this paper are those of the authors and do not necessarily reflect the views of the National
Science Foundation, the University of Pennsylvania, or Utah State University. Special thanks to Tomoko
Nakajima, Debora Lui, Justice Walker, Gayithri Jayathirtha, and Mia Shaw for their help with datacollection and analysis.

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Blikstein, P. & Worsley, M. (2016). Children are not hackers: Building a culture of powerful ideas, deeplearning, and equity in the Maker Movement. In K. Peppler, E. Halverson, & Y.B. Kafai (Eds.),
Makeology: Makerspaces as learning environments (pp. 64-79). New York, NY: Routledge.
Brennan, K., Balch, C. & Chung, M. (2014). Creative Computing. Harvard Graduate School of
Education. Retrieved February 21, 2018 at http://scratched.gse.harvard.edu/guide/ download.html
Buechley, L., & Eisenberg, M. (2008). The LilyPad Arduino: Toward wearable engineering for everyone.
IEEE Pervasive Computing, 7(2), 12-15.
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and challenges in a discovery-based program of game design learning. Educational Technology
Research and Development, 59(2), 267-289.
Fields, D. A., Quirke, L., Horton, T., Velasquez, X., Amely, J. & Pantic, K. (2016). Working toward equity
in a constructionist Scratch camp: Lessons learned in applying a studio design model. In A. Sipitakiat &N. Tutiyaphuengprasert (Eds.), Proceedings of Constructionism 2016 (pp. 290-297). Bangkok,
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program. ACM Inroads, 3(2), 47-53.
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Environments, 1(1), 1-32.
Hetland, L., Winner, E., Veenema, S., and Sheridan, K. (2013). Studio thinking 2: The real benefits ofvisual arts education. New York, NY: Teachers College Press.
Jayathirtha, G., Fields, D. A., & Kafai, Y.B. (2018). Computational concepts, practices, and collaborationin high school students’ debugging electronic textile projects. Conference Proceedings of International
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Kong.
Kafai, Y. B. (1995). Minds in play: Computer game design as a context for children’s learning. Mahwah,
NJ: Lawrence Erlbaum Associates.
Kafai, Y. B. Fields, D. A., & Searle, K. A. (2014). Electronic textiles as disruptive designs in schools:
Supporting and challenging maker activities for learning. Harvard Educational Review, 84(4), 532-556.
Kafai, Y. B., Lee, E., Searle, K. S., Fields, D. A., Kaplan, E., & Lui, D. (2014). A crafts-oriented approachto computing in high school. ACM Transactions of Computing Education, 14(1). 1-20.
Lui, D., Walker, J. T., Hanna, S., Kafai, Y. B., Jayathirtha, G., & Fields, D. A. (2018). Communicating
computational concepts and practices within high school students’ portfolios of making electronic
textiles. In proceedings of the International Conference of the Learning Sciences, London, UK.
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Learning Analytics in Education: Objectives,
Application Possibilities and Challenges
Aleksandra Klašnja-Milićević, akm@dmi.uns.ac.rs
Faculty of Sciences, University of Novi Sad, Serbia

Mirjana Ivanović, mira@dmi.uns.ac.rs
Faculty of Sciences, University of Novi Sad, Serbia

Abstract
With an increased amount of educational data in all domains of human activities, Learning Analytics(LA) become a powerful mechanism for supporting learners, instructors, teachers, learning systemdesigners and developers to better understand and predict learners’ needs and performances. In thispaper, we analyse the significant dimensions and objectives of LA, application possibilities and somechallenges to the beneficial exploitation of educational data. We identify the required skills andcapabilities that make meaningful use of LA techniques and technologies in this domain. They can act
as a useful guide for setting up LA services in support of educational practice and learner guidance, in
quality assurance, curriculum development, and in improving learning process effectiveness andefficiency. Furthermore, this paper proposes the most important constraints that affect LA technologies
in education.

Keywords (style: Keywords)
learning analytics; education; application; learning environments; effective learning system

Introduction
During the last years, developments of new learning technologies, devices and environments, such as
digital learner records, learner cards, mobile and eye-tracker devices, sensors, flexible classroom
design, and Massive Open Online Course (MOOC) are completely transforming the approach oflearning and teaching (Sharple, et. Al, 2014). Higher education institutions are collecting more data thanever before. Management of this vast amount of data, named “big data”, should offer valuable
comprehension about the learning process, insights about risk of learner’s dropping out, and support
for increasing learners’ success. In order to comprehend the patterns of value that exist within the large
amount of data, new or innovative approaches are required. Lot of exploration and researches aim tohandle the data with the proper techniques and new tools to produce real time solutions and predictionsin this certain area. The utilities of proper techniques and new tools could be: operative self-learning,
useful peer groups, available class time for creativeness, and possibilities for problem solving(Papamitsiou and Economides, 2014).
Analysis techniques that extract information from “big data” such as discovering patterns and applying
them to the education process are named Learning Analytics (LA). In its initial steps of evolving, there
has been numerous definitions used for LA. Siemens defines it as “the use of intelligent data, learnerproduct data and analysis models to discover information and social connections, and to predict andadvise on learning” (Siemens, 2012). Elias described it as “an emerging field, in which sophisticatedanalytic tools are used to improve learning and education” (Elias, 2011). Learners and teachers leave
many traces behind them in which LA can convert them to be beneficial for the education sector (Duval,2011). The most cited definition of LA came from an open online course on learning and knowledge
analytics (LAK11) and was adopted by the associated First International Conference on LA and
Knowledge in 2011 (Long, 2011): "the measurement, collection, analysis and reporting of data about
learners and their contexts, for purposes of understanding and optimizing learning and the environments
in which it occurs".
Many researchers and developers study the use of LA in different fields related to the data availability,
the competence, applicability, the cost, the privacy, the relevance and the ownership (Adams Becker,
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2017; xxx et al., 2017). In this paper we will concentrate on contemporary study examines theapplicability of LA in higher education institutions.
The rest of the paper is organized as follow. The second section gives an overview of key trends ineducation environments, dataset and analytics. The third section is devoted to educational LA
applications and their role in contemporary education. The challenges of implementation of LA in
education environments are pointed out in the section 4. The fifth section of the paper provides theconcluding remarks and possible future directions.

Educational data - benefits for Learning environments
LA can obtain advantages of available educational datasets from different Learning Management
System (LMS), Content Management System (CMS), Tutoring system and other learning proposedsystems (Davies, et al. 2017). Educational Institutions already have a large amount of student data anduse these for different purposes. Administering student progress and reporting to receive funding fromthe public authorities are the most commonly known purpose. Linking such available datasets would
facilitate the development of mash-up applications that can lead to more learner-oriented services andtherefore improved personalization. LA strongly relies on data about learners and one of the major
challenges LA researchers are facing is the accessibility of publicly available datasets to evaluate their
LA methods. Most of the data produced in institutions is protected, and the protection of student dataand created learning artefacts is a high priority for IT services departments. Nevertheless, analogous toOpen Access publishing and related movements, calls for more openness of educational datasets havealready been brought forward (Drachsler et al., 2010).
Greller and Drachsler (2012) developed a framework that includes six critical dimensions related to an
LA initiative. Each of the dimensions must be addressed to institutionalize an LA initiative successfully.
Figure 1 illustrates critical dimensions according to educational environments.

Figure 1. Critical dimensions of LA

Some researchers specified that higher education has to include the analytics tool into the system inorder to improve productivity (Daniel 2016; Hrabowski et al. 2011; Becker 2013). First, data analytics
software can provide feedback to learners and teachers about educational performances:
1. Feedback: Learner often might fail at a subject but not know what the reason was. It becomes
valuable when the learner can look not just at himself, but at other people who have had the same
experience. (S)he can get an insight either that would describe it so (s)he is not frustrated or that (s)hecould use to correct it so that (s)he could be successful again. The improvement of electronic learning
modules supports evaluation of learners in logical, real-time ways. In order to predict learner outcomessuch as dropping out, needing extra help, or being capable of more demanding assignments, this609

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approach can analyze underlying patterns. Pedagogic approaches that seem most effective withparticular learners could be identified.
2. Tracking: In order to understand the real patterns of learners more effectively tracker devices canbe used for teachers, by allowing them to track a learner’s experience in an e-learning course. In
observing the digital paths learners leave overdue. Teachers can track learners’ passage during thewhole learning experience.
3. Efficiency: LA can save many hours of time and effort, when it comes to the achievement of ourgoals and strategies that we need to reach them.
4. Understanding the learning process: By using LA in e-learning, teachers can see which parts of acourse were too easy and which parts were so difficult that the learner has failed to solve. Other partsof the learner’s path teachers can analyse after that and consider pages re-entered often, preferredlearning styles, sections recommended to peers, and the time of day, when learning operates at its best.
5. Collaboration: Experts from many different fields have to come together to retain a Learning
Management System function at its best. This encourages cooperation, teamwork, and interdisciplinary
thought processes.
6. Personalization: LA can be successful in the way we approach e-learning design by allowing
designers to personalize courses to adjust their learners’ individual needs. This will allow e-learning
developers to promote the standard for effective and exceptional e-learning courses.
Regardless of the educational performances, many faculties, and universities have confirmed thatanalytics can support significant improvement of an institution, including resource distribution, learnerachievement, administration, and finance. Some important features that LA offers to institutions arelisted below (Daniel, 2015; Siemens, 2011).










Assisting in creating common sense of complex topics through the combination of social networks
and technical and information networks. Algorithms can recognize and provide insight into data
and at-risk challenges.
Innovating and transforming the college and university system, in addition to educational models
and pedagogical approaches.
Improving administrative decision-making and organizational resource provision.
Helping leaders transition to holistic decision-making through analyses of “what-if” scenarios.
Exploring how different components within a complex discipline (e.g., remembering learners,
decreasing costs) connects and discovers the influence of varying essential components.
Increasing administrative efficiency and productivity by providing latest information and allowing
fast reaction to challenges.
Testing and evaluation of curricula.
Helping official leaders to control the hard (e.g., research, patent) and easy (e.g., quality ofteaching, reputation, profile,) value created by faculty activities.
Evaluating typical grading techniques and instruments (i.e., departmental and licensing exams).

Potential Application Areas of Educational Learning Analytics
Nowadays, researchers in educational LA field tries to answer increasingly important and complex
questions: what a student knowledge is and whether a student is engaged in learning process. Scientists
and researchers have analysed and experimented with new techniques and new kinds of learning
system data that have shown promise for predicting student learning outcomes (Jivet et. al. 2018,
Adams Becker et al, 2017). In this section we present review of potential application areas for
Educational LA. These areas represent the broad categories in which LA can be applied regarding
online activities. Figure 2 presents a summary of selected areas and appropriate data types needed foranalysis. In summary, LA systems apply models to answer such questions as:When are students ready to move on to the next topic?
When are students falling behind in a course?
When is a student at risk for not completing a course?
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Constructionism 2018, Vilnius, LithuaniaWhat grade is a student likely to get without intervention?
What is the best next course for a given student?
Should a student be referred to a teacher for help?

Responding to these questions involves the collection of students’ entry and correctness of the answer,
student activities on learning systems over time, when and to which group is specific for learning
strategy, and performance of students on pre / post-tests.
Research on the various components of the learning system can contribute to the design of better
learning systems as it has strong implications for student learning. This area represents a key point for
educational data processing and LA (Baker, 2010). Haixiang et al. (2017) proposed learning
decomposition as an alternative and effective method. Learning decomposition method involves
exponential learning curves to performance data and relating student success to the amount of eachtype of pedagogical support. The weights indicate how effective each type of pedagogical support is for
improving learning.

Figure 2. Application areas for Learning Analytics and data types needed for analysis

Scientists from Carnegie Mellon University have built cognitive models of mathematics, which havebecome the basis for high school programs that include intelligent tutorials (Ritter et al., 2007). In thesesystems, complex tasks are divided into individual components of knowledge, and the model is used to611

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improve the learning process and the problem-solving strategy. Each student activity is associated with
one or more skills. In this way, researchers can use data from the tutorial system to dynamically assess
the teaching efficiency. Evaluations and improvements to this model have been carried out over the last
15 years.

Challenges of Learning Analytics Technologies in Higher
Education
Regardless of the fast development of applications that support implementation of big data techniques
in higher education, there are also several apprehensions that affect LA technologies. The main issues
that can be observed in the application of LA in education are related to data profiling, privacy, and therights of learners with respect to their individual behavior recording (Boyd, 2010). For example, some
important issues must be taken into consideration (Picciano, 2012):
 Should learners be informed that their activity is being followed?
 How much information is necessary for faculty, students, parents, scholarships, and other issuers?
 In what manner should faculty affiliates act in response?
 Do learners have a requirement to look for support?
Protection should be undertaken to confirm that the well-known collections of personal data of learnertransactions are not hurt individuals. One encouraging approach to resolving these issues could bemasking the data at its source (Barlow, 2013). Masking is one type of creative approaches that will
make large-scale applications of data possible while still protecting the confidence of students’ andteachers’ information. New performances and competences for software applications permit sensitive
data to be masked at the database level, when brought into a data warehouse. It can be concluded thateven if someone has acquired physical access to database, delicate information like social security
numbers will still be confused.
With the adoption of LA in the educational fields, institutions are required to adjust their policies withlegislative framework. Many institutional policies failed to fully reflect the ethical and privacy implicationsof LA (Prinsloo & Slade, 2013). In the rest of the section, we specify several possible principles that an
ethical LA policy should describe:
1. Collection of personal information: sex, date of birth, address, ethnicity, occupational status,
qualifications and study records.
2. The usage of personal information, if it is for the benefit of the students, such as predicting
students’ behavior and advices based on LA, or if it is for research reasons to achieve LA
objectives.
3. Methodology of data collection either by the student’s input him/herself or by other services, suchas browser cookies.
4. Security principles for keeping the data protected.
5. A description of the time period of keeping learners’ data and rules for deletion process. (Singer,
2014)
All LA tools should follow convenient security principles to keep the analysis results and the students’records safe from any threat (Anciaux et al., 2006).


The confidentiality property promises that the data can never be accessed by an unauthorizedaccess.
 The integrity property promises that the data cannot be watched, changed, or transformed.
 The availability property guarantees that the data should be available for authorized parties toaccess when needed.
A key component of protecting learners’ information is encrypting their data in order to achieve theconfidentiality concept. Encryption guarantees that only authorized people can use the data. Moreover,assuring confidentiality can include: invoking file permissions and granting a secure operating
environment, while cryptographic hashing of datasets can assure the integrity property of students’records (Chen & Wang, 2008).

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As LA is an emerging research topic in the field of Technology Enhanced Learning and a forthcoming
trend (Ebner & Schön, 2013), accuracy and validity of information is highly questionable. Faults related
to selection a wrong dataset, or not recognizing the component relevant to data will negatively affectthe accuracy of the outcome (Krasnow Waterman & Bruening, 2014). Therefore, a wrong selection of
educational dataset will lead to inaccurate results. The questions we can ask here are: What if LA results
were wrong? And what if the predications or the interventions went wrong? Accordingly, LA would aim
to provide guarantees that it’s analyzing, and picking the data, fit quality criteria and produce an agreed
level of accuracy.
Data protection and copyright laws are legal restrictions that limit the beneficial use of LA. Such legal
restrictions are: limitations of keeping the data for longer than a specific period, which are regulateddifferently in each country; the data should be kept secure and safe from internal and external threats;
data should be used for specific purposes and the results of any process should be as accurate as
possible. The restrictions could be stronger when it relates to personal information. Applying social
network analysis as a method of LA causes the adoption of personal information.
There are two main perspectives about who own the data: students and institutions. Jones et al. (2014)concluded that neither the students nor the institutions should win the ownership of the data. Theysuggested a hybrid module that merges both perspectives. Institutions can invest the students’ data inanalytics, develop new personalized learning platforms and benchmark their learning management
system. While students want to enhance their learning and maintain their performance, they would liketo ensure that their information is kept confidential. An uprising question we like to address here is: What
if LA methods have to modify the students’ data for prediction purpose.

Conclusion
Learning Analytics is a promising research field, which provides tools and platforms that influence
researchers in Technology Enhanced Learning. Higher education institutions are applying LA to improve
the facilities they provide and to improve observable and measurable learning outcomes (e.g. marks,
success and awards, as well setback and obstruction) (Cooper, 2013). Nonetheless, this emerging new
field lacks an approach that explains a complete overview of its processes. This research study revised
the definitions of LA and the aspects that encourage its extension. This provides an entire overview
consisting of: learning environment, dataset, analytics, constraints and the interventions which areinterpreted to achieve the main goals of LA. Based on this approach, we identified the stakeholders,introduced examples of usage, presented methodologies and discussed the objectives. After that, we
covered the way of determining the challenges that surround LA and shed the light on the privacy,
security and ethical issues and anticipated questions that need a further research in near future.
In the future, educational institutions should try to balance the institutions own philosophy of learnerdevelopment on the one side and various federal privacy laws on the other side. It is significant thatorganizations comprehend the dynamic nature of educational success and retaining, offer surroundingsfor open dialogue, and enhance practices and approaches to solve these issues.
We hope that research achievements presented in this paper and the implications derived from themwill advance the discussion about building effective learning systems for learners, instructors, course
designers, and similar activities in educational institutions.

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Assessing Learning through Exploratory Projects in
Cnstructionist R-based Statistics Courses forEnvironmental Science Students
Maite Mascaró, mmm@ciencias.unam.mx
UMDI-Sisal, National Autonomous University of Mexico (UNAM), Mexico

Ana Isabel Sacristán, asacrist@cinvestav.mx
Centre for Research and Advanced Studies (Cinvestav), Mexico

Abstract
For years we have been developing sequences of constructionist and collaborative activities where
university environmental science students engage in computational programming tasks (using R code
–http://www.r-project.org/) for exploring and learning statistical concepts and ideas. In this practice
paper, we present a project-based method that we use for assessing students´ learning andappropriation of those statistical concepts.
The exploratory projects that we use for assessment, are open-ended realistic problems inenvironmental contexts. In these projects, students, working collaboratively in teams of up to 4
members, are expected to make choices of the most appropriate statistical procedures and graphic
representations to be used in portraying relevant statistical features in the data, and construct statisticalmodels using any tool they choose, such as R code. They then have to defend the line of reasoning
underlying their choices, through a formal presentation using their own communication resources, andwhere other students question and discuss the methods, results and interpretations presented. Suchprojects can be considered to be thought-revealing activities; i.e. they serve as a window into students’
thinking, and thus are means for assessing their learning. They also constitute an approach that is morein accordance with constructionist principles, since students explore and construct freely. Such projects
thus, not only serve as a means for us to assess students’ learning, but can be, in themselves, learning
experiences that help reinforce what has already been learned.

Keywords
statistics education; assessment; exploratory project; R-programming; constructionism

Introduction
For approximately 8 years, we have been researching how to enhance the teaching and learning ofstatistics and experimental analysis for environmental and biological sciences students at university
level, using the constructionist approach (Papert & Harel, 1991). As we expressed in Mascaró &Sacristán (2016, p. 102):
our concern is that environmental scientists require probability and statistical knowledge forexperimental data analysis, decision-making, evidence to support theory, and communication ofscientific results, so it is important for future researchers to have adequate understanding of these
topics. However, learners in university programs in biology and related sciences tend to havedifficulties in understanding and/or applying the concepts, and even researchers have a poor
understanding and use incorrectly many statistics concepts (Batanero, 2001). Many teaching
programs have had unsuccessful results (Bishop & Talbot, 2001) and there tends to be anaversion (including general ‘mathophobia’ – Papert, 1980) towards learning statistics.
In order to address this concern, we have developed sequences of constructionist and collaborative
activities where students engage in computational programming tasks (using R code –http://www.rproject.org/) for learning and exploring statistical concepts. However, we then face the issue of how toassess the learning that takes place through our approach, without reverting fully to more traditional
schooling methods, but rather utilizing means that are more in accordance with constructionist
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principles. In this practice paper, we focus on a project-based method that we use for assessment
purposes. Before that, we summarize the approach we take in our courses.

The R-based statistics course approach
Using as framework the constructionist philosophy (Papert & Harel, 1991) –which suggests that learning
can be facilitated if students engage in exploring through construction, such as programming– as statedabove, we have been developing and refining a series of approximately 35 student-centred computer
programming activities for students to explore, implement and develop meaning for statistical concepts
and ideas (see Mascaró, Sacristán & Rufino, 2016; Mascaró & Sacristán, 2016). The activities also
attempt to integrate the following aspects: (i) the use of concrete examples with data from real researchsituations; (ii) emphasis on the use of diagrams, since graphic representations are essential in data
organisation, statistical reasoning and analysis (Wild & Pfannkuch, 1999).
All the activities are based and presented in the R programming language through R-code “worksheets”
with instructions, examples, programming tasks, questions for reflection, and comments. We chose R,“because it is a transparent and powerful expressive ‘Logo-like’ language commonly used bystatisticians, but simple to use (using a set of intuitive and relatively simple commands)” (Mascaró &Sacristán, 2016, p. 102). The aims are for students to apply and explore statistical functions andconcepts, and develop meanings for them, through the R-programming tasks; as well as develop
competency in a statistical analysis tool (R) that they can use in their future research activities.
The activities are usually carried out in teams of 2-3 students and moderated by a teacher. They are of
two types: those for introducing the basics of R; and those on various topics of statistics courses (e.g.
activities that deal with frequency distributions; activities on binomial, Poisson, and normal distributions;activities on ANOVA and comparisons between means; activities on linear regression; etc.). A sample
task in ANOVA can be seen in Mascaró et al. (2016).
The different activities have been implemented, repeatedly over the years, in at least two undergraduate
courses (Probability and Statistics; Experimental Planning and Analysis) and three post-graduate ones(Experimental Design and Data Analysis; Introduction to Multivariate Statistics; and Univariate
Statistics): in total, in approximately 20 courses (counting repetitions) in Mexico and Portugal.

Assessing the learning and appropriation of statistics
In order to assess students’ learning and appropriation of the statistical concepts and ideas, one
approach that we have developed is through exploratory projects, carried out by teams of students, forsolving realistic problems in environmental contexts. In these projects, students need to make choicesof the most appropriate statistical procedures and graphic representations to be used in portraying
relevant statistical features in the data, serving both exploratory and confirmatory purposes. They thenhave to defend the line of reasoning underlying their choices, through a formal presentation using theirown communication resources, where other students question and discuss the methods, results andinterpretations presented.
These projects are similar to the learning programming activities of the lessons (including thecollaborative work by teams of students), except that they are completely open-ended. In that sense,
they are even more constructionist than the learning activities themselves. These projects share somecommonalities with what Lesh et al. (2000) called thought-revealing activities (or model-eliciting
activities) in the sense that students are faced with an open-ended realistic problem which they need to
explore, and for which they need to build statistical models with whatever tools and methods they
consider adequate, pose questions and present their conclusion in reasoned ways. Moreover, as Nossand Hoyles (1996) proposed, computer-based activities can serve as a window onto mathematicalmeaning-making. In those ways, students’ thought processes are revealed; we can thus assess the way
in which students are able to create meanings and apply statistical concepts in solving the problems.

Phases of the assessment exploratory projects
An assessment session centred on an exploratory project, consists of three phases:
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1. Each team (maximum 4 students) is given a concise, yet informative, description of a problem
to be explored and solved, the context and the data produced by the investigation stated in the
problem, together with an indicative list of important aspects to be considered as a guide for its
solution. Teams are given 90 minutes to work on the problem, during which they have open
access to every source of information: R scripts of their own, as well as those found on theinternet; online documents on statistics, blogs, books, class notes and corrected exercises andhomework.
2. Teams are then given 30 minutes to prepare a 15-slide presentation of their findings.
Presentations are expected to include a short introduction of the problem and researchquestions, a description of the exploratory and confirmatory techniques used, tables withstatistical descriptors, numerical and graphic representations of relevant features in the data,
and final conclusions.
3. After a short break, teams present their results in random order. After each presentation,
students in the other teams have to pose at least two questions that are graded depending on
the degree of complexity and pertinence. Answers by the presenting team are also graded
depending on precision, extensiveness and appropriateness. Students are previously instructed
to ask questions in relation only to the statistical procedures and interpretations of the results
obtained (thus knowledge on other contextual aspects –biological, chemical, geographical orsocial concepts– whilst superficially necessary for the discussion, is not considered for thegrade).

Assessment criteria
During the presentations, members of each team are graded according the following categories usinga scale of 1 to 3. (Note: we need to score students’ work, since we still are working in a formal school
setting):
A. How the problem and its general context are expressed and presented, and the explanation thatis given on the experiment that was carried out
B. Identification of the hypothesis of the problem (the cause-effect relationship)
C. Identification of the factors (types, number of levels, arrays), of the response variables (units,SU, number and balance)
D. Clear identification of the statistical model to be used and translation of the hypothesis of the
problem, as expected from the different variation sources.
E. Exploration of the data (trends of means, variances, internal distributions, extraordinary data)
F. Execution of the analysis (types of tests and programmatic tools)
G. Statistical interpretation of hypothesis tests
H. Interpretation in the terms of the problem and the scope of the results
I. Support elements to explain the results (use of tables, diagrams, graphs, etc.)
Questions from other teams and answers during the discussion stage are also marked from 1 to 3
according to the following criteria:
1.
2.
3.
4.
5.

Complexity of the question
Articulation of the question
Adequate question
Quality and efficiency in the response
Elaboration of the answer

Grades in criteria A – I, are worth 60%; whereas those in criteria 1 – 5, 40% of the total score.

Sample assessment related to Factorial ANOVA
We now present an example of an exploratory evaluation project used to assess the way in which
students apply statistical concepts related to factorial ANOVA for solving an environmental problem. As
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can be seen in the text given to students (Figure 1) for the sample project discussed here, there are no
specific questions but rather some guiding aspects to consider; so the project was open-ended and was
specifically directed to assess whether students were competent in:







Identifying situations where the estimation and prediction of variance components and interaction
terms between categorical explanatory variables (factors in nested and orthogonal ANOVA
designs) is required.
Identifying the cause-effect relations of all explanatory and response variables involved, and thecorresponding test hypotheses that need to be specified to confirm such relations.
Distinguishing between random and fixed effects in factorial designs, the computation of variancecomponents and its interpretation in the context of the problem.
The application of test hypotheses using the F statistic, and the identification and interpretation ofdifferent elements in the numerical output of such tests i.e. degrees of freedom, residual standard
error, probability of F value, coefficient of determination, etc.
Assessing the magnitude of estimates in the statistical model, particularly those related to nested
factors and interaction terms.
The construction of graphic representations used to portray relevant statistical features in the datathat will serve both exploratory and confirmatory purposes.

Programming objectives were to learn, or improve, the use of the following R functions and libraries:






R-package GAD to adjust balanced models with different combinations of fixed and random factors
in nested and factorial ANOVA designs.
The ‘anova’ function and compare its performance to that of ‘gad’.
The F-Distribution ‘pf’, ‘qf’, ‘df’ and ‘rf’.
The ‘interaction.plot’ and ‘coplot’ functions used to build working graphs that help assessinteractions between explanatory variables.
Functions in the R-package ‘ggplot2’ to build graphs with different elements used often for finalvisualizations of statistical models.

Specific example of a problem to be explored for the assessment
Below (Figure 1), we present the text of a problem that guided one of the assessment projects.
River San Pedro is the main body of superficial water in the Mexican state of Aguascalientes. It
runs in an almost straight line from North to South through 90 km, until it meets River Santiago in
the state of Jalisco. Prior to the construction and launching of the Industrial Park of the Valley ofAguascalientes, clustered in the southernmost part of the state, water pollution in the river, theaquifer and the soil in the river banks was negligible. Today, however, contamination by toxic
organic substances, such as phenols, anilines and sulfacetamide (SAAM) has been reported as
dramatic.
In the attempt of confirming the environmental impact of organic toxins in the water, researchers
from the Laboratory of Environmental Studies of University of Aguascalientes conducted a study
repeating the same sampling design that their colleagues had used before the region's
industrialization (1975). This consisted of taking 15 random water samples in 5 stations along theriver, covering from the far north end of the industrial park (Sit1), an intermediate (Sit2), one near
the industrial park (Sit3), one within the park (Sit4), and finally, one where most industries arelocated (Sit5). The concentrations of anilines (mg L-1) were determined in each water sample
following the same procedures used previously, despite the idea that the old determinations wereless precise than modern ones would be.
The data can be found in sheet 'anilines' of file 'ex 2.10.xlsx'.
Guide of important aspects to consider when solving the problem.
a) Cause-effect relations and derived hypotheses.
b) Type of statistical model, categorical factors included, level per factor and arrangement in theexperimental design.
c) Types of variables, experimental units, replication and balance in the model.

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Constructionism 2018, Vilnius, Lithuaniad) Strategy for the statistical analysis, including exploratory data analysis and the type ofcomparisons amongst means necessary to fully respond to the research questions posed inproblem.
e) Interpretation of results, both statistic and in the context of the problem.
g) Presentation of results (format, graphs, tables, relevant statistical values, etc.), including final
remarks and recommendations for future investigations.
Figure 1. The problem given to students for an assessment related to Factorial ANOVA

Sample answer by a team of students
We discuss here the presentation created by a 4-member student team, after their exploration of theabove problem, in one of courses in the Sustainable Coastal Zone Management undergraduate degree
at the National Autonomous University of Mexico. This team got an overall score of 88/100 – the highestof the group. The team unequivocally identified the cause-effect relationship, the corresponding
explanatory and response variables, and the fixed nature of the former; the experimental unit, numberof replicates and levels in both factors (Figure 2).

Figure 2. The factors of the problem from the student’s team slide

Figure 3. The hypothesis of the problem as presented in the students team’s slides

The team was also clear and resourceful in the description of the problem, supporting the introduction
with a before-and-after diagram to explain how the impact assessment experiment had been conducted.
They carried out an exploration for comparing, as an appropriate control, the data amongst years andthe use of time, something that was straightforward; this allowed them to express a general hypothesis(in this case of differences in aniline concentration between years), (see Figure 3). Whilst stating that“both time and site would affect aniline concentration in a dependant way (with differences in rate ofchange), there was some confusion in terms of which mean values would differ from each other. Despitesome contradiction, a final relation amongst means per sites and years was appropriate, and the typeof statistical model was correctly chosen, showing sufficient understanding of the need of an interaction
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term to be assessed. The team applied several exploratory techniques to reveal features relevant to the
problem and the data, including elements such as the magnitude and trend in the difference between
means, size of internal dispersion, absence of outliers, etc. An important part of that exploratory
analysis, is the construction of adequate graphical representations. In this case, the students used R toconstruct a variety of graphical resources (Figure 4), which they used effectively for describing
adequately the elements of the problem and their interactions.

Figure 4. The students team created different graphical representations to present the data of the problem: a) a
scatter plot, b) a box plot, and c) line graphs for the sample interaction.

This team of students carried out several statistical tests, such as ANOVA (see Figure 5) complemented
and verified through a SNK test (Figure 6), which were correctly interpreted. They confirmed the
hypothesis that the aniline concentration is dependent on both time and site. Nevertheless, whilst being
able to recognize the need to assess the interaction term from the beginning, their strategy lacked
consistence when they insisted on maintaining the analysis of the two main terms in the model. Also, inthe final visualization of the model, this team didn’t portray the important conclusive elements regarding
the interaction term drawn by them in their analysis, showing only differences amongst years; however,
the model was correctly executed and included a measure of dispersion (Figure 7).

Figure 5. ANOVA table created by the team ofstudents using R code

Figure 6. The SNK test carried out by the students
team and their interpretation.
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Figure 7. The team’s choice for the final visualisation showing the differences in concentration over time withmeasures of dispersion.

So, in general, this team of students carried out a fairly successful exploration where they showed anunderstanding of the problem, were able to apply statistical concepts and generated adequate graphicalrepresentations and conclusions. This shows a good appropriation and learning of the concepts. They
also used R programming for their statistical explorations, which shows, as well, that they appropriatedthat tool for their explorations. This example, although of the highest scored students, illustrates thepotential of this type of approach for assessing students learning.
We did not present here examples of the questions posed by individual students; but it is anotherimportant dimension of that assessment approach, since it reveals individual students thinking versus
that of the team.

Concluding remarks
In this paper we showed how we use exploratory problem-solving projects in realistic contexts, as wellas using a collaborative approach, in order for students to reveal their appropriation of the statisticalconcepts and ideas as they apply them in exploring, solving and presenting their interpretations,
statistical model and conclusions. We consider this assessment approach more in accordance withconstructionist principles, since students explore and construct freely, in a way that is also more realistic:
where they actually have to think and work, more as they will have to, in their future careers. These
projects thus, not only serve as a means for us to assess their learning, but are, in themselves, learning
experiences that help reinforce what has already been learned.

Acknowledgement
The work presented in this paper has been financed in part by the PAPIME PE204614 and PE207416
grants from DGAPA-UNAM.

References
Batanero, C. (2001). Main research problems in the training of researchers. In C. Batanero (Ed.),
Training Researchers in the Use of Statistics (pp. 385-396). Granada, Spain: International Associationfor Statistical Education and International Statistical Institute.
Bishop, G. & Talbot, M. (2001). Statistical thinking for novice researchers in the biological sciences. In
C. Batanero (Ed.), Training Researchers in the Use of Statistics (pp. 215-226). Granada, Spain: Intl.
Association for Statistical Education and International Statistical Institute.
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Lesh, R., Hoover, M., Hole, B., Kelly, A., & Post, T. (2000). Principles for developing thought-revealing
activities for students and teachers. In A. Kelly & R. Lesh (Eds.), Handbook of research design inmathematics and science education (pp. 591-645). Mahwah, NJ: L Erlbaum.
Mascaró, M., & Sacristán, A. I. (2016). Exploring randomness and variability in statistics through R
based programming tasks. In A. Sipitakiat & N. Tutiyaphuengprasert (Eds.), Constructionism in Action:
Conference Proceedings Constructionism 2016 (pp. 101–108). Bangkok, Thailand: Suksapattana
Foundation. http://e-school.kmutt.ac.th/constructionism2016/?p=772
Mascaró, M., Sacristán, A. I., & Rufino, M. M. (2016). For the love of statistics: appreciating and learning
to apply experimental analysis and statistics through computer programming activities. Teaching
Mathematics and Its Applications, 35(2), 74–87. doi: 10.1093/teamat/hrw006
Noss, R. and Hoyles, C. (1996). Windows on Mathematical Meanings. Dordrecht: Kluwer.
Papert, S. (1980) Mindstorms: Children, Computers and Powerful Ideas. New York: Basic Books
Papert, S. & Harel, I. (1991). Situating Constructionism. In I. Harel & S. Papert (Eds.), Constructionism.
Norwood.,
NJ:
Ablex
Publishing
Corporation.
Retrievedfrom
http://www.papert.org/articles/SituatingConstructionism.html
Wild, C. & Pfannkuch, M. (1999). Statistical thinking in empirical enquiry. International Statistical Review
67 (3), 223-265.

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How Students Struggled with Preparation of
Activities for a Leisure Time Robotic Workshop
Karolína Mayerová, mayerova@fmph.uniba.sk
Comenius University, FMFI, Bratislava, Slovakia

Michaela Veselovská, veselovska@fmph.uniba.sk
Comenius University, FMFI, Bratislava, Slovakia

Abstract
In this article we describe university compulsory elective course, which we taught in the winter semesterof 2017. We briefly explain specific conditions, which encourage transformation of the organization of
the course. The aim of this article is to provide an overview of the process and results of a case study
on integrating authentic learning into mentioned course. Not only from point of view of usage robotic
kits during course lessons but also from the point of view of creating specific activities for leisure timeworkshop. This workshop was taught by our student (let's call her Jane), who also conducted
observations there. In our research we used qualitative methods of data collection and data analysis
including observations, recorded video, students' work process and outcomes (created activities). We
introduce several activities with remarks from conducted observations. These activities were createdwith constructionist approach by our students during the course. We present several findings from Jane,from the teacher' point of view and also from pupils' point of view. She identified several requirements
for content of activities for robotic workshop, which should include solutions of all the tasks in theactivities and methodological guidelines for teaching.
Based on Jane observations, we found out that number of pupils in the workshop was growing especiallybecause of attractive nature of created activities. We hope that such increased internal motivation could
help pupils to acquire a positive relationship to the compulsory school subject Informatics and to direct
them in career growth.

Keywords
university elective course within teacher’s training; educational robotics; after school activities forprimary and lower secondary school pupils; authentic learning

Introduction
Teacher education in the field of robotics can have a positive impact on teaching practice, such as, i.e.
teaching focused on students (Bers, 2007). However, there are only few studies that include the trainingof future teachers with educational robotics (Kim et al., 2015). Nonetheless in these studies there is aserious lack of information and systematic evaluation about teacher education using robotics in teaching(Kim et al., 2015). Spolaôr and Benitti (2017) conducted a systematic review of studies about
educational robotics that is being used in higher education institutions (tertiary institutions) and foundout that studies do not focus on practical experience embedded in learning theories such as
constructivism (Strommen, Lincoln, 1992) and project-based learning (Blumenfeld et al., 1991).
For the several past years, we have been teaching course at the Comenius University in Bratislava thatis engaged in educational robotics. This course is taught both in winter and summer semester and itaims to provide students experience with educational robotic kits (Kabátová, 2010). We try to lead thelessons in a way that allow students to get knowledge that cannot be obtained only by lecturing. They
have the opportunity to get acquainted with the advantages and disadvantages of robotic kits in thecontext of teaching. This year, we had opportunity to apply the elements of authentic learning (Pasch,1991) in the natural way. During the winter semester of the academic year 2017/2018, in the course of
Robotic kits in Education 2 (hereinafter RKiE2) we had a student (future teacher, we will call her Jane)whom started to run a robotic workshop at a leisure centre. This workshop had different Robotics kits
than we have at our faculty. This was one of the reasons, why we decided to prepare opportunity for
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students to try to work on an authentic problem, which was the creation of assignments with different
robotic kits for real and diverse pupils at robotic workshop. So, we created activities that would be as
useful as possible for our students and prepared them for situations in which beginning teachers canfound them self.

Constructionism in robotics
In the creation of the syllabuses of our course, we were inspired by Papert et al. (1991), he states that
simplest definition of constructivism evokes the idea of learning-by-making therefore, the whole content
of the course is formed in a way that students acquire knowledge through active work. Therefore,
students at our course are not only familiarizing themselves with robotic kits but they are actively
exploring them and creating activities with them. This semester students also received feedback fromtheir classmate, whom taught activities created during the course lessons. Students then adapted newactivities, based on received feedback and new requirements. Papert et al. (1991) further states that:"Constructionism shares constructivism's view of learning as "building knowledge structures" throughprogressive internalization of actions. It then adds the idea that this happens especially felicitously in acontext where the learner is consciously engaged in construction a public entity, whether it's a sandcastle on the beach or a theory of the universe". In our case, it was about creating learning content fora leisure time robotic workshop. The main principles of constructivism include rich user-oriented
interaction, the use of authentic problem situations, learning collaboration and learning experience inthe process of constructing knowledge (Papert et al., 1991). Based on these principles, we have alsochanged the role of the student which verified the activity. She has become a helper for pupils in theprocess of actively construction their own concepts, structures of knowledge and skills. Thus, we have
implemented constructionism as a learning theory and educational strategy, which also takes into
account the importance of the enthusiasm for learning, as we confirmed by the results of our research.
This theory states that the pupils will be more actively involved in the learning process by working on
what is of individual significance (Kafai et al., 1996). In our case, it was mainly the student whom tested
activities that was created during that week. Similarly, Papert (1993) argues that the construction ofknowledge is more effective when pupils are involved in designing meaningful projects and creating
artefacts.
Papert et al. (1991) further states that digital technologies are perfect means of actively working on
something real, when we construct and learn in a natural way (Kabátová, 2010). Work on something
real takes place in authentic learning.

Authentic learning in robotics
In authentic learning, learning is organized with emphasis on the meaningful use of the learning material,
which we have set as our primary goal in teaching our course. This way of learning reflects the process
through which most of the knowledge and skills have been acquired through human history. It allows
pupils to build on learning context and to use acquired knowledge in a meaningful way (Pasch, 1991).
The basic characteristics of an authentic problem based on the Three-Step Model of Extension (Pasch,1991) are:

The real problem should be based on the interests of pupil or group of pupils, because it has
personal value and is interesting for them. We have already seen this at the beginning of thesemester, when we agreed with the students on the course details.
The real problem has no predetermined correct answer. However, while solving real problems,
pupils should make the most by using authentic methods – they should approach the problem aswell as experts. Each aspect of the project provides an opportunity to lead pupils to high-level work.
While solving the real problem, the pupils will in the end present gathered information to the realaudience. This audience depends to a great extent on the pupils age and the complexity of the
problem and should have and strong in interest in the problem. In our case, the audience wascomposed of real pupils from leisure time workshop.

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Authentic teaching activities can be the main goal, during preparation of the course lessons. The
ultimate goal in any course should be that pupils make meaningful use of acquired knowledge (Pasch,1991).

University robotic course within teacher training
RKiE2 is an elective course of master's degree program in Teacher's training during the wintersemester. It is intended for all combinations of student of teacher's training that we have at our faculty,
i.e.: mathematics, physics, chemistry, biology, physical education, geography. It continues the course
from bachelor degree program entitled Robotic kits in Education 1. The content of this course wasdeveloped by our colleague a few years ago (Kabátová, 2010). We still use some of her findings, but
we adjust content of the course according to the number of students and their needs. The aim of ourcourse was to prepare opportunity for students to try to work on an authentic problem, which was thecreation of activities with different robotic kits for real and diverse pupils at robotic workshop. Our
students were exploring the elements that would be included in the curriculum for leisure robotic
workshop. Experience with creating this kind of curriculum should be beneficial, because in Slovakia
educational robotics is most often appearing in leisure workshops.
Our school own robotic kits LEGO WeDo 1.0, WeDo 2.0 and LEGO NXT. From her own initiative, Janebrought robotic kits used at the workshop from leisure centre. Course RKiE2 at our faculty lasted 13
weeks (one lesson per week = 90 minutes) and we were dealing with following topics:o Simulation of an inexperienced teacher: The students simulated a teacher who does not haveany prior experience with robotics and is about to begin teaching it. They should search forsuitable materials, tutorials and blogs on the internet to inspire them or direct them how to teachwith robotic kit LEGO NXT / Ev3.
o

Getting to know the LEGO NXT and Sphero: Students have tried to work with the LEGO NXT
and the Sphero robot. At the end of the lesson they evaluated the advantages, difficulties andpredict how pupils could react to these robotic kits.

oGetting to know BeeBot, Albi Robot and Code-a-Pillar: At the beginning of the lesson
students get acquitted with three simple robotic kits. Subsequently, they should design a seriesof activities with these kits to be used at robotic workshop in leisure centre.

oGetting to know LEGO WeDo 1.0 and WeDo 2.0: The students experienced work with both
robotic kits by getting to know the programming environment and building a few simple models
according to the instructions.

oGetting to know Ozobot: The students were getting acquainted with the possibilities ofprogramming the Ozobot robot and creating simple activities for this robot.

oCreation of activities for robotic kits LEGO WeDo 1.0 and WeDo 2.0: The students choose
one tutorial for each robotic kit and created a story that incorporated both. For the first time, they
were given instructions that the created activities for workshop should include story, be focusedon design and program, develop interdisciplinarity and wrote down the learning objectives of theactivity.

o

Web searching and testing specific activity for the robotic kit LEGO Mindstorms NXT:
Modification of activity for workshop needs.

oCharacterizing and defining recommendations and experiences from whole process ofcreation of activities

Constructionist design of the course
During the design of the course we applied eight Papert's big ideas (Papert, 1999):


Learning by doing - we have implemented by the exploration of different robots, robotic kits andpreparation of assignments, that were used for real pupils at robotic workshop in leisure centre.
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Constructionism 2018, Vilnius, LithuaniaUsing technology as a building material - students worked with robots but also createdassignments for pupils using a text editor. At the beginning of the course assignments contained
mostly text, but later students also included e.g. images, various web links and samples ofprograms for robot control

Hard fun - robots are basically toys but solving complex tasks with them could be difficult (XXX,2010). Likewise, creating interesting and appropriate assignments that follow educational goals
for robotic workshop at leisure centre is demanding.



Learning to learn - students learned what assignments or types of tasks with different robots
are appropriate for specific groups of pupils.



Taking time – proper time for the job – syllabus of the course was flexible, so students hadenough space to explore robots and to create assignments. If needed, some of the assignments
were finished at home or took two lessons.

The opportunity to make mistakes and learn from them - students had enough space tocreate their own solutions where they committed many mistakes. We did not point them out
immediately but we tried to engage in dialogue with students to find out where the problem was
and with the help of questions direct them to the solution.

Teachers also learn - teachers cannot be fully prepared for any problems that may arise, butthey can take them as opportunity for further learning and so they learn with students. In ourcase, we did not know most of the new robotic kits.

The use of digital technologies for further learning - robot exploration and creating
assignments for pupils created opportunity for students to discover important information
technology concepts.

Methodology
The aim of this article is to provide an overview and results of a case study on integrating authentic
learning into university course about educational robotics. Not only from point of view of usage robotic
kits during course lessons but also from the point of view of creating specific activities for leisure timeworkshop, that take place each week. In the lessons we worked with robots and robotic kits BeeBot,
Albi Robot, Code-a-Pillar, Ozobot Evo, Sphero, LEGO WeDo 1.0, LEGO WeDo 2.0 a LEGO Mindstorms
NXT. The students' work process was reported in the moodle system, where they at the end of eachlesson submitted the assignments – the design of activities they created. This was the data that weanalysed by qualitative methods (Creswell, 2002). In addition, students shared all activities throughgoogle documents. We also recorded some audio from course lessons and final lesson was recordedon the video. The student who taught these activities at the workshop informed us about results andfeedback at the next lesson. Further she provided her field notes with the photographs for each
workshop lesson.

Participants
Our course was attended by five college students, one male and four females. Three students wereenrolled in the first year of master's degree program in Teacher's Training and other two students did
not attend any didactically oriented subjects or pedagogical-psychological subjects. They were students
of master's degree program in Applied Informatics and another in Mathematics of Economy, Financeand Modelling. The activities created by the students of the course were verified in the workshop at
leisure time centre which was taught by one of our students Jane. The number of pupils in the workshop
at leisure time centre has changed over the weeks. There were two groups of pupils. The pupils wereaged between 8 and 14 (mostly boys) and in one group there were from 2 to 10 pupils.

Activities created by students
In this chapter, we provide preview of the activities that students have created during the course. In the
figures below, we can see how length and form of the proposed activities have changed during the626Constructionism 2018, Vilnius, Lithuania

semester. After getting acquainted with robotic kits, students tried to create activities to be implemented
by two groups of pupils in a row and to be interesting. They tried to take into account the time constraints
and area in which the robotic workshop was running. They created a series of four activities that took
place during five lessons within workshop. The aim of activities created by students was to develop
pupil’s interest in the workshop theme and to explore pupil’s potential. The educational goals of activities
were defined by our students. During our course, students discovered why well defined educational
goals for teachers are needed. They did not define goals based on National educational curriculum,
because it did not exist for leisure workshops. Students focused mostly on increasing of motivation of
pupils. We did not edit the activities to see change in form, amount of text used, grammatical orformatting errors. To each activity we added a comment from the student who tested it in the workshop
at leisure centre.

BeeBot, Albi Robot a Code-a-Pillar

Figure 1. Activities with BeeBot, Albi Robot and Code-a-Pillar

Comment to activity Bee-Bot (left part of Figure 1): "In this assignment, we had to improvise on thelesson because of the lack of props. Some pupils had experience with this robot from the nursery. In
general, Bee-Bot was least attractive for pupils."
Comment to activity Albi robot (middle part of Figure 1): "Pupils were most familiar with this robot
because many have it at home or their friends have it. Pupils also highlighted an error in the assignmentand stated that assignment could be shorter."Comment to activity Code-a-Pillar (right part of Figure 1): "Activity was longer than expected.
Assignment could be shorter. The pupils very accurately measured the route and tested it several times,
so it was time consuming."

OzobotFigure 2. Activities with Ozobot

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Comment to activity Ozobot painting (Figure 2): "The pupils were very fond of drawing a trail for
Ozobot. Difficulties in this activity were cause by having only one robot available for all pupils. But thepupils were very creative and drawn a lot of trails for the robot."
Comment to activity Ozobot programming with the app: "The assignment was inaccurate, I had
troubles to see the aim of the students that created it. So, the pupils improvised but they really liked
results of how the robot lighted up."LEGO WeDo 1.0 and LEGO WeDo 2.0

Figure 3. Activities with LEGO WeDo 1.0 and LEGO WeDo 2.0

Comment to activity Lego WeDo (Figure 3): "This was very interesting for kids, only a pity that there
was only one computer. It would be better if we could have had more robotic kits, because there werea lot of pupils in one group."

LEGO Mindstorms NXT

Figure 4. Activities with LEGO Mindstorms NXT

Comment to activity LEGO Mindstorms NXT (Figure 4): "The construction was not difficult, but
programming was unrealistic for pupils this young. But they enjoyed moving of robot."Experiences from authentic learning
Jane was most engaged in authentic learning, so she summed up conclusions from the course. She
comments content, formal implementation, positive and negative aspects of the whole course. Most
positive comments are directly related to authentic learning e. i.: "I did not have to invent my own
activities.", "I really enjoyed the joy of the pupils. They relished the workshop mainly due to the activities
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that we have created." In a descriptive manner, she stated that methodical materials need to beprepared alongside created activities. Emphasis should be placed not only on correct content by also
on motivation aspect: "Attention should by focus on process of activity, not only on activity goals." Thedifferences between students of Teacher' training and other students were summarized as follows: "Thegroup made up of the future teachers cared too much about the pupils and their age, so they created
too simple activities – pupil could manage even more challenging tasks. Other students invented
awesome activities but lacked greater focus on pupils' abilities."
In addition, she stated, that from pupils’ point of view, they were the most fascinated by the results ofwhat they were doing – the movement of the robot and its interaction. The pupils were incredibly happy
when Ozobot began to shine as traffic light, when the Bee-Bot managed to get through the challenging
route, when Code-a-Pillar simultaneously sang and move to the marked spot. They were also very
happy when they could choose what to do with the LEGO building blocks and already knew how tocontrol robot, so on the basis of their new knowledge, they could create something completely alone.
From the teacher's point of view, it was demanding that the assignments were created mainly for thepupils. There were no instructions for the teacher, no expected results, no pre-programmed codes thatwere expected to be done by pupils. Furthermore, there were quite a lot of pupils on each lesson, so
they worked in pairs but each pair had their own assignment. This crated a lot of work, because teacher
needed to help each group on different tasks.

Conclusion and Discussion
In this article we presented activities, which our students created for robotic workshop at leisure time
centre. The content and the form of activities were developed based on observations of our student,
who taught these activities. Although we have been teaching this course for several years, we haveexperienced for the first time that students actively used their acquired knowledge directly in practice in real robotic workshop with diverse pupils. Over the past few years, only few students used theacquired knowledge from this course in practice as participation in the robotic competition Istrobot.
Based on the data analysis, we found out that during one semester students are able to get deeper
knowledge with a certain number of robotic kits - we used eight robotic kits and it was too many for ourstudents. Based on the observations from the workshop, we found out that number of pupils in theworkshop was growing especially because of attractive nature of created activities. We hope that suchincreased internal motivation could help pupils to acquire a positive relationship to the compulsory
school subject Informatics and to direct them in their career growth. Based on the experience from thissemester, we managed to buy other types of robotic kits within our department. In the current summer
semester of the academic year 2017/2018, our students have already been able to use some of these
new robotic kits with pupils during another leisure time robotic workshop.

References
Bers, M. U. (2007) Blocks to robots: Learning with technology in the early childhood classroom.
Blumenfeld, P. C. et al. (1991) Motivating project-based learning: Sustaining the doing, supporting thelearning. Educational psychologist, Vol. 26, No 3-4, p. 369−398.
Creswell, J. W. (2002) Educational research: Planning, conducting, and evaluating quantitative. Upper
Saddle River, New Jersey.
Kabátová, M. (2010) Constructionist approach at teaching pre-service teachers educational robotics.
Ph.D. thesis, Comenius University, Bratislava.
Kabátová, M. and Pekárová, J. (2010) Learning how to teach robotics. In Proceedings: Constructionism
2010 conference.
Kafai, Y. and Resnick, M. (1996) Constructionism in practice. Mahwah. Lawrence Erlbaum Asociates,
New Jersey. ISBN 0-8058-1985-1.

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Kim, Ch. M., et al. (2015) Robotics to promote elementary education pre-service teachers' STEM
engagement, learning, and teaching. Computers & Education 91, p. 14−31.
Papert, S. (1999) The Eight Big Ideas of the Constructionist Learning Laboratory. Unpublished internal
document. South Portland: Maine (1999) Cited in STAGER, G. Papertian Constructionism and the
Design of Productive Contexts for Learning. In Proceedings: EuroLogo X 2005.
http://www.stager.org/articles/eurologo2005.pdf [Accessed: 2018-05-02]
Papert, S. (1993) Mindstorms: Children, computers, and powerful ideas (2nd ed.). Basic Books, NewYork.
Papert, S. E. and Harel, I. E. (1991) Constructionism. Ablex Publishing.
Pasch, M. (1991) Teaching as Decision Making: Instructional Practices for the Successful Teacher.
Addison Wesley Publishing Company.
Spolaôr, N. and Benitti, F. B. V. (2017) Robotics applications grounded in learning theories on tertiary
education: A systematic review. Computers & Education, Vol. 112, p. 97−107.
Strommen, E. F. and Lincoln, B. (1992) Constructivism, technology, and the future of classroom
learning. Education and Urban Society, Vol. 24, p. 466−476.

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Constructionism as an Epistemological Option in
Courses of Youth Center for Science and Culture –Bahia – Brazil
Elmara Pereira de Souza, elmarasouza@gmail.com
Youth Center for Science and Culture – Vitória da Conquista – Bahia, Brazil

Luísa Souza Moura, luisasouzamoura@gmail.com
University of São Paulo (USP) – São Paulo, Brazil

Abstract
The goal of this paper is to report on the practice of the Youth Center for Science and Culture, Vitória
da Conquista, Bahia, Brazil, a public school for secondary school students. The Youth Center uses a
constructionist perspective as an epistemological basis in their courses. The students are the main
characters in the learning process and they use the coding languages Logo and Scratch in their projects’development.

Figure 1. Pedagogical structure of the courses of Youth Center for Science and Culture – Vitória da Conquista– Bahia - Brazil

This experience shows that it is possible to create spaces of authorship so that the public school’s
students don’t turn out to be just users of the technologies, but authors of socially relevant content, thatcan change their lives and that can provide future perspectives.

Keywords
constructionism; authorship; protagonism of young people

Introduction
We undergo, in Brazil, an educational crisis that has worsened with public political impositions andmethodological proposals that are unlinked to the students’ realities; pedagogies that reinforce thestatus quo and that, most of the time, are insufficient with regard to the diversity, to the multiplicity, tothe singularity and don’t favor the autonomy, the authorship of our students. In the Brazilian educational
system, the problems are innumerable.
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These problems can be seen by analyzing the indexes of the students’ evaluations, the indexes related
to the teachers’ training; also our own teaching practice in the basic education and, specially, in thesecondary school.
The Program for International Student Assessment (PISA, 2015), for example, was applied in 70
countries, and Brazil got the 63rd position in sciences, 59th in reading and 66th in math. Analyzing the
Basic Education Development Index (BEDI, 2015), it is verified that, in a scale from 0 to 10, Brazil got
a 3.7 average.
When we analyze the data related to the teachers’ training, we verify that, from the state public
secondary school teachers, 56.2% don’t have postgraduate studies and 74.9% don’t participate incontinuous training (Universidade Federal do Rio Grande do Sul, 2018).
We understand that there are a lot of variables to analyze Brazilian education, only the indexes are notenough to present the real situation of our education, but they can be used to cause a search forinnovational educational proposals, that are close to the interest and to the reality of the students andthat can deal with the youths’ desire for knowledge. The effective usage of information andcommunication technologies (ICT), of the cyberspace’s resources in the pedagogical process and of
the insertion of creative learning in the classrooms can be made possible, since the young have a close
relation with technology.
The usage of the ICT, of the mobility, of the ubiquitous resources in education can favor the agency ofthe student’s desire to build new knowledge. With pedagogical practices based on constructionism(Papert, 1994 and 1986), on dialogism (Bakhtin, 2000), on affection-joy (Deleuze, 2008; Spinoza, 2009)subjective singularities can be produced in education favoring the training of our young people.
We present, in this paper, the experience of the Youth Center for Science and Culture, a public schoolof the State of Bahia - Brazil, specially, the experience of the “ProgramAÍ” and “Criando Games”
courses, developed for secondary school students, in addition to the Project Incubator, having
constructionism as an epistemological option.

Youth Centers for Science and Culture
The Youth Centers for Science and Culture are public schools created by the Education Department of
the State of Bahia and instituted through the decree nº 12.829, of May 4th, 2011. They have the goal of
offering complementary education and diversifying the public school curriculum, as well as promoting
the students’ access to contemporary thematics, through studies and interdisciplinary activities. The
main pillars of the pedagogic activities developed in the Youth Centers are: (1) the student is the authorof his own journey; (2) the school's connection; (3) the knowledge is transmedia; (4) learning is fun.
The Youth Centers are interschool spaces and bring as a feature the logic “one-lots”, which means,they are after-school programs offering courses, workshops and activities to students of any state publicschool of the region covered by the Center. This feature multiplies the attendance and, at the sametime, promotes the interaction of students from different schools, ages and degrees, as in every
workshop, there may be students from elementary school to secondary school and from multiple
schools.
The Centers are a pedagogical innovation laboratory, they are active learning spaces, and they enhance
the usage of digital technologies in the creative learning perspective (Resnick, 2017). The proposal isto provoke the students’ curiosity and encourage them to create a new relation with the act of learning,
motivated by the pleasure of discovery. No Youth Centers activity is required. The students choose if,
when and how they’ll participate, making possible the effective protagonism of their training.
There are five Youth Centers for Science and Culture in Bahia in the following cities: Salvador, Senhor
do Bonfim, Itabuna, Barreiras and Vitória da Conquista.
In this paper we present the experience of the Youth Center from Vitória da Conquista, specially theexperience of the “ProgramAÍ” and “Criando Games” courses and the Project Incubator.

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The courses “ProgramAÍ”, “Criando Games” and the Project incubator
The “ProgramAÍ” is a course of introduction to programming logic and it was created to address theneed of implementing courses that are close to the students’ interest. It has the objective of providing
to the secondary school students the initial contact with robotics systems control and coding, and also
stimulate the development of logical thinking, creativity and teamwork for problem solving.
The course “Criando Games” aims to provide the students with the knowledge of Scratch programming
language to the development of games and animations. The activities are created in a practical way sothat the students can be authors of their projects and the gaming and animation development can be a
fun and attractive way of learning. The students are part of the Scratch community, sharing theirproducts and interacting with other youth.
The first experiences happened in 2016 with two classes of 20 students from 1st to 3rd years of secondary
school. The workload of the courses was 30 hours each, with two weekly face-to-face meetings anddistance activities in virtual learning environments (cjccvc.org60).
After one year of performance in the courses “ProgramAÍ” and “Criando Games”, we identified thepotential of many students in the digital technologies fields and we noticed that they had innovativeideas, but they couldn’t keep and/or finish their projects because the courses time was limited. After thatobservation, we created the Project Incubator, a space of authorship so the students can deepen theirprojects or develop research projects and innovative products after their own ideas. There is one weekly
meeting and what determines the time that each student will be in the Incubator is the project he/she isdeveloping. Any Youth Center student can integrate the Incubator at any time of the year; he/she only
needs to develop a project in the digital technologies field, join a project that is being developed or justbe welcomed in the group and learn with the others. The objective is to be an open and productive
space in which the students can exercise creativity and produce knowledges through research projects
and products development.
In all these courses and in the Project Incubator we follow the constructionism as epistemological basis.

Constructionist as epistemological option
The Youth Center is a public school opened to pedagogical innovation, so we chose constructionism as
an epistemological basis in the courses “ProgramAÍ” and “Criando Games”, and also in the Project
incubator.
In his investigations, Papert (1994) looked for different ways to learn, in which kids could be producers
of knowledge, leaving their spot as just users to become active in the knowledge construction process.
As an author, the students should take control of their own development along with the school as alearning place. So, inspired in Jean Piaget’s psychogenic theory, he created a set of ideas called
constructionism.
According to Papert (1986 and 1994), with the use of computers, the student visualizes and verifies
his/her mental constructions related to the concrete as well as the abstract, following an interactive
process that favors knowledge production.
The creation of active learning environments is one of the constructionism principles and, in Center’s
experiences, is one of the most important actions for the students to be able to develop creativity andproduce knowledge. The active environments allow the students to test their ideas, theories or
hypotheses and try the creation and implementation of projects. Papert (1993) realized that with thecomputers, there was the possibility of creating conditions for significant changes in the developing
process of the children and, along with a group of researchers from MIT, developed the Logo
programming language. Logo is a language that is considered simple, allowing the development ofprojects, and at the same time as it has the power of a professional programming language.

60

This is the address of virtual learning environment of Youth Center.

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Recently, the Lifelong Kindergarten Group of the MIT Media Lab, led by Mitchel Resnick, inspired in
Logo, created Scratch, a free programming language and an online community in which you can create
your own interactive stories, games and animations. Scratch has an easier and more friendly graphicalinterface that is free and it’s available on and offline. By using Logo and Scratch, the children andteenagers have the opportunity to be in control of the knowledge production and the development of
their own ideas through the exercise of creative learning.
Creative learning experience is based on four P´s: Projects, Peers, Passion, and Play. Resnick (2017)
describes each P:Projects. People learn best when they are actively working on meaningful projects – generating
new ideas, designing prototypes, refining iteratively.



Peers. Learning flourishes as a social activity, with people sharing ideas, collaborating on
projects, and building on one another’s work.

Passion. When people work on projects they care about, they work longer and harder, persist in
the face of challenges, and learn more in the process.



Play. Learning involves playful experimentation – trying new things, tinkering with materials,
testing boundaries, taking risks, iterating again and again. (Resnick, 2017).

In Youth Center, Logo is used in the course “ProgramAÍ” and Scratch in the course “Criando Games”.
Both languages promote the development of creative thinking.

Some projects developed in Project Incubator
In the Youth Center´s Project Incubator, many projects were produced using Scratch, among them we
can highlight the game “Choices”, the interactive animation “Are your behaviors ecologicallysustainable?” and the game “Aedes Adventure”. All projects have the objective of being educational
actions and social relevant proposals.
In Brazil, 8.4% of the teenagers from 12 to 17 years old are obese and 25.5% are above the ideal
weight. Identifying these indexes and knowing that the teenagers like using digital games, the project“Choices” proposed the creation of a fun and interesting game, that could be an educational channel tomake the youth aware of the importance of a healthy diet, physical activity and the consequence of the
choices (individual and collective) to life quality. (figure 2).

Figure 2. The first screen of the game “Choices”. Player can measure the body mass index - BMI (2017).

In the project “Are your behaviors ecologically sustainable? the students developed an interactive
animation with Scratch that was used as data production methodology (variables were created to keep634

Constructionism 2018, Vilnius, Lithuania

the users’ answers) to know if the students from a public school had sustainable attitudes related towater, energy, consumption, waste disposal and transportation usage.
This project was also an educational action, as the subjects of the research, by having fun with theanimation and answering the proposed questions, gave feedback about each theme related tosustainability and could reflect about their own attitudes (figure 3).

Figure 3. A girl using the animation “Are your behaviors ecologically sustainable?” developed in Scratch (2017)

The game “Aedes Adventure” was created so people could think about the importance of combating the
bug Aedes Aegypti’s breeder. In this game, we used augmented reality and, with a computer’s camera,
the players could use their bodies to “kick” the breeders and the bugs.
Besides providing the students’ authorship, we had exciting results with this project that have beenchanging these boys’ and girls’ lives, as well as their families’. (1) The game “Choices” has won 3 rdplace in the Edital Tecnologies for Education from FAPESB and the students got a R$ 5,000,00 prize;(2) we wrote an article about this experience in the development of the game “Choices” that waspresented in the Meeting of the Brazilian Society for the Progress of Science (boys who had never left
their hometown had the opportunity of being in one of the greatest events on the science field from Latin
America, of presenting a paper among ungraduate and postgraduation students); (3) we received
honored mention in this event, for the merit of the work; (4) we received a motion of Applause at the
Vitória de Conquista City Council because of the developed work; (5) the project of the interactive
animation “Are your behaviors ecologically sustainable?” was finalist of the Brazilian Science andEngineering Fair 2018; (6) the students presented their Scratch developed projects in the Campus Party
2017 and in the Meeting of Students that took place in Salvador - Bahia.
In the Project Incubator, there are no proofs or traditional learning measurements. The evaluation iseffectively developed in the process of knowledge construction. We evaluate every stage of the project,
the involvement of each student; we reflect about all the steps taken and we identify which path to follow,
which subjects to deepen and to study. The partnerships with teachers and Universities are fundamental
to the Incubator’s projects development.
In the Incubator, everything is built in partnerships, after the students’ ideas, who are protagonists inthis project. Each one learns and contributes according to his rhythm and knowledge. In the end, we alllearn about contents and about coding, about how to relate with the other, about group work, aboutovercoming our own limits and going beyond the imagined, about trusting our capacity. The Project
Incubator brings secondary school students in contact with University education, providing them with
future perspectives.
The projects of the Project Incubator are available in the repository of learning objects cjccvc.org.

Conclusion
Considering that the contemporary subjectivity is anchored in capitalistic devices that try to standardize,
form consumer subjects and social, economic and cultural products users, developing educational635

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proposals that allow the students’ authorship, the youth’s protagonism is relevant, especially in an
unequal country such as Brazil.
Constructionism as epistemological option in the courses and activities in Youth Center for Science andCulture from Vitória da Conquista have shown themselves very productive, since, in the courses“ProgramAÍ” and “Criando Games” as well as in the Project Incubator, the students express their
feelings and ideas through the project development; they, are authors, share their productions, work ingroups and learn with interaction and technological artifacts.
The experience of projects, games and digital animation production shows that it is possible to create
authorship spaces so that public school youth don’t turn out to be reproducers and users of what’s
already made, but producers and authors of socially relevant educational content.

References
Bakhtin, M. (2000). Estética da Criação Verbal. 4. ed. São Paulo: Martins Fontes.
Deleuze, G. Em medio de Spinoza. Buenos Aires: Cactus, 2008.
Papert, S. (1993). The Children’s Machine: Rethinking School in the Age of the Computer. New York:
Basic Books.
Papert, S. (1986) LOGO: Computadores e Educação. São Paulo: Brasiliense.
Resnick, M. (2017). Lifelong Kindergarten: Cultivating Creativity through Projects, Passion, Peers, andPlay. MIT Media Lab. MIT Press.
Spinoza, B. (1996) Ethics. E. Curley (trans.). London: Penguin.
Universidade Federal do Rio Grande do Sul (2018). Carvalho, M. J. S, Neves, B. G. B, Melo, R. S.
Cultiveduca. Brasil no. BR512014001340-5, 18 mai. 2014, 25 jan. 2016. Source:
http://cultiveduca.ufrgs.br/pg.index.html.

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Female Teenagers and Coding: Create Gender
Sensitive and Creative Learning Environments
Bernadette Spieler, bernadette.spieler@ist.tugraz.at
Institute of Software Technology, Graz University of Technology, Austria

Wolfgang Slany, wolfgang.slany@tugraz.at
Institute of Software Technology, Graz University of Technology, Austria

Abstract
The number of women in technical fields is far below the average number of males, especially indeveloped countries. Gender differences in STEM are already present in secondary schools in students
aged between 12 to 15 years. It is during this intermediate female adolescence that girls begin to makecritical career choices, which therefore makes this a key age to reinforce them and reduce the genderdisparities in ICT. Acquiring computational thinking (CT) skills, particularly coding, is important forbuilding a positive economic, developmental, and innovative future. To address the gender bias inschools, one of the goals of the European H2020 project No One Left Behind (NOLB) included
integrating Pocket Code, a free open source app developed by the non-profit project Catrobat, into
different school lessons. Through game design, Pocket Code allows teenage girls to incorporate
diversity and inclusiveness, as well as the ability to reflect their cultural identity, their likes, and theirways of interacting and thinking. To evaluate the impact of the use of the app in these courses, we
captured the results on engaging girls in design and coding activities. For this paper, the authors present
the data of surveys via a qualitative content analysis during the second cycle of the project. The results
let the researchers conclude that the organization and the setting of the coding courses (for example,
guidance and supporting material, freedom of choice) had much more inﬂuence on female students’
engagement than the coding aspects or the app itself. In contrast, male students more frequently
mentioned missing features in the app, and stated that they liked the coding. With a focus on female
teenagers, the results allow us to conclude that a suitable classroom setting is significantly more
important for them than the coding tool itself.

Figure 1. Providing inclusive coding environments for female teenagers

Keywords
pocket code; game design; gender inclusion; coding; mobile learning; STEM; social inclusion;
constructionism; girls; learning environment

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Introduction
Secondary school is the place where students make the critical choices which decide their future
careers, develop a more realistic picture of their future jobs, and assess their career-relevant abilities.
Researchers observe that girls’ interest in IT drops significantly from the age of 12 to 15 (Tsan, Boyer,
and Lynch, 2016). To address this gender bias at an early stage, a goal of the European project No One
Left Behind (NOLB) was to integrate Pocket Code, an app developed at Graz University of Technology,
into different school subjects, thus making coding more accessible and attractive to female students.
Although promoting gender equality is a longstanding policy which all European countries place on theiragendas (EC, 2014), a gender-based inequity in ICT still poses barriers for women. Thus, the acquisitionof digital skills is more important than ever and represents a key professional qualification (Balanskat
and Engelhardt, 2015, Kahn, 2017, Tedre and Denning, 2016). For the NOLB project, the authors
assumed that according to related literature it is possible to spark girls’ interests by getting themengaged in computational thinking through collaborative, creative, and engaging coding activities (Khanand Luxton-Reilly, 2016). Thus, the team studied whether the design of mobile games has an impact
on inclusion and satisfaction of female students. Thus, the following research question has been
defined: How can we organize coding activities to reinforce female teenagers in computer science?
The paper is organized as follows: First, we describe the challenges of teaching CS in Europe anddiscuss several learning theories, with a focus on constructionism. Second, we present solutions on
how to provide suitable learning environments for girls, followed by a brief description of the NOLB
project and our educational apps for coding. Third, we present the results of the quantitative andqualitative surveys which show insights for framing suitable classroom settings for coding activities forgirls. Finally, the last sections conclude the paper and describe our future work.

Learn Coding: A Worldwide Challenge
The European Commission states, “All of Europe's citizens need to be educated in both digital literacy
and informatics” (Informatics Europe/ACM Europe, 2013, EC, 2016). Thus, IT education is seen as aninterdisciplinary field that bridges the gap between the use of digital media and information-processing
technology as well as basic concepts and fundamental ideas of computer science. The EC recommendthat students should benefit from computer literacy at an early stage and that it is important to find astandardized definition of the informatics curricula through all countries. However, in Austria and inmany other European countries, computer science topics are underrepresented in school curricula,hence, teaching time for these topics is limited (Informatics Europe/ACM Europe, 2016). From primarythrough secondary school, only a few opportunities exist for students to explore coding and CS topics.
Learning theories from the past serve as an organized set of principles, clarifying how people acquire,
retain, and recall knowledge (Schunk, 2016). These theories helped researchers to gain a better
understanding of how learning occurs and help us to select appropriate techniques, tools, and strategies
to support learning and teaching how to code. Three basic types of learning theory exist: Behaviorism,
Cognitivism, and Constructivism, and some subtypes or variations, e.g., Instructionism, andConstructionism.
Figure 2 provides an overview of important findings of each theory, how each theory handles students’motivations, and shows details on how teaching and learning should be done.

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Figure 2. Learning theories of the 20th century: Behaviorism/Instructionism (Pavlov, 1927, Skinner, 1976, vonFoerster et al., 2009), Cognitivism (Piaget, 1968, Perry, 1999), Constructivism (Piaget, 1968, Vygotsky, 1978),Constructionism (Papert, 1980, Paper, 1991).

Constructionism
The Constructionist approach (Papert, 1980) is interested in building knowledge through active
engagement and personal experience. Papert noted that individual learning occurred more effectively
when students understood the world around them and were creating something that was meaningful tothem. This experiential and discovery learning by challenges should inspire creativity, and project work
allows for independent thinking and new ways of constructing information. The iterative process of selfdirected learning underlines that humans learn most effectively when they are actively involved in thelearning process and build their own structures of knowledge. In this theory, communication between
students about the work, and the process of learning with peers, teachers, and collaborators, is seen
an indispensable part of a students’ learning (Papert, 1993, Papert, 1991).
“The construction of knowledge through experience and the creation of
personally relevant products. The theory proposes that whatever the product,e.g. a birdhouse, computer program, or robot, the design and implementationof products are meaningful to those creating and that learning becomes active
and self-directed through the construction of artefacts.” [Papert, 1980, p.2]
Thus, Papert described the huge potential of bringing new technology into the classroom (Papert, 1993).
For this reason, he co-invented the LOGO programming language in the late 1960s at the MIT. LOGO
was designed to have a “low threshold and no ceiling” and was indeed used to help novice
programmers, and to support complex explorations and the creation of sophisticated projects (Tinker
and Papert, 1989). LOGO set the basis for later visual programming tools, such as Etoys (Kay et al.,1997) or Scratch (Resnick et al., 2009). Such block based visually oriented tools made programming
accessible for a large number of people and taught new skills such as engineering, design, and coding(Blikstein and Krannich, 2013). They allow students to recognize blocks instead of recalling syntax.
They are broadly integrated in schools, or even at universities all over the world (Meerbaum-Salant etal., 2010).
To conclude, psychologists and pedagogues from today following the constructionist approach state
three main goals. First, they wish to rethink traditional education without step-by-step guidance and tocreate new social and open environments. Second, they strive to allow students to engage in meaningful639

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and relevant problem-solving activities, and third, they want to integrate new tools, media, andtechnologies in school lessons (Neo and Neo, 2009).
Jeannette Wing, 2006 shaped the term “Computational Thinking” (CT):“Computational thinking involves solving problems, designing systems,
and understanding human behavior, by drawing on the concepts
fundamental to computer science” (Wing, 2006)
Wing’s idea that children who are introduced to CS learn more than just programming opened a new
way of thinking, e.g., it showed the beneﬁts of learning to think like a technician (Wing, 2008). Wing’s
findings have been incorporated into the CS curriculum of many countries (Kahn, 2017) and into K-12
movements (Mannila et al., 2014). However, CT is just a small subset of Seymour Papert's ideas in the
80s (see previous section). Papert was the first who used the phrase computational thinking and defined
it in a much broader way. For instance, Wing focuses mainly on computer programs, whereas Papert
stated that there are more kinds of constructionist projects, and computational ideas could serve
learning in a broad variety of subjects, this “can change the way [children] learn everything else” (Papert,
1980, p. 8). In comparison, CT concepts lack creativity and student-directed projects (Kahn, 2017). To
summarize, CT concentrates on the importance of coding and computer science activities, thus
delivering concepts that are more applicable and successful on a number of levels (Tedre and Denning,2016). However, critics argue that coding should not be seen as a unitary skill but instead as a metaskill for a complex network of other skills.

Creative Environments to Reinforce Female Teenagers in Coding
Teaching good game design and development skills is especially important for girls because they are
not that likely to play games (Krieger, Allen and Rawn, 2015). In addition, the literature argues that even
if the number of female students who plays video games increased in recent years, male students havea greater interest in playing games (Jenson, Castell and Fisher, 2007). However, a recent study which
examined American female players’ experiences showed that 65% of women play mobile games,
compared to 2011 when only 31% of women played mobile games (Google and NewZoo, 2017).
Furthermore, 64% of women prefer smartphones to other platforms (38% of the men do so). Thus, touse smartphones to design mobile games seems to be a very promising approach to attract young
females. Furthermore, framing a supportive classroom setting for coding activities is a critical factor,
and literature suggests that interventions should specifically target the classroom climate to strengthen
teenage girls’ confidence to motivate female students extrinsically as well (Beyer et al., 2003). Such first
positive experiences in coding may direct their future career choices towards STEM fields. If they
become game designers and creators of their own learning content through a constructionist, creative,and engaging learning environment, this can significantly contribute to closing the divide andparticipation gap in digital culture (Veilleux et al., 2013, Allison, Cheryan, and Meltzoff, 2016). Thus, theliterature states that for young women, creativity and interest in STEM professions are often related, butthere is a lack of practical relevance in a lot of these subjects. A European-wide study in which 11,500
young women between 11 and 30 were interviewed (Microsoft, 2017) showed that girls between theages of 12 and 16 are the most creative. Approximately every third women that has been asked (33%)
criticized how scientific topics were explained in schools and that those subjects are taught from a more"male perspective". Thus, it is important to make CS more attractive for girls in order to sustain a balancein this very strongly male-dominated IT labor market. It is therefore important to eliminate the prejudicethat STEM professions are not creative. Through game design activities, creative environments, andcustomization, a broader spectrum of girls can be reached (Subsol, 2005). If facilitators promote
especially IT careers that are more driven by creative thinking and design, more female students will
expressed their interest in IT careers (Wong and Kemp, 2017).

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The European No One Left Behind (NOLB) Project
The focus in the subsequent sections lies on the European No One Left Behind61 (NOLB) project andthe Catrobat62 learning apps, Pocket Code and Create@School. The NOLB project has been funded by
the Horizon 2020 framework and involved partners from Germany, Spain, the UK, and Austria. The
vision of the NOLB project was to unlock inclusive game creation and to construct experiences in formal
and informal learning situations from primary to secondary level, particularly for students at risk of social
exclusion. This project started in January 2015 and reached its conclusion in June 2017 by validating
its outcome in different phases: preparation phase, feasibility study, first, and second cycle. To limit thescope of topics to those relevant to this paper, the remainder of this section focuses on the results ofthe second cycle and on the Austrian pilot. In the past the authors’ work concentrated on the feasibility
study (Petri et al., 2016), evaluation of performed Pocket Code Game Jams (Spieler et al., 2016), andanalyzing female teenagers’ performance in regard to the learning goal achievement of submitted
programs (Spieler, 2018).

Educational Coding Apps
Our app Pocket Code (Slany, 2014), is a visual programming language environment that allows thecreation of games, stories, animations, and many types of other apps directly on smartphones or tablets,
thereby teaching fundamental programming skills. Programs in Pocket Code follow a similar syntax tothe one used in Scratch. During NOLB an enhanced version of the app has been developed andadapted for use in schools. This new version is called Create@School and integrated the results of theobservations during the pilot studies as well as feedback from teachers and students. Furthermore, a
web based Project Management Dashboard (PMD) for teachers was developed. The app was releasedin October 2016 for first test runs during the second cycle of the project. Important components of the
new Create@School flavor of Pocket Code were the gathering of analytics data, the integration ofaccessibility preferences for children with special needs, and pre-coded game design templates. In
previous work the authors focused on the evaluation of the Create@School features (Spieler et al., 2017[1]) and the teachers’ perspective during NOLB (Spieler et al., 2017 [2]). This paper focuses on theevaluation of the NOLB coding environments, thus the apps will not be explained in more detail.
In Austria, the NOLB project piloted in three different schools, in total, 478 students participated inAustria (281 female students and 197 male students). Altogether, 22 coding courses were conducted,
and the coding apps were integrated in the curricula of English, computer science, physics, fine arts,and music (Spieler et al., 2017 [2]).

Gender Inclusiveness in NOLB
The idea of NOLB included that game creation challenges in classes should enhance female students’abilities across all academic subjects, including logical reasoning, creativity, and the development ofsocial and computational thinking skills (see previous section). Moreover, students had the opportunity
to socialize with their peers during the game making process by working in teams. To address thegender bias in coding classes, the goal of the Austrian study included how to make the coding
environment more suitable for female teenagers. From the literature, the assumption for NOLB was toattract girls by:

61
62



Increasing their personal attachment to programming by improving our services with appropriate
example games/templates, new assets, and themed tutorials/templates.

Increasing their involvement by encouraging them to become active members of the Pocket
Code community by providing them a safe and interesting environment to join, with featured
games from female users for female users.



Asking them to design their own games rather than just to code programs.

http://no1leftbehind.eu/
https://www.catrobat.org/

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The intent was to discover how to organize the coding units in ways that specifically empower girls byengaging them with playful and creative activities.

Evaluation of NOLB Activities in Austria
In this section, the NOLB results from the Austrian case study are evaluated to investigate female
students’ experiences, behaviors, and outcomes when using our tools and services in different courses.
With the help of a quantitative and qualitative survey, students’ opinions about the user experience (UX)of the Create@School app and the courses in which the app was used has been collected to gain adeeper understanding of the experience evaluation. Overall, a total of 131 students filled out feedback
forms in Austria during the second cycle (63 male and 68 female students). Based on the research
design, a t-test was performed which compares whether two groups (female/male students) havedifferent average values. The evaluation is part of the NOLB Delivery 5.4 (Spieler and Mashkina, 2017).
Results are collected by answering like/dislike questions: How was your experience with
Create@School? (very good – very bad). Figure 3 illustrates students’ opinions per answer inpercentage. The percentage of female students (in orange) who rated the experience as “good” was
29% (38), and the percentage of male students (in cyan) account for 21% (27). The answer option “bad”
was chosen by 13% of the girls (17) and 14% of the boys (19). In general, girls rated the app experience
more positively (mean female = 2.63) as their male colleagues did (mean male = 2.37, but notsignificantly: p = .130, α = .05).

Figure 3. Distribution of answers about the experience with the Create@School app.

To clarify the motivation for these answers, it is necessary to take a closer look at the analysis of the
open-ended questions: What did you like the most? The answers of the participants describe theirpositive impressions about the Create@School experience. Answers could be classified into fivedifferent categories: “working process”, “the app”, “the results (their game)”, “organization”, or “others”.
The distribution of the answers among the categories can be seen in Figure 4. This time a Mann-Whitney
U test was performed to analyse whether the central trends of the two independent samples aredifferent.

Figure 4. Categorization of the positive impressions during NOLB.
Note that one answer could contribute to two categories, for instance the feedback "the finished products + thefacilitators" contributed to both, results and organization categories.
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The category “working process” contains feedback about preferred actions (e.g., I like to program, toplay the game, to design), or properties of the school units (e.g., having a freedom of choice, that you
could be very creative). The largest statistically significant difference between the responses of maleand female participants was observed for this category, namely 26% of the girls (36) and 13% of theboys (18) provided positive working process related feedback (z = 2.402; p = .016, α = .05). The
category “app” contains the answers mentioning the experiences directly connected with the app itself,e.g., “the simplicity of the app”, or “the different effects and backgrounds”. Male students seemed to besignificantly more positively impressed by the app than girls were (z = -1.969; p = .049, α = .05). A
number of 8 girls and 20 boys evaluated the app qualities as positive. Some students were especiallysatisfied with the results of their work or the concept of game creation, and the category “results (owngame)” contains this type of feedback. For instance, “the results and how everything turned out”, or “I
liked the idea of creating a game” demonstrates this feedback. Of the girls, 9% (12) and 11% of theboys (7) were satisfied with the results or their personal game. The answers from the category“organization” highlights feelings about how the unit was structured. This includes if students wereenjoying teamwork or solving the problems on their own, the presence of external people (facilitators of
the workshop), usage of tablets during the school units, etc. It was noticeable that students in theyounger age group of 12 - 14 years old were very excited about using the tablets during the school
units. Typical responses for this category were, e.g., “to work in a team with your friends”, “when you(the facilitators) explained to us how the app really worked”, “that we had our own tablets”. The same
number of the male and female participants’ feedback falls into the category of “organization” with nine
responses from boys and from girls. The replies that could not be clearly classified into any of thedescribed categories above were summarized into the “other” category. For example, answers with noclear message, or containing feedback connected to a particular game or classroom setting, as well assuch responses as “nothing” or “everything”.
Next, we will take a closer look on the categories “working process” and “app”. The category “working
process” was divided into six subcategories: “designing”, ”creativity”, “playing”, “freedom of choice”,“programming”, and “other”. Figure 5 shows the distribution of the responses.

Figure 5. Detailed overview of positive impressions category “working process”

The subcategory with the name “designing” summarizes the feedback related to drawing, taking
pictures, personalization of the characters, etc. For instance, “drawing our pictures” or “creating thecharacters” fall into this subcategory. There were 32% of the girls (17) and only two boys who identified
design related activities as the essence of their positive experience (significant: z = 2.151; p = .032, α =.05). The subcategory “creativity” represents the feedback praising the creative side of the school units,
for instance, “you could be very creative”, “you could do almost any game you wanted”. A total of sevenfemale students and two male students evaluated creativity as the positive aspect of the Create@School
experience. A total of six boys and three girls stated that they considered “playing” as a positive
experience. Answers like “you were allowed to play” were typical for this subcategory. No constraints inchoice or actions were valued by two boys and five girls. These responses were summarized within thesubcategory “freedom of choice”. A representative statement for this subcategory was “That you could
do whatever you want”. There were four boys and two girls who enjoyed the programming process itself.
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Typical responses for the subcategory “programming” were “programming the rocket” or “programmingby ourselves”.
The category “app” was divided into four subcategories: “LEGO®-style bricks”, “features”, and “design”.
The distribution of the feedback can be seen in Figure 6.

Figure 6. Detailed overview of positive impressions of the category “app”.

None of the girls but six boys stated that they were pleased about the LEGO-style bricks in
Create@School. Although the number of answers is too small to be significant, the insights areinteresting. The response “brick system” was representative for this subcategory. A total of nine boys
and four girls liked the “simplicity” of the app. Feedback included “It was relatively self-explaining!”, or“components are easy to understand”. The subcategory “features” consists of answers like “the different
effects and backgrounds” or “variables were available”. Four boys and two girls contributed to thissubcategory. Equally, two of each gender liked the design of the app.
What did you like the least? Any suggestions for app improvement? The answers to the questions were
the basis for the negative feedback evaluation. Note that some students responded with everything wasfine (6) and some did not give any answer to this question (10). Figure 7 categorizes the negative
impressions about the app and the school units.

Figure 7. Categorization of the negative impressions about the app and the school unit.
Note that one answer can contribute to two categories, for instance the feedback “It was too complicated and it
took too long to finish the game” contributed to both the “organization” and “complicated” categories.

The category “app” consists of the feedback related only to the Create@School app, for example, “itlooked kind of tacky, complicated, confusing”, “the axis of the screen”, or “it was buggy”. There were
33% of the boys (41) and 17% of the girls (21) who provided this kind of feedback (significant: z = 3.372; p = .0007, α = .01). Responses such as “it took too long to finish up the game”, “introduction of644

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the app was boring”, or “The instructors were not able to explain everything to us” about the unit weresummarized into the category “organization”. There were 8 boys and 14 girls who gave this kind of
responses. The feedback of the contents of the type “I found some things complicated”, “make it simpler
for people that are not technical”, or “you needed help a lot” were summarized under the category“complicated”. There were 21 girls (17%) and only 2 boys who gave this type of feedback about theirexperience with Create@School. The replies that could not be clearly classified into any of thecategories described above were summarized into “other”, for example, the answers with no clear
message, or complaints about the devices and other apps. A breakdown of the category “app” into
subcategories can be seen in Figure 8.

Figure 8. Detailed overview of negative impressions of the category “app”.

The subcategory “buggy” contains the feedback about the app behavior that students considered asbugs, responses about slow performance, and mentions of app crashes. 32% of the boys (23) and only
8% of the girls (6) gave this type of feedback about the app (significant: z = -2.73; p = .006, α = .01).
The representative answers for this subcategory were “Create@School crashes ALL the time”, “Itsometimes stopped, didn't always work smoothly”, or “it took AGES to load”. Seven boys and one girlstated that the app lacks structural clarity, and is confusing, e.g. “the app totally lacks the structure”, or“some things should be easier to find”. These answers were summarized within the subcategory“confusing”. Nine girls and eleven boys complained about the complexity of the app (subcategory“complicated”). Comments characterizing the subcategory “unattractive design” were, for example “Itlooked kind of tacky” or “the design is rather boring”. The subcategory “other” contains all other
responses that were related to the app, e.g., missing features (“the axes of the screen should be like in
Scratch”).

Conclusion and Discussion
The results of the UX experiences with Create@School showed many significant responses and helped
the authors to shape future workshops. Overall, the answers about likes and dislikes can be clearly
separated by gender. On the one hand, female students significantly preferred the aspects of theCreate@School units (“working process”) and mostly related to design activities, but did not mention
the programming aspect explicitly. Negative impressions from female students concentrated more on
the organization of the units or on the level of complexity, and less on the app itself. On the other hand,male students mentioned app related aspects, like programming and Create@School’s simplicity, as
positive impressions significantly much more often than the units. Their dislikes significantly
concentrated on the app, e.g., that it was buggy. This reflects the complaints of only six girls.
Surprisingly, answers from girls also included that they did not like coding at all, whereas this was not
an answer given by boys. The low performance and high error rate of the app is still a serious issue, but
seems to have been more problematic for the male students. Possible reasons are again that boys are
more used to utility apps (“tools”) and game engines than girls are (Krieger, Allen, and Rawn, 2015).
To conclude, there are statistically significant different aspects which are more important for girls thanfor boys. In the literature review, the authors already described that CS lessons are mostly constructedto suit the interests of males. However, this evaluation shows that the working process and thesequence of the units are particularly important for engaging female students. Thus, for them not onlyis the tool essential but the learning environment as a whole, as well as the ability to express their own
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interests, e.g., through designing and creative activities. Based on these statistically significant results
and our analysis of the existing literature, and after the completion of the NOLB project, we started todesign more suitable learning environments for girls, particularly focussing on aspects of gendersensibility and awareness (McLean and Harlow, 2017). We shortly describe them in the next section
and will publish a more detailed description in a future paper.

Outlook
One result of NOLB, the Create@School app, was a great opportunity to provide schools with a tailored
package of tools and services to help them integrate coding in their classrooms and to apply the app
for interdisciplinary project work. This is important for the future to support teachers in Austria with theupcoming challenge to integrate a basic set of digital literacy education63 in secondary schools. After
NOLB, the authors concentrated their research on how to apply the NOLB results to tailor the Catrobat
services to female needs. Therefore, we have developed a new course model that includes extrinsic
and intrinsic motivators as well as four key fields which have been considered as important for coding
activities: playfulness, engagement, creativity, and coding. This model has already been tested with twoclasses of 23 students (one mixed gender class and one girls-only class). This sample clearly was verysmall, and thus we plan further larger studies in the same and in different contexts (outside school). Asa first step, an intensive “Girls Coding Week” will be performed later in 2018 to test the model’s
efficiency.

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Visualizing Mathematics with the MathBot: aConstructionist Activity to Explore Mathematical
Concepts through Robotics
Christina Todorova, tina@esicenter.bg
European Software Institute – Center Eastern Europe, Sofia, Bulgaria

Carina Girvan, girvanc@cardiff.ac.uk
School of Social Sciences, Cardiff University, Wales, UK

Nikoleta Yiannoutsou, nyiannoutsou@ppp.uoa.gr
Marianthi Grizioti, mgriziot@ppp.uoa.gr
UoA ETL, National and Kapodistrian University of Athens, Athens, Greece

Ivaylo Gueorguiev, ivo@esicenter.bg
Pavel Varbanov, pavel@esicenter.bg
George Sharkov, gesha@esicenter.bg
European Software Institute – Center Eastern Europe, Sofia, Bulgaria

Abstract
In this practice paper, we aim to share our experience with the design and implementation of
constructionist educational robotics activities tailored to primary school students (4th grade, age 9-11
years) implemented in a series of robotics workshops, which took place within a real school setting in
Sofia, Bulgaria.
Through this contribution, we will further present an activity plan, which involves student engagementwith mathematical concepts (angle measuring and properties of the circle) in order to program thebehaviour of a robot. Our paper reports insights on the implementation of the activity plan focusing
students’ evaluation of their experience during the workshop. These insights are drawn from quantitative
data from 131 participants (63 boys and 68 girls), capturing the overall student attitude.
The activity plan behind this set of educational robotics workshops was designed, adapted and piloted
in alignment to the guidelines of the Bulgarian national curriculum for mathematics for the 4th grade.
However, it could function as a practical example, which could be adapted, enriched and modified tobenefit other age groups, nationalities and desired learning outcomes.

Keywords
educational robotics; mathematics; programming; Scratch; primary education

Introduction
The increasing difficulty related to maintaining students’ positive attitude and motivation towards theeducational process is not a new topic neither for the Bulgarian education scene, nor internationally.
Furthermore, against the backdrop of the rapidly changing technological landscape, which demands thecontinuous improvement of computer science curricula in general education schools, providing students
with a functional understanding of how technology works becomes a paramount and a challenge.

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The ICT general education in Bulgaria is already undergoing a renaissance of a sort with subjects suchas computer modelling646566 with Scratch for primary school students being gradually included within theofficial school curriculum. Furthermore, curriculum strategies are being developed in order to include amultidisciplinary approach towards learning, an example being the Ministry of Education’s “Innovative
Schools”676869 initiative granting schools a certain level of curriculum flexibility and allowing them toinclude innovative learning approaches and multidisciplinary subjects.
At the same time, there is a worldwide turn to robotics and STEM education alike, with a big number ofrobotics kits launched during the last few years. According to the International Federation of Robotics
in October 2016, from 2014 to 2015, unit sales of entertainment robots, including educational robotics
kits jumped by 29 percent to around 1.7 million units. The entire segment is projected to increase to a
total of 11 million units (2016-2019). The sales value adds up to around US$ 9 billion in the same
period.70 Robotics activities for STEM have been considered as interdisciplinary with a strong emphasison collaborative learning, and robots have been perceived as a potentially powerful “vehicle” providing
opportunities for construction, experimentation and collaboration between learners (Alimissis 2013).
However, most of the robotics kits and activities tend to focus mainly on Programming and Engineering
leaving aside the Mathematics and Science elements of STEM education. Additionally, in manyeducational robotics activities the element of construction and active exploration is limited as they arebased on instructional, “step-by-step” type of exercises and closed quizzes. Nevertheless, the field of
robotics bares an enormous educational potential as children’s fascination for robots, along with thevariety of fields and topics covered by robotics (Johnson, 2003), make robots a powerful idea to engage
with, including gears and computers (Papert, 1980). At the same time, robotics is an excellent tool forteaching science and technology (Mataric, 2004; Mead 2012), therefore many educational robotics
activities already focus on STEM (Baretto, 2012), which unfortunately attracts predominantly children
who have not lost their interest in these subjects.
We do believe, however, that robots are a powerful tool for teaching STEM disciplines not only to thechildren who are already interested in those subjects. Thus, inspired by the spirit of the undergoingeducational reform but also by the current state of educational robotics, involving mainly students withestablished interest in STEM, under the ER4STEM project, we undertook an exploration on how robotics
activity plans could be designed to support teaching concepts, stipulated within an official mathematics
curriculum. With the term activity plans, we refer to structured descriptions of robotics activities, which
are built on the ER4STEM Activity Plan Template. This template provides a generic design tool that
identifies critical elements of teaching and learning with robotics based in theory and practice and is
expected to contribute to the description of effective learning and teaching with robotics (Yiannoutsou
et al., 2017).
In this practice paper, we begin by introducing an activity plan, applied to a set of educational robotics
workshops carried out between November and December 2016, within one public general education
school in Sofia, Bulgaria. Each of the six 4th grade classes went through one educational workshop
which comprised two 4-hour sessions. By involving experimentation with virtual and physicalconstructions within the activity, serving as tools of learning, we attempted to design an activity plan,
which helps expand student’s access to powerful ideas, in the sense of being immediately useful to thelearner, by association to other productive ideas (Papert, 1980, 2000). We believe that this activity plan
64

Ordinance No 5 of 30.11.2015 on general education in force from 08.12.2015 issued by the Minister of Education and Science, obtained
online in Bulgarian on March 10, 2018 at http://zareformata.mon.bg/documents/nrdb5_30.11.2015_obshtoobr_podgotovka.pdf
65
Educational curriculum for computer modelling for the third grade issued by the Minister of Education and Science, obtained online in
Bulgarian on March 10, 2018 at https://www.mon.bg/upload/12205/UP_KM_3kl.pdf
66
Educational curriculum for computer modelling for the fourth grade issued by the Minister of Education and Science, obtained online in
Bulgarian on March 10, 2018 at https://www.mon.bg/upload/13660/pr_UP_KM_4kl.pdf
67
Again in Ordinance No 5 of 30.11.2015 on general education in force from 08.12.2015 issued by the Minister of Education and Science,
obtained online in Bulgarian on March 10, 2018 at http://zareformata.mon.bg/documents/nrdb5_30.11.2015_obshtoobr_podgotovka.pdf
68
Guidelines for application for inclusion in the network of innovative schools in Bulgaria for the academic year 2018/2019, obtained online in
Bulgarian on March 10, 2018 at https://www.mon.bg/upload/12192/Nasoki_2018_2019_iSchools.pdf
69
COUNCIL DECISION No 391 from 17 July 2017 on adopting a list of innovative schools for the academic year 2017/2018(promulgated, SG No. 60/25 July 2017), obtained online in Bulgarian on March 10, 2018 athttps://www.mon.bg/upload/12193/rms_391_17072017.pdf
70International Federation of Robotics, Press Release, Seoul, Oct 12, 2016, obtained online in English on March 10, 2018 athttps://ifr.org/ifr-press-releases/news/service-robotics

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could be adapted to fit a variety of educational and cultural contexts and might be of use to educators
looking for strategies to improve their student’s involvement in subjects such as mathematics.
In addition to the pedagogic goals of this set of workshops, qualitative and quantitative data werecollected through pre- and post-activity questionnaires completed by students (63 boys and 68 girls),
and 6 groups of learners participated in semi-structured interviews to explore students’ interactions andfeedback. For the purposes of this paper, we will focus on data collected through questionnaires at theend of the workshops. The specific question we aimed to answer was what are Bulgarian students’attitudes towards constructionist robotics activities.
Finally, in this contribution we further share our experience with integrating collaborative work, aimed atdeveloping an activity that could support the cultivation of some important 21st century skills within
students (Dede, 2010), namely problem solving, collaboration, flexibility and adaptability.

Implementation Context
Overview
This activity plan was developed for boys and girls (age 9-11 years) studying at the 4th grade of one
public general education school in Sofia, Bulgaria in 2016. The students have previously participated in
educational robotics activities carried out by the implementation team and are expected to have previous
experience programming with Scratch (however, previous programming knowledge was not required).
They were also familiar with the collaboration requirements of the activity as well as the research
protocol. This activity was developed following the guidelines of the Bulgarian national curriculum formathematics for the 4th grade, thus knowledge of third grade mathematics was a prerequisite for theparticipation.
The educational robotics activities took place within regular school time. For each 4th grade class aseparate workshop was organized, thus resulting in six workshops in total. Every workshop was of aneight-hour duration, with breaks, and was divided into two sessions of equal duration, with not morethan one-week time between sessions. A regular school class in a public general education school in
Bulgaria consists of anywhere within 24 and 28 students. Each session was led by 2-3 tutors.
Following our strong belief that the collaborative work plays a very important role for the effective
engagement of students in the educational process, we apply the engagement theory (Kearsley &Shneiderman, 1998) as a significant part of the Constructionist teaching methods. Based also on theirempirical conclusions that “technology can facilitate engagement in ways, difficult to achieve otherwise”(Kearsley & Shneiderman, 1998), we also asked students to assess their feelings on problem-basedlearning, working with robots and collaborative work.

Bulgaria’s Mathematics Curriculum for the 4th Grade for General Education
Schools
The fourth-grade curriculum in mathematics of Bulgaria71 is designed by the Ministry of Education inaccordance to the State Educational Requirements and is developed in accordance to a set ofprinciples, several of which served as a common ground for the design of the activity plan for theeducational robotics workshops:Arithmetic operations with one and two-digit numbers and the properties of these actions;
Deepening and expanding knowledge on the units of measurement by introducing angle
measurement;
Deepening and expanding the knowledge of the basic geometric shapes, types of triangles, typesof angles, rectangle, square, knowledge of the circle and its elements
Making evident how mathematics connects to subjects of other cultural and educational fields;

71

Educational curriculum for mathematics for the fourth grade issued by the Minister of Education and Science, obtained online in Bulgarian
on March 10, 2018 at https://www.mon.bg/upload/2645/matematika_4kl.pdf

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Improving collaboration, communication, formulating and expressing ideas and cultivating skills
such as observance, logical thinking, comparative thinking, critical analysis, formulatingstatements, making conclusions, experimentation;
 Develop interest and motivation to study mathematics and form a positive attitude towards the
subject;
Following these guidelines, we created a multi-layered activity plan, to support the mission of thecurriculum and possibly transfer this experience to fourth-grade mathematics educators, in order tocontribute to the student’s confidence in the subject and to inspire their curiosity in it.

Visualizing Mathematics with the MathBot
A Constructionist Activity Plan for Mathematical Experimentation
The educational robotics activities presented here are structured around the constructionist pedagogicalapproach, where the activity is organized around powerful ideas, facilitated through collaborative andgroup-centred games. Our focus was put on promoting a social dimension of robotics, which includes
sharing personal knowledge and experiences on STEM related concepts while learning from others(Kafai & Burke, 2015). In this context, robotic construction and programming are seen as expressive
medium to externalize ideas and thoughts, which through the sharing are becoming objects fordiscussion and change (Kynigos, 1995). Thus, all the activities are designed to take place in a common
space and integrate virtual and physical constructions (Papert, 1980, 2000).
The pedagogical approach, as well as the background for the elaboration of the educational robotics
workshops within the ER4STEM project are coordinated and structured with the means of an Activity
Plan Template. This template was developed in the project as a design tool for planning robotics
activities and depicts what we have identified as essential and transferrable elements of learning withrobotics.
The Activity plan template, addresses the following aspects: a) Focus and resources: reference to thedifferent domains involved, different types of objectives, duration and necessary material; b) contextual
information regarding space and characteristics of the participants; c) social orchestration of the activity(i.e. group or individual work, formulation of groups etc.); d) a description of the teaching and learning
procedures where the influence of the pedagogical theory is mostly demonstrated; e) expected student
constructions; f) description of the sequencing and the focus of activities; g) means of evaluation(Yiannoutsou et al., 2017)

Social Orchestration and the Role of Discussion
The students were assigned to groups, each group consisting of 3-4 members. Every group was working
around one table with one computer and one robot. We applied no specific grouping criteria, as we
wanted the students to be able to sit together based on pre-established friendships. We wanted students
to be positively influenced by the idea that the workshops they were participating in were a fun group
activity, which they could share with their friends.
As the students in one general education school class are about 25 boys and girls, this resulted in theformation of about 6-7 student groups in total.
Students were given discussion opportunities to reinforce their understanding by sharing with peers
their constructions and working strategies. Students were supposed to verbally, and visually present
their solutions to the tutors and the other teams, which required them to communicate their learning
curve and solution in an understandable and clear manner. Student groups were encouraged to invite
other student groups to present their solution and could go and ask for consultation with other student
groups. By answering questions from other students and student groups, the presenting group was
required to be able to explain their reasons and articulate their chosen strategy. The ability to clearly
identify and communicate the reasoning underlying group decisions is at the core of the “thinking about
one’s own thinking” (Han & Bhattacharya 2001) constructionist approach, which we aimed to integrate
in our activity. Furthermore, in relation to the subject of mathematics itself, language can facilitate652

Constructionism 2018, Vilnius, Lithuania

reflection and internal regulation, which is of importance to realize which parts of the mathematical idea
are important (Hoyles, 1985).
Furthermore, we wanted our students to remain motivated to learn and participate throughout theactivity. Motivation and understanding are shown to be increased when encouraged to explain
knowledge (Brown, 1988). By enabling such discussions, the robot’s behaviour or the code is becoming
a subject of common reflection, which empowers learning by providing the learner with an expertise toshare.
To this end, apart from meaning generation we argue that constructionist robotics activities can also
help the development of a number of 21st century skills including problem solving, flexibility andadaptability. With flexibility and adaptability, we refer to the abilities of a) incorporating feedback
effectively, understanding, negotiating and balancing diverse views and beliefs to reach workable
solutions and b) adapting to varied roles, schedules and context. In our workshops, we aim to reinforce
these abilities through collaborative reflection, public discussion, role rotation and redistribution.

Implementation
For this particular set of workshops, we chose to use the Finch Robot, designed and developed by
Birdbrain Technologies. Among the advantages of the Finch Robot is its cost-effectiveness, durability
and the minimal amount of required knowledge or preparations in order to install all necessary softwareand run it. The Finch Robot is a black-box technology, equipped with many programmable sensors to
support learning activities that emphasize programming the behaviour of the robot.
The Finch robot is programmable through the offline version of the visual programming language
Scratch, which makes it highly accessible to students of this age group with little to no experience with
programming and discrete logic. Furthermore, as Scratch allows the students to focus on tackling
mathematical problems and easily create logical constructions, which becomes increasingly challenging
with other, non-visual programming environments (Utting et al., 2010), where the focus falls on the code
itself, thus making the programming process a purpose, instead of a tool for learning.
The activity plan was structured in seven phases of gradual complexity, apart from the first and the lastone, which were introductory and evaluation-related. Every phase offered games with multiple subquests, which allowed for differentiation and made it possible for all groups to learn and problem-solveat their own pace. As cultural context, the activities were designed for Bulgarian students or students
representing Bulgaria-based minorities and required fluency in Bulgarian language as game tasks are
written in Bulgarian.
Phases 2 and 3 were more programming-oriented, allowing the students to reach a level of comfort inprogramming the robot and included basic command of the robot, programming it to perform a sequenceof actions, programming its nose to blink in different colours while performing certain movements andothers. Phase 4 included games related to drawing and measuring lines with the robot and rulers. In
phase 5, students are engaged in a mathematical game of measuring the angle at which the robot turns
with a protractor. Phase 6 aimed at exercising knowledge on the elements of a circle and included
drawing circles with the robot and measuring its elements.
Ultimately, at the end of the workshop students gain understanding of what a robot is and know somecommon robotic parts. They understand that robots are programmable and gain knowledge on sensors
and some different types of sensors and are aware that different sensors serve different purpose. At theend of the workshop, students would have exercised basic age-appropriate concepts from the subjectof mathematics.
The full activity plan, with more information about all phases is available at repository.er4stem.com.

Activity Evaluation
In order to be able to evaluate the activities and answer our research questions data was collected fromstudents whose parents had given informed written consent. From 6 workshops held with different
classes, data was obtained from 131 participants (63 boys and 68 girls). The majority of them had
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previously participated in educational robotics workshops in Bulgaria organized by the European
Software Institute, under the ER4STEM project.
Quantitative data was obtained through pre and post workshop questionnaires. Interviews wereconducted with one focus group from each class (6 interviews in total) and were transcribed, allowing
more insight of the participants’ experience and immediately after participating in the workshops.
Additionally, reflections and observations were collected from tutors, along with student reflections andartefacts of learning.
This section presents the findings of the analysis of the questionnaires aims to provide insight on theparticipants’ experience. It should be noted that some participants chose not to give an answer to somequestions. Considering this, the percentage of students who left a question blank will be indicated next
to the answers’ report.
It is noteworthy to mention that this paper will report on insights related to the implementation of
the activity plan focusing students’ evaluation of their experience during the workshop. The activity’squantitative evaluation was carried out under the ER4STEM evaluation protocol, which is not specificallytargeted at assessing student’s understanding of mathematical concepts or student’s attitude towards
mathematic in general. The quantitative evaluation results reported below focus more, but not only on
exploring boys and girls' interests in STEM, whether the approaches/activities appeal to them, on
reporting on their intra-relational and interpersonal skills, and their feelings towards collaborative work
experience.
Students’ understanding of mathematical concepts, specifically for this activity, is qualitatively assessed
through open answers in the questionnaires, focus group interviews, by collecting artefacts of learning,tutor observations and group reflections. The qualitative data obtained throughout ER4STEM
workshops under analysis (May 2018) and will be reported on following the project.

What are Bulgarian students’ attitude towards constructionist robotics activities?
The majority of the students who participated in the study reported the educational robotics workshop
activities for mathematics as interesting, fun and not very difficult.
Strongly
disagreeDisagree

Neither
Agree NorDisagree

Agree

Strongly
agree

Blank

Interesting

1%

0%

2%

9%

88%

0%

Difficult

39%

20%

28%

8%

3%

0%

Fun

2%

0%

3%

10%

83%

0%

Interesting

1%

0%

0%

8%

89%

0%

Difficult

42%

23%

22%

8%

5%

0%

Fun

2%

1%

0%

8%

89%

0%

Interesting

3%

0%

8%

16%

71%

0%

Difficult

44%

22%

15%

8%

7%

0%

Fun

5%

1%

7%

16%

68%

0%

The problems we had tosolve were:Working with robots was:Working in a team was:Figure 1. Aggregated students’ feedback related to the problems solved, their work with robots and teamwork

Most students reported working in a team as interesting, fun and not difficult (Figure 1). Tutors’
observation show that regardless of group conflicts that inevitably arose in some groups, the students
generally manifested positive attitudes towards group work and felt excited for the opportunity to workin a team.
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Constructionism 2018, Vilnius, Lithuania

Strongly
disagreeDisagree

Neither
Agree NorDisagree

Agree

Strongly
agree

Blank

9%

5%

23%

15%

45%

3%

4%

2%

5%

14%

73%

2%

2%

1%

2%

4%

89%

2%

2%

2%

15%

27%

50%

3%

80%

12%

3%

1%

3%

2%

3%

3%

8%

21%

64%

2%

74%

12%

7%

5%

1%

2%

6%

2%

20%

24%

48%

1%

During the workshop…
I solved a problem
I worked as part of a team
I programmed a robot
I was good at listening
I gave up quickly
I worked hard
I was bored
I helped someone

The students also gave a relatively high rating to the workshop they participated in with 4.93 stars outof 5.
Some students also reported increased confidence in mathematics and felt encouraged to participate
in more activities such as this one.

Closing remarks
In this practice paper, we concentrated on providing insights on the design, implementation andpedagogical theory underlying an educational robotics activity, designed in support to the guidelines ofa national educational curriculum in mathematics. We further provided aggregated feedback datareporting on students’ experience with this activity, obtained by the primary school students who took
part in the activities and we have given a brief overview on the evaluation protocol of the activities.
As practitioners in the field of educational robotics, we realize the importance of applying "powerfulideas" (Papert, 1980) as tools for learning. Through the Visualizing Mathematics with the MathBot
activities, we aim to engage students in experimentation with digital and robotics artefacts to solve
mathematical problems, in order to construct a better understanding of the interdisciplinary applicationof mathematical concepts and to further develop a collaborative mind set to problem-solving. We believethat constructionist activities such as this one, bare the potential to enhance the properties of the linked
curricula, and the possibility of adapting such activities to a plethora of robotics kits, programming
environments and learning contexts makes this activity an idea worth sharing.

References
Alimissis, D. 2013. Educational robotics: Open questions and new challenges. Themes in Science andTechnology Education, 6 (1), 63-71
Barreto, F. and Vavassori, B. 2012. Exploring the educational potential of robotics in schools: A
systematic review. Comput. Educ. 58, 3 (April 2012), 978-988.
Brown, A.L., 1988. Motivation to learn and understand: On taking charge of one’s own learning.
Cognition and Instruction, 5, pp.311–322.
Dede, C. (2010). Comparing frameworks for 21st century skills. 21st century skills: Rethinking how
students learn, 20, 51-76.
Johnson, J. 2003. Children, robotics and education. In Proceedings of 7th international symposium on
artificial life and robotics (Vol. 7, pp. 16-21), Oita, Japan.
Han, S. & Bhattacharya, K., 2001. Constructionism, Learning by Design, and Project Based Learning.
In Emerging perspectives on learning, teaching, and technology.
Hoyles, C., 1985. What is the point of group discussion in mathematics? Educational studies inmathematics, 16(2), pp.205–214.
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Kafai, Y. B., & Burke, Q. (2015). Constructionist Gaming: Understanding the Benefits of Making Games
for
Learning.
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Psychologist,
50(4),
313–334.
https://doi.org/10.1080/00461520.2015.1124022
Kafai, Y. B., Burke, Q., & Mote, C. (2012). What makes competitions fun to participate? The role ofaudience for middle school game designers, Proceedings of the 11th International Conference on
Interaction Design and Children, 284–287.
Kynigos, Chronis. 1995. Programming as a Means of Expressing and Exploring Ideas in a Directive
Educational System: Three Case Studies. Computers and Exploratory Learning, diSessa, A, Hoyles, C.
and Noss, R. (eds), Springer Verlag NATO ASI Series, 399-420.
Matarić, M. 2004. Robotics Education for All Ages, Proceedings, AAAI Spring Symposium on
Accessible, Hands-on AI and Robotics Education, Palo Alto, CA, Mar 22-24.
Mead, R.A., Thomas, S.L., and Weinberg, J.B. 2012. From Grade School to Grad School: An Integrated
STEM Pipeline Model through Robotics in Robots in K-12 Education: A New Technology for Learning,
IGI Global.
Papert, S. (1980). Mindstorms: Children, computers, and powerful ideas. New York: Basic books
Papert, S. (2000). What's the big idea? Toward a pedagogy of idea power. IBM Systems Journal,39(3.4), 720-729.
Utting, I., Cooper, S., Kölling, M., Maloney, J., and Resnick, M. (2010) Alice, Greenfoot, and Scratch: A
Discussion. ACM Transactions on Computing Education, Vol. 10, No. 4, Article 17, November 2010.

656

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Ethnomathematics in Teacher Education: Analysis
and Construction of Geometric Ornaments
Igor Verner, ttrigor@technion.ac.il
Technion – Israel Institute of Technology, Israel

Khayriah Massarwe, massarwe@technion.ac.il
Technion – Israel Institute of Technology, The Arab Academic College for Education, Israel

Daoud Bshouty, daoud@technion.ac.il
Technion – Israel Institute of Technology, Israel

Abstract
This paper presents a study in which we developed, implemented, and evaluated a teacher education
course for teaching geometry based on the ethnomathematics approach. The participants, prospective
and in-service teachers with different cultural backgrounds studied geometry through analysis andconstruction of geometric ornaments from diverse cultures, and acquired knowledge and skills in
multicultural education. The students inquired geometrical properties and symbolism of the ornamentsfrom the cultures they chose. In the study we analysed motivating desires observed in the course using
the theory of engagement structures proposed by Goldin and colleagues. We found that some of theengagement structures that are typical for conventional mathematics classes, emerged in our course
while other structures were not observed. We propose a new, additional engagement structure toembody motivational desires arising from multicultural interactions in diverse classes.
The figure below presents the Druze Star, the prominent symbol of the Druze culture. It also shows thegeometric construction of the Star performed by a Druze student. She figured out the meaning of coloursof the Star: green symbolizes nature; red symbolizes courage and love; yellow symbolizes
enlightenment; blue indicates patience and brotherhood; white indicates reconciliation and peace.

Keywords
ethnomathematics; geometric ornaments; teacher education; learning engagement.

Introduction
Creative hands-on practices and projects are increasingly included in the school mathematics curricula.
In these activities students learn mathematics with applications through practical activities intechnological environments. They construct artefacts that drive their thinking, inquiry, andcommunication. Teachers should have knowledge of the constructionist approach and skills to apply it
in choosing suitable learning environments, activities, and strategies of students' guidance.

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Constructionism 2018, Vilnius, Lithuania

The challenges facing the mathematics teacher in implementing the constructionist approach are toengage every student in the class, to make the activities accessible for the students, and provide them
opportunity for systematic mathematical learning. To meet these challenges, research points out to theimportant role of affective factors on the constructionist learning behaviour (Picard et al., 2004).
Geometry underlies construction of artefacts. As such, development of geometry is rooted in theconstructional practice while learning geometry is naturally embedded in the constructionist educational
processes. Since ancient times, geometric constructions, in addition to functionality, possessed
symbolism and cultural values. This duality stands out in geometric ornaments, decorative and symbolic
patterns of cultural value, composed of basic geometric figures that are repeated under different
transformations. Ornaments are complex geometric objects that carry within a variety of properties of
Euclidean geometry. They are part of the world cultural heritage, created in folk art and crafts throughout
the history of nations (El-Said, 1993(. Historically, people constructed ornaments with compass andstraightedge to express their feelings, spiritual believes, and cultural identity.
Our aspiration for teaching geometry with ornaments is to make geometry lovable to students who
perceived it as a dry subject, as expressed by Papert (2006) "Let's stop trying to make children like themathematics they hate. Let's make a mathematics they will love." For the past ten years, we developed
and guided practices of analysis and construction of geometric ornaments in mathematics teacher
education. The two theories that underlie our teaching were constructionism and ethnomathematics.

Ethnomathematics approach
D'Ambrosio (2004) defines ethnomathematics as the mathematics practiced among identifiable culturalgroups, meaning that ethnomathematics deals with mathematical conceptions and techniquesdeveloped in different cultures to solve real-life problems. In education, the ethnomathematical
approach generates learning processes, in which learners inquire into mathematical experiences fromtheir own and other cultures to understand how mathematical ideas are formulated and applied (Rosa,& Shirley, 2016).
The ethnomathematical approach promotes mathematical learning by activating the factors of students'
engagement related to context, culture, and ethnicity. Ethnomathematically based courses introduce
mathematical concepts in such a way that they are better understood, and their power, beauty and utility
are better appreciated (Achor, 2009). Affective processes in ethnomathematics courses are influenced
by both cultural context and diversity. The majority of ethnomathematically based courses in schools
and teacher colleges consider situations in which students in culturally homogeneous groups study
mathematics in the context of their culture (Gerdes, 2001; Rosa & Orey, 2011).
Presmeg (1998) studies a different situation in which pre-service teachers with different cultural
backgrounds inquired into mathematics embedded in their cultures and shared findings with the class.
Her study shows that using students’ cultural practices in the mathematics classroom had a positive
impact on their awareness of diversity and multiculturalism. Presmeg calls for seeing cultural diversity
as an asset and not a burden. Gay (2010) emphasizes the need to enhance teacher education throughspecial training to develop the skills of teaching in a culturally diverse classroom and creating crosscultural dialog. The teacher can reinforce students’ energy and learning engagement by encouraging
classroom discussions on interesting and joyful experiences and by asking questions that help maintainstudents' positive mood (Gay, 2002; Vomvoridi-Ivanovic, 2012).
Our research started with pilot experiments, in which high school students in geometry lessons
performed a constructionist exercise involving analysis and construction of geometrical ornaments(Massarwe, Verner, & Bshouty, 2010). The experiments revealed that the students and teachers
perceived learning geometry through ornaments as a meaningful and joyful experience. Then we took
part in the program of the Israel National Council of Higher Education for development of academic
courses on the theme “academy–community partnership for social change.” Our Technion course“Issues in ethnomathematics” attracted interest of prospective and in-service teachers of mathematics
and other subjects. It prompted us to study culturally responsive teaching of geometry and the ways to658

Constructionism 2018, Vilnius, Lithuania

treat issues of diversity and cultural identity. In this way we came to an understanding of the educational
value of ethnomathematics and its potential for promoting constructionist mathematical learning.
Our study explored learning engagement of students participated in the ethnomathematically basedteacher education course. The study applied the methodology engagement structures proposed byGoldin et al. (2011). The theory focuses on identifying typical patterns of engagement observed in thelearning process and characterizing the patterns by means of engagement structures. An engagement
structure is considered as a construct including the statement of the motivating desire, the scheme ofthe social interaction, the characteristics of situations which are likely to evoke the desire, and the
behavior pattern. Goldin et al. (2011) presented patterns of learning engagement typical for
conventional middle school mathematics classes and characterized them by a number of engagement
structures. The study presented in this paper identified and characterized the patterns of engagementthat are typical for our course.

Characteristics of learning engagement
This case study followed up the course delivered at the Technion Department of Education in Scienceand Technology. The goal was to investigate motivations that drive students to perform mathematical,
cultural and pedagogical activities in our ethnomathematically-based teacher training course. We
sought to answer the question: what are the features of student engagement in the course and how can
the methodology of engagement structures be applied to analyze these features?
The course "Issues in ethnomathematics" has been developed and given to prospective and in-service
teachers as part of the Technion teacher education program. The 29 students participated in the course
had different academic and cultural backgrounds. The 42-hour course included the following learning
activities:










Acquisition of geometrical skills required for construction and analysis of ornaments.
The students learned basic geometric constructions with compass and straightedge and applied
this knowledge to analyze and construct given ornaments. They learned to define geometricalproblems related to the constructed ornaments.
Studying mathematical and pedagogical concepts for teaching geometry in cultural context.
The students learned the principles of teaching mathematics with applications and cognitive
mechanisms of learning in context. They inquired how geometric concepts are embedded inculturally meaningful ornaments and serve to express symbolism and beauty. They discussed theconcept of diversity and the value of multicultural education.
Development of instructional units on analysis and construction of geometric ornaments.
The lectures exposed students to the common historical, mathematical, and cultural roots ofgeometric ornaments. They discussed applicability of the ethnomathematics approach in class.
Each student developed and gave in class a presentation of the selected culture, which included
a collection of ornaments, a detailed construction procedure of the chosen ornament, related
geometric problems, and their solutions.
Workshop "Joyful learning of geometry in multicultural context".
The workshop "Joyful learning of geometry in multicultural context" was the culmination of thecourse. Each of the students guided a multicultural group of school students to make a poster.
Each poster consisted of an ornament constructed by compass and straightedge, its variation
made using the graphics software tool Kaledomania, geometric problems and solutions, and an
essay on the chosen culture. The posters were publicly exhibited at the end of the workshop.
Analysis and presentation of the workshop results, and a final report.
In the last part of the course, the students analyzed the data collected during the workshop, andpresented findings.

When teaching the course we paid attention to the strong engagement of students in the
ethnomathematical learning activities and decided to investigate motivating desires that drive theengagement. We applied the methodology of engagement structures proposed by Goldin et al. (2011).
To answer the research question, observations of classroom activities were recorded and videotaped
throughout the course. We collected and analyzed repeated patterns of engagement that indicated the659

Constructionism 2018, Vilnius, Lithuania

presence of the engagement structures proposed by Goldin et al. and of new structures that emerged
through our teaching. We also analyzed the homework tasks and materials prepared by the students
for the workshop, as well as in-class discussions, final reports, and post-course reflections. In thesereflections, we paid attention to patterns associated with cultural awareness of students with different
cultural backgrounds. We found that studying geometry in cultural context touched student’s feelings ofcultural identity and raised their desires for learning. This way we came to a new engagement structurethat we named “Acknowledge my culture.”
Below we present structures from the ones proposed by Goldin et al. (2011) that appeared in our course.
Get the Job Done. In this engagement structure "emotional satisfaction accompanies fulfilling theobligation through task completion" (ibid.). Our observations in the course showed that this engagement
structure arises in the constructionist learning process. This feature was especially prominent when thestudents learned to construct ornaments and prove their geometric properties. In the beginning, thestudents had difficulties in constructing the ornament using compass and straightedge because they
lacked this skill. They asked for guidance to construct the first ornament step by step and they showedcommitment to accomplish the task correctly and accurately (Figure 1). The post-course reflections
indicated that students’ desire to “get the job done” in the task, was determined by the added value ofthe experience for developing construction skills and understanding geometry of ornaments.

Figure 1. Student's commitment to accomplish the construction task.

Look How Smart I Am. “The student's motivating desire behind this engagement structure is to impressclassmates and achieve positive self-regard by demonstrating high mathematical ability” (ibid.). We
found indications of this desire in our students when they strived to perform the course assignments inthe best way possible and to demonstrate their competences in geometry and culturally responsive
teaching. The desire for superior achievement was indicated also by creative solutions developed in thecourse.

Figure 2. Students expressing positive self-regard.

Check this Out. For this engagement structure the source of motivating desire is "that solving amathematical problem can give a benefit" (ibid.). This desire can be stimulated by extrinsic benefits, but
our course used intrinsic payoffs: (a) feeling of invention aroused when the students practiced in posing
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and solving non-conventional geometric problems; (b) satisfaction about coping with a real geometricproblem of practical value; and (c) discovery of geometric properties, methods of creation of ornaments,
and cultural meanings. An example of a geometric problem proposed by one of the students ispresented below.
A.

B.

C.

Figure 3. A. An ornament; B. problem related to the.
Problem: The ornament in Figure 3A is constructed as shown in Figures 3B and 3C. Given that theradius of the circles is R, find the area of the selected shape.
Solution: We draw a circle of radius R with the centre B. In triangle OAB , AOB 

3600
 600
6

from the construction and BO  OA  R . So, OAB is an isosceles triangle and its area equals

3 2
600
1
R . The area of the sector AOB is: S AOB 
 R 2   R 2 . Then the area of the selected
0
4
360
6

3 2

3 2
shape is S  2  ( S AOB  SAOB )  2  ( R 2 R )( 
)R .
6
4
3 2
SAOB I’m Really into This. The meaning of the structure is that “the student is intrigued by solving achallenging mathematical problem and tunes out from other elements of the environment (ibid.)”. Our
course involved students in the geometric analysis of ornaments, they proposed problems related on
the ornament and solved the applied geometric problems with enthusiasm. Being intrigued by thegeometric problem, the students did not abstract from the cultural meaning of the ornament and fromthe way of its construction.
Let Me Teach You. This structure presents “the motivation desire of a student to mentor the classmate
who has difficulties in solving mathematical problems. (ibid.)”. This role motivates the student to learn
and master the subject. Satisfaction from the progress of the classmate is an additional stimulator of
the motivation desire. In our course each student developed an instructional unit on geometricornaments from the culture that she/he selected. The student taught the unit to peers with the intentionof getting feedback for improving the presentation. The course created an atmosphere in which thestudents felt invited to share information on heritage artifacts, proposals for geometric constructions andproofs, and ideas on how to present the material to school students.
From our observations, in peer teaching the students shared proposals of alternative solutions forgeometric constructions and proofs, pedagogical ideas on how to present the material to school
students, and findings on traditional customs and visual artefacts in different cultures. A repeatedreflection:
The discussions on geometry greatly helped me to understand the way other people lookedat the same subjects and even taught me some new tricks.
Observations in the course indicated occurrence of motivating desires rooted in cultural diversity of theparticipants and pointed out the need to introduce a new engagement structure which is presented
below.
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Constructionism 2018, Vilnius, Lithuania

Acknowledge My Culture. Here, ”the motivating desire awakes when the student feels urged to be a
representative of his or her own culture” (Goldin et al., 2011). In our course some of the students elected
to investigate ornaments from their own culture and others from cultures to which their families belonged
before immigration to Israel. Passion, inflamed by exposure to artefacts from different cultures, drove
the students to indulge in collections of artefacts, and impress the class by distinguished mathematicalfeatures of artefacts from these cultures. A repeated reflection:
In the course I learned about my culture in a more elaborate way and tried to represent itin the best way possible.

Discussion and Conclusion
Today's students prefer hands-on, visual, joyful, and socially meaningful learning. To meet theirpreferences, teachers of mathematics should be able to effectively use the learning resources ofcontext, affect, culture, and ethnicity. Therefore, teaching in a cultural context becomes a necessary
subject of mathematics teacher education. Our course addressed this subject with regard to teachinggeometry based on the ethnomathematical approach. This approach focuses on learning by inquiry into
mathematical practices developed in different cultures to solve real-life problems.
In our course students inquired properties and symbolism of geometric ornaments from different
cultures. Following the constructionist pedagogy, the course included practice in constructing
ornaments by compass and straightedge, posing and solving geometric problems, creating aesthetic
presentations, developing instructional units, and teaching them to diverse groups of school students.
In the course we assessed student's performance in the construction of the ornament of her/his choiceusing compass and straightedge and the analysis of the construction procedure. Through theconstruction process the students gained insight into geometric analysis that justifies the practice. In
the assignment of posing and solving geometric problems, we assessed the ability to identify andformulate an interesting problem related to the complex geometric pattern, solve it formally, and present
the solution. Assessment of the instructional units on geometric ornaments and the feedback from thepeers and the school students indicated that the prospective teachers developed the skill of teachinggeometry in cultural context to diverse groups. The experiences were perceived by the prospective
teachers as interesting, joyful, and engaging.
Our research focused on students' engagement in the course. We applied the methodology ofengagement structures proposed by Goldin et al. (2011). The analysis of students' motivational desires
observed in our course revealed their significant differences from that typically observed in conventional
mathematics classrooms:High level of engagement of all the students participated in the course.
The diminished role of external motivation (rewards and sanctions), and of competition forsuperiority.
The increased role of interest, enjoyment, self-realization, and perceived relevance.
Strong impact of personally meaningful cultural context.

Based on the positive results of this study, we continue to develop and implement theethnomathematical approach in teacher professional development as a way to facilitate moreconstructive, contextualized, and culturally responsive mathematics education in schools.

References
Achor, E. E., Imoko, B. I. & Uloko, S. E. (2009). Effect of ethnomathematics teaching approach on seniorsecondary students’ achievement and retention in Locus. Educational Research and Review, 4(8), p.
385-390.
D'Ambrosio, U. (2004) Prefix. In Proceedings: Ethnomathematics and Mathematics Education, ICME
10 Discussion Group on Ethnomathematics, Copenhagen (p. V-X).
El-Said I. (1993) Islamic art and architecture. The system of geometric design. Reading, UK: Garnet
Publishing.
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Constructionism 2018, Vilnius, Lithuania

Gay, G. (2002). Culturally responsive teaching in special education for ethnically diverse students:
Setting the stage. Qualitative Studies in Education, 15(6), 613-629.
Gay, G. (2010). Culturally Responsive Teaching: Theory, Research, and Practice (2nd edition). New
York: Teachers College Press.
Gerdes, P. (2001). Ethnomathematics as a new research field, illustrated by studies of mathematicalideas in African history. In J. J. Saldana (Ed.): Science and Cultural Diversity. Filling a Gap in the Historyof Science (p. 11-36). Mexico, Cuadernos de Quipu.
Goldin, G. A., Epstein, Y. M., Schorr, R. Y. & Warner, L. B. (2011) Beliefs and engagement structures:
Behind the affective dimension of mathematical learning. ZDM, 43(4), p. 547–560.
Massarwe, K., Verner, I. & Bshouty, D. (2010) Fostering creativity through geometrical and culturalinquiry into ornaments. In: B. Sriraman and K.H. Lee (Eds.), The Elements of Creativity and Giftedness
in Mathematics, p. 217-241, Sense Publishers.
Papert, S. (2006) From math wars to the new new math. Plenary lecture at the 17th ICMI Study
conference, Digital Technologies and Mathematics Teaching and Learning: Rethinking the Terrain.
Hanoi, Vietnam.
Picard, R., Papert, S., Bender, W., Blumberg, B., Breazeal, C., Cavallo, D., Machover, T., Resnick, M.,
Roy, D., & Strohecker, C. (2004) Affective learning – A manifesto. BT Technology Journal, 22(4), p.
253–269.

Presmeg, N. C. (1998) Ethnomathematics in teacher education. Journal of Mathematics Teacher
Education, 1(3), p. 317-339.
Rosa, M. & Orey, D. C. (2011) Ethnomathematics: the cultural aspects of mathematics. Revista
Latinoamericana de Etnomatemática, 4(2), p. 32-54.
Rosa, M., & Shirley, L. (2016) In Guise of Conclusion. In: M. Rosa et al. (Eds.), Current and Future
Perspectives of Ethnomathematics as a Program. ICME-13 Topical Surveys, pp. 39-40, Hamburg,
Germany: SpringerOpen.
Vomvoridi-Ivanovic, E. (2012) Using culture as a resource in mathematics: The case of four Mexican–
American prospective teachers in a bilingual after-school program. Journal of Mathematics Teacher
Education, 15(1), p. 53–66.

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Coding to Learn – Informatics in Science Education
Michael Weigend, mw@creative-informatics.deHolzkamp-Gesamtschule Witten, Germany

Abstract
Programming is a way to explore and elaborate scientific content. Software projects might (to someextend) replace traditional pencil-and-paper textbook problems that require algebra skills. This
contribution discusses a Python workshop which has been conducted with 34 students from three
German high school chemistry classes in grades 11 and 12. The participants (mostly programming
novices) developed interactive programs solving quantitative Chemistry problems. They claimed toenjoy programming, the girls almost as much as the boys. A majority agreed that programming implies
practising logical thinking, precise communication and creativity as well as elaborating chemistryknowledge.

Keywords
science education; programming; computational thinking

Programming and Science Education
Computational Thinking (CT) is the thought processes involved in developing computer programs.
However, CT is not just relevant for professional software engineers. Like reading, writing andcalculating it is considered to be a “universally applicable attitude and skill set“ that everybody should
learn at school (Wing 2006 p. 32). This includes abstraction, decomposition, algorithmic thinking,evaluation and generalization (Selby and Woolard 2013). CT concepts are already (silently) used inscience and science education. For example, the concept of generalization is used in chemistry, when
students find principles and rules for chemical reactions based on individual observations. Chemical
formulas like H2O are abstract models of real molecules. There are several suggestions how to integratecomputational thinking into curricula. One of the goals of the Google “Online course on CT for educators”(https://computationalthinkingcourse.withgoogle.com) is to “increase the awareness of CT concepts
among educators”. Sometimes elements of traditional science curricula are reinterpreted as related toCT. On the other hand, coding (an obvious application of CT) can be a way to explore and elaborate
scientific content. Before discussing examples of science-related programming projects let me briefly
point out a few facets of traditional learning units in textbooks. Beside general information about a topic,
STEM textbooks contain elements that are meant to evoke active elaboration of content: worked out
examples, exercises (tasks) and solutions to tasks. Worked out examples have proved to be an efficient
way to learn (Stark et al. 2002). There are different patterns of elaboration, which correspond to different
levels of mental effort. Students may for example reconstruct an example in depth and anticipate thesolution by themselves or they just take the (metacognitive) information about a possible structure of asolution.
When the learning objects contain quantitative aspects, exercises usually imply algebraic
transformations and calculations. A typical task from a popular German chemistry text book is this:Calculate the pH of these solutions: Sodium hydroxide solution, c0(NaOH) = 1 mol/L Ammonia solution, c0(NH3) = 0.2 mol/L(Tausch& von Wachtendonk, 2015, p.52)
The solution of this task requires these activities:

Find relevant formulas like pH + pOH = 14.
Do algebraic transformations and solve equations.
Look up chemical constants (like pKB(NH3)) in a table.
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Constructionism 2018, Vilnius, Lithuania

 Use correct wording, in particular write correct units like mol/L for concentrations.
The primary goal of these exercises (as part of chemistry education) is not to practise algebra and tosolve these problems quickly and error-free but to elaborate chemistry concepts like acids, bases andchemical equilibrium. In Germany, high school students usually do not have to learn formulas by heart.
But they are expected to develop the competence of using given formulas to solve problems. For thisthey need a general understanding of relevant chemistry facts and a more general competence ofmathematical modelling and problem solving.
In contrast to conventional pencil and paper textbook exercises, a programming task requires to develop
a digital artefact, that is useful for other people. That implies almost the same activities as during solving
a textbook problem, but they are now part of a design process. The programmer must create verbal
system responses that are precise and well understandable and uses scientific vocabular and units
because they are essential, when asking for input data. Example “Input the molar weight M of the solved
substance (g/mol).”

An Introductory Python Programming Workshop for ChemistryClasses
In winter 2017/2018, students from chemistry classes in grades 11 and 12 at a German high schoolparticipated at a 90-min programming workshop. Among the 34 students whose data were evaluated,
there were 21 girls and 11 boys (2 did not tell the gender), the average age was 17.5 years. Most of
the participants (73%) had never written a program before. They got a short introduction and thenworked on their own, following a written tutorial. They were free to collaborate and ask questions. In
contrast to a regular computer science class, the primary goal of this workshop was not to discoverprogramming techniques as such but to get an idea about how to use programming as a tool to solve
Chemistry-related problems.
Thus, the 6-page tutorial was designed to be self-explaining for absolute beginners. It consisted of
instructions, Python listings, visual and verbal explanations and tasks. Except for the very first task, allexamples and tasks were related to chemistry. At some points the students were asked to note the time.
So it was possible to calculate the time they needed for the exercises.

Interactive Exercises
The tutorial starts with three exercises which must be performed in the interactive Python Shell (Idle).
The Python Shell displays a command line starting with the prompt >>>. When a user enters a Python
statement and hits the ENTER-key, the Python runtime system interprets the statement (for example amathematical expression) and returns the result in the following lines (read-eval-print loop, REPL).
Example:>>> 23*5.2
119.60000000000001
The first task is to try out four expression commands and discover the little differences between Python
syntax and mathematical notation. In the workshops, this took two minutes in average.
In the second exercise the students use the Python Shell as calculator for three little computational
problems from a typical chemistry textbook. Example: Calculate the mass (in g) of 0.2 mol sodium
chloride (NaCl). For this they needed seven minutes.
In the third activity the students are asked to follow step-by step instructions to solve a more complex
problem from Chemistry using the Python Shell. In this context the tutorial introduces variables to namedata and split a complex calculation into separate steps (CT concept: decomposition).
Task: Through a hole in a pipe, the amount of 100 g methane went into a cylindric building (height 20
m, radius 5 m). Calculate the concentration in mol/L.
Step 1: Calculate the volume of the cylinder (m³).
>>> V = 20 * 3.14 * 5**2665

Constructionism 2018, Vilnius, Lithuania

The result of the term is assigned to the variable V. One can check the value of V by calling the print()
function:>>>print(V)1570.0
Step 2: Calculate the number of mol:>>> n = 100/16
Step 3: Calculate the concentration c in mol/m³:>>> c = n/V>>>print(c)0.003980891719745223
And so on.
In cartoon-like explanations, variables are visualised as containers for data. The name of the variablein this metaphor is a label, making it possible to refer to the container.

Figure1. Two frames from a cartoon-like explanation of Python statements.

Interactive Programs
In the next exercise the students write a script consisting of six lines of code using the Idle editor. They
have to figure out how to use the editor and manage to write error-free program text. In the workshops
this took in average 16 minutes.
The interactive program has a simple Input-Processing-Output design. The user is asked to input the
number of carbon and hydrogen atoms. The program calculates the molar weight of a hydrocarbon
compound consisting if these atoms and returns the value using proper units (g/mol).
print("Calculate the molar weight of a hydrocarbon compound.")inp = input("Number of C atoms: ")n_C = int(inp)n_H = int(input("Number of H atoms: "))M = n_C*12 + n_H*1
print("Molar weight of the compound: ", M, "g/mol.")
The execution of the program is explained in a cartoon with five frames in the style of figure 1. The
average self-rated comprehension of the program was 75%. Note that understanding the calculation inline 5 requires Chemistry knowledge.
The students are then asked to extend this program and add a new feature on their own. The newversion should now be able to calculate the molar weight of organic compounds additionally containing
oxygen atoms. This program should be tested with methanol (CH3OH). 90% of the participants wereable to do this and theses students needed in average eight minutes to finish the extension.
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The final challenge is to implement an interactive program that helps preparing solutions. The students
have to transfer the techniques they learned from the prototype to create their own project.

Evaluation
Table 1 displays the results of the evaluation. The participants claimed to enjoy programming, the girls
almost as much as the boys. The average agreement to “Programming is fun” was 4.55/5 for boys and4.05/5 for girls. A majority said that programming implies practising logical thinking, precise
communication and creativity as well as practising chemistry knowledge. There were only a few critics.
Four of those seven students who stated that they had lost time doing unimportant things had theopinion that programming is useless knowledge anyway.
Table 1. Evaluation of the programming workshop (n=34).
Statement

Average level ofagreement (1 to 5)

Complete and partly
agreement

Programming was fun.

4.2

85%

While programming I have practised logical thinking.

4.2

79%

During programming I
knowledge.

3.9

74%

I could imagine to do more programming projects.

3.7

62%

While programming I have practised expressing things
precisely.

3.6

59%

While developing the programs I could be creative.

3.5

56%

For my projects I did some research on chemistryknowledge.

2.9

47%

While programming I lost time doing unimportant things.

2.4

21%

To me programming is useless additional knowledge.

2.3

18%

have practised chemical

Starter Projects for Science Education: Reconstruct-ImproveCreate
The Python workshop, which was discussed in the previous section follows a “Reconstruct-ImproveCreate” design pattern. This can be considered as a simple version of agile programming (Beck 1999).
“Stories” are implemented in a few short iterations. The tutorial contains these elements:
ReconstructA story: Visual and verbal description of a useful software covering a certain topic in chemistry(e.g. molar weight),
 An easy-to-understand prototype Python program implementing the story (starter project), Visual and verbal explanations helping to understand the program code.
Improve
 Suggestions, how to improve the prototype program.
Create A new story that can be implemented using the techniques introduced in the prototype.
Note that the students are supposed to use the editor, to type a few lines of code themselves and tounderstand and modify the program (reconstruction). This contrasts for example with the Google“Exploring Computational Thinking” approach, which is based on copy and paste rather than onreconstructing. In the example simulating radioactive decay, learners are supposed to copy and paste
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more than 40 lines of rather complex code in a VPython environment and then run the program(https://docs.google.com/document/d/1HotPQxfK1w1SytPtJg5qqKkxFrqmZpiZrxyaIHRKZIE/edit).

Programming Scientific Experiments
Taking advantage of the CT concepts “abstraction” and “generalization”, it is possible to create relevant
software without specific programming experience. This section discusses example projects using a
Raspberry Pi and a non-dispersive infrared (NDIR) CO2 sensor. These projects are part of a workshop
on sensor technology for chemistry teachers in North Rhine-Westphalia, Germany. The NDIR consistsof an infrared source, a filter and a photo sensor in a tube with some holes. The IR radiation travels
through the tube and is partly absorbed by the carbon dioxide molecules in the tube. A fraction of theIR with a certain wavelength passes the filter and is detected by the photosensor. A small digital device
processes the detector output and creates data that are sent to the Raspberry Pi via the serial I 2C bus.

Figure 2.Raspberry Pi with NDIR CO2-sensor module MH-Z16

Story 1: The carbon dioxide concentration in a room is an important factor of wellbeing. Connect thesensor to the GPIO of a Raspberry Pi with jumper wires and observe the carbon dioxide concentrationin the room (ppm) for five seconds. The output on screen might look like this:540 ppm
540 ppm
543 ppm
545 ppm
550 ppm
The hardware is prepared in a few minutes. This already gives an idea how the serial data transfer
works. The digital sensor device has four pins: VCC, GND, CLK and SDA, which must be connected
via jumper wires to the GPIO of the Raspberry Pi. The pins VCC (+5 Volts), GND (ground) are for the
power supply. The data are transferred bitwise via the SDA line. The CLK line is for a clock signal thatsynchronizes the data transfer.
The following Python program implements the story and illustrates the CT concepts abstraction andgeneralization.
from ndir import get_ppm
from time import sleep
for i in range(5):c = get_ppm()
print(c, "ppm")
sleep(1)

#1
#2
#3
#4
#5
#6

All text elements at the end of a line starting with # are comments and are not part of the formal text.
#1: Import the function get_ppm() from the module ndir.
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Constructionism 2018, Vilnius, Lithuania#2: Import the function sleep() from the standard module time.
#3: Repeat the following indented block 5 times.
#4: Get the current CO2-concentration in ppm and store the value in the variable c.
#5: Print the value of c in the current line of the output window. Then start a new line.
#6: Wait for one second and continue then.
Programming implies abstraction. A computer program can be considered as a simplified model ofreality. Unnecessary details are ignored. In this case, measuring the CO2-concentration is reduced toa function call in line #4. In industrial software development, abstraction takes place on many levels from system design to coding. Abstraction is considered as a key competence for programmers (Kramer2007). But even a tiny project like this example illustrates the benefits of abstraction by using a formal
language. From the perspective of working memory theory (Baddely 2013, Dehn 2008) abstraction isessential for problem solving. Humans can only keep a very limited number of chunks of information inmind while thinking about a solution. Unnecessary details would be disturbing. Therefore, for a human
it is easier to comprehend the semantics of a concise program, than the semantics of an algorithm
written informally in natural language. However, the prerequisite for understanding is that she or hemasters the programming language.
Thus, all language elements in a Python starter project must be explained well to the novice learner.
Some Python commands in the example can be understood almost at once, since they use familiar
concepts metaphorically. The function call sleep(1) makes the Python interpreter to stop the
execution of the program and to continue it after one second. The concept of falling asleep and being
awaked after a while is well known from everyday-life.
Some other language elements are more complex and might need some active exploration andrehearsal before they are fully understood. Learners might want to try out some calls of the functions
range() and print() in the Python Shell. Generalisation means solving new problems based on
already-solved problems. This includes reusing software (for example classes or functions) in newcontexts. In this example, the import of Python modules demonstrates the CT concept of generalisation.
The module ndir encapsulates all the details of reading sensor values vis the I²C bus. It was designed
to be useful in many projects.
However, this is only a rather weak demonstration. Learners get a deeper understanding, when they
define functions, classes and modules on their own and use them at different places in their projectsoftware.

Automatic Experiments With Human Actors
Measuring the CO2 concentration may be a part of a scientific experiment about diffusion. In special
environments like space satellites, experiments may be conducted completely automatically. In theclass room it is possible to involve humans as actors. The program tells the actor what to do (instead ofcontrolling motors directly) and calculates useful information from sensor data. This way the program
represents a precise description of the experiment. The program text is rather simple and concise, but
one can imagine that with some effort it is possible to automatize the experiment completely.
Story 2: Create an interactive program that helps to conduct an experiment that demonstrates the
diffusion of a gas.
The implementation of this story is an extension of the starter project.
from ndir import get_ppm
from time import sleep
print("Watch the diffusion of carbon dioxide.")number = input("Number of measurements: ")print("Now, breath to the NDIR sensor!")669

Constructionism 2018, Vilnius, Lithuania

for i in range(number):c = get_ppm()
print(c, "ppm" )sleep(5)Example Dialog
Watch the diffusion of carbon dioxide!
Number of measurements: 10
Now, breathe to the NDIR sensor!
722 ppm
709 ppm
935 ppm
1264 ppm
1181 ppm
1032 ppm
1012 ppm
1018 ppm
960 ppm
883 ppm

Summary and Conclusion
Developing interactive programs with Python may be an element of science education at high schools.
When students develop interactive assistant programs or semi-automatized experiments with sensor
readings, they both elaborate scientific content and apply computational thinking. During the process
they are challenged to design man-machine dialogs using appropriate scientific terms und units create and explicate precisely activities that are necessary to conduct an experiment apply scientific laws for calculating useful information from sensor reading.
The results of a study with 34 high school students from chemistry classes suggest that young people
find introductory programming projects in chemistry lessons instructive and stimulating. Using Python
as programming language, even novices can write relevant programs without previous training within aregular lesson. They may get an idea of some CT concepts. But there are limitations and several
reasons, why computational thinking is developed best in dedicated computer science classes:
To experience the benefits of decomposition and generalization, students need to create rather
complex software projects. Object oriented modelling by creating class structures requires a lot of
specific instruction and cannot be learned “on the fly” in chemistry classes.
 To deal with code that is more than a few lines, learners must know debugging techniques. These
include special technical knowledge about the IDE, debugging tools and defensive programming.
All this is not related to the core topics of traditional science.
 Beginners need a lot of time and some assistance, when they search for errors. Science teachers(who are not programmers) may not be able to help.
 Debugging – which is a necessary part of a complex project – is not very helpful for learningscience content. Debugging and testing are genuine software engineering activities. The goal is toincrease the technical quality of a software product. Science students might consider the need ofcoping with complexity as an annoying distraction.
Programming projects in pure science classes need to be simple. But they can still be relevant. The
pedagogical potential includes three facets:

Learning activities may get more interesting and more rewarding, since the students create usefuland relevant products.
Learning activities get more challenging, since the software design requires broad knowledge
about a field.

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

Learning can be more individual. In an iterative process, each student can individually decide how
far she or he wants to develop the project.

References
Baddeley, A. (2003): Working Memory Looking Back and Looking Forward. Nature Reviews
Neuroscience, Vol. 4, p. 829–839.
Beck, K. (1999) Extreme Programming Explained. Addison Wesley.
Beheshti, E., Weintrop, D., Swanson, H., Orton, K., Horn, M., Jona, K., ... & Wilensky, U. (2017, April).
Computational Thinking in Practice: How STEM Professionals Use CT in Their Work. In American
Education Research Association Annual Meeting 2017.
Dehn, M. J. (2008): Working Memory and Academic Learning. John Wiley & Sons, Hoboken, New
Jersey.
Google (2018): Computational Thinking for Educators.
https://computationalthinkingcourse.withgoogle.com/course .
Kramer, J. (2007). Is Abstraction the key to computing? In Communications of the ACM, 50(4), 37.
Swaid, S. I. (2015). Bringing computational thinking to STEM education. Procedia Manufacturing, 3,3657-3662.
Selby, C., &Woollard, J. (2013). Computational thinking: thehttps://eprints.soton.ac.uk/356481/1/Selby_Woollard_bg_soton_eprints.pdf .

developing

definition.

Stark, R., Mandl, H., Gruber, H., & Renkl, A. (2002). Conditions and effects of example elaboration.
Learning and Instruction, 12(1), p. 39-60.
Tausch, M. & von Wachtendonk, M. (2012) Chemie 2000+. Qualifikationsphase. Bamberg, Germany.

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Media Parkour– Experiential Learning Activities forMedia Education
Michael Weigend, mw@creative-informatics.deHolzkamp-Gesamtschule Witten, Germany

Fenja Göcking, fenja.goecking@gmx.de
Alexander Knuth, knuthalex@web.de
Patrick Pais Pereira, patrick-pais-pereira@hotmail.com
Laura Schmidt, laurakathleen2305@gmail.com

Abstract
This contribution presents a sequence of quick and easy experiential learning activities for sixth graders,which were developed, performed and evaluated by a high school pedagogy class. The activities mainly
take place outside in the schoolyard and challenge creativity, computational thinking and collaboration.
The learning arrangements are inspired by ideas of experiential learning and cover topics from media
education like appropriate reactions to cyber bullying, data transmission between mobile phones,
recognizing commercial advertisements, falsification of news etc. The Media Parkour was conductedwith almost fifty sixth-graders in the age of 11 to 12. They performed the activities in teams of four andthen rated them in a questionnaire.

Keywords
media education; computational thinking; gamification; experiential learning

Introduction
In most German schools, media education is not a dedicated subject. But since media are omnipresent,
media education is included in the curricula of several school subjects. Obviously, in language lessons
students learn to distinguish between different text types, while in art classes they create and interpret
visual media. In science digital models or simulations are used to visualize all kinds of processes.
Computer science education takes place, if digital technology is not just used as a tool but underlying
principles and ideas are made aware and discussed. This contribution presents a 90-minutes workshop
called “Media Parkour” for sixth graders, which has been created by a high school pedagogy class(grade 12). It consists of eight experiential activities with the primary goal to develop the competence ofusing digital technology in a responsible way. Additionally, computational thinking is challenged. The
design of the activities is based on the "NRW-Medienpass" and inspired by ideas of experiential
learning.

"NRW-Medienpass" and Digital Competencies
Germany is a federal republic and each federal state has its own regulations on media education at
schools. In North Rhine-Westphalia (NRW), the government has published a collection of media
competencies – the “NRW Medienpass” (media passport) – the schools are encouraged to consider intheir curricula. The primary goal is to help young people and children to use media in a safe, creativeand responsible way. The "NRW Medienpass" consists of six groups of competencies for different age
groups from Kindergarten to grade 13. These are "operating and applying digital technology","researching with digital technology", "communication and co-operation", "creating and presenting","analysing and reflecting" and since recently "problem solving and modelling". The "Media Parkour" hasbeen designed for pupils in grade five and six and focuses on "communication and co-operation withdigital technology". The skills include


using digital tools for communication,
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Constructionism 2018, Vilnius, Lithuaniaknowing and following rules for digital communication and co-operation,reflecting ethical principles and cultural and social norms when communicating or cooperating via
digital media,
being aware of social, personal and economical risks of technology usage, knowing consequencesof cybercrimes and knowing how to deal with them.

Learning Activities for Media Education
During the winter 2017/2018 two sixth grade classes of a comprehensive school in Witten, Germany
participated in the "Media Parkour".
The 90-minutes workshop has been designed by high school students from a Pedagogy class andconsists of eight learning activities or challenges, which were located at certain places community areasof the school building and on the schoolyard. Each activity was supervised by an adult student from thepedagogy class. The sixth graders went through the parkour in groups of four, solved tasks and received
points from the students responsible for the activity. Each group was guided by a pedagogy student. At
the end, the points were added up and a prize awaited the winning team.
The activity "What now?" is about correct comprehension of text messages and responsible
communication. The team receives cards with WhatsApp messages with different intentions of thesender (insulting a classmate, requesting to spread a chain letters, important information given by theclass teacher). The challenge is to recognize the sender's intention and suggest a procedure how todeal with the message.
In the arrangement "How to react best?" two members of the team must improvise a role play on cybermobbing in a WhatsApp group chat using role cards. The other two are audience. They must recognize
the roles and suggest how to react when watching such a dialogue.
The activity "Silent mail" is inspired by the well-known game and illustrates mechanisms of falsifying
information that is spread on the Internet. Each member of the team draws a card with instructions. The
first person additionally gets a photo depicting a playground scene. The children on the photo arelabelled with their names. One can see a harmless scene: "Anna" and "Lisa" are playing on a swing.
The first person’s instructions say that she or he should describe the situation to the second person(whispering silently into his or her ear) and mention the names of the kids on the photo. The second
person's role card says: "Anna once hurt you and you do not like her. When you tell stories about Anna,
you usually make her look bad." Person 3 often mixes up names and Person 4 is asked to tell the story
aloud, he or she had just heard but to exaggerate. At the end the team gets a list of possible reasons
why spread information might get falsified and find the one which was not effective in this case.
In the arrangement "Who is it?" three members of the team mime a bullying situation as living statues.
The fourth person is then asked to identify the different roles.
At the activity "The GPS" one member of the team (the “runner”) gets a blanket put over the head. Under
the blanket the “runner” can read text messages on a smartphone. The “navigator” writes text messages
thus guiding the “runner” through a course.
"The Maze" is very similar. A blindfolded person must find a way through a maze while the “navigator”
gives directions by tapping on the shoulders.
In "The Advertisement Quiz" the students must identify images from commercial advertisements.
The activity "Data Transmission" simulates a bit-by-bit data transfer from one mobile phone to another.
Three of these learning arrangements will be discussed in depth later.

Pedagogical Background of the Task Design
The content of the activities presented in the last section is based on the "NRW-Medienpass". The main
goal is to improve responsible media usage. This section discusses some methodological aspects.

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All learning arrangements are designed to challenge the children’s imaginations and creativity. This is
obvious in activities which contain role plays and “living statues”. But even if the main task is pure logicalreasoning, there is always space for creativity and individual solutions in details.
The activities have the character of experiential learning arrangements (Heckmeir & Michl, 2012). They
take place in the outdoors and many of them include physical activity (e.g. "The GPS" and “Datatransmission”. All challenges are performed in groups and require cooperation.
The French philosopher Roger Caillois(1958) defines game as a voluntary activity characterized bycompetition (agôn), unpredictability or chance nature (alea), adrenaline rushes (illinx) and masking(mimicry). According to this model, the “Media Parkour” is game-like. First, it is a competition. The teams
collect points according to their performance. The activities involving physical activity (like “The GPS”)
may (occasionally) lead to this emotional excitement that Caillois calls illinx. Role plays involvepretending to be someone (or something) else which is a type of mimicry. Elements of chance, whichmake an activity more suspenseful and game-like (alea) appear in little details like drawing role-cards.
The book "Computer Science Unplugged" (Bell, Witten, Fellows 2015) has inspired many educators todevelop learning activities on computer science without using a computer (e.g. Futschek & Moschitz
2010, Gallenbacher 2006, Weigend 2017). The "Media Parkour" follows the "unplugged" idea, but differs
in some points slightly from the classic concept:All activities challenge creativity in some way. The participants are supposed to invent(subjectively) new algorithms, visual representations, verbal explanations and ideas. According to
the constructionist idea, each activity should lead to some product that is individual and might beunique and surprising. On the other hand, “Computer Science Unplugged” contains a lot of(wonderful) tasks that are pure “brain teasers” and have just one correct solution, which must be
found out.
The "Media Parkour" is not completely "unplugged" since smartphones are involved.
The activities of CS Unplugged are explicitly related to computer science techniques like datacompression, binary numbers, fault tolerance and so on. The activities of the "Media Parkour" areprimarily related to media education. But many of them are connected to computer science at asecond glance. This will be discussed in the next section.

Computational concepts in media-related activities
Jeannette Wing (2006) popularised the term "computational thinking" as an approach to problem solving
that is typically adopted by computer scientists. Selby & Woollard (2013) identify five facets ofcomputational thinking: abstraction, algorithmic thinking, deconstruction, generalisation and evaluation.
This section discusses three example activities of the “Media Parkour” with respect to their contribution
to the development of computational thinking. We speculate about the educational potential; however,
we cannot provide an empirical evidence of effectiveness.

Identifying advertisements – algorithmic thinking
The team picks one of three cards with items (soda, chocolate, car). For each of these items there are
two photographs somewhere placed at the wall. One photo is from an internet advertisement and theother one is from an informative article just showing the item but not advertising for it. The task is: Go
to the photo, which is from an advertisement for the picked item. There are several strategies to identify
the correct photo:

Check whether the item appears in the photo.
Look for traces of post-processing. In one of the photos letters have been removed. By detecting
areas of pixels with exactly the same colour, such manipulations can be identified.
 Check the uniqueness of the item. For example, if the photo depicts bottles of multiple soda brands,
it is probably not from an advertisement for one of these brands.
 Check whether this photo is exciting and evokes positive emotions in the viewer.
While discussing how to distinguish ad photos from non-ad photos, the children develop algorithmic
ideas. Additionally, the limitations of algorithms may become obvious. For example, humans decide674Constructionism 2018, Vilnius, Lithuania

whether a photo is boring or exciting rather "intuitively" than algorithmically. In fact, modern software forrecognising commercials (perceptual ad highlighter) uses artificial intelligence (Hwang 2017).

Data Transmission – Evaluation of Algorithms
At the beginning, the children listen to a story: Tom (sender) wants to send a WhatsApp message to his
sister Tina (receiver). The characters of the message are (somehow) represented by zeros and ones
(bits) and sent via multiple intermediate stations form the sender to the receiver. This process will bereconstructed in a role play on the schoolyard. The challenge is to transmit a sequence of bits from thesender to the receiver whose location is at least 100 meters away from the sender and around thecorner of the school building so that the receiver cannot see the sender. The other two members of theteam are intermediate transmitters.
The children are not allowed to talk during the transmission and must represent the bits through body
postures. They get three minutes to develop a method for the transmission.
Then they form a chain of transmitting stations, the sender picks a piece of paper with a sequence ofzeros and ones and starts the transmission. The receiver at the other end of the chain writes down thereceived bits. After one minute the transmission stops and the received sequence is compared with theoriginal. The team gets points according to the number of correctly transmitted bits.
In this activity the students need to solve problems which are related to efficiency:

Zeros and ones must be mimed in a way that is well recognizable from the distance.

Sequences of identical bits must be handled. Transmitters must be able to recognize, when thetransmission of a bit starts and ends. Some teams chose relatively complex movements using
the whole body ("disco moves") to represent zeros and ones. This made is easy to recognize
individual bits correctly; however, this method was time consuming.
 The speed of the data transmission must be adjusted to a pace, in which each repeater station
has enough time to recognize a bit and to send it then to the next station. Usually, each student
watched his or her successor and waited until he or she was ready again for the transmission of
the next bit.
Of course there was not enough time to develop special data compression techniques to speed up datatransfer. But the students were put in a situation where they had to judge algorithms in terms of theireffectiveness and efficiency.

The GPS – abstraction and formal language
Each team chooses a "navigator" and a "runner". The runner puts a blanket over the head, which isthen attached to his body so that she or he cannot see the surrounding area. But he or she can readtext messages on a smartphone. The navigator (advised by the rest of the team) leads the “runner”
through a course by writing WhatsApp text messages (commands). Any other form of communication
is forbidden, and the runner is not allowed to ask questions.
During the exercise the team encounters the following problems related to controlling by verbal
commands:
The navigator’s commands must be clear and unambiguous.
Writing a command should not take too much time. The message must be short.
The navigator observes the runner to see if he or she interpreted and executed the latest commandcorrectly. If necessary the navigator can improve the commands.
 It is advantageous to have a limited set of commands that are fully understood.
During the workshop, not a single team had agreed on a set of commands beforehand. The WhatsApp
messages were semantically elementary ("right", "left", "straight ahead", "stop") but often decorated –sometimes (especially among boys) with friendly insults (“Go left, you bastard!”). Communication
problems were frequently observed. For example, the command "Go to the right!" does not clarify how
far the runner should move to the right. Some runners were cautious and had little confidence in thenavigator. They made just one single step and then waited for the next command. There were also
nasty navigators who occasionally guided their runners against a wall on purpose.




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The participants got the opportunity to “play with language” and recognize the benefits of a formal
language with well-defined semantics. Short commands involve abstraction since all unnecessary
details are omitted.
Another activity of this kind was developed at the Vienna University of Technology. Two players sit backto back and cannot see each other. Both players have the same set of building blocks. Person A builds
a figure. Afterwards, person B must reconstruct the figure according to A’s oral instructions. Person B
has only three ways to give feedback: "Ok” ("I’m ready for the next instruction”)
 "Repeat the last instruction”
 "All over again!”
The activity "The GPS" follows the same idea but is much simpler, which makes it suitable for younger
students. Additionally it includes physical exercise and can be performed on the schoolyard in the fresh
air.

Evaluation
At the end of the workshop, the children filled a questionnaire. The data of 38 persons (20 boys and 18
girls, average age 11.4 years) could be used for evaluation. It turned out that most children loved the"Media Parkour". They were asked to give a grade from 1 to 6 (equivalent to A to F in American schools).
The average grade was 1.9.The workshop has been designed as a contest. There is a controversial
discussion on the question whether competition has a positive effect on learning (see for example
Cantador 2015). In the questionnaire most of the participants claimed that that the competitive characterof this workshop was important to them. On a scale from 1 ("super important") to 6 (“totally unimportant")
the score reached an average of 2.4. There was only a small difference between girls (2.6) and boys(2.2).
Table 1 shows the results of the evaluation of the individual activities. The students could choose amaximum of three activities for each of the following categories:






Activities that were the most fun.
Activities that were the least fun.
The easiest activities.
The hardest activities.
Activities that dealt with important topics.
Table 1. Evaluation of the eight activities
Activity

most funleast fun

easy

hard

important

What now? (1)

16% (6)29% (11)24% (9)11% (4)42% (16)

How do I react correctly? (2)

13% (5)24% (9)34% (13)

13% (5)50% (19)

Silent Mail (3)26% (10)37% (14)

16% (6)

42% (16)

24% (9)Who is it? (4)26% (10)21% (8)42% (16)

13% (5)11% (4)The GPS (5)66% (25)

8% (3)

11% (4)34% (13)

24% (9)The Maze (6)55% (21)0% (0)

18% (7)24% (9)13% (5)The Advertisement Quiz (7)18% (7)42% (16)

45% (17)26% (10)

8% (3)

Data Transmission (8)61% (23)

13% (5)34% (13)

8% (3)

24% (9)The children had most fun with those tasks that involve self-directed activity and body movement. At"The GPS" (66%) and "The Maze" (55%) one person had to go through a path while being "remotecontrolled" by a classmate. In "Data Transmission" (61%) digital zeroes and ones were represented bybody movements. Other activities were more like a quiz or exam offering the children relatively little
opportunity for free action or interpretation. In the exercise "What now?" the children must find out theintentions of a sender of various text messages and assign them to the categories "chain letter", "cyber
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bullying" or "informative message". Only 16% of the children rated this activity as the most fun and 29%
rated it as one of the least enjoyable exercises. In "The Advertisement Quiz" the children had to
distinguish normal photos from advertisements (most fun: 18%, least fun: 42%).
Most participants claimed that "The Advertisement Quiz" was especially easy (45%) while others foundit to be difficult (26%). Other tasks that were found to be easy are "Who is it?" (Interpreting a “living
statue” and recognizing roles, 42%), "Data Transmission" (34%) and "How do I react correctly?" (34%).
On the other hand children found the exercise "Silent Mail" (falsification of news, 42%) particularly
difficult.
All activities were on topics that were relevant for media education according to the “NRW Medienpass”.
But how do the children rate the relevance of the activities? A very important topic seems to be
cyberbullying which was addressed in two activities, “How do I react correctly?" (50%) and "What now?"(42%). Only 8% considered the recognition of advertisement photos in "The Advertisement Quiz" asparticularly important. The activities "Silent Mail" and "Data Transmission" which are about explaining
how certain things work, were marked as relevant by 24% of the participants each.

Media and Computer Science – Educational Perspective
At the beginning of each activity the children got a little introduction, explicating the related technology
concepts and media competencies, if they were not obvious: Recognizing commercial advertising is an
important media skill. When sending messages, data is transmitted, and so on. For explaining thecomputer science aspects in more detail, specific technical expertise is required. But the tutors of thesmall groups were pedagogic students and not computer scientists. An improvement of the setting might
be the use of audio-visual material (designed with the support of experts) presented on tablet
computers. This could help the students to connect the activities to serious computer scienceknowledge.

References
Caillois, R. (1958) Les jeux et les hommes. Paris 1958.
Bell T. ,Witten, I. H., Fellow, M. (2015) CS Unplugged, Computer Science Without a Computer. URL:
https://classic.csunplugged.org/books/ 2015.
Boroditsky, L.; Ramscar, M.; Frank, M. C. (2001) The Roles of Body and Mind in Abstract Thought. In
Psychological science, 13(2), p. 185 – 189.
Cantador, I. (2015). An example of healthy competition in education. In Proceedings of the 2nd
International Workshop on Gamification in Education (gEducation 2015).
Caillois, R. (1958) Les jeux et les hommes. Paris 1958.
Futschek, G, Moschitz, J.: Developing algorithmic thinking by inventing and playing algorithms. In
Proceedings: Constructionism 2010.
Gallenbacher, J. (2006)Abenteuer Informatik. IT zum Anfassen. Spektrum Akademischer Verlag
Heckmair, B., & Michl, W. (2012)Erleben und lernen. 7th edition
Hwang, Y. (2017) The Best Adblocker – Using Artificial Intelligence to Perfectly Block Ads. In IT for All,
18. April 2017. URL: https://www.iotforall.com/best-adblocker-artificial-intelligence-to-perfectly-blockads/
Selby, C., &Woollard, J. (2013) Computational thinking: the developing
https://eprints.soton.ac.uk/356481/1/Selby_Woollard_bg_soton_eprints.pdf, 2013.

definition.

Weigend, M. (2017) Smartwalk: computer science on the schoolyard. In Proceedings: IFIP World
Conference on Computers in Education. Springer, Cham.
Wing, J. M. (2006) Computational Thinking. In Communications of the ACM, 49/3,p. 33–35.
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A Creative Learning Sequence in an Introductory
Programming MOOC
Elisabeth Wetzinger, elisabeth.wetzinger@tuwien.ac.at
Vienna University of Technology, Austria

Gerald Futschek, gerald.futschek@tuwien.ac.at
Vienna University of Technology, Austria

Bernhard Standl, bernhard.standl@ifs.tuwien.ac.at
Vienna University of Technology, Austria

Abstract
Since January 2017 we have designed and developed a Massive Open Online Course (MOOC) for
learning computer programming, addressing high-school students without or with little experience inprogramming who intend to start studying at a technical University. The objective of this MOOC is toconform the heterogeneous levels of our freshmen students’ pre-knowledge in computer programming.
We decided to use Processing72 as programming language because Processing supports an easy
learning start to programming by using graphics and animations. We included already at a very earlystage of the MOOC a challenging learning sequence that involves creativity, peer feedback andcommunication with other participants: We asked students to create a graphical artwork through coding
using previously in the course presented graphical procedures. Further, we ask them to create a fakecopy of an artwork coded by a peer. Finally, artists and fakers should discuss their experiences. This
paper summarizes and discusses our experiences with this learning sequence gained during the firstrun of the MOOC in summer 2017.

Keywords
creative learning sequence; MOOC; learning computer programming

Introduction
In the first-year of the bachelor programs of Computer Science and Business Informatics (CSBI),
students have a heterogeneous knowledge and experience levels in coding due to different high-school
pre-experiences. This fact challenges teaching and studying at the Faculty of Informatics and lead to ahigh drop-out rate73. In order to address these issues, our faculty has set measures to improve thebachelor programme curricula as well as teaching practice. The faculty also has established pre-study
bridging courses supporting students at the beginning of their studies in September before the actual
lectures start by learning basic skills in programming, maths and other topics of Computer Science. The
bridging courses are implemented as on-site workshops of which the programming course is limited to
100 participants following a first-come-first-serve rule. This means, that approximately 80% of students
accepted for a CSBI bachelor programme are not able to attend the programming bridging course.
As an additional bridging course for prospective students of the Computer Science and BusinessInformatics (CSBI) and other STEM undergraduate programs, we developed and provided a massive
open online course (MOOC) on introductory programming in German language called “Programmieren
mit Processing” (engl.: Programming with Processing). We followed the guidelines published by Ebneret al (2014). Especially prospective students, who are not able to attend on-site workshops benefit fromthe online course as it can be accomplished at one’s own pace and can be accessed from anywhere
only requiring internet access. As it supports massive numbers of participants, literally every prospective
or interested student is guaranteed to be able to accomplish the MOOC.

7273

http://processing.org (last access: 06/2018)
Source: http://www.tuwien.ac.at (Data from our institution)

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This MOOC has started in April 2018 at the public MOOC platform iMoox.at 74 and aims at serving
everybody who is interested in learning programming using Processing. Our goal is to provide a coursethat involves constructive learning sequences that provoke the students’ creativity, interest, andpassion.
In the next sections, we first present the idea of our MOOC and then the description of the learning
sequence in detail. We will close our paper with an overview on students’ outcomes and conclusions.

BackgroundResnick (2014) claims to give P’s a chance: Projects, peers, passion, play. He calls them “The 4 P’s ofCreative Learning: Projects, Peers, Passion, Play”. He argues that active working on meaningfulprojects supports new ideas in finding solutions. Also, exchange with peers and building on work ofothers can boost motivation and creative learning processes. Therefore, he also remarks that projects
should be rich enough to create passion in finding a good solution. Further, he underlines that a playful
way with room for experimentation helps to explore different solutions. Based on that, we are convinced,
that learn to code can also be more efficient when these 4 P’s of Creative Learning are involved in the
instructional design. In order to trigger these so called 4 P’s we need not only a suitable programming
system but also excellent learning activities. Nevertheless, typical Massive Open Online Courses(MOOCs) rely mostly on passive instructional video learning combined with text-based scripts andmultiple choice self-assessment. Koedinger et al (2015) showed that interactive activities support
learning more than instructional MOOCs.
For over a decade, computational thinking has been in the focus of educators and researchers incomputer science, particularly when it comes to learn how to code. Even though computational thinking
has become popular since Wing discussed and reintroduced the topic in 2006 (Wing 2006),
computational thinking still has a long tradition in history over decades (Tedre et al 2016). Despite thelack of an overall definition of computational thinking, there is for the most part consensus in including
problem-solving involving the thinking skills abstraction, decomposition, algorithmic thinking, evaluationand generalization (Dagiene et al 2017). Hence, learning programming should by its nature support
computational thinking skills. At any rate it comprises algorithmic thinking, but while analysing programs
and program output also evaluation skills are trained. Since a program is a kind of model of its outcome
also abstraction skills may be learned.
Considering both, Resnick’s 4P as a framework for building a useful learning environment that
conformed to the constructivist approach to coding, and computational thinking as a definition for
describing problem-solving in the field of computer science, we developed creative coding tasks as partof our MOOC. In particular, the learning sequence, as presented in this paper allows a playful
experimentation while building on another’s work and bringing in own ideas leading to a motivating andpassionate learning environment. Techniques and skills required in the implementation of the solution
are based on the decomposition of existing work, algorithmic reconstruction and the evaluation of thesolution.

MOOC: Learning Programming with Processing
The MOOC comprises ten course lectures and is implemented on the virtual learning environment
Moodle hosted by our college. The first lecture includes a course welcome and starts the course with abrief overview on fundamental questions about CS and programming, such as “What parts docomputers consist of?”, “How can we communicate with computers in order to make them solve ourproblems?”, “What are algorithms and abstraction?” and “Why should I learn to program?”
In order to comply with the curricula of the on-site pre-study courses and the Introductory Programming
lecture (which is compulsory for all CSBI bachelor students) the remaining course lectures follows anon-objects-first approach, which means, that the object-oriented programming paradigm is not
introduced from the beginning explicitly. Course lectures 2 to 10 introduce the Processing Integrated
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Development Environment (IDE) and basic programming concepts, such as: variables, operators, datatypes, basic animations and user interactions, decisions using if- and if-else-statements and loops aswell as functions, arrays and recursions.

Learning Sequence: Creating Visual Art with CodeThe first practical programming exercise is positioned at the end of lecture 2. This lecture introduces
the Processing Programming Integrated Development Environment (Processing IDE) which is usedthroughout the MOOC to write the Processing source code. Three videos and three handouts guide thestudents through downloading and installing the IDE as well as using it. In addition, they learn how to“draw” simple geometric objects and text by programming basic Processing commands. These include
lines, triangles, rectangles or ellipses of different sizes, orientations, positions and the use of colours
and transparencies. Finally, an overview of the Processing Reference is given which the course
participants are encouraged to use to look up available Processing commands, functions and operators.
The overall exercise topic is to create graphical artworks through coding and to fake-copy a peer’s
artwork. The assignment consists of three parts:
Part 1: Programming an artwork
In the first part of the exercise, the participants are asked to create a new sketch (i.e. Processing
program) and program a graphical artwork of their own choice by using their previously acquired
Programming skills and exploring the Processing Reference.
To keep the challenge of the second part of the exercise feasible especially for programming beginners,
we have set the following constraints for each artwork: The sketch canvas size should not exceed 800
by 800 pixels. Only geometric objects of the types “2D Primitives” and “Typography”, referring to theProcessing Reference, are allowed. The artwork should not consist of more than 20 of such objects.
However, we did not limit the use of colours, contours and transparencies. As part of the exercise, we
guide the participants through saving and executing their programs as well as we support them increating a screenshot of their visual outcome. The submission consists of two parts:
1. Uploading the respective Processing source code file as well as the screenshot of the sketch window,
which displays the artwork upon code execution.
2. Creating a thread in a forum provided for the exercise and uploading only the screenshot (but not thesource code file) of the artwork, a fitting artwork title, as well as a short description of oneself(programming skills, motivation to take part in the course, optionally intended study programme, orsimilar).
Part 2: Faking a peer’s artwork through coding
After students have programmed and uploaded their artwork to the forum, every participant is asked tobrowse through the forum and choose one artwork programmed by a peer. Based on the provided
screenshot he or she has to fake the artwork by programming it with Processing. The goal is to createa replica which is as close to the original as possible. The result, both the source code as well as ascreenshot of the artwork copy, should be uploaded as a reply to the original forum entry. Along with itthey are assigned to describe the main challenges they faced while copying the artwork and encouraged
to respond to the personal introduction of the peer by stating similarities or differences concerning
motivation for course participation, programming skill, study programme or similar.
Part 3: Discussion
Every participant is encouraged to check his or her own forum thread regularly for replies (i.e. artwork
fakes programmed by peers). If a reply is received, the student should compare the fake to the original
artwork and explain in the forum how the copy can be identified as such. Also, the original author is
supposed to check the fake source code, compare it to the original code and discuss how the Processing
programs differ and whether as well as how this is apparent in the resulting visual output or not.
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Our main objective behind the exercise is to follow the four P’s by Resnick, to foster a creative, playful
and individual experimentation with coding and to overcome potential fears or doubts of programming
easily. Therefore, we set only the first part of the exercise as compulsory. This means, for the overall
course accomplishment we took into account if a student has uploaded an artwork as well as created aforum topic.
The second and third parts are voluntary yet strongly recommended, but not taken into account forgrading or a successful course accomplishment. The reason for this decision are the students’heterogeneous pre-course programming skills which result in various levels of individual challenge andtime needed to accomplish the assignments. Our priority was to challenge both beginners as well as
advanced students at their individual levels. They should have fun and be able to solve the exercise to
their own abilities, rather than leave anyone feeling overloaded or even unable to solve the exercise
especially at this early stage of the course.
Didactic Background
With this exercise, we aim at encouraging the students to experiment with code and to take their firststeps in programming in a playful, creative and interactive way while practicing the use of the Processing
language and the use of the IDE. Without being much aware of the underlying programming concepts
or processes the participants should explore and experiment with the use of functions, parameters andthe computer graphics coordinate system as well as with the order of code execution. The graphicalfocus of the exercise and the immediate visual output of their programmed code enables visible
feedback of the impact of their programmed code to the output sketch immediately. This supports themin reflecting their written code and the corresponding graphical results through learning by doing.
As the assignment is implemented as the first hands-on programming exercise in the whole course,
programming skills are explicitly not needed as a prerequisite. Participants are provided only with basic
tools they need to solve the exercise and are encouraged to explore and “play” creatively andindividually using these tools as a starting point in order to create visual art.
Through this playful hands-on experience, we foster the students’ curiosity and support them inexperimenting with coding independently. With the programming reference, they are also equipped totry out further commands and extend their programming skills already at this early stage of the course.
Using this approach potential fears and barriers concerning programming and technology can beovercome easily and stress as well as pressure to perform or deliver are avoided. Students aresupported to study at their individual level, i.e. by creating simpler or more sophisticated and detailed
artworks depending on their own programming experience.
Finally, this exercise aims at community-building among the course participants, i.e. prospective CSBI
students already before they start their study programs. We consider this as important as our university
is located in downtown Vienna and its distributed building structure does not support informal student
socializing well. This means, that in addition to the challenges freshmen face with regard toadministrative and organizational aspects as well as the contents of their studies, they also have to start
networking with their peers, find friends and build study groups on their own at semester start. With theinteractions encouraged between peers especially during this exercise, we support prospective students
in getting to know each other prior their studies start so they might be able to master the challenging
first weeks of their studies in teams rather on their own.

Results
During the summer holiday break in 2017 we pilot-tested the course with prospective students of the
CSBI bachelor programmes. From a total of 315 active course participants, 126 students (i.e. 40%)
submitted an artwork (exercise part 1). Active refers to having at least one compulsory course activity
accomplished successfully. Figure 1 shows examples of artworks programmed and uploaded by MOOC
students.

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Figure 1. Examples of artworks programmed by course participants (ref. exercise part 1)

These examples visualize the huge variety and level of creativity of the resulting artworks programmedby the course participants using simple Processing commands only.
In total 38 artwork fakes were created by the students. Despite the fact that not every artwork was faked,
the resulting copies are incredibly close to the original as Figure 2 shows exemplarily. Based on the
results, it can be assumed that the course participants took high efforts in copying the original artworks
as exact as possible. Hence, only subtle differences are perceivable, such as in different colour ortransparency nuances or command order i.e. occlusion (e.g. artworks in Figure 2, rows 2 and 5) or small
differences in position and scale of objects (e.g. artworks in Figure 2, rows 1, 3 and 4).

Participants’ Feedback
At the end of each course lecture we asked the students for a brief feedback about what they liked anddisliked about it and how they would grade the lecture. In addition, we conducted an anonymous online
survey at the end of the course in order to get feedback about the overall course. With respect to theassignment the received feedback shows that the assignment was well-accepted among the students.
“The exercise was very much fun…”“The exercises were cool. I personally find the interaction with other course participants through a
discussion forum a really very good idea. By analysing the others’ contributions one gets to know new
ways of thinking and becomes more familiar with the geometric objects to be used in the exercise ingeneral. The more participants in the course, the better the result!”
On the one hand, two students were concerned that their artworks should be shared in a forum, on theother hand there were also requests for more exercise of this style throughout the course, because itsupports creativity and requires interaction with peers.

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Figure 2. Examples of original artworks (left) and their fakes (right) programmed by course participants(ref. exercise parts 1 and 2)

In several comments, they appreciated that the artwork exercise allowed them to apply the learned
content in a creative task and that they could independently create and program their own artwork as
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well as having the opportunity to choose and be creative in copying artworks of peers. However, one
student found it “very challenging to create an exact copy of the original artwork”. The “simple
introduction to programming allowing for visible feeling of success immediately” was appreciated andanother course participant evaluated the exercise as “playful yet professional introduction toprogramming with Processing”.
We compared the feedback on this learning sequence with feedback received on other parts of thecourse and conclude that this exercise was experienced as very exciting and encouraging.

Conclusion and Discussion
Although we intended to create a task that allows creativity, communication and peer feedback, we were
overwhelmed by the passion and playful way of learning by the participants. It is a further proof that the
4 P’s of Creativity are powerful. It also shows that creative tasks can enrich a mainly instructive MOOC.
The only disadvantage was the relatively low completion rate of the fakes compared to the original
artworks observed in our first run of the MOOC. In our opinion task part 2 provokes less creativity, hasa higher difficulty and needs a high effort to complete, although it boosts learning evaluation CT skills.
In the next runs of the MOOC we will improve this learning sequence according to our gained
experiences and we will include further learning sessions that support creativity.

References
Dagienė, V., Sentance, S., & Stupurienė, G. (2017). Developing a two-dimensional categorization
system for educational tasks in informatics. Informatica, 28(1), 23-44.
Ebner, M., Lackner, E., & Kopp, M. (2014, October). How to MOOC? - A pedagogical guideline forpractitioners. In The International Scientific Conference eLearning and Software for Education (Vol. 4,
p. 215). " Carol I" National Defence University.
Koedinger, K. R., Kim, J., Jia, J. Z., McLaughlin, E. A., & Bier, N. L. (2015, March). Learning is not aspectator sport: Doing is better than watching for learning from a MOOC. In Proceedings of the second(2015) ACM conference on learning@ scale (pp. 111-120). ACM.
Resnick, M. (2014, August). Give P's a chance: Projects, peers, passion, play. In Constructionism andcreativity: Proceedings of the Third International Constructionism Conference. Austrian Computer
Society, Vienna (pp. 13-20).
Tedre, M., & Denning, P. J. (2016, November). The long quest for computational thinking. In
Proceedings of the 16th Koli Calling International Conference on Computing Education Research (pp.
120-129). ACM.
Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33-35.

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Posters

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Educational Robotics for STEM: From Workshops
to Curricula and Framework
Carina Girvan, girvanc@cardiff.ac.uk
School of Social Sciences, Cardiff University,
Wales, UK

Wilfried Lepuschitz, lepuschitz@pria.at
Practical Robotics Institute Austria, Austria

Ivaylo Gueorguiev, ivo@esicenter.bg
Christina Todorova, tina@esicenter.bg
European Software Institute – Center Eastern
Europe, Sofia, Bulgaria.

Chronis Kynigos, kynigos@ppp.uoa.gr
Marianthi Grizioti, margrizioti@gmail.com
UoA ETL, National and Kapodistrian University ofAthens, Athens, Greece.

Angele Giuliano, angele@acrosslimits.com
Annalise Duca, annalise@acrosslimits.com
AcrossLimits, Malta

Julian M. Angel-Fernandez ,julian.angel.fernandez@tuwien.ac.atMarkus Vincze, vincze@acin.tuwien.ac.at
Technical University Wein, Vienna, Austria

Abstract
This poster presents the outcomes of the first two years of a three-year project exploring and refining
the use of educational robotics to engage young people in STEM education. It shows the developmentof activity plans which leverage constructionist concepts such as powerful ideas, objects to think withand the construction and sharing of personally meaningful artefacts in order to explore, test and extend
understanding within STEM domains. Workshops in which these activity plans have been tested andrefined, have so far engaged over 3,000 children between the ages of 7 and 18 in six European
countries. Additionally, conferences and competitions for young people have been held each year where
they compete and collaborate with others. These workshops and competitions have been systematicallyevaluated through QUAL+quant mixed methods, in order to inform the development of future activities
and refinement of existing ones. Through this process we identify key components of successful
educational robotics activities for STEM education. These have, in-turn, informed the design of aframework for educational robotics. The activity plans have informed the bottom-up development of ageneric curriculum for educational robotics in STEM and are available on a dedicated repository. This
poster presents a snapshot of each of these project outcomes.

Keywords
Constructionism; educational robotics; science; technology; engineering; mathematics; learning; STEM

Introduction
Constructionism and the use of robots in education seemingly go hand-in-hand. Yet, any review of theliterature on the potential of educational robotics to engage young people in science, technology,
engineering and mathematics (STEM), shows that it is often only lip-service that is paid to the ideas of
Seymour Papert. Simply stating an activity to be constructionist, does not make it so. Educational
robotics, as an area of research, is also one that cuts across various fields with academics fromeducation, psychology, mathematics, engineering, computer science and science conducting researchin this space, with various pedagogical approaches, research methods and objectives, with activities
and research often implemented outside of the constraints of the traditional school system. Teachers,
keen to embrace robotics in the classroom, can struggle to know how to design activities. They facepressures such as demonstrating measurable learning outcomes for individual students, siloed subject
teaching and packed curricula. Should concepts be taught in advance and applied to robotics activities,
should activities be structured to provide or require engagement with specific concepts to be completed,or should we allow students to explore, test and extend their own understanding in their own time andin their own way, potentially never explicitly engaging with the concept which should be learned?
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The project presented in this poster aims to foster the interest in STEM and powerful ideas that children
have at a young age (Lammer, et al., 2017), but is often lost as they progress through formal education
and are increasingly exposed to societal pressures. In particular we are interested in the potential of
educational robotics to engage all young people with STEM, particularly girls (Nugent, et al., 2010). The
project involves the development and redesign of robotics tools and learning spaces for young people,
implementing these and other commercially available robotics tools in workshops and competitions withover 4,000 children ages 7-18, in six European countries. The workshops and competitions have beensystematically evaluated through a large-scale, QUAL+quant mixed methods research design, involving
in-depth case studies in each country. This research has informed the design of new and thedevelopment of existing educational robotics activities. It has contributed to the development of activity
plans which guide the design of activities and exemplify a generic curriculum for robotics. As this work
matures, we aim to develop a framework for the design of educational robotics activities, which isaccessible to both teachers and researchers.
Constructionist concepts such as powerful ideas, objects to think with and the construction and sharingof personally meaningful artefacts in order to explore, test and extend understanding within STEM
domains, are used to underpin the design of activity plans and in-turn workshops. They also provide away to explicitly introduce 21st Century Skills, the creative arts and creative expression in sucheducational robotics activities.

Activity Plans and Curricula
A standardised activity planning template acts as a common learning design instrument through which
project partners design and communicate their design of educational robotics workshops to each other.
The activity plan template aims to raise awareness of the key characteristics of constructionist
educational robotics activities (Yiannoutsou, et al., 2017) and has developed in response to partner
feedback as well as issues and opportunities highlighted in the evaluation. This template has also
informed the design of a bespoke online repository of workshops for teachers and others interested in
the use of educational robotics.
Activity plans, act as a mediating artefact through which various groups involved in the project such asteachers, academics, commercial partners, engineers and computer scientists, are able to share theirown beliefs and experiences. They provide a way to identify and question assumptions. Ultimately, they
act as a tool through which a shared understanding of the key components of the design of educational
activities can be developed and expressed.
Reviewing the activity plans and other freely accessible educational robotics resources, a bottom-up
approach is currently being used to organise the implemented activity plans and develop a series ofgeneric curriculum paths, organised around STEM domains and complemented by arts, business and21st century skills.

Workshops and Competitions
By the end of the project, activity plans will have been implemented in workshops involving more than
4,000 students in Austria, Bulgaria, Greece, Ireland, Malta and the UK, between the ages of 7 and 18.
Workshops typically take place in schools during normal hours. Some workshops are held as one/twofull-day events, where the normal timetable is suspended, whilst others occur over several weeks,
integrated into subject specific lessons and the regular school timetable. A wide variety of robotics tools
and learning environments have been used or created and integrated into the workshops, including
LEGO Mindstorms, LEGO WeDo, Finch, Hedgehog (Koza et al., 2016) and also SLurtles (Girvan et al.,2013) in purpose designed virtual worlds. Workshops may be delivered by a small team including theclass teacher, or delivered solely by the teacher, without any additional support. There are advantages
and disadvantages to these different approaches, both in terms of learner outcomes and research.
There are a range of national and international robotics competitions for young people in Europe. This
project examines the European regional Botball tournament which involves multiple rounds and different
forms of competition. While teams competing against each other are the most common form of robotics
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competitions, the Botball competition includes an Alliance round in which two teams collaborate to scorethe most points together. Alongside the competition there is also a conference for which each teamprepares and delivers a paper. These often focus on technical developments or suggested
improvements to the kit identified by the students and tend to focus on the engineering and technology
domains.

Research
One of the primary aims of the project was to increase young people’s interest and engagement in
STEM subjects and careers. The research team were interested to find out about what concepts
students participating in workshops and competitions learned, whether gender stereotypes werechallenged and whether those who were originally not interested in STEM became interested throughthe activities, and why. The research explores whether there is a gender dimension to the roles students
take and their participation in activities, in addition to whether children develop soft-skills. Another aimof the project was to develop multiple ways for students to engage with robotics activities and forstudents to take ownership of the activities they were involved in.
To evaluate whether the aims of the project have been met and to begin to answer the researchquestions, data collection has been ongoing throughout this project. Students involved in the workshops
and competitions have been invited to participate in the research, with parental consent. A single
evaluation kit is used by all partners, which provides all data collection instruments and schedules,
including: pre and post workshop/competition questionnaires, interview and observation schedules,
procedures for artefact collection (robots constructed, code written, drawing produced, etc) andreflective activities for teachers and students.
In the first year of the project the aim was to pilot the evaluation kit and explore existing practices, toinform the development of the framework and activity plans, which in turn would influence the design ofactivities in the second year of the project. Each workshop or competition was treated as a single casestudy. Within each case study, data was analysed using the constant comparative approach, allowing
the researcher to remain open to emerging themes throughout. Single case studies could then be
drawn together to allow analysis across or within countries, age groups, gender or other factors.
The second year of the project focused on the recommendations which emerged from the first year,
their implementation and the impact, in addition to exploring the original research aims of the project.
While analysis of the data was more focused, it remained largely interpretivist, allowing researchers toremaining open to emerging codes and themes. Currently the project is in its final year, in which thedata analysis has become yet more focused on a specific set of issues raised in the second year,
however interpretivist approaches remain central. In addition, the third year provides an opportunity to
explore the impact of the developing framework by contrasting the findings with the previous two years.

Developing a Framework
The development of an inclusive framework for the design of educational robotics activities in STEM
began with a review of the literature and existing frameworks. Synthesising this work and considering
it in relation to the findings of the research evaluation in years one and two, demonstrated that whilst
many aspects of these frameworks were relevant, there were aspects such as team formation andmodes of collaboration which were either missing or lacking insufficient detail.
Informed by the evaluation, the project team are currently developing a framework for educational
robotics activities which is underpinned by constructionist ideas.

Acknowledgements
The project has received funding from the European Union’s Horizon 2020 research and innovation
program under grant agreement No. 665972. Project Educational Robotics for STEM: ER4STEM

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References
Girvan, C., Tangney, B. & Savage, T. (2013). SLurtles: A tool to support constructionist learning in
Second Life. Computers & Education 61(1) 115-132.
Lammer, L., Lepuschitz, W., Kynigos, C., Giuliano, A., & Girvan, C. (2017). ER4STEM Educational
Robotics for Science, Technology, Engineering and Mathematics. In M. Merdan, W. Lepuschitz, G.
Koppensteiner & R. Balogh (Eds.) Robotics in Education Advances in Intelligent Systems andComputing, vol. 457. Switzerland: Springer International Publishing, 95-101.
Koza, C., Lepuschitz, W., Wolff, M., Frank, D., & Koppensteiner, G. (2016, November). Hedgehog light–a versatile, white box educational robotics controller. In International Conference EduRobotics 2016
(pp. 229-232). Springer, Cham.
Nugent, G., Barker, B., Grandgenett, N., & Adamchuk, V. I. (2010). Impact of robotics and geospatial
technology interventions on youth STEM learning and attitudes. Journal of Research on Technology in
Education, 42(4), 391-408.
Yiannoutsou, N., Nikitopoulou, S., Kynigos, C., Gueorguiev, I., & Fernandez, J. A. (2017). Activity plan
template: a mediating tool for supporting learning design with robotics In M. Merdan, W. Lepuschitz, G.
Koppensteiner & R. Balogh (Eds.) Robotics in Education Advances in Intelligent Systems andComputing, vol. 457. Switzerland: Springer International Publishing, 3-13. doi:10.1007/978-3-31942975-5_1

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Towards a Generic Curriculum for Educational
Robotics in STEM: From Scientific Concepts toTechnologies and Powerful Ideas
Ivaylo Gueorguiev, ivo@esicenter.bg
Christina Todorova, tina@esicenter.bg
European Software Institute – Center Eastern
Europe, Sofia, Bulgaria

Nikoleta Yiannoutsou,
nyiannoutsou@ppp.uoa.gr
Xristina Greka xristgreka@gmail.com
UoA ETL, National and Kapodistrian University ofAthens, Athens, Greece

Pavel Varbanov, pavel@esicenter.bg
George Sharkov, gesha@esicenter.bg
European Software Institute – Center Eastern
Europe, Sofia, Bulgaria

Carina Girvan, girvanc@cardiff.ac.uk
School of Social Sciences, Cardiff University,
Wales, UK

Julian M. Angel-Fernandez ,julian.angel.fernandez@tuwien.ac.at
Vienna University of Technology, Vienna, Austria

Lisa Vittori, vittori@pria.at
Practical Robotics Institute Austria, Austria

Annalise Duca,annalise@acrosslimits.com
AcrossLimits, Malta

Abstract
This paper and its corresponding poster present a “work in progress” concept for the visualization of 19
activity plans, into a generic curriculum map for teaching STEM concepts through constructionist
robotics activities. There are six educational paths that represent potential use cases and these havebeen validated through 148 educational robotics workshops implemented with children between theages of 7 and 18 in six European countries.

Keywords
educational robotics; curricula; robotics; workshop; science; technology; engineering; mathematics;
learning; STEM

Introduction
Many children lose their natural curiosity for how things function and interrelate to each other along theway into their lives as young adults. The ER4STEM project aims to turn curious young children into
young adults passionate about science and technology with a hands-on use case: robotics. In this poster
we present our effort to develop a generic curriculum for robotics using a bottom-up approach: i.e.
analysing several activity plans produced as part of the project. Our analysis follows different
educational paths aiming to construct a curriculum that addresses the different facets of educational
robotics: a) powerful ideas; b) scientific concepts and skills; c) technologies and engineering; d)mathematics. The result of this analysis is presented in the form of a ‘metro map’ showing how an
educator could navigate through the educational robotics activity plans taking each of these educational
paths.
We embarked on this effort understanding that a curriculum is a complex concept, which ends up beinga matter of profound philosophical discussion, as it yet revolves around the notion of knowledge andnotably, of what is of importance to be learned for the individual within the context of the society, inwhich they function (Bers, 2003). However, in our case, instead of following a top-down approach andlooking at what should be learned in terms of educational robotics, we followed a bottom-up approach.
Consequently, we analyse how we can identify teaching and learning elements, which are connectedto the nature of educational robotics: i.e. constructionist technology, multidisciplinary domain, subject
matter and a domain for contextualized learning of other subject matters.
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ER4STEM project is funded by the European Commission through the Horizon 2020 Program (H2020,
Grant agreement no: 665972).

A Generic Curriculum for Applying Educational Robotics in
STEM-Related Education
Among the core goals of the project is to deliver a generic educational robotics curriculum. It aims tofacilitate usability of the project’s products by a broader community of relevant stakeholders such as,
but not limited to, teachers, researchers and 3rd sector organisations.
Assuming the general definition of curriculum being a general plan for educational activities, Adams andAdams (2003) define a curriculum as everything that goes in the learners’ life such as planned and notplanned interaction of pupils with educational objectives, instructional content, materials and resources
used and materials and resources not used, the sequence of courses, objective, standards andinterpersonal relationships.
In order to accomplish the project objectives and to research the domain of using robotics in education,
we organise educational robotics workshops. The activities developed follow a learning design
instrument “Activity Plan Template”, which aims at raising teachers’ awareness of the key characteristics
of educational robotics implemented in constructionist ways (Yiannoutsou et al 2017).
Taking into account the above mentioned curriculum definition and the experience gained so far, we
decided to use a bottom-up approach for organising the 19 Activity Plans as adoptable genericcurriculum paths organised around Science, Technology, Engineering and Mathematics domains andfurther complemented by arts, business and 21st century skills.
The activity plans were designed, implemented and validated through 148 educational robotics
workshops with more than 3500 students implemented in Austria, Bulgaria, Greece, Malta, Ireland andthe UK, over two years. However, due to time constraints, the entire curricula paths, consisting of an
educational sequence of activity plans as a single entity were not implemented. Therefore, we consider
the curricula on the level of educational paths of a generic nature by design and they will need to befurther adapted to meet context specific desired learning outcomes, specific objectives and classroom
needs.

The Six Educational Paths Visualised as a “Metro Map”
With this generic educational robotics curriculum, we want to assist teachers in adapting a learning path,consisting of a set of educational robotics workshops or single activities within the workshops activity
plans, that are suitable for their context, students, specific objectives and prior knowledge.
The six educational paths built around the STEM domains, are based on the following use cases:
1. An educator aims to teach technology and engineering with Lego Mindstorms robots. Thisis a typical use case of using robots for education
2. An educator aims to teach concepts of science, technology and engineering while
demonstrating various technology platforms such as Arduino, Beebots, Hedgehog, LEGO
Mindstorms and LEGO WeDo as well as a vast plethora of programing languages C++,
Python, LEGO Mindstorms Programming, Scratch or SNAP.
3. An educator aims to cover all STEM domains, Science, Technology, Engineering andMathematics, while also developing creativity and business skills among the students.
4. An educator aims to teach concepts in science, technology and engineering through “whitebox” platforms such as:” Arduino, Hedgehog, LEGO WeDo with a stronger focus on electronics
and widely used industrial programming languages such as C++ and Python
5. An educator aims to teach Mathematics by using educational robotics technology. This pathcovers multiple mathematical concepts;
6. An educator aims to teach concepts in Technology, Engineering and Mathematics.
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While providing those generic educational paths, we still consider it of utmost importance for teachers,
pedagogues and researchers, while following the suggested curriculum paths, to tailor the activity plans
to their context and thus design their own, separate curriculum.
The generic learning paths are a curriculum property, which will be further elaborated in the next months
and will represent certain relationships between the activity plans contained in the curriculum.

Figure 1. Six educational paths of generic curricula presented as a metro map in which each point is an activity
plan for and educational robotics workshop

To illustrate the concept, we could take for instance red line 3, as presented in Figure 1. The entire
educational path, represented by the red line, consists of 37 hours of educational robotics content tocover all STEM domains complemented by concepts from domains such as business and art. The first“metro stop” where we “start the journey” is ES01 named “Educational Robotics and Creativity
Workshop. Within this workshops, students with no prior experience, related to the implementation of atechnical project in a collaborative environment explore the fields of technology and engineering whileconstructing and programing a robotic vehicle and applying mind-mapping to generate ideas on theapplication of robotics in various fields. The provoked students’ interest and curiosity in technology arefurther developed within the second workshop ES02: Visualising Mathematics with Mathbot” where thestudents, who are already familiar with the basics of programming robot in Scratch, are exploring
mathematical concepts, such as measuring angles, types of triangles, properties of a circle andproportions. In the next “metro stop”, UA01 named “An introduction to robotics - exploring planet ektonis”
students have a chance to learn, further develop and practice concepts of science, technology andengineering while also using mathematics and art. At the last “metro stops” TW01 named “Robot-video
elementary” and TW02 named “Robot-video advanced” the students are embarking on a hands-on
learning experience in technology and engineering, while developing business skills and expressing
their creativity through video and robotics. The same “metro line” could be taken the other way around,just by following the dashed red lines. As one can notice, the “metro map” illustrated in Figure 1, threeof the five “metro stops” of the red line are also part of other proposed generic educational paths, serving
different desired learning outcomes.

Powerful ideas
Our work in this dimension is in progress and focuses on what the central ideas are around which theactivities evolve and how these are expressed in the robotic constructions. Powerful ideas are a central
theoretical construct of constructionism and involve the use of knowledge in new ways where learners
can establish personal epistemological connections with various domains of knowledge (Paret, 2000).
Furthermore, analysing powerful ideas Bers (2008) describes them as domains of knowledge where
many diverse topics or subjects co-exist and become explicit. Viewing activity plans from this lens poses
the following challenges: a) powerful ideas might not imply a “progress” from one activity plan to another692

Constructionism 2018, Vilnius, Lithuania(in terms of student age, prerequisites etc) so we need to seek other connections that can bemeaningful; b) not all activity plans are built around powerful ideas but they might have the potential toevolve in this direction. While traveling in the STEM metro line, students also develop collaborativeskills, take roles within groups, solve practical problems in teams, communicate with other teams to
exchange ideas and tips in the social and actions domains.

Conclusion
We are confident that the presented approach for the organization of a generic curriculum will provide
value to educators that are considering to apply educational robotics activities in their classrooms orwould like to generate ideas on how to diversify their already applied approach. Furthermore, the work
presented will attempt at visualizing the context and content of the educational robotics activities
designed and developed within the project.

References
Adams, K. L., & Adams, D. E. (2003). Urban Education a Reference Handbook. Santa Barbara,
California: ABC Clio.
Bers, M. (2008). Blocks, robots and computers: Learning about technology in early childhood. New
York: Teacher’s College Press
Papert, S. (2000). What’s the big idea? Towards a pedagogy of idea power. IBM Systems Journal, 39(34), 720–729.
Walker,D.F (2003) Fundamentals of curriculum: passion and professionalism, Mahwah, N.J: L. Erlbaum
Associates.
Yiannoutsou N., Nikitopoulou S., Kynigos C., Gueorguiev I., Fernandez J.A. (2017) Activity Plan
Template: A Mediating Tool for Supporting Learning Design with Robotics. In: Merdan M., LepuschitzW., Koppensteiner G., Balogh R. (eds) Robotics in Education. Advances in Intelligent Systems andComputing, vol 457. Springer

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Heuristic Potential of Open Institutional Models in
Researchers Education
Liudmyla Kryvoruchka, kryvoruchkald@ukma.edu.ua
National University of Kyiv-Mohyla Academy, Ukraine

Abstract
The national and cross-border training networks for PhD education established in accordance with EU
Principles for Innovative Doctoral Training (2011) and Open Science agenda have already contributed
to significant transformation of traditional researcher education settings across Europe. This poster
presents the preliminary results of Erasmus+ DocHub project (2016-2018) being implemented in
Ukraine by partnership of 5 Ukrainian and 4 EU HEIs together with 6 Ukrainian research institutions.
The obvious impact of such collaborative efforts at organisational and systematic level often hides
substantial heuristic potential of diverse instructional and mentoring strategies as well as learners’
experiences. The jointly established learning environment of DocHub is structured by the idea of
openness and aimed at engaging PhD students and early researchers in active and continuous
exchange and thus enhance dialogic and innovative mind-set and support experimentation.

Keywords
Openness, inter-institutional cooperation, dialogic and innovative mind-set

BackgroundThe need for educating doctoral students as competent innovative researchers has been actively
discussed during the last decade. Rapidly increasing complexity of research work in the open global
research community encourages HEI to search for the best practices and institutional structures toensure their PhD graduates are ready for future challenges.
Doctoral education is considered a key contribution to all 3 main policy goals of EU research andinnovation introduced in 2015: Open Innovation, Open Science and Open to the World. Open Sciencecreates the vision of research as collaborative and transparent, therefore, recommendations from the
Bologna Process (“Salzburg Recommendations” 2005, “Salzburg II Recommendations” 2010; “EU
Principles for Innovative Doctoral Training” 2011) have to be re-interpreted by HEIs to implement an
open collaborative model of doctoral programs.
In the context of Ukrainian educational system the need for the collaborative model is crucial toaccelerate the Bologna 3rd cycle reform. Traditional one-to-one mentorship system remains a basis forthe academic practices of Ukraine, so implementation of structured PhD training according to the Law“On Higher Education” (enacted in 2014) is still challenging for both HEIs and research institutes of the
National Academy of Sciences. Though in 2016 the structured PhD training model became obligatory,
only few institutions have developed PhD training programs of sufficient quality and relevant teaching
methodologies compliant with the EU recommendations. National reform of the 3 rd cycle in Ukraine
requires cultural change through exposure to an alternative approach to researchers’ training.
The EU funded Erasmus+ project “Structuring cooperation in doctoral research, transferrable skills
training, and academic writing instruction in Ukraine's regions/DocHub (2016-2019), engaged 5
Ukrainian universities and 6 research institutes of the National Academy of Sciences of Ukraine (withthe expertise input from 4 EU HEIs) into establishing national centers of excellence in PhD training for
aggregation of critical mass, boosting research capacity and provisioning of high-quality transferrable
skills and language training programs.
In this paper, we present and discuss the tools and models of collaborative doctoral training to showhow open culture being established by DocHub project activities can foster both institutional
development and the personal development of doctoral candidates and educators.
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Impact of the DocHub community
In the diverse landscape of European doctoral education we see many examples of successful openmodels of doctoral training implemented on institutional and/or national level, such as: Innovative
Training Networks (ITN), European Industrial Doctorates (EID) funded by Marie Skłodovska-Curie
actions in Horizon2020, Doctoral Centers of Excellence, Doctoral Training Networks (UK), Finnish
Doctoral Training Network (Finland), Regional Doctoral Schools (France). The key benefits ofcollaborative practice are establishing a high-level research environment with complementarycompetences, sharing of knowledge and infrastructure, the exchange and sharing training
modules/courses, building supervision and mentoring groups for individual research projects, creating
possibilities to work in interdisciplinary area, enhancing future employability of doctoral candidates, andan increase in mobility.
As we can see the networks are based either on regional models (i.e. Finnish Doctoral Training
Network, Finland) or research subject model (i.e. Doctoral Training Networks, UK). The DocHub projectwill use both models in Ukraine by establishing:(1)

5 HEI-based regional centers of excellence (DocHubs in Kyiv, Lviv, Dnipro, Mykolaiv andKharkiv) with developed infrastructure and systems for training advanced academic writing in
English, including components of the blended learning format which has become crucial for theinternationalization of research and innovation at the national level; providing validated curriculaof transferrable skills according to EU quality assurance standards;(2)

5 subject-based inter-institutional research groups (Educational Policy, Chemistry/Biochemistry,
IT, Political Sciences, and Finances, more than 110 in total) to provide collaborative professional
training, supervising and mentorship by the cooperation of university researchers and scholars
from research institutes.

To accomplish the above goals the project group is developing (with the support of the Ministry of
Education and Science of Ukraine) the regulatory framework for inter-institutional cooperation andfurther development of the 3rd cycle of reforms based on the EU best practices. This involves drafting
and adopting policies and national regulations (including changes to legislation) related to licensing andaccreditation of PhD programs, funding of doctoral education, procedures of qualification. Project group
initiated a regular open seminar with the participation of doctoral education stakeholders for 2018-2019
academic year.
A regional-based networking model in Ukrainian context proved to be an effective tool of enhancing
institutional capacity of the regional universities: the modular courses (Advanced Academic
Communications, Transferrable Skills Curricula) were designed and developed as a result of joint effort
and active discussion of peer-group with the expert input from EU partners; but after internal andexternal evaluation the curricula was piloted by each lecturer/trainer individually with episodic coteaching sessions. Using more advanced course materials increased the level of teaching skills andknowledge. As a result of DocHub activities all institutions introduced new courses and research topics
in their doctoral training programs
In a subject-based model DocHub partners actively used the complimentary expertise and developed
training programs based on the individual research interests and achievements of engaged researchers
from research institutes and universities (36 courses). The aggregated groups of PhD students fromdifferent HEIs (up to 900 in total) participated in piloting curricula introduced by academicians fromdifferent regions and institutions.
Both models of networking can be a solution for most of Ukrainian HEIs that typically receive just a few
state-budgeted PhD positions in each subject area and have difficulties with provisioning effective
training and establishing critical mass. One of the objectives of the DocHub is to develop a legal
framework and a model for “national credit mobility” to boost inter-HEIs cooperation and overcome therapture between institutions of the National Academy of Sciences of Ukraine and universities inheritedfrom the USSR.

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Adoption of the DocHub Code of Practice and the formal establishing of collaborative units at theinstitutional level was re-scheduled from the 1st to the 2nd year of the project to give time for defining acommon vision and a shared understanding of responsibilities based on realistic research-based
connections. On the basis of agreed general framework (principles of inclusiveness, high academic
standards, internationalization, research integrity) each DocHub should now set up a working model for
decision making and management (via Academic Councils and administrative group) and tools for
effective communications (seminars, conferences, working groups, groups for supervision andmentoring).
Mandatory national regulations on doctoral training (enacted in 2016) require provision of structural
educational programs for at least 2 years of study totaling from 30 to 60 ECTS. In accordance with
“Salzburg Recommendations” and “EU Principles for Innovative Doctoral Training” the significant part(at least 20 ECTS) of obligatory credit workload should be aimed at developing transferrable skills(research ethics, informational literacy, research management and language skills). However the issue
of instructional design differentiated from 1st and 2nd cycles in content and purposes and suitable
instructional methods for effective language and transferrable skills training is the most challenging areaof doctoral education in Ukraine due to the lack of skills required by new framework of global research
practice (like Open Science model) and issues with research integrity. Therefore project group initiatedtraining on trends in 3rd cycle best practices and established a regular seminar on 3rd cycle teaching for
2018-2019.
DocHub project team is actively promoting collaborative scheme of doctoral training at regional andnational level. Upon project completion regional DocHubs should be considered as centers of expertisefor provision not only PhD students training, but for continuing professional development of researchers
at all stages of career. DocHubs research communities will contribute to regional development serving
as points of cooperation with interested public and business entities.
Collaboration and networking in doctoral education is a transformative concept related to the emerging
Open Education practices and Open Learning culture and thus will move the discussion about the modelof researchers’ professional development forward.

Heuristic potential of open institutional models
DocHub allows doctoral students access to a large pool of concepts, techniques, methods, skillset aswell as personal experience and micro-networks of each senior scholar. The obvious efficiency of suchcollaborative efforts at the organisational and systematic level of doctoral education hides substantial
heuristic potential of diverse instructional and mentoring strategies.
Any partnership strives to seamless connections, but institutional collaboration not only increases therates of knowledge exchange, but also conflicts of approaches and values that need to be resolved
through individual reflective efforts.
DocHub networks generate a unique learning environment driven by epistemological tension between
distinct academic cultures (often under-articulated) and scholar approaches, though aligning in the basic
objective to produce original research.
According to the EU Principles of Innovative Doctoral Training (2011) “research excellence” isexpressed by a researcher that was trained as a “creative, critical and autonomous intellectual risk taker,
pushing the boundaries of frontier research”. In this context DocHub sets the “attractive institutional
environment” for researchers that would be ready to take risk of holding the uncertainty of open
dialogue.
To ensure continuation in such open research collaboration an individual is to be committed to accept
the complementary role of any individual position or expertise and this is possible when a participant
develops dialogic mind-set. At the institutional level partnership should establish the scheme forconstant communications to minimize misunderstanding.
When co-teaching and group discussion of doctoral projects in an institutionally open supervisory group,
as well as national and international mobility, become a routine practice amongst PhD students, we caninfer that such settings encourage open learning culture. In collaborative schemes, both doctoral
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students and senior researchers are directly exposed to diversified communities and practices. This will
aid them in the transition to new technology-driven and societally engaged research.

Conclusions
It is apparent that Ukrainian universities and research institutions are highly committed to develop
innovative training networks to increase their research and learning capacities to equip doctoral students
and early researchers with competences needed in a knowledge-based economy.
The Open Institutional Model is now considered a very effective tool for engaging researchers in acooperative model of research work in globalized context.
The joint learning environment of DocHub is structured around the idea of openness, the support ofexperimentation, and focused on engaging researchers in an active and continuous exchange, thus
promoting the development of a dialogic and innovative mind-set.

Acknowledgments
The author gratefully acknowledge the support of the Erasmus+ KA2 – Cooperation for innovation andthe exchange of good practices – Capacity Building in the field of Higher Education. In particular throughthe funding of the three year project “Structuring cooperation in doctoral research, transferrable skills
training, and academic writing instruction in Ukraine’s regions / DocHub” with project code 574064-EPP2016-LT-EPPKA2-SNHE-SP.

References
A Renewed EU Agenda for Higher Education.COM (2017)
Doctoral Education – Taking Salzburg Forward. Implementation and new challenges. (2016) EUA-CDA
Castaño Muñoz, J., Punie, Y., Inamorato dos Santos, A., Mitic, M. & Morais, R. (2016): How are Higher
Education Institutions Dealing with Openness? A Survey of Practices, Beliefs and Strategies in Five
European Countries. Institute for Prospective Technological Studies. JRC Science for Policy Report,
Huitt, W G, & Monetti, D M. (2017). Openness and the Transformation of Education and Schooling.
Open: The Philosophy and Practices that are Revolutionizing Education and Science. Ubiquity Press,
43–65.
Principles for Innovative Doctoral Training (2011).
Statement from the EUA-CDE Global Strategic Forum on Doctoral Education (2013).

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Construction of a Project Monitoring Application
Iteratively and Incrementally
Pekka Mäkiaho, pekka.makiaho@uta.fi
Timo Poranen, timo.t.poranen@uta.fi
Katriina Vartiainen, katriina.vartiainen@uta.fi
University of Tampere, Finland

Abstract
Developing complex software applications is a challenging task. It is hard to recognise all neededfeatures in advance, and therefore software development is often done iteratively and incrementally.
Every iteration usually contains activities like planning, refining requirements, implementation andtesting. In this paper, we describe how university student teams developed Metrics Monitoring Tool
(MMT) application during the years 2014-2018. The construction process of the MMT has contained
five larger development iterations so far. All versions have been tested comprehensively with dozens ofreal users.

Keywords
Iterative and incremental development; metrics monitoring; application

Background“Learning by making” is a simplification of ideas behind constructionism learning theory (Papert andHarel, 1991). Constructionism underlines student-centered learning where students use information
they already know to acquire more knowledge. (Alesandrini and Larson, 2002)
In computer sciences, students learn by studying different concepts and then implementing the concepts
as small exercises (simple programs, algorithms, user interface designs etc.) or larger course works.
The course works often implement many concepts at the same time. Larger software application arevery complex, and they can contain hundreds or thousands of minor concepts that the developer team
integrates to work as a whole.
To manage complexity, iterative, incremental and agile software development models (Sommerville,2010) have become mainstream in software construction. Main idea behind these models is to build
software piece by piece in iterative manner. This ensures that there is no need to plan everything
beforehand, but instead it is enough to plan next development iteration carefully and to have a wider
vision of the project goal. Knowledge of the development team increases, and vision can be adjusted
regularly.
Design science research methodology focuses on the development of artifacts, like software. Hevner
and others (2004) have given seven principles for design science research: i) Design as an artifact, ii)
Problem relevance, iii) Design evaluation, iv) Research contributions, v) Research rigor, vi) Design as
a search process, and vii) Communication of research.
In this paper, we describe incremental construction of Metrics Monitoring Tool (MMT) application. The
research follows design science methodology principles.

Incremental development of the MMT
Metrics Monitoring Tool (MMT) is a tool designed to support project managers, project members andupper management in reporting and observing projects’ proceedings. Version 2.9 was introduced in anarticle “MMT: A tool for Observing Metrics in software Projects” (Mäkiaho et al., 2017). Main screen of
the MMT is shown in Figure 1.

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Figure 1. Main screen of the MMT

The Figure 2 shows how MMT was developed incrementally and iteratively. The current version of MMT
is 4.0. The tool was developed in Tampere University’s Software Project Management and Project Workcourses during three academic years. The paradigm used was Design Science Research (Hevner etal., 2004). According to the Design Science principles an artefact (increment) was built to solve a
problem and then evaluated how it was succeeded.

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Figure 2. Development process of MMT

Version 0 - proof of concept
The first versions (0.1-0.16) were developed during the academic year 2014-2015. The client evaluatedthe version by reading the test report and decided that the quality of the software was too low for taking
this version to pilot use. The versions 0.17-0.19 were developed during the summer 2015 by one
individual member. However, it was also evaluated that the mature of the software was not good enougheven for the piloting.
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Version 1 - piloting
A new project team was formed at fall 2015. The first task of the team was to evaluate the previous
version and the decision was made to begin the developing from the scratch. The version 1.0 was readyby the end of the fall semester and it was piloted on the project work courses during the spring semester
2016. The new versions (1.2-1.3) were also developed parallelly so that the version 1.3 was deployedby the end of the semester.

Version 2 - production use
During the summer 2016 it was evaluated the experience of the piloting and a single student from theteam continued the developing by correcting bugs, adding some new features and increasing theusability. The main features of this version were Logging Hours, Viewing Reports, Viewing Charts andComposing Reports (Mäkiaho, et al., 2017). This version was on the production use on the Project Workcourses at fall semester 2016.

Version 3 - different roles and rights
During the fall semester 2016, the developer of the version 2 continued the developing as a project
manager of a new team. The tool was on the production use, it was evaluated and the team developed
and deployed new increments . The version 3.0 was deployed by the end of the semester having newfeatures based on different user rights and roles like project manager, member, supervisor and client.

Version 4 - mobile use and interfaces
During the production use of the version 3 at spring 2017, the tool was still continuously evaluated andfeedback was gathered. A single student developed new increments and the version 4.0 was deployedby the end of the semester. The version 4.0 is currently in the production use and it handles new metric:
risks. There are also interfaces to common software project tools like Trello and Slack. Moreover, thesoftware was made mobile friendly by adding responsive UI. Example visualisations of number ofcommits, requirements, test cases, and the working hours by type of the version 4.0 are shown in the
Figure 3.

Figure 3. Example visualisations of number of commits, requirements, test cases and working hours
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Conclusions
Construction of the MMT has been a long process that started in 2014. There have been roughly 20
people involved in the development of the MMT. Roughly 160 students from 30 projects have so far
used the application. There have been also projects clients and teachers using the application.
Experiences on constructing and using the application have been positive among students, teachersand project clients.
MMT is a software artifact that solves project reporting and monitoring problem. Design has been
evaluated in dozens of software development projects. Future plans for further developing the MMT
include enhancing user experience and adding a possibility to archive projects.

References
Alesandrini, K. & Larson, L. (2002) Teachers bridge to constructivism. The Clearing House, 119-121.
Hevner, A., March, S., Park, J., & Ram, S. (2004) Design Science in Information Systems Research.
Management Information Systems Quarterly, 28(1), 75–105.
Mäkiaho, P., Vartiainen, K., and Poranen, T. (2017) MMT - A Tool for Observing Metrics in Software
Projects, International Journal of Human Capital and Information Technology Professionals 8(4), 2017.
Papert, S. and Harel, I. (1991) Constructionism. Situating constructionism. Ablex Publishing
Corporation, 193–206.
Sommerville, I. (2010) Software Engineering, 9th edition, Pearson.

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Exploration of Algorithm Abstraction Process with
Cubetto and Middle Grade Elementary Kids
Yoshiaki Matsuzawa, matsuzawa@si.aoyama.ac.jp
Misako Noguchi, a8115165@aoyama.jp
Issei Nakano, a8115156@aoyama.jp
Aoyama Gakuin University, Japan

Abstract
In this research, the question of how lower grade elementary school students can solve a programmingtask which requires algorithm abstraction, was examined using the programming kit "Cubetto" designed
for kids. In order to scaffold kids thinking abstraction, the following tools were introduced: (1) the"human-size robot simulator" where kids can simulate their algorithm using their embodied knowledge,and (2) the "Whiteboarding" method where kids can think of an abstraction by finding patterns in avisualized concrete algorithm. In-depth qualitative analysis conducted for the two task-solving episodes
illustrates the contrast of the procedures: the one where a young kid could maintain high concentration
over thirty minutes with the prepared scaffolds, whereas another where the kids lost interest in theirhaphazard trial-and-error process.

Keywords
computational thinking; algorithm abstraction; cubetto; lower grade elementary kids

Introduction
At Montessori school, distinct educational practices have been carried out over hundred years with theaim of fostering a love of learning. One of the insights of Montessori is the discovery of what children
learn a lot through play (Montessori, 1936). The Montessori toys designed for kids are attractive enoughfor students to maintain a high concentration, and while kids "play" (called "work" in the school) with thetoys, they can learn embedded scientific concepts deeply beyond the developmental stage.
“Cubetto” is a programming kit which was designed with being inspired by the Montessori toys. The
wooden, lovely face robot can be controlled by physical programming blocks. The kit, itself, highly
abstracts the concept of programming in children’s playing activities. In this study, we developed alearning environment with Cubetto, and practiced it with elementary school kids. The main theme is toclarify how children learn the concept of abstraction, which is the core of Computational Thinking (Wing,
2006) through their experiences in this learning environment.

MethodCubetto"Cubetto" is a programming kit in which a user controls a wooden robot by the instructions defined byprogramming blocks on the board. A user can use four types of blocks. The green block works as "moveforward," the yellow block works as "turn left," and the red block works as "turn right." Thus, the robot
can move around on a map which is made of 6 x 6 tiles, on each tile has a figure which characterizes
the tile place (mountain, tree, castle, etc)(Figure 1). A blue block works as a function call: the block calls
the operation which is defined using other blocks at the bottom part of the programming board.

The Task
We designed a curriculum to develop skills of playing Cubetto for early elementary kids. The curriculum
is composed of some tasks that are categorized into five levels. Students are expected to start from thefirst level which is comprised of simple, easy tasks and then go up to the upper level comprised of more
complex, difficult tasks.
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The task which used in the analysis, described in the result section, is categorized as level 5 (mostdifficult). The task is described as “Start from compass, and pass through tree, mountain, and finally getship.” The trajectory of a robot expected as an answer of this task is shown in Figure 1.

Figure 1. Robot(Cubetto), Board, and Map, and The Trajectory of Robot as an answer of the task.

The task requires algorithm abstraction due to the limitation of the number of blocks. The number ofcells which a robot has to pass through is nine, whereas the number of green blocks (move forward) isfour. In order to solve the task, students have to find a pattern to repeat, and implement it using function
definition and calling (by blue blocks). The abstraction is one of the keys in Computational Thinking(Wing, 2006); accordingly, we thought this would be an appropriate task for use as a benchmark to
explore the learning process.

Tools to Scaffold Thinking
We developed tools to scaffold children’s thinking to solve the designed tasks. The developed tools
are shown in Figure 2.

Figure 2. Scaffolding Tools for Thinking (left: human-size robot simulator, right: whiteboarding)

The “Human-sized robot simulator" (hereinafter called "Self Simulator") is shown in the left of Figure 2.
It includes the same map as the moving robot, but it is scaled up to the children's size. The aim of thetool is to think of an algorithm by simulating it by they themselves becoming a robot, so that the children
can use their embodied knowledge in their thinking. "Whiteboarding," a sub tool used with “Self
Simulator,” is shown in the right of Figure 2. It includes coloured sticky notes aimed to support thinking
program abstraction by temporarily avoiding constraints of the number of blocks. Learners can at once
illustrate concrete algorithm without the constraints, which enables them to easily discover the patternsof a block sequence.

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Empirical Study Field and Analysis Method
The educational environment was examined at the workshop for kids. Approximately 30 elementary
school kids participated over two days. University students participated as a teaching assistant foreach elementary student. Learners' learning process in practice was recorded by a camcorder, andtwo episodes were selected for in-depth qualitative analysis.

Results
The timeline of the selected two episodes in this study are illustrated in Figure 3. In the figure, a star
indicates a notable event described in a script, white circles indicate trial points using “Self Simulator,”and black circles indicate execution points with the Cubetto program.

(a) Episode I(b) Episode II

Figure 3. The Timelines of the two episodes analysed in this study

Episode I
The learner of Episode I is a boy (hereinafter called Kid A) who is estimated to be a mid-grade
elementary school student. Before tackling this task, Kid A solved 9 tasks in about 30 minutes. As shownin Figure 3, the process is roughly divided into two phases. Phase I is the 21-minute phase of thinking
without running Cubetto. Phase II is a 7-minute phase in which Cubetto is examined using "Self
Simulator," and finally succeeded to implement a workable program with Cubetto.
Three patterns of actions by Kid A were observed in Phase I. They include "Thinking without moving","Thinking while moving your finger in the air", and "Thinking while actually getting up and confirming thedirection with your body”. No blocks had been put on the board until Ev1 (09 '17’’). After Ev1, Kid A
started putting blocks on the board, but it had not been run.
Kid A was concentrating on thinking by himself. For example, although an instructor advised, "Why do
not you think about using simulator?" (06'40"), or his mother advised, “It may be difficult to think withyour head, it might be better to try it by yourself "(11'20"), this advice was ignored and he kept thinking
in his own way.
Phase II has started from C1 (21'00"), C1 is the first trial with “Self Simulator.” Unfortunately, there wasa bug in the program and it did not work correctly. C2 (23'06’’) is a scene where the second trial bycorrecting the bug found in C1. Although it did not work correctly, Kid A seemed to get some clue, as
he said “better, almost.” Although his father tried to give advice, he refused it by saying "I'm thinking
myself, do not say anything”.
C3 (24'13") is a scene where he corrected the bug found in C2. During the trial, Kid A suddenly hit his
hands and said, “I should use a (particular) block here.” He seemed to come up with something[sL1].
After putting a yellow block to the board, Kid A raised his voice saying "Yeah, I made it!" Even if he had
not run yet at this point, he seemed to have gained strong confidence with his success.
Ex1 (25'12 ") was the first time to run Cubetto, and surprisingly it worked correctly. Kid A was watching
the execution with a pose of praying. When Cubetto approached the destination, a smile came. Finally,
Cubetto arrived, he screamed "Ya-ha!" with a big gut.

Episode II
The learners of Episode II were two girls (hereinafter called Kids B, and C) and three boys. They were
estimated to be mid-grade elementary school students. The durations were 48 minutes and 59 seconds.
As shown in Figure 3(b), the process was divided into three phases.
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Phase I is the first “attack” to the task by all five kids. During three runs of Cubetto during this phase,students became aware that the number of blocks was short for the solution by a concrete algorithm.
They were also aware that they must use blue blocks. However, they failed to solve it within 13 minutes.
As they felt a difficulty in the task during Phase I, they went back to solve lower level tasks. Phase II
was about 10 minutes where the kids tried the lower level tasks. They succeeded to solve several tasks,
but they became tired as kid C commented during the phase.
C: “I got tired playing with (Cubetto). But Cubetto is cute.”
Phase III was about 25 minutes where kids tried the initial task again. However, three boys dropped
out, and the only two girls (B and C) participated in this trial. The girls finally succeeded to solve after
eight times execution [sL2]of Cubetto, without the “Self Simulator.” They had already understood theyshould use blue block; however, the patterns they tried seemed to be at random. The phenomena they
were getting boring could be observed as the following conversation implies:
B: “I wish Cubetto could run faster”
C: “I wish we had a button that ran faster. I hate this kind of slow system.”
After this conversation, C seemed to neglect participating in the thinking process. She tried to check thenext Cubetto booth (there were two Cubetto booths), and tried to copy the next program. B continued
to try alone and finally found a solution. Although she had a happy face, she looked tired and she did
not look like she found something deeply.

Discussion
In both two Episodes, the children could reach a workable solution. Algorithm abstraction is known as
requiring a high level cognitive operation for kids at this age[sL3]. One notable thing is that Kid A in
Episode I maintained a high concentration over 30 minutes on the cognitive operation and achieved thesolution. The episode indicates to us that constraints are the mother of invention (Stokes, 2005).
However, contrasting the two episodes clearly shows us the importance of supporting their tinkering
process. In Episode II, the kids lost interest in their haphazard trial-and-error process, and a single kidbarely achieved the solution on the sacrifice of the four drop-outs. Kid A ran the program with aconfidence in his solution, whereas kid B found a solution almost by accident through her trial-and-error
process. The difference of deepness in their level of understanding is obvious. We believe theexperience of kid A merely fostered “a greater sense of empowerment” (Papert, 1987).

References
Montessori, M. (1936): “The Secret of Childhood”, (Mass Market version, Ballantine Books, 1982).
Wing,J.M. (2006): Computational Thinking, Communications of the ACM, Vol.49, No.3, pp.33–35.
Stokes, P. (2005): "Creativity from Constraints: The Psychology of Breakthrough 1st Edition", Springer.
Papert, S. (1987): "A Critique of Technocentrism in Thinking About the School of the Future" talkpresented at Children in an Information Age: Opportunities for Creativity, Innovation, and New Activities.

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Influence of Students’ Self-perceived Use of
Metacognitive Strategies and Sensory Preferenceson Academic Achievement in Science andTechnology
Enric Ortega Torres, eortega@florida-uni.es
Florida Universitària, Valencia, Spain

Vincent Sanjosé López,Joan-Josep Solaz Portolés
Universitat de Valencia, Spain

Abstract
The present work gives empirical evidence of the influence of Secondary students’ self-perceived useof learning strategies (including self-efficacy beliefs) and sensory preferences on academic
achievement in Science &Technology subjects. MLSQ scales and VARK scores were used as
predictors of the average academic marks. A significant percentage of the variance was explained, witha specific contribution from strategies with a metacognitive component.
Our data suggested that about 17% of the variance in academic achievement could be explained by thestudents’ perceptions on their use of learning strategies and individual sensory preferences. When only
the strategies with metacognitive basis were considered, the explained variance reached 16%. In
particular, main contributions were obtained from Self-Efficacy and Metacognitive Regulation strategies,
and also from the Kinaesthetic score from VARK questionnaire.

Keywords
science and technology education; MSLQ learning strategies, VARK sensory preferences, secondary
students, academic achievement

Introduction
In Science and Technology academic subjects (S&T onwards), the personal construction of meaning isa fundamental learning process, so meaning construction should be related to academic success in
S&T. As Ausubel exposed in The Psychology of Meaningful Verbal Learning (1963), that the personal
construction of meaning has some prerequisites of different nature: 1) Well-organized, relevant priorknowledge student’s structures (cognitive prerequisite); 2) Conceptually clear learning materials, with apotential logical meaning (logistic-instructional prerequisite); 3) A learner’s emotional commitment tomake the necessary effort to integrate the new information with his/her prior knowledge (emotional
prerequisite). Constructivism also emphasized the student’s self-implication in learning because he/she
is considered as “active meaning builder”. Therefore, when the instructional materials provided to
students are not well-adjusted to their cognitive capabilities and emotional dispositions, constructional
learning could be impeded. Thus, knowing students’ preferences on particular learning materials, or on
combinations of different types of learning materials, it could be useful to adapt these materials tostudents.
Among the variated features in learning materials influencing students’ preferences, the format exciting
particular sensory channels to capture and process information seems to be of main interest. In fact,
Mayer (2005) developed the multimedia learning theory on the basis that human brain uses different
cognitive resources to process incoming information with different physical support (images, sounds,
text, textures, etc.). By means of associative processes, the brain relates these inputs and then builds
a mental representation. Therefore, appropriate combinations of formats for the information provided
could increase students’ comprehension. Students’ preferences for particular formats could be
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associated with the easiness of processing and comprehension. In addition to the cognitive, affective
and logistic-instructional prerequisites, metacognition has proved to be another influencing factor inacademic success. Consistent evidence has been obtained on the academic benefits of a properdevelopment of metacognitive skills (Wang, Haertel and Walberg, 1993). Developing metacognition
implies making students’ aware of their own strengths and weaknesses, and also the possibility oftransferring responsibility from teachers to students themselves to make decisions on learning
processes. This is also the claim of the Constructivist approach to learning and teaching (Dewey, 2007):
students have to engage in planning, monitoring and evaluating the learning goals, processes andoutcomes (Brown and Palincsar, 1982) in order to be active learners and meaning-constructors. Thus,
developing metacognitive skills should imply greater academic success, especially in S&T: theconsciousness of the internal construction of meaning allows the early detection of discrepancies andincoherencies, and fosters the student’s active use of resources at hand to overcome perceived learning
obstacles.

Methodology
A total of 365 male and female high school students in grades 7th to 11th participated, belonging to 8
high schools of different ownership located in the surroundings of a big Spanish city.
The instrument called Motivated Strategies for Learning Questionnaire (MSLQ, Pintrich, Smith, Garcia,
and McKeachie, 1993) was used to obtain the students’ self-perceived use of metacognitive strategies
in S&T learning.
The VARK questionnaire (Fleming and Mills, 1992) was used to assign one of the 15 different types of
SP to each participant: V: Visual; A: Auditory; R: Read/ Write; K: Kinaesthetic, or combinations of these“pure” preferences (i.e. VK, AR, ARK, etc.).
The academic achievement of each participant was obtained directly from the science and technology
teachers of each secondary school. We used a scale of 4 levels, whose relationship with the usual 010 scale in Spain was chosen as follows: D: up to 5,0 points; C: 5,5-6.5; B: 7,0-8.5; A: 9.0-10.0.

Results
Influence of students self-perceived use of strategies on academic achievement in S&T
The scales included on MSLQ and the five scores considered in the VARK-questionnaire appeared to
be independent, as any of their cross correlations were significant (r< 0,093; p> 0,089 in any case).
Hence, MSLQ and VARK might have specific, unique contributions to the students’ achievement in S&T.
Together, all scales of the MSLQ explained almost 13% of the students’ general achievement in S&T(F(15,341) = 3,240; p< 0,001; R= 0,35). However, only a part of the scales involved in this instrument
had significant correlations with the achievement as shown in Table 1. These scales with significant
correlations together accounted for 11% of the variance of general academic achievement in S&T(F(10,346)= 4,081; p< 0,001; R= 0,33).

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Table 1. MSLQ scales with significant correlations with achievement in S&T
MSLQ scalesr-Pearson (p)MOTIVATION
Extrinsic Goal Orientation: EO

0,12 (0,013)

Task Value: TV

0,17 (0,000)

Self-Efficacy: SE

0,26 (<0,001)

COGNITIVE & METACOGNITIVE
Rehearsal: R

0,20 (<0,001)

Elaboration: E

0,15 (0,005)

Organization: O

0,15 (0,005)

Metacognitive Regulation: ME

0,19 (<0,001)

MANAGEMENT
Time and study environment: TE

0,19 (<0,001)

Effort regulation: ER

0,18 (0,001)

Support of others (or Help seeking): SO

0,14 (0,009)

As for the VARK concerns, four of its five scores had significant correlations with the students’achievement in S&T (Table 2): the V-score reached only a marginal signification.
Table 2. Correlations between VARK scores and students’ achievement in S&T

VARK scoresr-Pearson (p)V

0,08 (0,082)

A

0,11 (0,014)

R

0,11 (0,015)

K

0,16 (<0,001)

Total Responses

0,20 (<0,001)

When the VARK scores were added to the MSLQ scales, this extended set of predictors explained 17%of the variance of the academic achievement in S&T (Method: Intro; F(13,313)= 4,772; p> 0,001; R=0,41). Using the back-step method to keep only the significant contributions, the linear regression
explained 15% of the variance of the dependent variable (F(5,321)=11,494; p< 0,001; R= 0,39), with a
sub set of remaining predictors: Self-Efficacy (= 0,25); K-score (= 0,17); Support of Others (or HelpSeeking; = 0,13); Organization (= 0,11); R-score (= 0,09).

Importance of the metacognitive strategies on students’ achievement
Some MSLQ scales have metacognitive essence, when this set of scales was taken as a predictor ofacademic achievement in S&T, the explained variance was also 11% (Intro method), A very close result
to the one explained by all the MSLQ scales together. Using the back-step method to compute the linear
regression, only GO, SE; CT, ME, SO remained as significant predictors explaining again 11% of thevariance of the academic achievement in S&T (F(5,351)= 8,467; p<0,001; R=0,33), being SE the mostinfluent predictor ( = 0,33), followed by ME ( = 0,14) and SO (or Help-Seeking;  = 0,11). A detailed
inspection showed that the negative contributions of GO (= -0,16) and CT (= -0,11) were due to finetuning compensatory adjustment in the regression, but not to negative correlations with the achievement
in S&T.
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Conclusion and Discussion
The main purpose of the present study was to shed light on the importance of the students’ use oflearning strategies and sensory preferences to improve their academic achievement in S&T. Our datasuggested that about 17% of the variance in academic achievement could be explained by the students’
perceptions of their use of learning strategies and individual sensory preferences. When only thestrategies with metacognitive basis were considered, the explained variance reached 16%. In particular,
main contributions were obtained from Self-Efficacy (= 0,26), and Metacognitive Regulation (= 0,16)
strategies, and also from the Kinesthetic score (= 0,16) of VARK questionnaire. It seems that
instructing students to properly use metacognitive learning strategies and to be aware of their individual
preferences to process instructional materials is a promising way to improve S&T achievement. A better
achievement, together with a greater responsibility in managing the own learning should increase
students’ self-efficacy beliefs.

References
Ausubel, D. P. (1963). The psychology of meaningful verbal learning.
Brown, A. L., & Palincsar, A. S. (1982). Inducing strategic learning from texts by means of informed,
self-control training. Center for the Study of Reading Technical Report; no. 262.
Dewey, J. (2007). Essays in experimental logic. SIU Press.
Fleming, N. D., & Mills, C. (1992). Not another inventory, rather a catalyst for reflection. To improve theacademy, 11(1), 137-155.
Mayer, R. E. (Ed.). (2005). The Cambridge handbook of multimedia learning. Cambridge university
press.
Pintrich, P. R., Smith, D. A., Garcia, T. and McKeachie, W. J. (1993). Reliability and predictive validityof the Motivated Strategies for Learning Questionnaire (MSLQ). Educational and psychologicalmeasurement, 53(3), 801-813.
Wang, M. C., Haertel, G. D., and Walberg, H. J. (1993). Toward a knowledge base for school learning.
Review of educational research, 63(3), 249-294.

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Modeling Across the Subjects
Barbara Sabitzer, barbara.sabitzer@jku.at
Johannes Kepler Universität Linz, Austria

Abstract
Jeannette Wing defines computational thinking as fundamental skill for everyone. We postulate thatmodeling is a fundamental tool for everyone for constructing knowledge and, certainly, for teaching andlearning computational thinking. This is the basis for our new project “Modeling across the Subjects”
with two main aims: (1) integrating computational thinking as transversal theme in primary, secondary
and teacher education and (2) supporting learning and teaching in different subjects through modelingtechniques from the field of computer science (e.g. UML diagrams). This contribution presents the
project, its research focus, and some preliminary results from interviews with teachers and students.

Keywords
modeling; computational thinking; learning strategies; brain-based learning

Introduction
With the implementation of the new Austrian curriculum “Basic Digital Education” in autumn 2018 computational thinking becomes obligatory for all secondary schools (grades 5-8), be it as specific subject
or integrated in different subjects. However, many schools do not have teachers with computer sciencebackground and will teach computational thinking as transversal theme. With our project “Modelingacross the subjects” we want to help these schools and show them a practicable way for a useful and“easy” integration of computational thinking in different subjects.
Modeling or building models, which are abstract descriptions of real or planned systems (Hubwieser,
Mühling, & Aiglstorfer, 2013), is well-known and important in the field of computer science andinformatics education. According to (Diethelm, 2007), models even should be taught “strictly first”(before programming). In the Bavarian informatics curriculum for secondary schools modeling is a
crucial topic. From the point of view of general education we strongly agree with Hubwieser, who wassubstantially involved in its implementation and articulates “that among all themes of informatics it isobject oriented modelling that promises the most benefit for the students” (Hubwieser, 2006).
Certainly, not only computer science but also some other subjects like mathematics or geography havetheir own modeling techniques, or use e.g. the well-known flow charts like economy. In a wider sense,modeling is used and applied, often implicitly and unconsciously, also in all other subjects and daily life
e.g. in the case of mental, verbal and physical modeling or also in form of mathematical formulas. For
persons without computer science background modeling with diagrams can be compared to concept
mapping, which is already used and investigated as effective teaching and learning strategy (Chang,Sung, & Chen, 2002; Horton et al., 1993; Novak, 1990). However, the use of computer sciencemodeling, e.g. with UML (Unified Modeling Language), in non-related subjects is quite new. So far, there
is not much relevant literature except few papers that propose some possibilities of application, e.g. theapplication of the UML for analysis of dramatic words (Tagliati & Caloro, 2008) or the use of flow charts(Al-Fedaghi, 2013) and other models (Kosara & Mackinlay, 2013) in storytelling.
In our project “Modeling across the subjects” we focus on modeling techniques from the field ofcomputer science (e.g. UML-diagrams) and apply them as “brain-based” and constructivist learning
strategy and/or tool in other subjects in order to construct and elaborate subject-specific knowledge(e.g. learning vocabulary in a foreign language by means of class or entity-relationship diagrams). In awider sense concept maps may be compared to the diagrams we want to use in this project. However,modeling techniques and diagrams from the field of computer science have much more potential andpossibilities because they can visualize not only structures and relationships but also processes,
instructions, situations, events, etc. and train competences like abstraction, problem solving, algorithmic
thinking, text comprehension, creativity etc., in short, they train many 21st century skills. In this way we711

Constructionism 2018, Vilnius, Lithuania“kill two birds with one stone” and can teach computational thinking and problem solving implicitly in
different subjects. At the same time, students use the diagrams as so called graphic, visual or advanced
organizers, which support the brain in the learning and memory process. Additionally, such visualorganizers are particularly effective with students who have learning disabilities (Kim, Vaughn, Wanzek,& Wei, 2004). And in our opinion they are ideal for constructing knowledge in different subjects, too.

Modeling across the subjects
The project “Modeling across the subjects” started in March 2018 and aims at1) integrating computational thinking as transversal theme in primary and secondary education and2) introducing appropriate modeling techniques from the field of computer science in order tosupport and improve learning and teaching in different subjects.
Based on different primary and secondary school curricula we will develop in the next three years-

a framework for “Modeling across the subjects”,-

guidelines and useful hints for teachers without computer science background and-varied cross-curricular and/or multidisciplinary teaching materials suitable for all subjects.

During this three-year-project we hold modeling workshops for (prospective) teachers of primary andsecondary schools, who want respectively have to teach computational thinking as transversal theme.
In these workshops all participants are supposed to develop teaching materials based on different
sample models for different purposes and to introduce them to their pupils as tools especially forstructuring and elaborating subject-specific knowledge, describing activities, planning and summarizing
texts, learning vocabulary, etc. Some of the introduced sample models and activities were developed ina former project of the author, where we could already gain positive experiences with flow charts, entityrelationship and activity diagrams (Sabitzer & Pasterk, 2015). In the current project we develop andintroduce further UML diagrams like use case or communication diagrams and study the acceptanceand usability as well as possible effects on learning outcomes. All materials will be collected, adaptedand finally provided in an online collection.
We will investigate the following main research questions by using a mixed-methods design:(1) How and where can we introduce modeling in primary and secondary schools in order to teachcomputational thinking?
(2) Which diagrams are useful and practicable for teachers and pupils without informatics
background in order to construct subject-specific knowledge and to train subject-specific andgeneral skills like abstraction, problem solving or text comprehension etc.?
(3) To what extent shall modeling and computational thinking be taught to everyone and what shallbe part of computer science education?
Our previous experiences show that modeling is considered as very useful and effective, but concrete
sample units and activities as well as empirical studies are still missing. Hence, this project may closea gap with providing teaching materials and studying their use and possible effects on learning
outcomes in different schools.

Conclusion
The project “Modeling across the subjects” introduces and investigates the use of computer sciencemodeling techniques as brain-based and constructivist learning tools for different subjects. It further
shows a practicable way of teaching computational thinking as transversal theme in primary insecondary education. Regarding our previous positive experiences and the preliminary results we
suppose that the frequent and varied use of modeling in different subjects may not only train generallearning skills like problem solving or abstraction but also improve subject specific knowledge andcompetences like vocabulary acquisition or text comprehension in foreign language lessons.
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References
Al-Fedaghi, S. (2013). Schematic representation for storytelling. In IEEE Int. Conf. Computational
Intelligence and Cybernetics.
Chang, K.-E., Sung, Y.-T., & Chen, I.-D. (2002). The effect of concept mapping to enhance text
comprehension and summarization. The Journal of Experimental Education, 71(1), 5–23.
Diethelm, I. (2007). Strictly models and objects first: Unterrichtskonzept und-methodik fürobjektorientierte Modellierung im Informatikunterricht. Pro Business.
Horton, P. B., McConney, A. A., Gallo, M., Woods, A. L., Senn, G. J., & Hamelin, D. (1993). An
investigation of the effectiveness of concept mapping as an instructional tool. Science Education, 77(1),95–111.
Hubwieser, P. (2006). Functions, objects and states: teaching informatics in secondary schools. In
International Conference on Informatics in Secondary Schools-Evolution and Perspectives (pp. 104–
116). Springer.
Hubwieser, P., Mühling, A., & Aiglstorfer, G. (2013). Fundamente der Informatik: Funktionale, imperative
und objektorientierte Sicht, Algorithmen und Datenstrukturen. Walter de Gruyter GmbH & Co KG.
Kim, A.-H., Vaughn, S., Wanzek, J., & Wei, S. (2004). Graphic organizers and their effects on thereading comprehension of students with LD: A synthesis of research. Journal of Learning Disabilities,
37(2), 105–118.
Kosara, R., & Mackinlay, J. (2013). Storytelling: The next step for visualization. Computer, 46(5), 44–
50.
Novak, J. D. (1990). Concept mapping: A useful tool for science education. Journal of Research in
Science Teaching, 27(10), 937–949.
Riley, D. D., & Hunt, K. A. (2014). Computational Thinking for the Modern Problem Solver. Taylor &Francis. Retrieved from https://books.google.at/books?id=7AQNAwAAQBAJ
Sabitzer, B., & Pasterk, S. (2015). Modeling: A computer science concept for general education. In
Proceedings
Frontiersin
Education
Conference,
FIE
(Vol.
2014).
https://doi.org/10.1109/FIE.2015.7344062
Schwill,A.
(n.d.).
No
Title.
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31,
2018,
http://www.informatikdidaktik.de/didaktik/Forschung/VortragsfolienFundIdeenMNU.pdf

fromTagliati, L. V., & Caloro, C. (2008). UML and Object Oriented Drama. Journal of Object Technology,
7(1), 85–101.
Wing, J. M. (2006). Computational Thinking. COMMUNICATIONS OF THE ACM March, 49(3).
Retrieved from http://www.cs.cmu.edu/~wing/publications/Wing06.pdf

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Constructing What? Knowledges of the Pwerful,and Powerful Knowledges
Michael Tan, michael.tan@nie.edu.sg
National Institute of Education, Nanyang Technological University, Singapore

Abstract
If we consider constructionism as a pedagogical theory, it can be easy to neglect curriculum
considerations—what is it we ought to communicate, and why. One approach to this problem is to bedeliberate about the nature of knowledge, and the sociological implications of its differential distribution.
The epistemologies of the knowledges of Science, Technology, Engineering, and Mathematics (STEM)
may be distinguished by its tendencies towards generalisation (S/M), or its contextual application (T/E).
Understanding this distinction may be a key to being clear what is being constructed, and how
constructionism may be deployed for particular goals. In this theoretical paper, I describe thefoundations for such a project.

Keywords
epistemology; nature of science; nature of engineering; curriculum

A curriculum for making?
While many educational jurisdictions exist with autonomy to develop and assess their own curricula, notall schools have that luxury. Standardised testing by state and regional boards of education can compel
teachers to narrow outcomes on the ostensible grounds of social justice and the equity of outcomes for
public schooling. It would not take much for teachers to sense the cultural logic of the assessment tailwagging the dog of schooling, and recognise that an optimal method of deploying makerspaces andmaking activity is as a means to the standard curriculum ends of ‘concept acquisition’ in Science andMathematics specifically.
Research attention on making as a learning activity has been on the conceptual gains from making(Davis, Schneider, & Blikstein, 2017), or as means for increasing engagement in the STEM disciplines(Norris, 2014), or in contexts outside of the mainstream school classroom (Sheridan et al., 2014).
Notably, in sites such as public makerspaces and science museums, a question that has yet to have aconvincing response remains the query by Bevan and associates (2015): “It looks like fun, but what are
they learning?” While the pedagogical case for making as instructional activity remains convincing, it
would appear that a similarly strong curriculum argument is not present. To be particular, looking beyondthe ‘workforce development’ arguments for STEM (Blikstein & Worsley, 2016), what justifications do wein the making community have that students ought to design and make? What ought students make?
Why? Should the Arts (and the Humanities?) be part of the STEM/design and making movement? Why?
In what ways should these other disciplines contribute to the project of making, and what roles shouldeach play?

Investigating binaries of knowledge
An important method to thinking about curriculum considers the nature of the knowledge and thesociological outcomes of a differential distribution of knowledge. A key question exists: whatknowledge
is of most worth, or, as modified by Michael Apple (1979) with a sociological angle:
whose knowledge is of most worth. Implicitly, it is this sociological approach that is being advocatedwhen calls for the inclusion of STEM are accompanied by visions of economic futures. However, suchsociological approaches suffer from the problem of arbitrariness—of what exactly constitutes
phenomena of interest, what simplifying assumptions are to be made, and what are rules for acceptable
explanations. If these high status knowledge claims was the result of social convention among thosealready in positions of high status, then there is a social justice concern in making sure that as many
people as possible can have access to the social construction of scientific knowledge. This is especially714

Constructionism 2018, Vilnius, Lithuania

so if we believe in universalism of participation as a central value of the natural sciences (see, e.g.,
Collins & Evans, 2017).
Such a perspective drives efforts to increase participation of under-represented minorities, and which
also criticizes ‘Western Modern Science’ as a White Men’s Science. A useful shorthand to describe theissues at stake may be the conceptual distinction between the knowledges of the powerful, and powerfulknowledges (Young, 2008). Here, Young points out the significance of the empirical grounding in astable knowable reality for some knowledge forms. In other words, there are actually truth claims thatare obligatory; reality is truly as we describe it to be; or even if it were a matter of social convention, an
exceedingly high fraction of humans, with similar sense organs and cognitive apparatus sensitive tocertain ranges of phenomena, would arrive at similar frameworks for understanding and description.
The point of the foregoing, then, is to introduce the concept of the social construction of knowledge, andthe arbitrary/obligatory distinction, or more informally, the distinction between knowledges of thepowerful, and powerful knowledges. Powerful knowledges are so called because, transculturally, suchknowledge claims can confer upon its users access to high status deliberations about the world.
Powerful knowledges can transcend the contexts of its generation, and find applicability across space
and time, as when ‘outdated’ mathematical theorems are used in contemporary cryptographic
algorithms. On the other hand, in invoking a concept such as the knowledges of the powerful, thereference is to situations where things are so, because “the Chief, the Party, or the Pope says so”(Moore & Muller, 1999, p. 193). While we may at first glance consider such epistemic warranting
strategies to be largely obsolete in our enlightened times, it may not take us long to find claims that we
accept purely on the grounds of scientific authority. This is not to cast doubt on scientific authority, but
to minimally point out that students should learn accepted epistemic warranting strategies as part oftraining as scientists. At this point, the outlines of a plausible justification for educative value should be
visible: it may be ideal for students to construct understanding of powerful knowledge for themselves(e.g., science), and not quite so much knowledges of the powerful (e.g. wine appreciation). Tentatively,
the distinction lies in the ability of these knowledge forms to transcend their contexts of generation, or,in other words, possess at least an ambition to the nomothetic, generalising tendency that is typical of
the natural sciences. Conversely, it can be easy to reject or at least find less valuable idiographic formsof knowledge that concern themselves with the particular implementation details of a particular project.
However, it would appear that such a model may be incomplete, in that, given the inherent complexityof many systems in the ‘real world’, there is at least a practical difficulty in obtaining general principles
that can make predictions outside of their contexts of generation. Sociocultural interactions, for instance,
are not amenable to law-like generalisations of the form of the natural sciences. Much closer to ourconcerns, the disciplines of design and engineering present similar challenges; I elaborate on these
below.

Natural and Artificial Sciences
While the foregoing may provide suitable justification for the natural sciences and mathematics(considered as the language for describing patterns and variations), the nature of the knowledge oftechnology and engineering provide challenges to this framework. As the subsection header suggests,
technology and engineering provide us knowledge of the sciences of the artificial, in that its core concern
is with the artificing of solutions to problems. For this insight, we are all, of course, in the debt of Herbert
Simon (1968/1996). Simon helped to define the study of design as an intellectual pursuit, as a key
concern of engineering:
The artificial world is centered precisely on this interface between the inner and outerenvironment; it is concerned with attaining goals by adapting the former to the latter. The
proper study of those who are concerned with the artificial is the way in which that
adaptation of means to environments is brought about—and central to that is the processof design itself. (p. 113)
There is much contained in this passage that is worthwhile of careful study. In considering the artefact
as occupying the interface between the inner and outer environments, it is important not to be too literal;
software programs can be engineered into existence, and have an internal code environment and user
facing interfaces to interact with the code in goal directed ways. The significance for our purposes here
is in the goal directedness of the efforts to adapt the inner and outer environments. When discussing
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the goal directed nature of efforts of engineering, perhaps an underappreciated perspective is the natureof intention, and its relationship to our knowledge of the natural sciences. While we often believe that(design) solutions become clear when problems are correctly identified, we often fail to understand thata very fundamental gap exists between an accurate apprehension of the problem, and the developmentof a design goal, an intention for the artefact (see, e.g., Nelson & Stolterman, 2003). This gap is obvious
if we were to observe that accurate apprehension is an act of description, and goal setting is one ofderiving a prescriptive course of action. David Hume’s device of the guillotine reminds us that, at leastin ethical considerations, there is no necessary connection between the descriptive and normative. In
other words, and pertinent to typical instructional sequences in STEM and makerspace projects, justbecause we can understand a problem in a particular manner, does not mean that the solution
necessarily follows. For instance, just because we discover that a local waterway is being contaminatedby industrial pollutants, does not mean that a device that can remove this pollutant is necessarily
advised.
From this perspective then, the synthetic disciplines of engineering and technology are distinct from theanalytic disciplines of science and mathematics in their foci—while we require accurate analyses of the
problem in order to obtain a good description of the reality that we are confronted with, we also requirea particular ‘wisdom’ to be able to develop appropriate goals for the problem at hand. Such wisdom is
unfortunately not reducible to law-like statements as in the natural sciences. Perhaps the closest thing
may be design principles, which are often obscure and opaque to novices with little exposure to problem
scenarios. Even then, exceptions abound, and correct application of these principles requires wisdomthat can only be gained through rich experiences, or, as is typical in schools of design, a familiarity withcase studies that explore the boundaries of our knowledge. Similarly, the disciplines of the humanities
and the philosophical inquiries associ ted with ethics point us in general directions to our best
accumulated wisdom on what ought to be, often statements of taste of powerful groups.

Towards rapprochement
It should be clear that a productive engagement with both poles of this debate is needed, if only so thatthe compromises are known. We ought to communicate the nature of knowledge in the STEM
disciplines accurately, so that we do not misrepresent these knowledges to students—we certainly
should not want that they eventually enter weapons engineering firms expecting the fun and games oftypical makerspace activities (Banks, 2018). Similarly, if we desire a more ethical engineering practice,
we need students to see more clearly the contextualised, idiographic detail that requires thoughtful
application of generalised principles, and not merely attempts to force-fit people and nature into amoral,
abstract systems of thought. As educators, it is timely indeed to consider the problem of what, exactly,
we are constructing. At the very least, students ought to become familiar with the distinctions between
knowledges of the powerful, and powerful knowledges: where is it that knowledge claims are based on
obligatory relationships with the way the world is, and where it is that arbitrary decisions have beenmade. Significantly, students should learn the nature of the interests to which such arbitrary decisions
have been made, in order that they be able to make these decisions for themselves in future.

References
Apple, M. W. (2004). Ideology and curriculum (3rd ed.). New York: RoutledgeFalmer. (Original work
published 1979)
Banks, D. A. (2018, January 24). Engineered for Dystopia. Retrieved February 8, 2018, fromhttps://thebaffler.com/latest/engineered-for-dystopia-banks
Bevan, B., Gutwill, J. P., Petrich, M., & Wilkinson, K. (2015). Learning Through STEM-Rich Tinkering:
Findings From a Jointly Negotiated Research Project Taken Up in Practice. Science Education, 99(1),98–120.
Blikstein, P., & Worsley, M. (2016). Children are not hackers: Building a culture of powerful ideas, deeplearning, and equity in the maker movement. In K. Peppler, E. Halverson, & Y. Kafai (Eds.), Makeology:
Makerspaces as Learning Environments (pp. 64–80).
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Collins, H., & Evans, R. (2017). Why democracies need science. Cambridge, UK: John Wiley & Sons.
Davis, R. L., Schneider, B., & Blikstein, P. (2017). Making the invisible visible: A new method for
capturing student development in makerspaces. In B. K. K. Smith, M. Borge, E. Mercier, & K. Y.
Y. Lim (Eds.), Making a Difference: Prioritizing Equity and Access in CSCL, 12th International
Conference on Computer Supported Collaborative Learning (CSCL) 2017 (Vol. 1, pp. 175–182).
Philadelphia, PA: International Society of the Learning Sciences.
Moore, R., & Muller, J. (1999). The discourse of “voice” and the problem of knowledge and identity inthe sociology of education. British Journal of Sociology of Education, 20(2), 189–206.

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Towards Girls’ Self-perception in Technology and
Craft: Challenges and Implications
Sawaros Thanapornsangsuth, Nathan Holbert Monica Chan
{st2839, holbert, mmc2265}@tc.columbia.edu
Teachers College, Columbia University USA

Abstract
This poster explores findings on elementary girls’ perception, confidence, and personal experiences intechnology and craft. We look into how they define technology and their exposure to technology as
consumers and producers. We also asked questions about their experiences with craft materials andactivities. Despite coming from families that encourage and financially support extracurricular activities
in STEM field, the majority of girls did not feel confident about their knowledge and skills in technology.
They also had a tendency to position their own expertise as inferior to their male family members. When
asked about crafts the girls showed high interest and engagement and each identified herself as
someone who is good with crafts.

Keywords
girls in technology; technology education; craft; technology confidence

Introduction
A large number of articles show that stereotypes and low expectations of women in math, science, andtechnology play a major role in women’s loss of interest in the field (Cohen et al, 2016; Spencer et al.,
1999; Steele, 1999). These stereotype threats also contribute to the notion that they are technologicallyinferior to their male counterparts (Hyde et. al, 1990; Margolis & Fisher, 2003). These data may
contribute to gender disparities in STEM professions. The most recent data from National Center for
Women & Information Technology (2015) reveals that women make up only 15% of practicing
engineers. When technology is described as being primarily for hackers, programmers, and engineers
those who do not identify themselves as such are pushed out (Worsley & Blikstein, 2016). Martinez(2015) suggested educators should be sensitive of their classroom environment as girls can react
negatively to surroundings that reflect stereotypical “hacker culture” in making, by rejecting their interestin technology and engineering. Literature has suggested creating an inclusive and supportive learning
environment and drawing wider examples of what counts as STEM can also help nurture women’sinterests in these domains (Hill, Corbett, & Rose, 2010; Intel Corporation, 2014; Margolis & Fisher,
2003)
Digital fluency is a concept that has been discussed by many scholars (Kay, 1991; Jenkins, 2006;
Resnick, 2012). The perception of being good at technology maps onto the concept of digital fluency,
which has been formalized by the National Research Council (NRC), where it states that “people fluent
with information technology are able to express themselves creatively” using their chosen technologies(Barron, 2004). Combining the NRC’s definition about digital fluency to Resnick’s (2012) stance—that
true mastery of technology involves being both a confident producer and consumer of technology, we
project that if the girls see themselves as a producer of technology, this change in perception might
improve their confidence towards their technical abilities. Specifically targeting girls, scholars such as
Buechley have explored using computational textiles to bridge the gap between traditional perceptionsof technology and craft (Buechley et al., 2008). Crafting techniques such as sewing has a more feminineorientation traditionally, but combining craft and electronics such as e-textiles to cultivate a stronger
producer of technology mindset has indicated positive engagement in females toward electronics andcomputing (Buechley & Eisenberg, 2008).

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There is a paucity of research examining the barriers that impede young girls to identify themselves withhigh competency in technology. Likewise, little work has been done to explore these perceptions forgirls from various backgrounds and experiences. This study is a part of the wider Bots for Tots project(Holbert, 2016). We are engaging young learners from diverse communities to build toys for younger
kids in their school. In this paper, we explore how girls from privileged backgrounds who have accessto high-tech tools and come from well-supported families feel less confident about their technology
competency. In examining this population our research aims to answer the following questions: (1) How
do the girls describe their experience with and knowledge of technology? (2) What tools, toys, or
activities do the girls consider to be “technology?” (3) How do the girls describe their experience withand knowledge of crafts?

Methods
Population and Site
The data presented here is part of a larger Bots for Tots study on fourth grade students (aged 9-11) at
a high-resourced all-girls private school in a suburban area in the North-Eastern United States.
According to the school’s website, 71% of the students are white. Forty-one fourth-grade students
participated in the study as a part of their Making and Engineering class, a bi-weekly maker education
class which ran 45-minutes per session. Out of the 41 girls, 12 were randomly selected to beinterviewed. In addition to the many extracurricular activities and programs available in the school,
students in 4th grade and beyond have access to an Engineering and Design lab (EDL) which is filledwith cutting edge equipment including a CNC machine, laser engraver, multiple 3D printers. This
population is unique and interesting as they represent the group of girls who receive the best exposuresand opportunities to technology. Many of the girls have parents who are engineers, have the opportunity
to take extracurricular STEM courses such as robotics and programming, and have access to manyhigh-tech toys and construction kits. Due to their upbringing, these girls are well suited for success in
STEM fields. All names used in this paper are pseudonyms chosen by the girls interviewed.

Data Collection and Analysis
Twelve girls were randomly selected to participate in pre and post interview, before and after theEngineering and Design class. Using a semi-structured cognitive-clinical interview format (Ginsburg,
1997), the fourth-grade girls were asked questions about their 1) electronic toys or devices at home, 2)
self-perception of technology, and 3) experience with making and crafts, both for themselves and forother people. We did not define technology or provide any specific examples as we sought tounderstand what the girls regarded as technology. Upon analyzing interview transcripts, we developed
a profile for each girl and grouped them in categories and developed a code scheme (Bogdan & Biklen,2007). This scheme focused on how the girls self-report their “technology and making confidence.”
Examples included phrases such as “Umm not really,” and “My brother like to build [..] but I don’t build.”
We also coded and categorized the girls based on their different craft activities and experience. Example
codes include, “girls making crafts”, “girls talking about family members”, “girls making by or forthemselves.” After grouping data in coding categories, we reviewed them for thematic connections(Seidman, 2013).

Results
Low confidence in technology and male family members as experts
When the twelve girls were asked if they considered themselves as someone who is good withtechnology in the pre interview, eight girls showed lack of confidence and said they were not. Answers
included: “no,” “I don’t know,” “not that good,” “I’m not the expert,” and “not really.” One girl expressed
uncertainty, answering “kind of”. Only 3 students indicated that they are good with technology. Out of
the 12 girls, 6 girls mentioned male family member such as a dad, brother, or uncle, as someone who
is good with technology.

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For instance, Betty talked about her extensive experience spending her free time working withelectronics and technology. She did not have a phone but she enjoyed assembling and playing with
“Meccano” (a sophisticated robotics toy that requires 168 steps to assemble) and building bird houses
with her dad who is an engineer. She liked technology and electronics, however, when we asked about
her technology competency, she immediately responded “No.” She later shared that she did not feel
confident as her dad would not let her do the work, “normally he’s like doing the whole thing.” She often
looked at her dad building from behind. Similar to Betty, when Panni was asked about her technology
competency, she answered “I don’t know” while shrugging her shoulders. Constantly denying her
competence with technology or construction (“I’m not a big, big builder”) she referred to her brother asa person who fitted the description better: “I don’t really build stuff that much. My brother likes to build.
If he sometimes gets something to play with, and it doesn’t come all together, he can build it. But I don’t
build much.”
The girls also mentioned female family members but not necessarily referring to them as technology
savvy. For instance, Aditi told us that if something goes wrong with her electronic devices, she would
not go to her mom but will seek help from her dad or brother. Lightbulb said that she could not fix acomputer but she often taught her my mom how to use different functions on her phone. Kate was the
only girl who referred to a female family member as someone who “very good with technology.” Shesaid that she would seek help and learn from her twin sister if she had difficulties with electronics.
Additionally, we also learned about the girls’ experiences and usage of the technology. The mostcommon use of technology was to play games (6 girls), watch videos (5 girls), learn an online lesson or
do homework (4 girls), programming (2 girls), make videos (2 girls), build a robot (1 girl).
Nine months later, after participating in the Bots for Tots project, we asked them again in the postinterview about their perception of technology. Five of the students said they were good with technology
but 2 out of the 5 expressed uncertainty when probed further about their experiences. For example,
Kate switched between positive and negative comments about her technology competency, “I feel likeworking, trying to figure something out either on a phone or computer, I'm pretty good at, I'm not very
good. But I think I'm still good.” Similarly, Erica showed a little hesitation and answered “Well, it kind of
depends on what kind of technology, but when we were using the circuits during games I think it was
great fun.” 7 out of 12 girls said that they were still not good with technology. However, 2 of the 7 girls
thought that they were better because they had been building more projects but they still thought thatthey were not good with technology. For example, Betty said that unlike last year where she was justwatching her dad building, he now let her do more hands-on building as she got older. “[My dad] is liketeaching me more about [technology] so like I’m learning more from my dad.”
In post interview the girls’ perceptions of what counts as technology shifted somewhat to include
programming (5 girls), robots (2 girls), switches (2 girls), fixing and putting things together (2 girls) andsewing machines (1 girl), in addition to including the usual devices such as computers, phones, iPads,
and iPods (5 girls)

Confidence and experience in craft
We asked the girls about their confidence in craft and whether they enjoy making craft or not. In boththe pre and post interviews, all of the 12 girls said that they enjoyed making crafts. Out of 12 girls, 11
indicated they often shared their craft projects with family members and friends—six of these doing so
unprompted. Erica talked about her plan to make a handmade present for her cousin in exchange ofthe gift he gave her, “recently, my cousin made me a little bicycle for one of my dolls, and so I'm thinking
I'm going to make him something.” Moreover, craft was perceived as a family activity for 6 of the girls.
For instance, LillyJane talked about gathering random things at her room and make something out ofthem with her sister and baby sitter. She often made craft when her friends came over to her house.
Lightbulb also said that her mom liked to do crafts and together they made small furniture and food outof clay for her dolls. In her free time, Lightbulb also enjoyed watching DIY videos on YouTube with her
mom and sometimes by herself.
Additionally, the girls used craft to experiment and express. Kate talked about her experience
experimenting with homemade slime putty or Play Doh. Describing her experiment, she said, “Play
Doh–you need like flour, salt and water. And then for slime you need like detergent. You can actually720

Constructionism 2018, Vilnius, Lithuania

use shaving cream.” She also added that making things out of homemade Play Doh with her female
friends was “exciting to me, making it and trying to like discover what could make it and what can’t makeit. It’s fun to experiment.” Erica enjoyed making things from cardboard to illustrate the stories she wrote.
She made a fairy house, trees, and castle as props to her story. Similarly, Betty made castles andcharacters that went with it, “I made little people with swords and spears and stuff.”

Conclusions and Implications
The participants of this study came from privileged backgrounds with an abundance of exposure totechnology. Their parents and schools actively sought to provide them with powerful STEM opportunities
and experiences. However, 8 out of 12 girls still did not consider themselves competent with technology.
The girls’ self-report on their technology confidence during interviews aligns with the literature on
stereotype threats suggesting that women can feel less confident and significantly underestimate theirabilities in the field of math, science, and technology. Moreover, half of the girls interviewed mentioned
male family members as technology experts and compared their own expertise to that of the male family
members. For example, Panni denied her identity as a builder by suggesting this designation belonged
to her brother. Erica also instantly mentioned her uncle as a technology expert when asked about her
own experience with technology. This reflects the literature on women feeling technologically inferior totheir male counterparts (Hill, Corbett, & Rose, 2010; Hyde et. al, 1990). When we investigate moreclosely what the girls considered to be technology, we found that programming and mobile devices werementioned the most. Those activities and tools represent a narrow definition of technology. Furthermore,men are more represented in the professions related to these artifacts and practices, withwomen holding only 25% of all computing occupations (NCWIT, 2015). Broadening what counts astechnology could increase diversity in these fields (Buechley, 2016) as a male-centric articulation oftechnology likely discourage girls from joining (Buechley, 2013; Margolis and Fisher, 2003).
In contrast to technology, all of the girls expressed confidence and interest in craft and enjoyed making
craft as a social activity. While male family members were often associated with the girls’ experience
with technology, female family members and friends were a big part of girls’ experiences with crafts. For
example, Lightbulb’s mother taught her and her sister to make toy furniture and food out of clay. Margie
spent every Tuesday drawing and painting with her female babysitter while LilyJane frequently madecrafts when her female friends visited her house. Unlike the girls’ experiences with technology, theirexperiences with craft falls under the category of the producer rather than the consumer. Girls seem tohave a deeper, more meaningful experience producing crafts, compared to playing games and watching
videos which was frequently mentioned when asked about their technology usage. The girls also usedcraft to express themselves, as seen when Erica and Betty created scenes and characters of theirstories from cardboards. Taking active roles in making crafts could positively influence girls’ confidence.
Additionally, the social implication of making crafts and sharing them with others makes the experience
meaningful and enjoyable.
In an effort to improve girls’ confidence in technology, we suggest that future pedagogical methods
should first focus on widening what counts as technology. Limiting the definition of technology to malecentric tools and activities may discourage girls from seeing themselves as competent. We should also
engage girls more as a producer of technology, as the true mastery of technology involves being botha confident producer and consumer (Resnick, 2012). Lastly, incorporating social implications to the girls’
technological experience makes learning more enjoyable and meaningful.

References
Barron, B. (2004). Learning ecologies for technological fluency: Gender and experience differences.
Journal of Educational Computing Research, 31(1), 1-36.
Bogdan, R. & Biklen, S. (2007). Data analysis & interpretation. In Qualitative Research for Education:
Chapter 5 (5th ed.). Boston: Allyn and Bacon.
Buechley, L. (2013). Thinking about making. Presented at the Fablearn 2013. Retrieved August 9, 2017from http://edstream.stanford.edu/ Video/Play/883b61dd951d4d3f90abeec65eead2911d
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Buechley, L. (2016). Inclusive Maker Education: STEM is Everywhere. Presented at the Fablearn 2016.
Retrieved
August9,
2017
fromhttps://edstream.stanford.edu/Video/Play/a33992cc9fb2496488c1afa9b6204a571d
Buechley, L., & Eisenberg, M. (2008). The LilyPad Arduino: Toward wearable engineering for everyone.
IEEE Pervasive Computing, 7(2).
Buechley, L., Eisenberg, M., Catchen, J., & Crockett, A. (2008). The LilyPad Arduino: using
computational textiles to investigate engagement, aesthetics, and diversity in computer scienceeducation. In Proceedings of the SIGCHI conference on Human factors in computing systems (pp. 423432). ACM.
Cohen, G. L., Garcia, J., Apfel, N., & Master, A. (2006). Reducing the racial achievement gap: A
social-psychological intervention. science, 313(5791), 1307-1310.
Ginsburg, H.P. 1997. Entering the Child’s Mind: The Clinical Interview in Psychological Research andPractice. Cambridge University Press.
Hill, C., Corbett, C., & St Rose, A. (2010). Why so few? Women in Science, Technology, Engineering,and Mathematics. American Association of University Women. Washington, DC
Holbert, N. (2016). Leveraging cultural values and “ways of knowing” to increase diversity in maker
activities. International journal of child-computer interaction, 9, 33-39.
Hyde, J. S., Fennema, E., Ryan, M., Frost, L. A. and Hopp, C. (1990), Gender comparisons ofmathematics attitudes and affect. Psychology of Women Quarterly, 14: 299–324.
Intel Corporation, MakeHers Report: Engaging Girls and Women in Technology through Making,Creating,
andInventing,(2014).
Retrieved
August
20,
2017
from
http://www.intel.com/content/www/us/en/ technology-in-education /making-her-future-report. html
Margolis, J., & Fisher, A. (2003). Unlocking the Clubhouse: Women in Computing. MIT Press.
Martinez, S. (2015). Making for All: How to Build an Inclusive Makerspace. Retrieved August 26, 2017
from
EdSurge:https://www.edsurge.com/news/2015-05-10-making-for-all-how-to-build-an-inclusivemakerspace
National Research Council. (1999). Being fluent with information technology. National Academies
Press.
NCWIT. (2015). Women in IT: The Facts Infographic. Retrieved August 26, 2016, fromhttps://www.ncwit.org/resources/women-it-facts-infographic-2015-update
Resnick, M. (2012). Mother's Day, Warrior Cats, and Digital Fluency: Stories from the Scratch Online
Community. In Proceedings of the Constructionism 2012 Conference: Theory, Practice and Impact (pp.
52-58).
Seidman, I. E. (2013). Analyzing, interpreting, and sharing interview material. In Interviewing as
qualitative research: A guide for researchers in education and the social sciences (3rd ed.). New York:
Teachers College Press.
Spencer, S. J., Steele, C. M., & Quinn, D. M. (1999). Stereotype threat and women’s math performance.
Journal of Experimental Social Psychology, 35(1), 4–28.
Steele, C. M. (1997). A threat in the air: How stereotypes shape intellectual identity and performance.
American psychologist, 52(6), 613.
Worsley, M., & Blikstein, P. (2016). Children are not hackers: Building a culture of powerful ideas, deeplearning, and equity in the Maker Movement. In Makeology (pp. 78-94). Routledge.

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Collaborative Creative Music Activity with ICT:A Case Study for Children in Grade Five
Sayaka Tohyama, tohyama@inf.shizuoka.ac.jp
Yugo Takeuchi, takeuchi@inf.shizuoka.ac.jp
Shizuoka University, Japan

Abstract
How to support learning new knowledge without spoiling children’s creativity seems a shared question
in both constructionism community and STEAM education. We designed a workshop for 5th graders toscaffold learning musical knowledge using Jigsaw and “Vocaloid for Education” as “object-to-think-with”.
The result was that fourteen children out of twenty succeeded to make harmonies to given melodies
using knowledge which they learned in the workshop even though they had no previous experience ofVocaloid, and five out of the fourteen created unique ones.

Keywords
keywords: Music; vocaloid for education; collaborative learning, STEAM

Introduction
Recently STEAM is used in the context of programming or ICT in education instead of STEM. As weknow, children can make creative products including “art” essences using Scratch (Resnick, 2017). It
seems that they learn about programming and art in their constructional process if we see it from theviewpoint of Papert’s “Mathland” (Papert, 1980). Although the effect of constructional activity foreducational context is expected, the process of the children’s learning about programming and arts isstill a hot topic in educational research.
In this study we focused on music as “art” in STEAM context. Music is widely accepted as an enjoyable
and creative activity, and it has synergistic power with ICT. One of the previous researches suggested
that children have musical sense and they could show their senses (Bamberger, 1991).
Our research question is to know what kind of scaffolds work for children to improve musical knowledge
based on their creativity in a sense of Constructionism. To the question, our hypothesis is thatcollaborative learning with ICT may function for improvement of musical knowledge because
collaborative work improves finding new viewpoints and deepening the participants’ understandings(Miyake, 1986). To confirm it in a real situation, we used the jigsaw method (Aronson & Patnoe, 1997)
and “Vocaloid for Education” which may work as “object-to-think-with”.

Methods
We held a three-hours workshop for twenty fifth graders (12 boys and 8 girls) in an elementary schoolin December 2017. The children were chosen by lot from who hoped to participate this workshop. We
posed the question throughout the workshop as “How to make beautiful harmony?” to enhance thechildren’s constructional activity using Vocaloid for Education (Figure.1) in collaboration. The children’s
main activity was making harmony for a song. The target song was “It’s a small world” because it is wellknown all over the world and in a music textbook for 4th graders in Japanese elementary schools. The
venue of the workshop was the PC room in the school, and we specially installed Vocaloid for Educationon the PCs. The first author conducted this workshop, and two adults who had experience in supportingchildren’s learning using ICT during the workshop. The children worked in pairs when they created theirown harmony, and in three-member groups when they collaborated in the jigsaw method.
The set of activities for the children in the workshop was as follows:Answering the questionnaire (preQ), and harmony-making test (pre-test)
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 Correcting the disharmony of “Twinkle twinkle little star” using Scratch as introduction
 Answering the harmony-making test (mid-test) Planning how to create the harmony for “It’s a small world”
 Learning musical knowledge in the jigsaw method
 Creating harmonies using Vocaloid for Education
 Answering the questionnaire (postQ), and harmony-making test (post-test)
The children answered to each questionnaire and harmony-making test individually. In thequestionnaire, we checked the children’s previous experience of music and programming, and attitude
to musical creation. We evaluated the pre-, mid- and post-test for comparison of the harmony-making
test, and checked the children’s pre-experience using the answered questionnaires. In the three timesof harmony-making test, we showed the final four bars of “It’s a small world” and asked the children to
write a harmony. We videotaped all the activities and collected the memos which were written by thechildren, and referred to them if we needed additional information for our analysis.

Scaffolds
Jigsaw Method
The jigsaw method is one collaborative learning method used to enhance the children’s ability forcontributing and discussing a given question using handouts which contain knowledge or technique forhelping children to generate their answer for the given question. Each child has responsibility to explain
his/her assigned handouts to the other members, so they prepare it in collaboration with classmates
who have the same handouts. After that, children who has different handouts share the contents of theirhandouts with each other. They generate their answer to the question through discussion for integrationof their handouts.
The jigsaw method could be a scaffolding for constructional activity because the children can try to
make relations between their prior knowledge or experience and new knowledge or experience.
In this research, we made three kinds of handouts to give the children musical knowledge. Each handout
fits in A4 size and contained some figures to help the children’s understanding. The theme of thehandouts is as follows:

A: Codes and its structuring notes (C is made by C and 3rd notes and 5th notes, and so forth)
B: How to choose harmonized notes (octave, avoiding 2nd notes, and so forth)
C: How to make smooth and natural melodies (match with intonation of lyrics, and so forth)Vocaloid for Education
Vocaloid for Education is a music creating application for Windows, developed by YAMAHA corporation.
The target user is elementary to junior-high school children in Japan. Children can create mixed chorus
in four voices by putting boxes onto a two-dimensional screen: tone length is horizontal and pitch isvertical. We can also enter lyrics to each melody. The artificial voice sings the set of melodies
simultaneously when we push the “play” button, and the progress bar shows where the voice sings.
These characteristics allow children to create music who have only little musical knowledge and ability
to read and write musical scores.

Results
The harmonies made by all the pairs in the workshop differed from each other, and the harmonies madeby individual children in post-test differed slightly from the pair results. From these results, it is suggested
that they used given musical knowledge in their own ways, not stereotypically.

Questionnaire
The preQ revealed that nineteen children had no previous experience of Vocaloid for Education andprogramming. All the children liked musical activity and using ICT in preQ and postQ. Only sevenchildren preferred expression using music over appreciation in preQ, hence twelve children preferred724

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expression in postQ. The number of children who felt good at reading musical scores was ten in preQand raised by twelve in postQ.

Figure 1. Vocaloid for Education (Main melody is purple boxes, and harmony is light blue boxes)

Harmony-Making Test
We coded the nineteen children’s answers about harmonies (missed collecting the question sheet fromone child) of pre-, mid- and post-test into seven categories from the viewpoints of uniqueness,stereotype (rule-based), and harmonization. The categories are as follows: Major 3rd: Consecutive intervals which mainly consists of major 3rd of the main melody
 Major 3rd+Arrange: Adds a little unique harmony to Major 3rd
 Unique: A harmony which does not depend on major 3rd of the main melody
 Major 2nd: Dissonant intervals which mainly consists of major 2nd of the main melody
 Major 2nd+Arrange: Adds a little unique disharmony to Major 2nd
 Random: No rules. Randomly assigned notes, spreading notes, and so forth.
 N/A: No answer
Major 3rd, Major 3rd+Arrange and Unique means smoothly harmonized melodies. Major 2ndand Major
2nd+Arrange suggests incongruent melodies. Random is partly harmonized. In these categories, Uniqueis the most qualified answers because it appears when the children succeed orchestrating the musicalknowledge and the creativity from their own viewpoints. Random is the lowest quality because it seems
the children did trial and error without plans.
The results were shown in Figure 2. It suggested that the children tended to answer randomly, gave noanswer, or made incongruent in pre-test. In mid-test, which is located after experience making
harmonies using Scratch, the number of children who used major 3rd notes systematically increased
from two to ten. Furthermore, the children who answered randomly disappeared. It suggested that theylearned simple rules for harmonization and incongruency in harmony in their Scratch activity. The posttest which is located after the jigsaw method with Vocaloid shows that two children created uniqueharmonies for the first time. It appears the combination of the jigsaw method and Vocaloid worked forthe children’s creation of harmonies.

Conclusion
Our case study showed that children could discover and use simple musical knowledge such as 3rdnotes from the main melodies’ notes only experienced correcting disharmony activity. However,725

Constructionism 2018, Vilnius, Lithuania

Figure 2. The number of the children’s answers in harmony-making test.

to enhance the children’s production of unique and qualified outcomes, the jigsaw method with ICT may
function. In our study we used the jigsaw method and Vocaloid for Education simultaneously to scaffold
the children from the viewpoint of constructionism because collaboration may emphasize thecharacteristics of “object-to-think-with” in Vocaloid for Education. For the future we will try to evaluate
the effect of each one by protocol and physical-performance analysis using video data.
In programming education, the problem seems the same with this study because scaffolding inprogramming education is expected not to spoil children’s creativity, but to enhance learning how tosolve problems or disciplinary knowledge in constructional activity. We think finding principles for
implementation such kind of activity is one of important themes in this research field. In this paper, weonly suggested the effect of designed collaborative activity. For the future, we will try to find what is
important principles in collaboration to cause meaningful constructional activities and share the resultswith STEAM educators.

Acknowledgments
We would like to thank JSPS Kakenhi (No. 17K17786) and foundation of promotion for engineering in
Shizuoka University. We wish to thank Junko Tsutsui, Nobuhiro Shirai and Tomoyuki Kuno (Yoshinkita
elementary school, at that time) who gave us great help to implement this workshop, and Yukako Enya
(Smart Education System group, YAMAHA Corporation) who gave us helpful advice and materials for
Vocaloid for Education. The handouts were inspired by the YAMAHA website.

References
Aronson, E., and Patnoe, S. (1997). The Jigsaw Classroom (2nd Edition.). Longman.
Bamberger, J. (1995). The Mind behind the Musical Ear. Harvard University Press.
Miyake, N. (1986). Constructive Interaction and the Iterative Process of Understanding. Cognitive
Science, 10(2), 151-177.
Papert, S. (1980). Mindstorms: Children, Computers, and Powerful Ideas. Basic Books.
Resnick, M. (2017). Lifelong Kindergarten: Cultivating Creativity through Projects, Passion, Peers, andPlay. The MIT Press.

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Applied Constructionism: Critical Reflection andLearning Through Play in Adult Learning
Nalin Tutiyaphuengprasert, nalin@stanford.alumni.edu
Darunsikkhalai School for Innovative Learning, King Mongkut’s University of Technology, Thailand

Abstract
This poster discusses the combination of constructionism and critical reflection in a workshop design
for adults in Thailand. Thailand applied constructionism in different fields including professional
development for employees focusing on facilitating self-development and soft skills. In these situations,learners work together to create complex Rube Goldberg machines in less than 24 hours. Time pressure
helped learners to reconnect with others and cope with their negative thoughts. While Papert (1999)
stated “This acceptance of “negatives” is very characteristic of the Logo spirit”, Mezirow’s criticalreflection (1990) can support safe expression of “negatives” and put them as an “opportunity” tounderstand and transform.

Keywords
applied constructionism; critical reflection, adult learning; professional development; agency; teambuilding; metacognition; transformative learning; perspectives transformation

Constructionism Intertwined with Critical Reflection and U
TheorySince 1997, constructionism has been applied in Thailand both in formal education and non-formal
education. For adult learning, constructionism has been applied in different professional development
programs in businesses, industries and farmers’ communities in rural area. “Think-Make-Reflect” is amotto that has been used as a core process in these situations as well as a learning outcome of
constructionism in Thailand to foster continuous improvement and self-directed learning.

Figure 1. Rube Goldberg machine and Critical Reflection Workshop in Thailand

Our Rube Goldberg machine workshop was designed to tackle the solo mentality problem inworkplaces, boost a more active learning environment and improve soft skills among employees.
Companies who participated are from different businesses such as healthcare and service, satellite
service providers, food and dairy manufacturing and conglomerate businesses. In this workshop,
constructionism learning tools played a main role in allowing learners to express themselves throughphysical artifact creation. We applied critical reflection (Mezirow, 1990) to help learners when they arefacing challenging tasks by facilitating learning from reflection, including facing negative thoughts andemotional challenges. U theory (Scharmer, 2009) was also used as a support for learners to reassess
thinking habits and show a path to explore and experiment with new possibilities.

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Figure 2. Secret Objects, design of contraptions and brainstorming Session

Explore Your Internal World with Critical Reflection and U Theory
Adults can learn from changing their perspectives from their past experiences. As Mezirow stated“Adulthood is the time for reassessing the assumptions of our formative years that have often resulted
in distorted views of reality” (1990). Critical reflection helps adults be aware of their own assumptions
about work, life, ability, values, rules, and so on, which impact how they perceive the world and makedecisions in life. We applied critical reflection in the workshop to bring participants’ thinking habits oremotions to the surface. Short guided meditation was also used to help learners feel calm, be able torecognize their emotions, and think clearly.

Figure 3. Daily reflection and examples of post-it notes
Translation of reflection in pink notes: “Tools didn’t fit with my expectation.”“It’s amazing that I can do it which I didn’t want to do this at the beginning because I’m scared.”“I’m concerned that the task wouldn’t be finished on time.”

In Thai culture, reflection can be useless if we do not take into account cultural sensitivity. It is normal
for people to cover up their thinking or say things to please audiences, especially if they perceive
hierarchy or seniority in the environment. I tried to build atmosphere of freedom of expression with somecore principles: 1) participants choose what, when and how to express 2) critical or negative feedback
is welcome 3) participants do not have to speak if they do not want to ,and 4) silence is acceptable anda legitimate way to show respect to allow everyone to think, contemplate and decide to when to speak
and when to listen.

Figure 4. Making contraptions (left and middle) and reflection time at the end of each day (middle right) andcelebration of Rube Goldberg (right)

U theory gives us a point of view of how people respond and decide to react, especially when it issomething uncomfortable or related to negative experiences in the past. This model introduced twopaths to participants: knee jerk reaction (automatic reaction without thinking) or moving down through728

Constructionism 2018, Vilnius, Lithuania

the U shape model to explore our mental stages. Voice of judgement, cynicism and fear were explicitly
introduced to learners with an encouragement to observe and refrain from these types of normal
reactions. U theory explained the stages through which we can first hold back immediate reactions anddetect negative thoughts in our minds, then be fully present, and finally allow the mind to recreate a
different reaction without bias or judgements from the past experiences.

Conclusions
Construct and Reconstruct Your Inner World in a Playful Context
The combination of the Rube Goldberg machine challenge and critical reflection provided a playfulenvironment where adult participants learned to recognize and transform their views and behaviour ina friendly way. As Ackermann (2014) stated, playfulness can give space to tolerate uncertainty and set
the stage for shifting perspectives or repositioning oneself. Being playful also includes the notion of safe
exploration. It provided the right touch of empowering learners to keep asking “what if” questions, giving
learners opportunities to suspend their old assumptions and explore their internal thinking and emotional
process. This enabled them to change their thinking if they wanted and at their own time.
Thinking and emotions are dynamic and rooted in deeper levels of belief. The critical reflection process
helps explicitly capture feeling and thinking and helps learners to develop metacognition processes.
Some feedback, which may initially be perceived as negative by adult learners, can be very useful forparticipants in observing their pattern of reactions, leading to deeper analysis of internal assumptions
that they might not have been aware of before.
Disorientation

Reintegration

Experiment

Rumination
Thinking Habits

Finding Options

Figure 5. Process of Reconstructing Thinking Habits or Assumptions

Figure 5, shows the process of change when a learner decides to tackle their own negative thoughts.
Detecting negativity helps liberate learners from self-deception and be more open to alternatives. We
have often found that many learners challenged their fears and transformed their responses by the endof the workshop.
“I usually have phobia with microcontroller. I was shocked when I have to do
this task. “Sh**!” I talked to myself when I first looked at the Gogo board package.
I had a bad experience in my high school. I asked my friend to do it for me. I canpass those tests because my classmate did it for me. But now I can’t run away.
But when I learned about input, process and output, I can jump out from myfear. I can get rid of that fear now and I feel really good about it. This is what I
want to share with you guys. Fear is in us and it is going to be one day that we
can jump out of it!” (Reflection transcription by participant in the workshop).
People can learn and grow from mistakes and negativity. Creating a safe environment allowed people
to transform their misconceptions and heal from past distortions. Constructionism can serve as a safeand playful situation for adult learners to nurture confidence in facing challenges in life and learn fromthem.
“I made a lot of mistakes and when it was the 20th time, I thought that it was fun
to fix problems. I’m ready to solve problems that arise and I learned so much from themand gained more confidence. In the past, I was so scared of making mistakes.
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I avoided all kinds of mistakes but now I learned to be open to mistakes.
I promised that I will now dare to make mistakes and learn to fix them quickly.
I promised that I will do mistakes in everything but I will learn and fix them all.”(Reflection Transcription by participant in the workshop)

References
Ackermann, E. (2014). Amusement, Delight, Whimsy, and Wit, the Place of Humor in Human Creativity.
In Constructionism 2014 International Conference, Vienna, Austria, August.
Mezirow, J. (1990). How critical reflection triggers transformative learning. Fostering critical reflection in
adulthood, 1, 20.
Papert, S. (1999). Logo philosophy and implementation. Logo Computer Systems Inc.
Scharmer, C. O. (2009). Theory U: Learning from the future as it emerges. Berrett-Koehler Publishers.
Senge, P. M. (2006). The fifth discipline: The art and practice of the learning organization. Broadway
Business.

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Different Cultures – Different Approaches toReasoning and Algorithms
Valentina Dagienė, valentina.dagiene@mii.vu.lt
Lina Vinikienė, lina.vinikiene@mii.vu.lt
Vilnius University, Lithuania

Abstract
The following five strands of mathematical actions are important in school curricula: 1) conceptual
understanding; 2) procedural fluency; 3) strategic competence; 4) adaptive reasoning; 5) productive
disposition. These strands have implications for mathematics teaching of both the practical andspecialised perspectives and are more or less cultural sensitive. One of the challenges facing
mathematics educators is to incorporate each of the mathematical strands in a multicultural classroom.
We developed the module for teacher educators that provide theoretical background based on the roleof reasoning and algorithmic thinking in mathematics. The module involves practical activities based on
the understanding of etnomathematics and Seymour Papert ideas. Through this module prospective
teacher are able to investigate the understanding different approaches of reasoning and algorithms,
explore examples of different approaches, experiment and reflect on the use of tasks, practice different
algorithms, develop pedagogical approaches. The module is prepared under Erasmus+ project“Intercultural learning in mathematics and science education, IncluSMe”. The project aims to increase
the quality of higher education curricula for prospective teachers by linking maths and science education
with intercultural learning (http://inclusme-project.eu). The poster describes the module content.

Keywords
Algorithmic thinking, etnomathematics, intercultural learning, reasoning in mathematics

Background
Teaching mathematics require a deep understanding about previous student’s knowledge and that theyneed to learn. The main challenge is the support them during the learning process. Due to that, teacher
educator have to be prepared to show the same things using different approach or instruments.
Astuti&Purwoko (2017) emphasize that learning instruments integrated with ethnomathematics involvethe construction of mathematical knowledge by instilling positive cultural values. According to authors,model of learning based on culture-related mathematics is based on constructivism learning theory.
Ethnomathematics is associated with mathematics education, social and cultural background.
D’Ambrosio stated that in order to understand how mathematics is created, it is necessary to understandthe problems that precipitate it. It involves the cultural context that drives them (Rosa&Orey, 2011).
Ethnomathematics is described as a branch of mathematics acknowledging the fundamental differences
in mathematics content, mathematics understanding, and mathematics application that links culture andmathematics (D’Ambrosio, 1985). According to Rowlands, Carson (2002), Rosa, Orey (2011), teachers
should know various teaching methods in order to teach formal, abstract concepts in different cultural
background. The ethnomathematics perspective into the mathematics curriculum “help student todevelop skills in critical thinking and analysis that can be applied to all areas of life and provide aneffective environment for developing skills to solve real-world problems”. (Rosa, Orey, 2011, p. 48). In
addition, it helps students to achieve better academicals results, know more about reality, culture,society. Rosa&Orey (2011) stated that etnomathematics is a program that includes relevance and builds
knowledge around the local interests, needs, and culture of students. According to Saymour Papert, as
well as connecting with the formal knowledge of mathematics, it also connects with the "bodyknowledge".
As a way to accommodate different learning styles and demonstrate several way to solve the sameproblem, Fisher&Davis (2008) distinguished the importance of algorithms. Algorithms engage students
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to learn mathematics in an active, fun environment. According to Kantner (2008), algorithms is steps tosolve problems which are not universal across cultural and nations. He mentioned, that learning mathaffect different types of representation systems, concepts, native language, world views, informal
mathematical experiences, conceptual differences in logic, reasoning, cognitive styles. However,teacher educator should present and know not only one algorithm, he/she should be interested in morealternative. In this way, in the cultural diversity classroom confusion and not understanding will be
avoided (Perkins, Flores, 2002). Identification of algorithms and description why the algorithm works let
students to rethink mathematical ideas. By solving mathematical problems student have to getelementary knowledge about mathematics methods and challenge the belief that this algorithms work
(Philipp, 1996).
In this paper, we describe the module that is prepared under Erasmus+ project “Intercultural learning inmathematics and science education, IncluSMe”.

Module for mathematics prospective teachers
Module “Different cultures – different approaches to reasoning and algorithms in mathematics” is design
to introduce pre-service teachers to intercultural learning in mathematics using different definitions. This
module involve an introduction into reasoning and algorithms in mathematics, theoretical backgroundbased on the understanding etnomathematics and Seymour Papert ideas. During the module, we
present the connection between theory and educational practice. The module consist for three mainparts and one additional section. In addition, we introduce computational thinking as one of the way ofmathematical reasoning. The main goal of the module is to engage student to try different problem
solutions, share and explain their own ideas. Students discuss about text that present different
reasoning methods, study specific examples, explore sources of culture-related contexts, solve
problems using different approaches of reasoning and algorithms, compare solutions in pairs or groups,
analyse text or identify examples and opportunities to use culture-related context, build knowledge using
constructionist approach.

Introduction
This part of module involve discussion and introduction of theoretical background.
First activity. Firstly, students find video about cultural understanding, reasoning and algorithms inlearning and teaching mathematics. During the lesson, they present the most interesting video anddiscuss about their findings in groups, and fill table. Filling the table, they have to describe the meaningof cultural understanding, ethnomathematics, reasoning, and algorithms. The idea of these tasks is not
only to engage students share ideas about particular video, but also their experiences. Together
students compare understanding and meaning, try to get one common problem solution. At the end ofactivity, teacher educator sum up discussion and provide the basics of theoretical background: provide
the meaning of etnomathematics, reasoning, algorithmic thinking from the literature view.
The second activity introduce Seymour Papert’s book “Mindstorms: Children, Computers, and Powerful
Ideas (1980). In this book Seymour Papert argues about the benefits of teaching computer literacy andillustrate many powerful mathematical ideas. Students have to analyze examples of text and discuss
about reasons to use culture-related context, the most important characteristics of good approach of
reasoning and provide approach for reasoning or algorithms in mathematics using personally. Analysing
text student are able to compare constructionism and constructivism, concrete thinking and formal
thinking, explain the meaning of assimilation, importance of LOGO, etc. Later, students are asked toanalyse the following example (Figure 1a) and apply algorithms in practice.
Difficulties experienced by children are not usually due to deficiencies in their notion of number but infailing to appropriate the relevant algorithms. Learning algorithms can be seen as a process of making,using, and fixing programs. When one adds multidigit numbers one is in fact acting as a computer incarrying through a procedure something like the program in Figure 18.

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Constructionism 2018, Vilnius, Lithuania[Seymour Papert’s book “Mindstorms: Children, Computers, and Powerful Ideas, 1980, pp. 152]

Figure 1a. Text example
The idea of algorithms practice is that students compare the differences in culture diversity. At the endof lecture, teacher educator provide several example such as arithmetic operations in different countries,
different multiplication algorithms, and different calculation methods. These examples should be indifferent type, such as text, video, practical exercise. In this way, students will be engaged to finddifferences between reasoning, understandings in different cultures.

Culture-related context (practical reasoning examples)
The second part of module involve practical examples of juggling, mathematical task, and programming.
Firstly, student analyse video, than read the text from S. Papert “Mindstorms” (p. 105-112) and practicejuggling by using pieces of light material.

Figure 1b. S. Papert. MINDSTORMS. Juggling (p. 105-112)

The goal of this activity is to compare how students understand the same things in different ways andcompare each student’s experience. Not all student will be successful in juggling, due to that otherstudents should help and explain how to reach the goal of task. This activity woke up discussion between
students how to do the tasks step-by-step using various methods, such us juggling with one ball or
more, juggling with a scarf. Presenting steps of juggling on the sheet of paper students describe thealgorithm how to find the best solution.
The second activity is the analysis of the example “A string around the circumference of the earth” from
S. Papert. MINDSTORMS… Students discuss about text and ways of reasoning.
The third activity could be implemented in a computer lab using programing language as Scratch. Using
programming language students have to draw triangle, square, rectangle using Turtle graphics. After
practice students compare solutions and read the text about Turtle from S. Papert book
“MINDSTORMS: Children, Computers, and Powerful Ideas (pp.75-76).

Practical methods
The third part of module consist of attractive tasks examples and Computer Science Unplugged activity.
Solving these tasks and participating in the unplugged activity students practice different algorithms andcompare solutions.
Tasks examples focus on decimal and binary numbers (Figure 2), pattern recognition, rules, cycle,
Dijkstra’s algorithm, calculations, programming. Teacher educator have to pay attention how student try
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to find the solution, how they understand the tasks, compare solutions and provide a short overview atthe end. Students in groups compare solutions, discuss about differences and similarities, methods
used to get the correct answer.
A BINARY SCALE. A Beaver scale shows weight both in decimal (left) and binary (right) numbers. A
fish weights 1100 kg in binary number system. Which weights you need to put on the scale plates thatyou can see the fish’s weight in decimal numbers?

Figure 2. Task example

Later students practice Computer Science Unplugged activity “Orange game” (Figure 3). This is a cooperative problem solving game. The aim is for each person to end up holding the oranges labelled with
their own letter (Bell, et al., 2015). This activity demonstrate constructivist approach.

Figure 3. Orange game (Bell, et al., 2015)

Conclusion
The main idea of module “Different cultures – different approaches to reasoning and algorithms inmathematics” is to provide deeper understanding of diversity of approaches to reasoning and algorithms
in mathematics. We focus on practical tasks using computers, technologies and various type of material.
Practical, attractive activities and tasks engage students to reflect and share their own ideas,
experience. All activities are covered with theoretical background on reasoning in mathematics andalgorithms. Educator has opportunity to choose the particular activity depending n the student skills,
practice, and related topic.

Acknowledgment
The presented module is based on the work within the project Intercultural learning in mathematics andscience education (IncluSMe) of the Erasmus+ programme. The project grant no. 2016-1-DE01-KA203002910. The authors gratefully acknowledge the project coordinator Prof. Dr. Katja Maaß, International
Centre for STEM Education (ICSE) at the University of Education Freiburg, Germany.

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References
Astuti, E. P., Purwoko, R. Y. (2017). Integrating Ethnomathematics in Mathematical Learning Design for
Elementary Schools in Proceeding “Research and education for developing scientific attitude insciences and mathematics”, 4th International Conference on Research, Implementation, and Educationof Mathematics and Science, 2017 May 15-16.
Bell, T., Witten, H., Fellows, M (2015). CS Unplugged. An enrichment and extension programme forprimary-aged students.
D’Ambrossio, U. Ethnomathematics and Its Place in the History and Pedagogy of Mathematics. For the
Learning of Mathematics, Vol. 5, No. 1 (Feb., 1985), 44-48.
Fisher, J. F., Davis, J. F (2008). Algorithms: through the ages and around the world.
Kantner, J. (2008). The only absolute truth in mathematics is the myth of mathematics as universal.
Perspectives: The New York Journal of Adult Learning 6(2). New York: Fordam University.
Rosa, M., Orey, D. C. (2011). Ethnomathematics: the cultural aspects of mathematics. Revista
Latinoamericana de Etnomatemática, 4(2). 32-54.
Rowlands, S., Carson R. (2002). Where would formal, academic mathematics stand in a curriculum
informed by ethnomathematics? A critical review of ethnomathematics in Educational Studies inMathematics.Vol.50, No.1, 79-102.
Perkins, I., Flores, A. (2002). Mathematical notation and procedures of recent immigrant students in
Mathematics teaching in the middle school, 7, 346-351
Philipp, R. A. (1996). Multicultural mathematics and alternative algorithms: Using knowledge from many
cultures. Teaching Children Mathematics, 3(3), 128-135.

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The Web – A Platform for Creation
Márton Visnovitz, visnovitz.marton@inf.elte.huGyőző Horváth, horvath.gyozo@inf.elte.hu
Eötvös Loránd University, Faculty of Informatics, Dpt. of Media & Educational Informatics, Hungary

Abstract
The web is a platform that is becoming more and more accessible for many people. With theadvancements in web technologies programming on the web is becoming easier to learn. The browser
provides a programming API that can be used to express ideas quickly and easily. Using web
technologies in learning programming has a lot of advantages, and this platform can be used to apply
the constructionist methodology as well. With creative thinking, and basic programming knowledge
students can use the web realise their ideas be it a simple computer game, a simulation or any otherapplication. The same programming logic can be applied to many different scenarios; thus, students
can explore the way to make their own ideas come to life, learning technology and programming in theprocess.
Every web application can be represented by three main components. These Three Pillars of a webbased application are: describing the underlying data structure, defining how that data can be displayed
in a user interface and how that user interface behaves. Getting started with these steps does not
require any technological knowledge about the platform itself, so students can aquire that during theprocess of implementation.

Keywords
Constructionism; web; JavaScript; game programming

Introduction
Nowadays the web is becoming a general, ubiquitous, rapidly developing and ever popular application
runtime platform, that is used by billions of users every day. Its ubiquitous nature means that informationand services are continuously available regardless of place, time and device. Web-based applications
are becoming widespread and are present on almost every platform from the browser to smart phones,
embedded systems and TVs.
Teaching of programming also uses the web for its purposes. There are many good examples ofteaching various aspects of programming using videos, tutorials and online programming environments,
e.g. Codecademy75, CodeCombat76. The programming language used in these environments can beanything from C# to Python, but HTML and JavaScript, the lingua franca of the web, noticeably occur
often in these programming platforms. Altogether, these online learning environments consider the web
as a runtime platform.
Web technologies are good for introducing programming to students in an exciting environment(Mahmoud et al., 2004). In this approach the web is not a platform, but the target of the teaching process.
It provides all the necessary aspects for successful programming teaching: easy-to-learn and easy-touse languages, modern tools and development environments, spectacular applications, familiar piecesof software like the browser (Horváth & Menyhárt, 2014). The whole teaching material (languages,
technologies) can be introduced step by step, telling just the next necessary information (Horváth &Visnovitz, 2017).
Students can easily start programming with this platform as its main technologies are easy to get started
with. Another benefit is that the definitive programming language of the web, JavaScript has many
positive aspects from the educational point of view (Horváth & Menyhárt, 2014). A huge benefit of the75
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web platform that the markup language used for user interfaces (HTML) and the scripting language
used for programming (JavaScript) both are extremely easy to get started with (Visnovitz, 2017). Bothare just simple text files that don’t require any boilerplate code to work, thus the source file only contains
the code that represent the desired functionality, In this article we present how the web platform can beused as the target of a programming teaching in a constructionist way.

The Three Pillars of Web Applications
Creating applications for the web consists of three steps that can be executed one after another. The
foundation of all these “pillars” can be CS Unplugged activities that don’t require any previous
knowledge. This way students can explore the process of implementing their ideas and learn about
technologies that are required to build a web application.
Declarative
Programming

Variables andConstants
Application State

User Interface Design
Stylesheets
FunctionsTemplates

User Experience
Interactive UIEvent Handling

Data Types

User Interface
Generation

Application State
Representation

Data Modelling

Event Types
Event Handlers
Event Binding
State Mutation

Figure 111. The Three Pillars of web applications

Application State Representation
To create any kind of computer program the first step is to describe the set of variables that represent
the state of the application at any given time. This step can be entirely independent from theprogramming part and can be done even without computers. Students must answer simple questions
like: What do I have to know about this program at any given time?
 What are the variables, what are the constants?
 How can I structure this data?
After answering those questions, they must translate the answers to some form of data model. Thisdata model can be very basic with only names of variables and a description of what they represent, orthey can be very complex with data type descriptions and custom data types. The complexity of thisstep can be adjusted to the previous knowledge of the students so that they can use their previously
acquired knowledge but also can learn something new in the process.
During this step students can explore concepts of data modelling, data abstraction, basic and composite
data types, the concept of constants and variables.

User Interface Generation
All users interact with a program using a user interface. Each UI can be a combination of static anddynamic parts. Dynamic sections of an interface are dependent on the applications current state while
static parts do not change depending on the state. Students should identify the static and dynamic partsof the UI. Some governing questions for this step are:
 What do I want to show to the user?
 What does the user need to use my program?
 Which parts are static (state independent) and dynamic (state dependent)?
 What is the relationship between the state of the application and the user interface?
Students can design the user interface and user experience using simple tools or even pen and paper.
This designing phase can help them to understand the various forms of user interactions Also, this step
allows students to express their creativity when creating the looks and other visual elements of the UI.
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For coding it is recommended to first create a static mock-up for the program that shows how it is goingto look. This allows students to explore the markup language and styling without worrying about
behaviour. The next step is to describe how the state of the application can be translated into this UI,
what is the connection between the two. The last step is to create functions that implement thistranslation.
During this process students learn about declarative programming, user interface design, functions andtemplates. Alternatively, students can use 2D graphics techniques provided by the Canvas API of thebrowser to minimize the HTML knowledge requirement of the entire process. Instead of creating the UI
with HTML elements students can use basic raster graphics, basic shapes and images to create the UIof a game (Horváth, Menyhárt & Zsakó, 2016).

Event Handling
The third thing to do with a web-based application is to make it interactive. Students can start this stepby describing how the program behaves and what kind of interactions can users make. This step can
be started by brainstorming and creating sketches and diagrams of the possible user actions. The threecomponents of every interaction that students must identify are the following: What part of the user interface the user interacts with? (e.g. a button…),
 What is the nature of the interaction? (e.g. clicking, pressing a button…),
 What is the effect of the interaction? (e.g. the button turns red, a counter increases…).
Using these three components pupils can create event handlers that make the UI react to user input.
Event handlers are basic programs with input/output handling and data processing, thus this is a very
good exercise to learn or practice basic programming. The I/O in this case uses the JavaScript
representation of the HTML page (DOM) to access UI elements and read or modify their content. A
common event handler reads data from the UI and modifies (mutates) the application state. Aftermodifying the state, the UI must be re-rendered in accordance with the changes. Sometimes it is overly
complicated to store every single aspect of the UI in the state. In these cases, event handlers can also
have side effects on the UI, such as modifying the visibility of an element or setting the focus. Thismeans that the event handler directly taps into the DOM in an imperative way, modifying its contents orproperties.
During this process students learn about the concept of events in computing, event types, event
handlers, event properties, event binding and state mutation.

Practical considerations
For all the previously introduced Three Pillars there is a shared workflow that can be applied to each ofthem. This consists of brainstorming, planning & researching, and implementing. During brainstorming
students collect their ideas and note them down using either paper and pen or the computer. This
collection of ideas will be the basis of their execution plan and their research for corresponding
technologies. The research phase can be assisted by the teacher. After students have a basic
understanding of the technology that they shall use to realize their plan they can start coding the actual
program. For coding the teacher can again function as a guide but can take a more direct and frontal
educational approach if necessary.

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It is important to note that using the Three Pillars concept does not haveto mean that students must plan the entire program from the start. The
business principles of Minimum Viable Product (MVP) and incremental
development can be applied in this case as well. This means that at firststudents can create a program with a minimal feature set and later add
new features by extending the existing three main components of theirapplication. Helping questions for extending a program in such way are:






What additional information do I need to know everything about the
program with this new feature?
What additions/modifications do I need to make to the user
Interface?
How do I translate this new state component user Interface?
What additional interactions do I need to add this functionality?
How do I have to change the current interactions to fit this newfeature?

Brainstorming

Planning,
research

Implementing

Figure 2. The cycle of adding
new features to a program

Using this form of incremental development, the process of “brainstorming, planning/researching andimplementing” becomes a cycle (Figure 2). From the start all the new features can be added using thesesteps.

Conclusion
The web is an easy to access platform that is available for all computer systems that have a web
browser. Using client-side programming not even Internet connection is required to use JavaScript tocreate rich, web-based applications. Students can realize their ideas using a platform that they are most
likely familiar with. Using the Three Pillars principle it is possible to start form CS unplugged activities
and create a fully-fledged web application or game. The three main activities can all help students tolearn about different aspects of programming while realizing their ideas.
By the nature of the browser and its main programming language, JavaScript, using web technologies
makes it possible to create applications that can be deployed to both desktop computer, smartphones
or other smart devices. This also can be very engaging for students.
Web technologies are widespread and are improving rapidly. HTML and CSS are well known
technologies that are easy to get stated with. In many countries HTML and CSS are even part of thehigh school curriculum. The JavaScript programming language is easy to learn and apply to many areas
including server-side, client-side and mobile programming. Programming skills acquired with thislanguage can be applied later in many different fields.

References
Horváth, Gy., Menyhárt, L. (2014). Teaching introductory programming with JavaScript in higher
education. In Proceedings: Proceedings of the 9th International Conference on Applied Informatics,
Eger, Hungary. p. 339-350.
Horváth, Gy., Menyhárt, L., Zsakó, L. (2016). Viewpoints of programming didactics at a web game
implementation. In Proceedings: Proceedings of the XXIX. DidMatTech 2016 Conference, Budapest,
Hungary. Eötvös Loránd University, Faculty of Informatics.
Horváth, Gy., Visnovitz, M. (2017). Egy bevezető webfejlesztési kurzus módszertani megfontolásai[Methodological Considerations of an Introductory Web Development Course]. In Proceedings:
Informatika a felsőoktatásban 2017 [Informatics in Higher Education 2017], Debrecen, Hungary,
University of Debrecen, Faculty of Informatics. p. 265- 274.
Horváth, Gy., Visnovitz, M. (2018). A böngésző mint alkalmazásfejlesztési platform [The Browser as an
Application-development
Platform].
Retrieved
March
30,
2018,
from
http://webprogramozas.inf.elte.hu/tananyag/kliens/kliens.pdf
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Constructionism 2018, Vilnius, Lithuania

Mahmoud, Q. H., Dobosiewicz, W., Swayne D. (2004). Redesigning Introductory Computer
Programming with HTML, JavaScript, and Java. In Proceedings: SIGCSE '04 Proceedings of the 35th
SIGCSE technical symposium on Computer science education, Norfolk, Virginia, USA, ACM SIGCSE
Bulletin. p. 120-124.
Visnovitz, M. (2017). Szöveges programozási nyelvek a közoktatásban [Textual Programming
Languages in Public Education]. MA thesis, Eötvös Loránd University, Budapest.

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Constructionism 2018, Vilnius, Lithuania

Programming Lessons for Kindergarten Children in
Japan
Takeshi Watanabe, watanabe@viscuit.com
The University of Electro-Communications, LLC. Digital-pocket, Japan

Yuriko Nakayama, nakayama@uec.ac.jp
Kagawa-fujimigaoka Kindergarten, Japan

Yasunori Harada, hakase@viscuit.com
LLC. Digital-pocket, Japan

Yasushi Kuno, y-kuno@uec.ac.jp
The University of Electro-Communications, Japan

Abstract
Viscuit is a programming language developed by the one of the author. Distinguishing feature of Viscuitis that the programs are made of pictures only, and no characters are required to understand or makeprograms. This feature makes it possible to experience programming for pre-school children. Actual,
LLC. Digital-pocket cooperates with Kagawa-fujimigaoka kindergarten in Japan for regular Viscuitclasses since November 2015. In this poster, we analyzed children 's programs and videos taken during
the lessons held for them in 2017. In addition, we took questionnaire on the difference between theusual state and the state of the Viscuit lessons of the children to the teacher in charge of each class,and examined the features of the programming lesson at the kindergarten.

Keywords
keyword; programming for kindergarten; visual programming language; ViscuitPreface
Background
In Japan, programming education is going to start from elementary school year 2020. As the Ministry of
Education of Japan states, the aims for programming education in elementary school is not to acquire
coding skills, but to acquire logical thinking abilities and problem solving skills or computational thinking
skills (Wing, 2006). As for the programming experiences, children are expected to organize or combine
primitive actions or symbols to construct the movement they want. As the result of a survey in 2016, thenumber of Japanese private programming classes are increasing, along with the children’s age
decreasing. Particularly, programming workshops or classes for preschool children are getting popular,
perhaps parents are willing for their children to be get ready for elementary schools programmingclasses.
We observed that children who joined full-year lessons during 2016 had apparently changed in theirprogramming abilities. And programs made by them became more complex and rich. In this poster, weshow results from our analysis, along with actual programs made by children.

What is Viscuit
Viscuit is a programming language created by Yasunori Harada who is one of the authors, in Japan
2003 (Harada & Richard, 2003) (Harada, 2010), which is a rule-based visual programming language
like KIDSIM (Smith, Cypher, & Spohrer, 1994). The most distinguish character of this language is user
can make program without using any letters or numbers, but only with drawing. On Viscuit, you canmake programs by putting your drawing on the rules that are similar to glasses (actually we call thoserules glasses). Right lens of the glasses means “before” situation and left means “after” situation. If you
put a picture upper on the right lens than picture that is in left, a picture on the stage get to move up
direction.
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Figure 1. How to make program by Viscuit

Viscuit has a function of sharing program in a group. Thisfunction is called “Viscuit Land”. Programs created on each
student’s tablet are gathered at another tablet through the
network. For example, if the theme of a lesson is “sea”,
participants will draw something that moves in the sea, makedrawings move, send network and share programs in a group.
In kindergarten, we have lessons using group work, notindividual production.

Kagawa-fujimigaoka Kindergarten
Programming lessons are held by kindergarten’ teacher.
Nakayama, who is also one of the authors, is in charge of
Figure 2. Recital time by watching“Viscuit Land”
Viscuit lessons. Initially, the equipment has been brought by
LLC. Digital-pocket, but then they bought iPad mini morethan 30 units, also prepared Wi-Fi environment. So now, lesson has been carried out using allkindergarten equipment.

PurposeIn this research, we will clarify the growth of children that is shown in the continuing lesson ofkindergarten. Also, clarify what preschool children are learning through programming lessons.
Specifically, their programs that run in the computer are made as whether or not they wish. Also, we will
clarify the features and important points of programming practice in kindergarten.

Research Method
Subject
The subjects are 56 children (5 and 6 years old) (28 people, 2 classes). In addition, a teacher in chargeof each class is conducted a questionnaire survey on the state of the child at the end of each lesson. In
the questionnaire, we asked the teacher how different between the child 's usual state and the state of
the programming lesson, and who shows characteristic behaviour in the programming lesson.

Content of Lessons
There are some challenges for children to study advance programming every lesson. One lesson is 40
minutes. Lessons consist of three parts. Two practice tasks are carried out in the first half, and a freeprogram production concerning those tasks follows. In the practice tasks, we prepare pictures forchildren to focus on their tasks. In the free production time we let children draw as they like unless it
has relation to the task. Lastly, we have recital time that we appreciate what they make in groups on thescreen like Figure 2.
On the other hand, it was found that even if the level of lesson did not raise every lesson, it could beenjoyed enough just by changing the picture of the practice. So, some important parts of programmingare repeated again and again by changing the picture to fix the study without progress.
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The layout of the classroom is like Figure 3. In this layout there are clearly separate "teaching space"and "production space". And by only placing the tablets in "production space" and do not allow children
to bring tablets in "teaching space", we can make children listen to what the teacher says in the "teaching
space". When the teacher teaches
something to children, they aregathered in “teaching space”, andafter they listen what they should to
do, then go “production space”. This
will allow children who cannot
concentrate when the tablet is in
front of them make concentrate on
what the teacher says. This way ofgoing and coming is only done at thetime of the practice. When they workon free program, they remain“production space” and focus onwhat they want to make.

Figure 3. The layout of the classroom

AnalysisAnalysis of program
From programs saved at each time, we counted the numberof works saved, the number of pictures drawn, the numberof pictures on the stage, the number of glasses. And we told
whether those glasses were valid or not, whether the
direction of drawings and movement matched and whether
the task and the movement of the pictures they madematched.

Analysis of video
I felt it difficult to understand what the children were learningwith only quantitative data of programs. Therefore, we
executed video research. We chose 4 characteristic children
and shot and analyzed the situation of free tasks in video.
As a result of observing the video, it turns out that each of
the four people shows different changes and attitude.

Figure 4. Lesson in kindergarten.
Children make programs with their fingers.

Inquiry of teachers
Regarding the questionnaire of each class teacher, we got a glimpse of the difference between theordinary activity of the children and the programming lesson using Viscuit. In the free description ofquestionnaire the following comments came out. ”Children's aggressiveness is high.” “I think in thislesson we provide a different experience from the drawing on paper.” “ I was surprised that they had
been able to maintain their concentration for 40 minutes.” These are suggesting hints to clarify thefeatures of programming lessons at kindergarten.

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Conclusion
Now we have done analysis of the program of 9 children during May to July. As a result, 70% of children
shows that a program that they made consistent with the tasks. In addition, we found that even ifpracticing tasks are completed in the lesson, children might start doing other challenges on the samestage and destroy previous program after they completed tasks. So, we could not understand whether
they understand when they tried to practice tasks. Therefore, it necessary to take screenshots during
lessons to check whether they manage to do tasks.
By observing each person about the video, we felt the possibility of being able to grasp the change ofeach person that cannot be found by program observation alone. Thus, we felt the possibility to grasp
what kind of changes caused through programming lessons by combining log of program andobservation of each person by video research.

References
Harada, Y. (2010). Computer Programming Education Using the Visual Programming Language Viscuit.
NTT Technical Reiew , 8 (11), 1-5.
Harada, Y., & Richard, P. (2003). Fuzzy rewriting: soft program semantics for children. Proc. of the 2003
IEEE Symposium on Human Centric Computing Languages ans Enviroments , 1 (1), 39-46.
Smith, D. C., Cypher, A., & Spohrer, J. (1994). KidSim: programming agents without a programming
language. Communications of the ACM , 37 (7), 54-67.
Wing, J. M. (2006). Computational Thinking. Communications of the ACM , 3 ed.:, 33-35.

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A Practical Report on a Programming Course with
“Making” Using Micro:bit
Aoi Yoshida, aoi@si.aoyama.ac.jp
Kazunari Ito, kaz@si.aoyama.ac.jp
Kazuhiro Abee, abee@si.aoyama.ac.jp
Aoyama Gakuin University, Japan

Abstract
We design and practice hands-on course aimed at acquiring problem solving skills and thinking skills inprogramming through physical computing based on constructionism. We held 15 weeks classes, andthere were 28 students who are third-year or fourth-year in this course. This course provided to makeproducts with micro:bit using digital fabrication and physical computing. This poster describes the course
content and students' final products.

Micro:bit (Left) Class scenery of this course (Right)

Keywords
physical computing; making digital fabrication; programming education

Introduction
We design and practice hands-on course aimed at acquiring problem solving skills and thinking skills inprogramming through physical computing based on constructionism. While previous studies have foundthat physical computing can be effectively incorporated into K-12 programming education, we believethat it can also contribute effectively to university education.
At Aoyama Gakuin University in Japan, we offered a course titled “General Practice in Information
Science”. An objective of this course is to acquire knowledge and skills listed below:knowledge about sensors and actuatorsskill of controlling sensors and actuators by programming

knowledge about technologies to constitute IoTskill of realizing own idea using information technology
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Constructionism 2018, Vilnius, Lithuania

For this purpose, we employed a method of learning by “making.” Students learn by making what theywant to make. Lectures did not give students too much instruction.
Research question of this practice is to clarify what kind of knowledge and skill students acquire throughthis course.

Practice Outline
ParticipantThis course was offered in the second semester. There were 28 students who are third-year or fourthyear in this course. Most of them are studying programming (Java, Visual Basic, and Scratch). However,
because of our multidisciplinary department, some students are not so good at programming. There
were 2 instructors and 1 TA(teaching assistant, a graduate student).

Hardware and software
We used micro:bit, a an ARM-based embedded system designed by the BBC for use in computer
education in the UK. Micro:bit equips accelerometer and magnetometer sensors, Bluetooth and USB
connectivity, a display consisting of 25 LEDs, and two programmable buttons. There are some coding
editor. We mainly used the JavaScript block editor on the web browser. ( https://makecode.microbit.org/) On demand, students switched coding editors from block to text, and switched coding editors fromJavaScript to Python. We rented “micro:bit box” to each student in order to use it freely outside theschool hour. There are a micro:bit, a valuable resistor, a buzzer, a USB cable, and five cables with clips
in the box.

Figure 1. Micro:bit box that we rented (Left) Programming editor on the web (Right)

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Course content
Table 1. The course content and activities

Phase

1

2

Class

Content

Activities

1

Basic of programming using micro:bit - Self-study on a text websites(except electrical work)- Make a program freely

2~4

Basic of programming using micro:bit- Hands-on practice(with electrical work)

5~6

Making (Individual work)- Develop a product7

Presentation- Show-and-tell

8

Digital fabrication

9~13

Making (Group work)(12)

Intermediate presentation

3- Design 3D model for micro:bit case- Print out it on a 3D printer- Think of creative ideas- Develop a product- Show-and-tell- Peer review- Show-and-tell each other

14

Interactive presentation

15

Award and review- Invest virtual money for favorite
products in crowd-funding styleStudents’ Final Products
All teams could develop and present their products, and most of them used CS technology other thanwhat we taught in phases 1 and 2. Students’ final products listed below:










Shooting monsters game
Music box
Detector people entering the roomo BLE communication, machine learning, various sensors, server-side programming
Hourglasso BLE communication, 3D printing
Savings box
o servo motors, handcraft, structure
Lier checkero electric resistance
Remote cat feeding machineo Raspberry Pi, web camera, various sensors, server-side programming, laser processing
Wake up deviceo a servo motor, handcraft
Automatic switch press machineo BLE communication, various sensors

We introduced two examples of students’ final products.
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Constructionism 2018, Vilnius, Lithuania

Shooting monsters game
This is a shooting game by Unity like an arcade game. When monsters appear, a player shoots themusing a controller like a model gun. There is a micro:bit on the head of the gun. It is sent the value of
inclination sensors from micro:bit to PC by serial communications. On the PC, a targeting is controlledusing the received value.
This group made it consists of three students. Students1 made a controller by 3D printing(Figure 2).
Student2 made a program on the PC side by Unity. Student3 made a program on the micro:bit side byJavascript (block editor).

Figure 2. Shooting monsters game

Music box
This is a box that plays music by cooperating with multiple players. All players touch the objects of heart
made by aluminum with one hand, and touch the aluminum buttons with other hand. And then, they canplay one sound. It uses the electric resistance value from micro:bit.
This group made it consists of three students. They did all the work together.

Figure 3. Music box

The videos of this course and students’ final products are available at https://goo.gl/za13nY .(only inJapanese. English version is to be prepared.)

Results
Students’ final products show that each students acquired in a different knowledge and skill. We thinkthat this is because the necessary knowledge is different when making what they want to make.
After all classes, we conducted questionnaires about this course. All the students except one weresatisfied with this course, and also wanted to continue “making” and programming.
Moreover, we asked students to evaluate achievement for objectives of this class on five-grade.
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Constructionism 2018, Vilnius, Lithuania






Q1. Can you understand the structure of various sensors?
Q2. Can you acquire how to control with micro:bit?
Q3. Can you understand technologies to constitute IoT?
Q4. Can you realize your own idea?

Figure 4. the result of students’ self-assessment

Figure 4 shows that most of all students achieve an objective of this course by themselves thoughlectures did not give students too much instruction.
According to free description in a questionnaire, a lot of students answered that they thought deeper
and participated in classes more aggressively than usual.
As future work, we plan to improve this course content more effectively. We also plan to expand target.

References
Papert, S.(1977) A Learning Environment for Children. Computers and Communication: Implications for
Education. New York, Academic Press, p. 271-278.
Papert, S. (1980) Mindstorms, Children, Computers, and Powerful Ideas. Basic Books, Inc.
Resnick, M. (2017). Lifelong Kindergarten: Cultivating Creativity through Projects, Passions, Peers, andPlay. MIT Press.
Sylvia, L.M., Stager, G. (2013) Invent to Learn: Making, Tinkering, and Engineering in the classroom.
Constructing Modern Knowledge Press.

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An Experimental Exploration of the Development of
Design Thinking in University Maker Courses
Jinbao Zhang, Zhangjb@bnu.edu.cn
Beijing Normal University, Department of Education, China

Abstract
At present, Maker activity in colleges and universities rarely appears in the formal curriculum, and mostof them exist in makerspace, training activity of entrepreneurship. In the research, we design a newundergraduate course named as 3D Printing and Maker Education, including two parts: (1) theapplication instruction about the technologies of 3D printing, 3D software, Arduino and APP Inventor;(2) design thinking based innovative and creative design practice. In the process of the curriculum, theauthor does some observation and interview. The primary findings include the design thinking
application level in creating scenarios and formation, the bottlenecks and obstacles encountered invarious stages of design thinking.

Keywords
design thinking; maker; curriculum design; instructional design

Introduction
The Maker Movement and Maker Space, which have risen in recent years, have won recognition andattention from most people as a new educational phenomenon (Li et al., 2016). In universities, there islittle research conducted in the form of a just-in-time and formal course. Whether this kind of education
idea is suitable for the university curriculum implementation model, what problems will exist, how todesign to play the value of such a teaching concept for students, are the starting point of this study.
Design is a purposeful behavior that requires creativity and innovation (Liu, 2015). The process ofdesign thinking is a combination of divergence and concentration of thought (Lawson, 2005). Design
thinking contains deep insight into the target group and efficient prototype testing, making the proposedsolution more effective, far beyond the traditional hypothetical thinking. Besides, the emphasis on design
thinking is to activate students' original thinking and expression methods, emphasize the cultivation ofcreativity and individuality. As it is closer to the essence of design education, it is "teaching for creativity"
to become one of the main objectives of higher education teaching and has high educational values.
Therefore, it is necessary to take "design thinking" in the design and creation process as the primaryobjective of the practical curriculum.
Based on the analysis of the idea of creating customers and the characteristics of undergraduate public
elective courses, the researcher proposes an innovative curriculum that takes design thinking as theprimary educational goal. With a relatively flexible teaching arrangement, through the implementationof the concept of "genius hour" (Liu, 2016), the undergraduate course at Beijing Normal University
named as 3D Printing and Maker Education is practiced to study whether students’ design thinking canbe well developed.

Research design
Given many students and the limited learning time, curriculum designers have thought that the core
goal of this course is to "design thinking education under the guidance of creating for customers" rather
than merely learning kinds of tool. Then researchers developed curriculum modules, teachingprocesses, and lesson schedules. The teachers at each stage are not the researchers themselves. It isbeneficial to avoid the influence of the subjective approach of the researcher on the research andguarantee the objectivity of the study.

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Constructionism 2018, Vilnius, Lithuania

Research hypotheses
This study proposes the following hypotheses:
1) Design thinking as a guiding method is more conducive to designing innovative courses thanprevious teaching methods;
2) After the course, students can master 3D printing, open source hardware, open source software,and other standard maker tools and software, as well as a complete set of solutions to problems (design
thinking);
3) Compared to traditional curriculum, this kind of teaching arrangement based on the theme ofmaker activity can be more favoured by students.

Research methods
To collect enough procedural data, this study adopts an educational observation method to record theentire process of class (teacher’s instruction methods, teacher-student interaction process, andresponses of most students). Students are interviewed after class. The research data collection tools
are shown in the table 1.
Table 1. Data Collection methods
Data collection methods

Dimensions

Classroom Observation

Teacher PerformanceStudent performance
Interaction between teachers and students

Structured Interview

Degree of satisfaction of course content
Satisfaction degree of teaching effectTeaching Suggestions
Self-capacity growth

Data analysis and key findings
Findings in classroom observation
1) By observing the progress of each group's project, it was found that most of the teams had"disagreement of work" and "less cooperation". Team building may require teacher intervention.
2) In the group work, there are crucial problems in their learning: single player working hard without
cooperating with others, team leader without relying on team member, more time to catch up and lessto follow the plan, ignoring the actual printing, less logical planning of design thinking and so on. The
team project may require more attention and guidance during the process of advancement.
3) But there is still a lot of good news: liberal arts students (Chinese, art, calligraphy, etc.) have a
lot of creative ideas and some have gotten the supported by schools; students are enthusiastic andpassionate, if they are supported and continue to be payed attention, students will come to the 3D
Printing laboratory every day; young students have strong learning ability, they can master theknowledge quickly.

Structured interview conclusions
After the course is over, one-on-one interviews will be conducted with students in class. The following
aspects need to be focused on:
1) Not all modules have gotten students’ confirmation. students are deeply impressed by thefollowing courses: First-course "Introduction to 3D Printing and Maker Education", the sixth-course
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Constructionism 2018, Vilnius, Lithuania"Mixly Maker Education", and the seventh course "General "Application of 3D printing technology intechnology courses" and the eighth course "APP Inventor".
2) The great reaction is that the curriculum is too fast. There are too many contents of the sevencourses, and the time may be insufficient to be completely digested.
3) At the knowledge level, all students have a deeper understanding and unique understanding of
3D printing knowledge, open source hardware categories and principles, and app mobile terminal
development;
4) Regarding core concepts, most of the students have a deeper understanding of the spirit of theMaker Movement which has promoted their practice in action. And in the final interview, we learned thatapart from the group project, almost all students have things they want to make;5) In the aspect of improvement of design thinking ability, almost all students can combine andcomplement one of their core concepts (user thinking, empathy) and the feature of maker (customeroriented thinking makes them more targeted, continuous improvements) have a clear understanding;6) Most students can recognize one of the core concepts of design thinking, but only a small partof these tools can be used proficiently (for example, in the development of group activities, the "planassessment diagram"). Most of the tools can be used to eliminate small components and obtain a unified
opinion. Many tools cannot be applied in actual activities.

Conclusion
Although it is trial and experiment, it also can get some inspiration for the future research and instruction:
1) The full range of design thinking tools is not required for every project. For example, the "userfeedback" tool for Empathise. Since almost all student projects are modelled on real-world scenarios
rather than actual projects in real society, the actual curriculum, and after-school activities are mostly
substituting team members for playing customer roles in different situations to observe customer needs.
2) The application environment of some design thinking tools is relatively complex and not easy tomaster. A set of tool theory in precision manufacturing and quality control needs to be combined withthe “brainstorming” method in order to be mastered. There are not many environments that need to be
used and skilfully used in student projects.
3) Some of the design thinking tools have similar functions to each other. For example, the "priority
chart" and "plan assessment chart" can compare and evaluate different programs.
For this course, we did not adopt a task-driven but fully open curriculum model. Students' performance
was smooth after initial exposure to confusion, repeated selection of project plans, and selection of
directions. The lecture-style teaching mode that is different from the ordinary course teaching mode isadopted. After the interview, most students will compare with the ordinary course how much they havelearned the specific knowledge in the classroom. For example, if there are seven groups or lessfeedback did not learn or master three-dimensional design software, open source hardware, and App
Inventor. This is not consistent with the goal of this course, which mainly aims at cultivating students'
design thinking ability. How to improve it still needs further study.

References
Li X., Gao H., Zou J., & Wan K. (2016). Changes of STEAM Education to Maker Education in the“Internet+” Background - from project-based learning to the development of creative abilities. Journal ofDistance Education, (5), 28-36.
Liu Jing-wei (2013). Design Thinking. Beijing: Chemical. Industry Press.
Liu, L. (2016). Inquiry and innovation in the classroom: using 20% time, genius hour, and PBL to drive
student success. Innovations in Education & Teaching International, 10(1), 472-472.
Lawson, B. (2005). How Designers Think: the design process demystified (4th ed.). London: The
Architectural Press.
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Panels / Workshops / Demonstrations /Working groups753

Constructionism 2018, Vilnius, Lithuania

PanelsConstructionism across Cultures: Commonalities
and Differences of Constructionist Implementations
around the World
Paulo Blikstein, paulob@stanford.edu
Stanford University, USA

Abstract
The goal of this panel is to bring together scholars from around the world to discuss commonalities anddifferences amongst implementations of constructionist projects in different countries. Seymour Papert
himself refused to precisely define what constructionism was, which opened up the possibility of local
contextualization and definition by scholars and practitioners. The papers in this symposium analyze
how constructionist theory was used, defined, and implemented within rich cultural contexts, discussing
particular characteristics that might have emerged as a result of the combination of local educational
practices and philosophies and constructionist theory.

Keywords
Constructionism, cultural context, implementation

Constructionism at Scale
Celia Hoyles, c.hoyles@ucl.ac.uk
Richard Noss, r.noss@ucl.ac.uk
University College London, UK

Yasmin B. Kafai, kafai@upenn.edu
University of Pennsylvania, USA

Kylie Peppler, kpeppler@indiana.edu
University of Indiana, USA

Deborah A. Fields, Deborah.Fields@usu.edu
Utah State University, USA

Nathan Holbert, holbert@tc.columbia.edu
Teachers College, Columbia University , USA

Abstract
Constructionist designers have used new technologies to engage leaners in rich opportunities to build
personally meaningful artifacts for decades. In recent years, new technologies have brought theseexperiences to large numbers of learners in distributed places. In this symposium we bring together
expert designers and scholars that have successful developed constructionist innovations using
emerging technologies in a range of domains and have brought these innovations to scale. In a panel
discussion format, participant highlight key challenges for scaling constructionist design and discuss
how these tools and environments evolve as the community of learners increases by orders ofmagnitude.

Keywords
programming; construction; making; mathematics; scale
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Inside the Trojan Horse – A Discussion Among the
Next Generation of Constructionists
Sylvia Martinez
Constructing Modern Knowledge, USA

Abstract“I think the technology serves as a Trojan horse all right, but in the real story of the
Trojan horse, it wasn't the horse that was effective, it was the soldiers inside thehorse. And the technology is only gong to be effective in changing education if you
put an army inside it which is determined to make that change once it gets throughthe barrier...
Just 100 years ago, John Dewey was saying things about educational change, notvery different from what I believe in. He couldn't get very far. And the reason why hecouldn't get very far is that he had only philosophical arguments. He didn't have anarmy. You must have an army, and it's an army primarily of children and the adults
also are a political force in this.” (Papert 1999)
This discussion, comprised of practicing educators from around the world, will address personal
challenges and triumphs bringing constructionism to life on a daily basis. Examples of classroom
practice, student projects, professional development, and strategies for sustaining constructionism will
be shared.
The panelists:Sylvia Martinez – President, Constructing Modern Knowledge (moderator)



Amy Dugré – Director of Technology, Innovation, and Curriculum: Dusseldorf International
School, GermanyAngela Lombardo – MalpighiLaB & CoderDojo Bologna, Bologna, Italy

Susana Tesconi - Lecturer and researcher at Department of Information Technology /UOC/
Universitat Oberta de Catalunya DARTS/Interdisciplinary research group in arts, technoscienceand society, Barcelona, SpainTracy Rudzitis – Faculty, Constructing Modern Knowledge, USABrian C. Smith – Computing & Learning Technology Teacher: Hong Kong International School,
Hong Kong

Jaymes Dec – Fab Lab Integrator: Marymount School, New York City, USA

Papert, S. (1999). "Ghost in the Machine: Seymour Papert on How Computers Fundamentally Change
the Way Kids Learn." Interview of Seymour Papert by Dan Schwartz.

Keywords
constructionism, Logo, Seymour Papert, Scratch, physical computing, coding, teacher education,
fabrication

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Workshops

WS1: The Essence of Programming at School –Learning for Life
Jacqueline Staub, Jacqueline.staub@inf.ethz.ch
ETH Zurich, Switzerland
Pädagogische Hochschule Graubünden, Chur, Switzerland

Abstract
School is responsible for priming and preparing pupils such that they develop a deep understanding oftechnology. Computer science education serves a vital role in fostering algorithmic thinking and problem
solving skills, as exemplified by programming. This form of learning is constructive, enriches creativity
and teaches precision. We have been introducing primary school pupils and their teachers toprogramming in Logo for more than a decade and thousands of children across Switzerland havelearned to program using our curriculum and purpose-built programming environment. In this workshop,
we give insights into how our curriculum guides pupils to progress individually and how we make pupils
building up competence by recovering from their programming errors autonomously. This workshop
caters towards educators and people interested in how to introduce computer science to novices.
Participants gain practical insights into our curriculum and discuss its didactic structure.

Keywords
algorithmic thinking; constructionism in novice programming; Logo; spiral curricula; modular design;
debugging; syntactic errors

Materials Provided
In this workshop, participants will be given the opportunity to learn about our approach of teachingalgorithmic thinking to novices by actively programming in Logo. Workshop attendees will solve selected
exercises from our curriculum at a computer and later discuss the thought processes children go
through. One major theme of discussion is the role of errors; we claim that children who learn to recoverfrom errors autonomously gain higher confidence and a deeper understanding for core computerscience concepts.
Participants will be enlightened with the core design ideas behind our curriculum involving the conceptsof repetition, modular design and parametrization as a preparation for variables – all neatly woven in aspiral curriculum.

Figure 1. Example of an introductory exercise from the chapter on regular polygons and circles

Exercises will address:756

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




A basic instruction set used for drawing lines, rotation and moving a turtle
A simplified looping construct which does not make use of variables
A mechanism to extend the language by introducing new commands and nesting them tosolve more complicated exercises
Generalizing solutions to existing problems by parameterizing custom commands

Figure 2. Drawing polygons with custom side lengths and number of sides

Resources:
Both programming environment and curriculum are freely available online in various languages. They
can be downloaded from http://www.abz.inf.ethz.ch/logo

Agenda1. Introduction: A decade of introducing a programming curriculum at Swiss schools
2. An Example: Algorithmic thinking as a problem solving strategy – a linguistic view
3. Our approach: Problem decomposition and an iterative refinement of solutionsi.
Theory: Why use modular design in the first place? Implications on the cognitive developmentof novice programmersii.
A practical example: From squares to circles
4. Hands-on: Our curriculum illustrated
5. Conclusion: Our approach introduces children to algorithmic thinking, a skill for a lifetime
6. Plenary Discussion: Participants ask questions on the matter of experiences, setup in schools,
limiting factors, typical errors and debugging strategies

Biography
Jacqueline Staub is a second-year PhD student in didactics of computer science at ETH Zurich and a
researcher at the STEM-Research-and-Services division at Pädagogische Hochschule Graubünden - auniversity of education based in Chur, Switzerland. She works on the topic of how to introduce pupils toalgorithmic thinking and enabling them to progress autonomously. Seven years ago, she joined thegroup as a student and henceforth acted as a Logo teacher in many primary and lower secondary school
programming courses. She completed her studies in Computer Science at ETH Zurich and, furthermore,
graduated the teachers’ education program. In her Masters’ thesis she developed XLogoOnline, aprogramming environment for novices which is tailored around the curriculum we present and use inthis workshop. Lately, her main focus lies within finding a model to identify the struggles pupils go
through while programming and how to counteract these struggles by providing additional help throughthe programming environment.
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References
Heidi Gebauer, Juraj Hromkovic, Lucia Keller, Ivana Kosírová, Giovanni Serafini, and Björn Steffen.
Programming in LOGO (Status as of March 18, 2018). http://abz.inf.ethz.ch/wpcontent/uploads/unterrichtsmaterialien/primarschulen/logo_heft_en.pdf.
Hromkovič, J., Kohn, T., Komm, D., & Serafini, G. (2016). Examples of algorithmic thinking inprogramming education. Olympiads in Informatics, 10(1-2), 111-124.
Hromkovic, J., Kohn, T., Komm, D., & Serafini, G. (2017). Algorithmic thinking from the start. Bulletin of
EATCS, 1(121).
Hromkovic, J. (2014). Einführung in die Programmierung mit LOGO. Springer Vieweg.
Serafini, G. (2011, October). Teaching programming at primary schools: visions, experiences, and longterm research prospects. In International Conference on Informatics in Schools: Situation, Evolution,
and Perspectives (pp. 143-154). Springer, Berlin, Heidelberg.
Hromkovič, J., Serafini, G., & Staub, J. (2017, November). XLogoOnline: a single-page, browser-based
programming environment for schools aiming at reducing cognitive load on pupils. In International
Conference on Informatics in Schools: Situation, Evolution, and Perspectives (pp. 219-231). Springer,
Cham.

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WS2: Developing Body Tracking Software with
Scratch and Kinect
Stephen Howell, stephen.howell@ucdconnect.ie
SMARTlab, University College Dublin, Ireland

Lizbeth Goodman, lizbeth.goodman@ucd.ie
SMARTlab, University College Dublin, Ireland

Abstract
In this workshop participants will design, develop and test programs that can be controlled with theuser’s body. The code is developed with a visual programming language (Scratch) and an infrared bodytracking camera (Kinect).
Starting with simple, familiar retro games, and a creative art and music performance application
controlled by keyboard and mouse, participants will learn how to add tracking blocks so prescribedmovements including individual joint positions, gestures, and stances can be continuously tracked andresponded to.

Scratch sample code with a spaceship sprite tracking the user’s left hand on the y axis, and tracking the user's
right hand on the open/closed gestureThis workshop is appropriate for educators and researchers interested in teaching Computational
Thinking by having students develop proprioceptive programs.

Keywords
visual programming languages; body tracking; proprioception; body syntonicity

WorkshopThis workshop explores Kinect2Scratch, a hardware extension for Scratch that enables body tracking
games and applications to be developed simply and rapidly (Howell, 2016). This extension was originallydeveloped to enable children learning Computational Thinking code more complex Scratch projects
using novel input controls. This includes moving their heads, limbs, and making gestures such as
clapping, jumping, and pointing. This is made possible by adding additional event and reporter blocks
to Scratch that receive data from an infrared sensor with body tracking capabilities. The body tracking
data is normalised for the Scratch stage and streamed to the Scratch project continuously. Each new
block allows the developer to detect, track, and respond to bodily movements of either the closest
participant to the sensor or up to 6 different users at once.

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The Kinect2Scratch extension blocks

Format
The workshop format will be interactive and include an initial demonstration of how the body tracking
Scratch programs are developed, including a simple retro game and creative art and music performance
program. No prior knowledge of Scratch is required but familiarity with a visual programming language
would be useful. The samples will be ‘live coded’ to explain the reasoning for their inclusion and notpresented as previously completed samples.
Following this, participants will learn how to respond to gestures including Boolean gestures (e.g. is auser’s hand open or closed) and complex gestures (e.g. is a user clapping, kicking, or jumping). Basedon participant suggestions, we will then build a new Scratch program incorporating the gestures andmovements the audience wants to track. Participants will then be able to test and refine the gesturesusing the workshop sensor.

Participants
A typical participant may be interested in teaching Computational Thinking principles or exploring
syntonic learning. However, several researchers without software development backgrounds havepublished work using Kinect2Scratch to conduct research that required body tracking. For that reason,
participants who are interested in writing body tracking software themselves but not necessarily teaching
it to students may find this workshop useful. Participants do not need any special equipment, as the
Scratch programs will be developed using a sensor and the workshop presenter’s laptop.

References
Howell,S.
(2016).
Kinect2Scratch
https://stephenhowell.github.io/kinect2scratch/

v3[Computersoftware].

Retrieved

from

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WS3: Group-based Simulation and Modelling:
Technology Supports for Social Constructionism
Corey Brady, corey.brady@vanderbilt.edu
Vanderbilt University, Nashville TN, USA

Walter Stroup, wstroup@umassd.edu
University of Massachusetts Dartmouth, North Dartmouth MA, USA

Tony Petrosino, ajpetrosino@austin.utexas.edu
University of Texas at Austin, Austin TX, USA

Uri Wilensky, uri@northwestern.edu
Northwestern University, Evanston IL, USA

Abstract
Through a US National Science Foundation-funded project called Group-based Cloud Computing(GbCC), we have created a web-based system for designing and implementing group-centred modelling
activities on top of the NetLogo Web platform. For the past two years we have been iteratively building
and testing this system in our own classrooms and in the University of Texas’s UTeach and UTeach
Computer Science programs. In this workshop, we introduce GbCC to the Constructionism community,
bringing participants to a point where they can create and publish their own activities. Removing barriers
to designing one’s own group-based activities, we open up the discussion to engage the research andteaching challenges and opportunities for a deeply social form of constructionism.

Keywords
collaborative learning; collective learning; generative design; social constructionism; agent-basedmodelling; participatory simulations

Introduction
Perhaps the most salient feature of learning in schools is the group-based nature of classroom life;
however, typical classroom instruction focuses primarily on individual learners, emphasizing
approaches such as assignment sets of procedural exercises and questioning that follows an initiationresponse-evaluation (IRE) sequence (Mehan, 1980). In contrast, with a generative approach, instructors
design situations for groups of students to construct relations between stored knowledge, experience,and new information (Wittrock, 1991). Using the taxonomy of generative design provided by Stroup,Ares, and Hurford (2004), our project uses the “resonant” technological infrastructure of collaborativeclassroom networks to engage learners in generative learning around the core practice of modelling.
We build upon the broad cross-disciplinary reach of agent-based modelling and the NetLogo language
and platform (Wilensky, 1999), to support activities in the Social Sciences and STEM disciplines,
including Computer Science.
Attempts to “scale up” individual learning approaches fail to capitalize in essential ways on the diversityof thinking that can be found in the group. In contrast, our GbCC project aims to use the technologicalinfrastructure of collaborative classroom networks to engage learners in generative learning around thecore practice of scientific modelling. In particular, we aim to support activities in which the in-processlearning of one’s peers can act as a learning environment to cultivate not conformity and convergence
toward a single, “right answer” but rather diversity and a collective exploration of the space of possible
ideas.
In the GbCC project, we connect innovative approaches to computational and participatory modellingto theoretical roots in Constructionist thought. In addition, the project is creating detailed analyses ofactivity designs and implementations fostering authentic STEM practices in group-centred learning
environments, as well as nuanced studies of high-leverage teacher “moves” and strategies within such
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environments. This workshop offers a hands-on introduction to the ideas of generative and groupcentred design that have inspired GbCC, and our plan for the session is driven by the goal of inviting
attendees into the group-centred discussion and enabling them use the technology to express their own
ideas for activity designs.

Workshop objectives
The workshop will be of interest to participants who engage with learners at middle-school, high-school
and university levels. A projector for the facilitator is required, and all participants need internetconnected computers. Thus, having a few extra computers available would be highly desirable. By theend of the workshop, our intention is for every participant to have created their own GbCC activity andeither made it “live” on their personal (new or existing) Heroku account, or sent the activity to theworkshop facilitators to publish it on the GbCC project’s Heroku account. We choose this objective inpart to ensure that participants take away something concrete and useful, and in part to demonstratethat we have achieved the goal of making social constructionist learning as simple to support
technologically as activities that do not offer group-centred collaborative supports. Of course, successhere then allows us to turn attention to the challenges of adopting group-centred pedagogies anddesigning activities that make effective use of the group. But this is a challenge we welcome and where
we have also made headway, as we will illustrate through brief experiences of several “genres” of groupcentred activities that we have identified. (This is not an exhaustive list of genres the project has
explored.)
Genre 1: Exploring Together. A common pattern in classroom agent-based modelling instruction is to
explore the “parameter space” in an existing model by changing the values of key inputs to thesimulation. Key goals of such activities are to investigate the range of behaviours the model can exhibit
and to determine how sensitive these behaviours are to changes in the model’s parameters. However,with NetLogo and NetLogo Web alone, it is difficult to bring different students’ discoveries together inan aggregate display of the class’s exploration, to facilitate group discussion. With GbCC, any NetLogo
model can be converted easily and deployed as a group exploration activity. Each student can publish
her findings to a shared “Gallery,” which can permit others to see and also repeat (to examine and/or
verify) the findings that she has published. “Going public” in this way with in-process findings has beenshown to enhance classroom learning in agent-based modelling and to help identify emerging themesin group exploration (Brady et al, 2015).
Genre 2: Constructing Together. Whether as an introduction to the NetLogo language as an
expressive medium, or as a means of identifying and exploring the impact of design choices in modellinga particular phenomenon, GbCC can be used to open a space for a classroom group to rapidly createand share “snippets” of NetLogo code that can define agent or model behaviors in a way that supports
radical remixing and collective construction. For instance, in an activity we have called “IntroButtons,”
learners are given an array of buttons that allow beginners to NetLogo code to explore Turtle Geometry
collectively. By producing “something beautiful” on the screen, they begin to explore what turtles andpatches can do. On publishing to the Gallery, learners share the graphic results of their explorations aswell as text-based code representations of the command sequences that produce these results. Sharing
these personally meaningful objects helps the classroom group to make the move to text-based coding,
together. For more advanced groups, similar activities to IntroButtons can invite learners to create
varying implementations of a particular agent behaviour and help the classroom to “think together” aboutmodelling and code representations of phenomena they are studying.
Genre 3: Participatory Simulations. Since its earliest releases, the NetLogo platform has incorporated
the HubNet architecture (Wilensky & Stroup 1999a) as a technological support for a genre of groupcentred modelling activities called Participatory Simulations, or PartSims (Brady et al, 2017; Colella,
2000; Colella, Borovoy, & Resnick, 1998; Wilensky & Stroup, 1999b; Klopfer, Yoon & Perry, 2005). The
GbCC system also provides support for PartSims, and it removes many of the implementation barriers
that past research has encountered with these activities, by being fully web-based. Any browser-capabledevice (including cell phones, netbooks, and single-board computers, as well as tablets and laptops)
can participate in GbCC activities over the standard HTTP network connection permitted by almost all
school networks. Moreover, the GbCC’s constructs of activity “Rooms” and the role of “Room Creator”
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Constructionism 2018, Vilnius, Lithuania(which can be filled by the teacher or even by a student small-group leader) makes setting up andrunning a PartSim as easy as following a link and giving the new Room a name. These technicalinnovations have come out of a decade of design research on making HubNet activities more feasible
for real classroom implementations, and they have also radically broadened the design space for
PartSims allowing us to engage, for instance, with in-and-out of classroom activities and activities
involving data collection and the Internet of Things (IoT).
Genre 4: “Fish Tank” and “Program to Participate” Simulations. A common thread between agentbased modelling in general and PartSims in particular is that they offer opportunities for social structuresand interaction patterns of the classroom to be mapped onto structures and agent-interactions in thephenomena being modelled (Stroup, Ares, Hurford, & Lesh, 2007; White, Brady, Huang, & Stevens, inpress; Wilensky & Stroup, 1999b). This enables groups to identify a social syntonicity (Brady, Weintrop,
Anton, & Wilensky, 2017) in their shared modelling work. One approach we have explored to use social
syntonicity to drive collective modelling has been to identify key behaviours of agents (whether of one
breed or several breeds), and to create a model similar to a PartSim, but where students’ participationconsists of contributing the code that their agent ‘avatars’ will use to execute these behaviours. In thisway, the traditional PartSim genre can move beyond an activity structure in which learners are immersed
in a simulation, role-playing agents, to creating behaviours for those agents, which then run collectively.
We do not see these “program to participate” activities as replacing traditional PartSims; rather they
foster a different perspective on the emergent phenomena studied in the simulation and they offer
opportunities for new ways to distribute the expression of model behaviours over the room. Moreover,as behaviours can be expressed in multiple agents and multiple agent breeds, the students’ connection
to the simulation can be varied to exploit resonances with social and group structures.
Integrating other Representational Infrastructures. While NetLogo Web itself has enabled us to
support a wide range of activity designs, we have also identified additional representational capabilities
that are attractive for new GbCC activities. These include a Mapping and Geotagging Interface (using
Leaflet); a Cartesian and Euclidean environment (using GeoGebra); a Data Modelling interface (using
CODAP); and a Physics Engine, with force and collision simulation (using Box2d). Extensibility of
NetLogo Web and the GbCC system allows us to integrate these other open-source, browser-based
tools. If time permits, we will show how these environments are integrated with GbCC to permit smoothinteroperation between NetLogo Web and the representations and interactions they support.

Conclusion
Cooperative and collaborative learning approaches have long been heralded as a powerful resource foreducators to use in supporting student achievement (Johnson & Johnson, 2009), fostering dialogic
interaction and shared meaning-making (Stahl, 2006), and encouraging equitable participation (Cohen,
1994). Constructionist approaches offer learners environments for engaging authentically with thepowerful ideas of a discipline, creating personally meaningful artefacts that express their perspectives,
and their understanding, and their interests (Papert, 1980; Papert & Harel, 1991). Generative design
bridges the traditions of cooperative and constructionist learning, carefully formulating activity
environments to achieve 100% participation and to enable the classroom group to accomplish
explorations and produce insights that the individual members of the group could not accomplish alone.
Group-based, generative activities can take many shapes, and the GbCC project and system aims toprovide technological support for a wide range of these. In this workshop participants will learn to create
their own GbCC activities, and in the process, will engage with some of the important theoretical andpractical questions of group-based teaching and learning.

References
Brady, C., Holbert, N. R., Novak, M., Soylu, F., & Wilensky, U. (2015). Sandboxes for model-based
inquiry. Journal of Science Education and Technology, 24(2-3): 265-286.
Brady, C., Orton, K., Weintrop, D., Anton, G., Rodriguez, S., & Wilensky, U. (2017). All Roads Lead toComputing: Making, Participatory Simulations, and Social Computing as Pathways to Computer
Science. IEEE Transactions on Education, 60(1), 59-66.
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Brady, C., Weintrop, D., Anton, G., & Wilensky, U. (2016). Constructionist Learning at the Group Level
with Programmable Badges. Proceedings of the Constructionism 2016 Conference.
Cohen, E. G. (1994). Restructuring the classroom: Conditions for productive small groups. Review of
educational research, 64(1), 1-35.
Colella, V. (2000). Participatory simulations: Building collaborative understanding through immersive
dynamic modeling. Journal of the Learning Sciences, 9(4), 471-500.
Colella, V., Borovoy, R., & Resnick, M. (1998, April, 1998). Participatory simulations: Using
computational objects to learn about dynamic systems. Paper presented at the Computer Human
Interface (CHI) 1998 Conference, Los Angeles, CA.
Johnson, D. W., & Johnson, R. T. (2009). An educational psychology success story: Social
interdependence theory and cooperative learning. Educational researcher, 38(5), 365-379.
Mehan, H. (1980). The competent student. Anthropology & Education Quarterly, 11(3), 131-152.
Papert, S. (1980). Mindstorms: Children, computers, and powerful ideas. New York: Basic Books.
Papert, S. & Harel, I. (1991). Situating constructionism. In I. Harel & S. Papert (Eds.), Constructionism(pp. 1-12). Norwood, NJ: Ablex Publishing Corp.
Stahl, G. (2006). Group cognition: Computer support for building collaborative knowledge (pp. 451-473).
Cambridge, MA: MIT press.
Stroup, W., Ares, N., & Hurford, A. (2005). A dialectic analysis of generativity: Issues of networksupported design in mathematics and science. Mathematical Thinking and Learning, 7(3), 181-206.
Stroup, W. M., Ares, N., Hurford, A., & Lesh, R. A. (2007). Diversity by design: The what, why and howof generativity in next-generation classroom networks. In R. A. Lesh & J. J. Kaput (Eds.), Foundationsof the future: Twenty-first century models and modeling. New York, NY: Lawrence Erlbaum.
White, T., Brady, C., Huang, J., & Stevens, M. (in review). A Distributed-by-Design Approach to
Supporting Collaborative Learning with Dynamic Mathematics Software. Educational Designer.
Wilensky, U. (1999). NetLogo (Version 6.0.3) [Software]. Evanston, IL: Center for Connected Learning
and Computer-Based Modeling, Northwestern University.
Wilensky, U., & Stroup, W. (1999a). NetLogo HubNet [Software]. Evanston, IL: Center for Connected
Learning and Computer-Based Modeling, Northwestern University.
Wilensky, U., & Stroup, W. (1999b). Learning through participatory simulations: Network-based design
for systems learning in classrooms. Paper presented at the International Conference of the Learning
Sciences (ICLS), Atlanta, GA.
Wittrock, M. C. (1991). Generative teaching of comprehension. The Elementary School Journal, 92(2),(169-184).

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WS4: AI Programming in Snap!
Ken Kahn, toontalk@gmail.com
Department of Education, University of Oxford, UK

Abstract
We have added new blocks to Snap! for speech synthesis, speech recognition, image recognition, andmachine learning. These run in a Chrome browser without any extensions or installation. An interactive
guide has been developed. Twelve sample programs are available. During the workshop participants
will explore these new blocks. They will have the opportunity to modify the sample programs. Familiarity
with Snap! or Scratch will be very helpful but not absolutely necessary. While this software can run ontablets and smartphones we recommend you bring a laptop.
More details can be found in the full research paper with the same title in these proceedings.

Keywords
Visual programming; machine learning; block languages; Snap!, AI services; cloud services; speechsynthesis; speech recognition; image recognition;

Speech Synthesis and Recognition
New Snap! blocks will be explored that build upon the browser’s Web Speech API (Mozilla 2018). The
most complex of these blocks enables control over the rate, pitch, volume, voice and language of thegenerated speech.
The Web Speech API also supports speech recognition. New Snap! blocks enable programs to respondto speech after it has been recognised. The most complex version enables one to receive partial results
as speaking is still ongoing. It also supports the specification of the language being recognised. One
can ask for several alternative interpretations of what was spoken and the confidence the system hasthat they are correct.

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Image Recognition
There is no web API standard for image recognition. However, many companies provide web-based
vision cloud services. The new Snap! blocks can send image recognition queries to Google, IBM, orMicrosoft. While these are commercial services all the providers have free quotas for limited use. In
addition to being able to obtain image labels and their confidence scores the most advanced Snap!
blocks provide access to other information these services provide such as locations of faces, image
properties, and more.

Machine Learning
Snap! blocks could be created to use machine learning web services. We, instead, use the tensorflow.js
library (tensorflow.js 2018) to perform machine learning locally in the browser. This library is able to use
the GPU (graphics processing unit) to run very fast. Currently the new Snap! blocks are limited to training
to classify images. Programs using these blocks can interleave training (either from the camera or fromsprite costumes) and classification of new images. A sample program using these blocks implements a
Rock Paper Scissors game where players make moves by configuring their hand in front of the camera.

Conclusion
Creating AI programs in Snap! can be fun. One can learn about perception and machine learning. And
one can be creative in making programs that do very impressive and interesting things.

References
tensorflow.js (2018) https://togetherjs.com/.
Mozilla (2018) Speech Synthesis API. https://developer.mozilla.org/enUS/docs/Web/API/Web_Speech_API.

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WS5: Developing Affordable STEM Maker Projects
with BBC Micro:bits and Microsoft MakeCode
Stephen Howell,
Stephen.Howell@microsoft.com
Microsoft Ireland

Neeltje Berger,
Neeltje.Berger@microsoft.com
Microsoft ResearchPeter Heldens,Peter.Heldens@microsoft.com
Microsoft Netherlands Kevin Marshall,kevmar@microsoft.com
Microsoft Ireland

Clare Riley, Clare.Riley@microsoft.com
Microsoft UK

Abstract
In this hands-on workshop, we will challenge the participants to build 3 projects including wearables andother physical artefacts using affordable microprocessors (BBC Micro:bits) and common arts and crafts
materials.

A typical bill of materials including BBC Micro:bits, batteries, paper plates and cups, crocodile leads, Velcro andcardboard coffee cup holders

Each project was devised by a multi-disciplinary team of teachers and industry educational technologists
in an initiative to develop free, high-quality resources with a depth of content that could be taught in aclassroom but breadth of accessibility so that students working at home (without a mentor or educatorto guide them) could complete the projects.

Keywords
constructionism; STEM; computational thinking; inclusive design; teacher training

Workshop
Sophisticated tools have become a common purchase for educators wishing to teach Computational
Thinking, and popular microprocessor and robotics kits are expensive when purchasing for a class. This
workshop presents 3 constructionist projects that each require just one or two microprocessors (BBCMicro:bit) and some cheap maker/electronic components such as LEDs and crocodile leads. A range
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of arts and crafts, including markers, paper plates, cardboard cup holders, and Velcro make up theremainder. The development environment used to code the BBC: Micro:bits is Microsoft MakeCode(Ball, 2017). These projects were developed by teachers, researchers, and technologists from England,
Scotland, Netherlands, and Ireland as part of a Maker Champions STEM initiative from Microsoft (Riley,2017).

Become a Maker Champion Website and MakeCode snippet showing radio communication

Format & ParticipantsParticipants will build a wearable to act as doorbell for a deaf user (and build the doorbell), make a maskor holiday decoration, and build a traffic light. This will be a hands-on workshop with minimal coding(MakeCode is a visual programming language) but some arts and crafts skills would be useful. The
expected participants for this workshop are interested in learning about BBC Micro:bits and Microsoft
MakeCode.

Teachers workshop with arts and crafts enhanced with LEDS to make masks and Christmas decorations
powered and controlled by BBC Micro:bits and Microsoft MakeCode

References
Ball, T. (2017). Physical computing for everyone. 2017 IEEE/ACM 39Th International Conference on
Software Engineering: Software Engineering Education and Training Track (ICSE-SEET), 3.
http://dx.doi.org/10.1109/icse-seet.2017.31
Riley, C. (2017). Digital Skills: Become a Maker Champion [Website]. Retrieved fromhttps://www.microsoft.com/en-gb/athome/digitalskills/discover/#maker768

Constructionism 2018, Vilnius, Lithuania

WS6: NetsBlox: A Constructionist Environment for
Creating Distributed Applications
Brian Broll, brian.broll@vanderbilt.edu
Vanderbilt University, Nashville TN, USA

Corey Brady, corey.brady@vanderbilt.eduÁkos Lédeczi, akos.ledeczi@vanderbilt.edu
Vanderbilt University, Nashville TN, USA

Abstract
NetsBlox is a collaborative learning environment extending Snap! with carefully-selected abstractions
that enable students to create distributed applications. It makes distributed programming accessible toyoung learners using simple yet powerful visual programming primitives, an intuitive user interface anda sophisticated cloud-based architecture. This workshop introduces the environment and gives
participants hands-on experience with activities that demonstrate NetsBlox’s utility for creating multiplayer games and client-server applications that access public-domain scientific data sources. Our early
work using NetsBlox with students suggests that integrating these connected features into learners’
early experiences of programming widens their perspective on the role of computation in both inquiry
and broader social life.

Keywords
Block-based programming; visual programming; distributed computing; network programming

Introduction
The majority of computer applications we interact with daily are distributed, that is, they involve morethan one computer to function. The web, texting, Twitter, Facebook, online games, Pandora, Netflix,
Amazon Echo, Siri, Google Maps and YouTube are just a few of the most popular examples. Self-driving
cars, home automation and the Internet of Things (IoT) are other prime examples of distributed systems.
Teaching distributed programming then constitutes both a necessity and a great opportunity. It is anecessity because distributed computing is becoming part of basic computer literacy. And it is also an
opportunity because children already use the technology every day and their natural curiosity will
provide excellent motivation for them to learn more about it.
NetsBlox (https://netsblox.org) is an open-source, web- and cloud-based visual programming
environment that enables users to create distributed applications (Broll et al, 2018). NetsBlox extends
the widely used Snap! environment building on its JavaScript codebase. NetsBlox supplies simple
abstractions for synchronization and communication across computers to enable students to create truly
engaging distributed programs.
Peer to peer communication in NetsBlox is supported by Messages. Messages are very similar to
Events already present in Snap! and in Scratch. In NetsBlox, a Message is an Event that contains astructured data payload and can be sent to other NetsBlox programs across the Internet. Messages are
typed and the message type is defined just like custom blocks are. The sent data shows up as
appropriately named variables on the receiving end. Addressing is simple yet powerful. An example
message sending and receiving block is shown in Figure 1.

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Figure 1. Sending and receiving messages in NetsBlox

Remote Procedure Calls (RPC) allow for invoking code that will be executed at a remote location, andthen (optionally) getting back the results of the computation. From the user’s point of view, RPCs arealso similar to custom blocks. The main use for RPCs in NetsBlox is making some of the large numberof publicly available interesting data sets and data-centric web services on the web available to NetsBlox
programmers. Examples include maps, weather, air pollution, seismic data, astronomic image sets, amovie database, stock quotes and many others. Essentially, the NetsBlox server provides a mapping
between RPCs and the corresponding public web API. Related RPCs, that is, RPCs interfacing with thesame web API, are grouped into Services. For example, NetsBlox wraps Google Maps into a Service
providing RPCs for getting maps, satellite images and coordinate transformations. Relying on thismapping service, it only takes a few blocks of NetsBlox code to provide a fully navigable map of theworld as an interactive background.

Figure 2. Earthquake project using data from Google Maps and USGS with the help of RPCs

In another example, the project shown in Figure 2 displays historical earthquake events for the
anywhere on Earth. The code shown is that of the stage: the +/- keys zoom in and out, while the arrow
keys pan the map. When the map is clicked, the sprite code (not shown, but it is of similar complexity
to the stage scripts) requests USGS data and displays red dots whose radius is proportional to themagnitude of the given seismic event. In this case, the minimum magnitude is set to 6, so only severe
quakes are shown. Notice that the project only assumes students know variables, if-statements andcustom blocks from the traditional curriculum. Conceptually, the only challenging aspect is thecoordinate transformation from x and y stage coordinates to latitude and longitude. But the map service
provides RPCs for the actual computation.
NetsBlox also supports collaboration: students can work on a shared project from their own computersjust like they would collaborating on a Google Docs document. This enables team projects and online
mentoring. Collaboration support requires keeping a log of all editing events. This log also enables
unlimited undo/redo as well as providing learners with the means to “replay” the entire history of the770Constructionism 2018, Vilnius, Lithuania

project creation via a YouTube-like media player interface. This supports them in reflecting on theconstruction process and enables teachers and researchers to study the process as well.

Workshop objectives
The workshop will introduce the environment and give participants hands-on experience with asequence of activities that demonstrate NetsBlox’s utility in supporting middle and high school students
in creating engaging distributed applications. A projector and Internet access, as well as several sparecomputers would be desirable for running the workshop. Participants may form two-person groups if
desired and work on the problems together using the collaboration features of the tool. Based on theinterests of the participants, a subset of the following applications will be included. Though building
these applications efficiently illustrates important features of the platform in an engaging context,
workshop participants will be encouraged to consider other constructionist activities that can be uniquely
supported with NetsBlox.
Weather app: the program will display current conditions anywhere on Earth. The background will be afully interactive map just like the one in Figure 2. When the user clicks on the map, the sprite costume
will change to the appropriate weather icon and the current temperature and the name of the closest
city will be displayed as well.
Movie app: the program will ask the user for the title of a movie and it will display photos of the threeleading cast members. This project, just like the weather app, will introduce how to use RPCs in
NetsBlox.

Figure 3. The Movie app

Chatroom: the presenter will create a chatroom server app, while the attendees will create a client that
sends text messages to the server which it broadcasts to other members of the chatroom. This program
will illustrate message passing and addressing in NetsBlox.
Shared Whiteboard: participants will implement a simple two-user drawing program. As one user draws
by dragging the mouse on the stage, it also sends a list of the mouse coordinates to the other client that
display the trace and vice versa. To make it more challenging, erasing the board will require a consensus
of the two users.

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Constructionism 2018, Vilnius, Lithuania

Figure 4. The Shared Whiteboard project

Mesh Network: participants will form a mesh network where everybody is only allowed to send
messages to one other person (e.g., right-hand neighbour around a circular table). The goal of the
project is to be able to send messages to anybody in the room by forwarding messages not meant to
the given person. This will be a good opportunity to use the built-in visual network debugger of NetsBlox.
Multi-player game: attendees will select a simple, turn-based multi-player game such as Tic Tac Toe,
21 Pebbles or Ghost. Participants will have the option to utilize the N-player Game service that provides
a set of RPCs and messages that facilitate the creation of turn-based games or implement the gamewith custom messages only.
The last part of the workshop will be a discussion in which the presenters will solicit feedback on thedesign of the environment and what directions its development should take.

Conclusion
The distributed programming abstractions in NetsBlox provide access to vast arrays of data on theInternet directly in the visual programming environment empowering students to create innovative
science projects and simultaneously bring STEM concepts into CS education. The ability to create multiplayer games and other engaging networked programs provides increased motivation and encourage
young learners to be creators and not just consumers of digital entertainment. The workshop will expose
attendees to the large number of possibilities created by opening the Internet to a block-based
programming environment via a small set of simple but powerful abstractions.

References
Broll, B., Lédeczi, A., Zare, H., Nguyen Do, D., Sallai, J., Völgyesi, P., Maróti, M., Brown, L., Vanags,
C. (2018) A visual programming environment for introducing distributed computing to secondary
education,
Journalof
ParallelandDistributed
Computing,ISSN
0743-7315,
https://doi.org/10.1016/j.jpdc.2018.02.021.

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WS7: The ER4STEM Repository for Educational
Robotics
Annalise Duca, annalise@acrosslimits.com
Angele Giuliano, angele@acrosslimits.com
AcrossLimits Ltd, Hilltop Gardens, Triq L-Inwkina, Naxxar, Malta

Sofia Nikitopoulou, sophieniki@ppp.uoa.gr
Nikoleta Yiannoutsou, nyiannoutsou@ppp.uoa.gr
Chronis Kynigos, kynigos@ppp.uoa.gr
University of Athens, Athens, Greece

Abstract
Type: Workshop
Using robotics in education is an engaging method for student motivation towards STEM subjects andmore. Teachers, educators and researchers who are newly experimenting with the use of robots in theclassroom are all asking a very similar question. “Where can I find inspiration to introduce
constructionism in my teaching?”, “What can I do to teach my subject using robotics?” The answer tothis is “The ER4STEM Repository” which will be full of educational resources, activity plans andsuggestions for educators. “The ER4STEM Repository” has been underpinned by the basic pedagogicaltheory underlying its’ design in constructionism. This happens through an Activity Plan Template. This
template is aligned with the Repository and provides a generic design instrument that identifies criticalelements of teaching and learning with robotics based on theory and practice and is expected tocontribute to the description of effective learning and teaching with robotics.

Keywords
repository; educational robotics; STEM; robots; sharing ideas; collaboration; educational robotics for
STEM; educational activities; constructionism; OER

Introduction
Top EdTech stories are constantly reporting how and why educators need to start educating andpreparing our kids to be able to tackle future jobs that are already changing in nature. This includes
understanding how one will have to work with robots and technology in different industries, may it be
medicine or in manufacturing companies.

The Educational robotics for STEM (ER4STEM) Project
The ER4STEM project is an European Funded project, and some of its’ activities are workshops on
robotics for approximately 4000 students in four countries, evaluation and the development of an
educational robotics repository to address the needs of the market.

The Repository
Different educational repositories already exist, however none of them are focused on providing specificeducational robotics lesson plans. Further to this, many robotics kits suppliers, promote their robotic kit
for the use of STEM education, however most of the time, one can only find few examples on theirwebsite. These are normally not enough for educators for many reasons. One needs to consider that inmost cases, the educator need to master how to use the robotic kit first before teaching it or using it toreach a particular educational goal. Creating activity plans take a considerable amount of time.
Additional to this, more resources and effort is needed to ensure usability.

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The ER4STEM repository is a user-centred repository and it is based on a pedagogical activity plan
template which was developed by the University of Athens. The offline version of the activity plan has
been already used by universities, robotic centers and training centers for the creation of various
robotics workshops and the refined version is now available as an interactive online repository.

Accessing the Repository
The repository is a user-friendly and guided portal that will encourage users to both search for existing
resources while also sharing their own. The portal can be accessed from repository.er4stem.com.
Figure 1 shows the landing page for the repository. Users can sign up for a free account using either
Facebook Single sign-on or using their email address. The portal is available in 7 languages (English,
Bulgarian, Czech, German, Greek, Turkish and Italian).

Figure 1. Landing page for the ER4STEM repository

The Activity Plans in the Repository
Logged in and guest users can easily search for different open resources. This can be done with any
keywords, or else using an advanced search which gives the additional opportunity to the user to search
by: subject, age and language. All users can share any activity plan via social media, and also download
an offline version in PDF format. When logged in, users also have the option to mark any activity as afavourite, so they can easily find it again later on.
The activity plans are split structurally in different sections, to ensure ease of use. Each activity plan is
split in; Outline Information; Objectives and Skills; Artifacts; Who? Where? How long?; Interactions;Technology; Learning and Teaching; How To; Assessments; Supporting Materials and Feedback (fromother users). Simple terminology was used to ensure that even those whose English is not their native
language, find it easy to navigate and use the repository.

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Figure 2. ER4STEM Framework on the ER4STEM Repository

Figure 2 also illustrates how with the use of an interactive diagram, one can visually see where theactivity plan fits in relation to the ER4STEM framework.

Submitting an Activity Plan
Once logged in, users will have the option to create and submit their own activity plans. This process is
split into 9 different steps. In each step, detailed information is given to the user to ensure that theactivity plan is written in line with the ER4STEM framework. Our aim for the creation of an activity plan
into the repository is to find balance between a) a level of abstraction that will make the template
adaptable to different settings and b) a level of detail that will demonstrate the influence of a specificpedagogical approach, it will address the particularities of Robotics and it will augment the affordancesof the specific robotic kit used in each activity plan. Therefore, when the users create their own activity
plan they are tacitly and implicitly guided to think as pedagogical expert. This method ensures that thepedagogy aspect of the activity plan is not lost, yet at the same time users do not need to be familiar
with the framework per se to use it as it will guide their input step by step. Some of the fields required
to create the activity plan include: Learning Outcomes: Subject Related, Skills Learning Outcomes,
Actions, Relationships, Roles in the group, Support by the tutor/s, Digital Artifacts, Robotic artifact,
Gender, Age Group, Class Size, Group Size, Grouping Suggestions, Prior Knowledge, Special needs
and abilities, Environment, Style of Room, Duration, Technology Used, Price per Kit, Programming
Languages, Technology Needed, Activity Blocks and any Supporting Material.
When a user fills these fields we ensure that once available, anyone reading the activity plan, can follow
it and implement the activity plan accordingly in their own classroom environment. All activities
submitted are reviewed by our team of experts to ensure that the quality is maintained.

The Blocks
Activity blocks are a new construct focusing mainly on the practical aspect of the activity plan. Thus, therole of Blocks is to help users create activities in the classroom by giving them concrete ideas on thesection of activity plan “how to”. In several cases, activity blocks are accompanied by relevant
worksheets that are designed to facilitate the implementation of the activity. Consequently, the activity
blocks were designed as a result of the first and second year evaluation of the project, aiming to support

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the fostering of creativity, collaboration and reflection and to offer multiple entry points for the activity
plans.

Figure 3. Activity Blocks that can be added to the activity plan

As in figure 3 below, previously used and tried “blocks” are presented to the user, to either arousecuriosity or implement as is. These blocks are filtered according to their type. Types found are
Introductory, Reflection, Assessment, Evaluation, Exploration, Experimentation and Construction.

The Workshop
During this 90-minute workshop, we will be doing an introductory session to show how the repository
functions, following by an interactive session where participants will be brainstorming on activity plansthat they can create. Participants should ideally have a laptop or tablet during this session.

Conclusion
The ER4STEM repository answers a need in the educational market for a simple one stop online shop
to find teaching materials and inspiration for teachers that want to use robotics in their subjects. All
materials are open and free of charge, and with constant inputs from the teaching community they willcontinue to increase in number and variety spanning all subjects and ages.

Acknowledgements
This work is funded by the European Commission through the Horizon 2020 Program (H2020,
Grant agreement no: 665972). Project Educational Robotics for STEM: ER4STEM.

References
https://www.inc.com/greg-satell/we-need-to-educate-kids-for-the-future-not-the-past-heres-how.html
http://www.edtechupdate.com/2017/robotics/trends/?open-article-id=7667685&article-title=-robots-arecoming-for-your-children-&blog-domain=hackeducation.com&blog-title=hack-education
Papert, S. Mindstorms: Children, computers, and powerful ideas. Basic Books, Inc. (1980).
Nikoleta Yiannoutsou, Sofia Nikitopoulou, Chronis Kynigos, Ivaylo Gueorguiev & Julian AngelFernandez. "Activity Plan Template: a mediating tool for supporting learning design with robotics". 7thInternational Conference on Robotics in Education, 14-16 April 2016, Austria.

776Constructionism 2018, Vilnius, Lithuania

Demonstrations
The Relationship between Computer Programmingand English Language Skills
Nevin Akcay, nevin.akcay@yahoo.com
Gumuspala Vocational and Technical High School, Istanbul, Turkey

Hulya Avci, hulyaavci@beykent.edu.tr
Beykent University, School of Foreign Languages, Istanbul, Turkey

Ali Gungor, ali.gungor@bau.edu.tr
Bahcesehir University, Vice President, Istanbul, Turkey

Tufan Adiguzel, tufan.adiguzel@es.bahcesehir.edu.tr
Bahcesehir University, Faculty of Educational Sciences, Istanbul, Turkey

Abstract
Computational thinking is a problem-solving method applying computer science techniques andencourages programming or coding skills among children. A programming or coding language is a
special language for describing computation and expressing a set of instructions on what tasks acomputer needs to execute. There has been an ongoing discussion in the literature whether
programming languages with a vocabulary of keywords are based on English. However, there is notmuch literature on how computer programming affects other aspects of life beyond the skills acquired.
In this study, we demonstrated the current literature on the relationship between programming andEnglish language skills and the methods of teaching both languages.

Keywords
Computer programming, coding, English language skills, learning

Introduction
Information technology has gained value and recognition widely in the fields of education, economics,
health, agriculture, social life and entertainment (Uzunboylu, 2005). In the last few years, with the rapid
development of information technology in almost all areas, having coding and programming skills is
expected to become more fundamental than ever for individuals of all sectors in the 21st century(Uzunboylu, 2005; Sayin & Seferoglu, 2016). “Today, computer programs are genetic code of our world
and many educators are starting to think that students need more than a passing knowledge of computer
coding” (Gow, 2015, p. 20). While the internet continues to expand, computer programming and coding
continue to play an important role in education (Uzunboylu, 2005; Balanskat & Engelhardt, 2015).
According to the Grand Coalition for Digital Jobs, there might be a shortage of 900.000 ICT
professionals in Europe and in the ICT related sectors in the next ten years (Burke, 2013). However,
over the past year, a number of non-profit organizations have attempted to investigate coding with
innovative training approaches and looked for people who can write code to meet their urgent needs inmany workplaces (Sayin & Seferoglu, 2016). In recent years, many governments around the world havebegun to modify the curriculum in schools to foster the improvement of computational thinking of primaryand secondary students through computer programming or coding (Moreno & Robles, 2015). The
purpose of integrating coding into curriculum is to equip students with skills such as problem-solving
and logical thinking skills that are increasingly crucial within the current digital community (Balanskat &Engelhardt, 2015). Additionally, these skills are beneficial not only for computer scientists but foranyone, regardless of personal features, interests or profession (Resnick, 2013). The use of computer
programming in schools as an educational tool to improve learning in other disciplines is becoming
highly prevalent at all levels of education in many countries (Moreno & Robles, 2015).
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Constructionism 2018, Vilnius, Lithuania

The language used in programming is not understood by an untrained person (Computer Programming)
In the process of learning coding, people not just learning the coding, but they are also coded to learn.
In addition to that, they have the opportunity to learn many other abilities, for example, problem solving
strategies, programming, logical thinking, algorithmic and analytical thinking, and the ability to transmit
ideas regardless of the programming language selected (Ersoy et al., 2011; Resnick, 2013). “Teachingof programming logic is the first and most important phase of programming teaching” (Arabacioglu etal., 2007, p. 193). The primary goal is to promote coding rather than teaching and using it as a tool toimprove other skills (Sayin & Seferoglu, 2016). Factors such as student attitudes and perceptions,consistency of selected programming language and aims of foreign language education programs might
influence students’ success in programming and transfer of skills to other domains (Sayin & Seferoglu,
2016; Arabacioglu et al., 2007; Ersoy et al., 2011).
As English is the dominant language, but isn’t a prerequisite, in the IT world, the majority of programming
languages are based on keywords in English (King, 2015; Basak, 2016), some of which are declarativeand mark-up languages like HTML and XSLT, and query languages like SQL. Java and C++ are also
English-based computer programming languages which are provided in introductory programmingcourses in many colleges (Krpan & Bilobrk, 2011). Therefore, many programmers can learn little English
while studying programming (Basak, 2016). Students who are competent in many subjects are
sometimes incapable of succeeding at programming. On the other hand, some students who perform
well in computer programming classes can also become good at English courses (Moreno & Robles,2015).
Most programming languages look like Romaic to an untrained person at first glance (Malan & Leitner,2007). Although they have been generated in a rigorous and artificial way instead of naturally evolving,
they precisely embody linguistic knowledge as well as other features shared with human languages(King, 2015). Unless prospective programmers have adequate knowledge about these linguistic
varieties including the study of meaning (semantics) and sentence structure (syntax) as well as
terminology in their native language, the complexity of programming increases promptly. As forexperienced programmers who are quite competent in the first language, their second language
acquisition must have been easier (Riker, 2010). To illustrate, considering the use of prepositions
including "from," "through," "to," and so on, COBOL language could be notably similar to English
language. However, the most legible and English-like language is Smalltalk (Kenneth, 2016). The
syntax of Smalltalk consists of <object> <message>, where <message> which is a command oroperator the user sends to an object, which can be anything from numbers and strings to classes andcode blocks. The <message> can be unary, binary, or keyword, the latter taking on a parameter asfollowing:
renderer: = three WebGLRenderer new.
renderer setSize: (window innerWidth) height: (window innerHeight) (Kenneth, 2016).
Scratch, another simple object-oriented programming language generated by MIT Media Lab’s Lifelong
Kindergarten Group (2013), has aroused interest among many teachers aspiring to promote coding intheir English language classes as it might facilitate the second language acquisition (Quan, 2015). In a
research project, students participated in a story writing activity using Scratch in a writing workshop.
The findings suggested that students were motivated to progress in learning English and developed
positive perception towards English, besides enhancing their digital literacy and language content areas(Quinn & Kafai, 2012). According to a study on coding, most students consider that coding has a positiveeffect on not only learning English but also improving other essential skills such as teamwork (Moreno& Robles, 2015). Furthermore, computer programming may have a positive impact on students' learning
outcomes in English language skills.

Conclusion and Discussion
As a result of literature review, as the programming languages are mostly English, and especially sharecertain similarities in terms of English syntax, one (Scott, 2015) says that English learning skills ofstudents improve while there are also studies and interpretations (Moreno & Robles, 2015; Kenneth,
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Constructionism 2018, Vilnius, Lithuania2016) that indicate otherwise. In the literature review, several results of the studies were found as
following:
As students who know the programming language learn programming, they can easily comprehend
English sentences (Moreno & Robles, 2015).
Learning other languages besides English gets more practical for learners of programming (Galvin,2016).
Computer programming promotes the abilities such as logical thinking and algorithm to solve problems
in many areas, regardless of the language used, and even the ability of analytical thinking (Basak, 2016;
Malan & Leitner, 2007).
Even if programming languages have a specific semantics and syntax, which is written in English, thewords used in programming languages will be almost equal to those who speak English and who do not
speak English (Resnick, 2013).
It should be noted that some students who take a programming course might encounter certain
challenges due to the programming language. Further research needs to be conducted to reinforce thelearning process and to determine the underlying causes of the problems about programming languages
confronted by students (White & Sivitanides, 2002). Future studies should reveal the relationshipbetween programming and English language skills through qualitative and quantitative studies. In
addition, the interrelation between programming and other fields (e.g. science and mathematics) should
be examined. Thus, we can conclude there is a need for studies which determine whether the study ofEnglish and other skills is affected by the development of calculation thinking and whether the students
should be able to generalize and transmit this problem-solving process to other areas.
In conclusion, the first phase of interdisciplinary study between programming and English language
skills was revealed in this study. Regarding current technological innovations, information-age in 21st
century, and digital traces that produce massive data in online environments which are constantly andexponentially increasing, it is thought that more work will be done in programming and coding education.

References
Arabacioglu, T., Bulbul, H.I., & Filiz, A. (2007). A new approach to computer programming teaching.
Proceedings of the 9th Academic Informatics Congress (pp. 193-197). Dumlupinar University, Kutahya,
Turkey.
Balanskat, A., & Engelhardt, K. (2015). Computer programming and coding priorities, school curriculaand initiatives across Europe. European Schoolnet (EUN Partnership AIBSL), Brussels, Belgium.
Basak, S. (2016, April 28). Re: Do people in non-English-speaking countries code in English? [Online
forum comment]. Retrieved from https://namasteui.quora.com/Do-people-in-non-English-speakingcountries-code-in-English
Burke, A. (2013). Grand coalition for digital jobs aims to fill 900,000 predicted European ICT vacancies.
Retrieved from https://www.siliconrepublic.com/jobs/grand-coalition-for-digital-jobs-aims-to-fill-900000predicted-european-ict-vacancies.
Computer
Programming.
(n.d.).
In
onlinebusiness
dictionary.
http://www.businessdictionary.com/definition/computer-programming.html

Retrievedfrom

Ersoy, H., Madran, R.O., & Gulbahar, Y. (2011). A model proposed for teaching programming
languages: Robotic programming. Proceedings of the 13th Academics Informatics Congress (pp. 731736). Inonu University, Malatya, Turkey.
Galvin, G. (2016, October 13). Some say computer coding is a foreign language [Online news article].
Retrieved from https://www.usnews.com/news/stem-solutions/articles/2016-10-13/spanish-frenchpython-some-say-computer-coding-is-a-foreign-language?context=amp

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Gow, P. (2015). Teaching computer programming is back. Why now? A new culture of coding. Retrieved
fromhttps://propertibazar.com/article/2015-computing-our-future-future-classroom-labeuropean_5a31a244d64ab24ed47f3677.html
Kenneth, R. (2016, March 7). Re: What computer language is most similar to human spoken language?
[Online forum comment]. Retrieved from https://www.quora.com/What-computer-language-is-mostsimilar-to-human-spoken-language
King, E. (2015). Java as a second language: Thoughts on a linguistically informed transition to typing
languages IB Design. Proceedings of the 2015 IEEE Blocks and Beyond Workshop (pp.11-12). IEEE
Computer Society, Washington, D.C., USA.
Krpan, D., & Bilobrk, I. (2011). Introductory programming languages in higher education. Proceedingsof the 34th International Convention. MIPRO, Opatija, Croatia.
Lifelong Kindergarten Group, MIT Media Lab (2013). SCRATCH 2.0 (programming language).
Malan, D. J., & Leitner, H. H. (2007). Scratch for budding computer scientists. SIGCSE Bulletin, 39, 1,
223-227.
Moreno, J., & Robles, G. (2015). Computer programming as an educational tool in the English
classroom. Proceedings of the Global Engineering Education Conference (EDUCON) IEEE (pp. 961966). Tallinn University of Technology, Tallinn, Estonia.
Quan, G.C. (2015). Student teachers evaluating and assessing SCRATCH in the applied linguistics
classroom. Social and Behavioral Sciences, 174, 1450-1456. doi:10.1016/j.sbspro.2015.01.774
Quinn, B., & Kafai, Y. B. (2012). The writers' workshop for youth programmers: digital storytelling withscratch in middle school classrooms. Proceedings of the 43rd ACM technical symposium on computerscience education (pp. 433-438). ACM, New York, NY, USA.
Resnick, M. (2013). Learn to code, code to learn. How programming prepares kids for more than math.
Retrieved from https://www.edsurge.com/news/2013-05-08-learn-to-code-code-to-learn
Riker, A. (2010). Natural language in programming an English Syntax-based approach for reducing thedifficulty of first programming language acquisition (Doctoral dissertation). Retrieved fromhttp://hdl.handle.net/10192/23861
Sayin, Z., & Seferoglu, S.S. (2016). Coding education as a new 21st century skill and effect of coding
on educational policies. Academic Informatics. Adnan Menderes University, Aydin, Turkey.
Scott, B. (2015, April 9). Is the English language too important to learn a programming language?
[Online forum comment]. Retrieved from https://www.quora.com/Is-the-English-language-tooimportant-to-learn-a-programming-language
Uzunboylu, H. (2005). The effectiveness of web-assisted English language instruction on theachievement and attitude of the students (Unpublished doctoral dissertation). Ankara University, theInstitute of Educational Sciences, Ankara.
White, G. L., & Sivitanides, M. P. (2002). A theory of the relationship between cognitive requirementsof computer programming languages and programmers' cognitive characteristics. Journal of Information
Systems Education, 13, 1, 59-66.

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Synthesizing the Mesh: Using Constructible
Authentic Representations to Gain Intuitive
Understanding of Bayesian Reasoning
Monica Chan, monica.chan@tc.columbia.edu
Teachers College, Columbia University, USA

Gary C. F. Lee, garyleecf@mit.edu
Massachusetts Institute of Technology, USA

Abstract
In recent years, there has been a massive push towards building basic computer science knowledge
and skills at K-12 levels. However, less has been done towards introducing more complex andapplication-based fields of computer science, such as machine learning, and connecting these fields
with mathematics and statistics education. This game Synthesizing the Mesh has been developed toprovide a constructionist learning platform that helps upper middle to high school students gain intuition
and understanding for Bayesian reasoning, an inherent algorithm of machine learning that is traditionallyperceived as an advanced topic. The design principle Constructible Authentic Representations (CAR)
has been used to guide the game design process.

Keywords
game design; middle school; high school; constructionism; problem-solving; strategy; bayesian
reasoning; technology

Introduction
Bayesian reasoning is one of the most fundamental algorithms of machine learning methods, and has
been a powerful approach for describing uncertainty and conditional probability. However, the topic of
Bayesian reasoning is typically avoided in introductory undergraduate mathematics (statistics) andcomputer science education (Satake & Murray, 2014), and hardly taught at all in high schoolmathematics or computer science classes, due to the perception that Bayesian reasoning is anadvanced topic. However, the applications of Bayesian methods are widespread in everyday life, foundin fields such as healthcare, law, manufacturing, etc.
Although there has been a larger push for K-12 computer science education in the recent years (K-12
Computer Science Framework; Next Generation Science Standards, 2018), there is still a lack in theuse of mathematics and statistics at K-12 levels to teach and learn essential concepts related tofundamental applications of computer science. This proposed educational game named Synthesizing
the Mesh uses constructible authentic representations as a design principle. The game aims to provide
a constructionist learning platform, where upper middle to high school students can gain an intuition
about the overarching concept of how Bayesian reasoning works. This is an effort to spur students’interests not only in learning about basic programming methodology, but also in delving more deeply
into the logic behind computer science algorithm development.

Literature Review
Prior WorkIn the past years, researchers have begun developing constructionist approaches to teach STEM
concepts that are traditionally perceived as higher-level or advanced. For example, MaterialSim wasdeveloped as a computational constructionist learning environment that aided undergraduate students
in learning materials sciences concepts (Blikstein & Wilensky, 2009).
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In the field of mathematics, and more specifically statistics, Equidistant Probability was a NetLogo
microworld with a series of computer-based probability experiments targeted at helping middle school
students gain intuition in probability and statistics (Abrahamson & Wilensky, 2003). Weintrop andcolleagues (2016) had also conducted a study on the development of computational thinking throughconstructionist online/video games. Such games combine features of traditional video games withconstructionist learning and design theories to create a core playing experience around building artifacts
in the game environment (Weintrop et al., 2016). We have used the discoveries about player/learner
experiences from these previous studies to create Synthesizing the Mesh, with a focus on Bayesian
reasoning as the choice of the advanced level topic that this educational game concentrates on.

Constructible Authentic Representations
Constructible Authentic Representations (CAR) is a design principle that may be incorporated into
educational game design to engage players in constructing artifacts that are epistemologically aligned
to representations used in the target domain (Holbert & Wilensky, 2014, p. 53). A main objective of thisdesign principle is to encourage the player to connect seemingly disparate knowledge resources to gaina higher probability that in-game experiences would be co-activated with formal representations in nongame contexts (Holbert & Wilensky, 2014, p. 57). Building upon the principle of concept integration(Cheng, 2011; Kafai, 2012), games designed and guided with CAR emphasize on the player’s
interaction with the game in creating meaning out of the concepts that the game accentuates.
CAR is also a build-on from the concept of computation-based restructurations (Wilensky & Papert,
2010), whereby computational objects (agents) replace traditional mathematical or scientificrepresentations to extend the learner’s understanding of a certain topic or idea. These computational
objects that are restructured typically have “power properties” that attract domain-experts, but also have“cognitive, affective, social and diversity properties” that make it accessible to the wider public (Wilensky& Papert, 2010, p. 4).

Synthesizing the Mesh Game Design
Currently, Synthesizing the Mesh has been built using a HTML/CSS/JavaScript platform, guided by the
CAR design principle. Specific game design features and mechanics include the use of different typesof sensors with varying costs and coverage range, different types of bugs that multiply at various rates
or probabilities, and different player choices on whether or not to perform predictive maintenance thatsignificantly reduces chances of bugs resurfacing. These decisions to implement certain choices aregoverned by the amount of cash units each player has.
The premise of Bayesian reasoning hinges upon a concept in probability, Bayes’ theorem, where one’s
belief about a hypothesis (or probability that the hypothesis is true) is updated as more evidence isobserved (D'Agostini, 2003). The game mechanic models this form of reasoning, where players attempt
to identify locations of the bugs based on the observations of the sensors in the neighborhood. Further,strategizing the locations and types of sensors may also help provide evidence about the guesses todiffering degree. The game is collaborative yet also competitive – players work together to unearth all
the bugs in the game, but also aim to accurately find the most bugs while optimizing their sensor
placements and minimizing cost.

Learning Goals
Listed below are the learning goals of Synthesizing the Mesh:
1. To cultivate a more holistic intuitive understanding of the method of Bayesian Reasoning: basedon observations (sensors in the game), figure out the probability of outcomes/guesses (bugs in
the game)
2. To gain more confidence about approaching thought processes that may be unfamiliar
3. To learn to collect data strategically and collaboratively to achieve a communal goal782

Constructionism 2018, Vilnius, Lithuania

Game Play
Each players starts with $100, and begins by setting sensors of his/her choice on the blue spots on thegame board. Player 1 begins from the left, Player 2 from the top, Player 3 from the right, and Playerfrom the bottom of the 21x21 square grid. The sensor placed on the board will turn green if there is nobug (machine bug) in its scope, and will turn red if there is a bug present in the vicinity. Each player isable to end his/her turn anytime and then the next player goes.
Various game mechanics are listed below:
Players need to spend money implementing sensors to find where bugs are, but they have alimited amount of money
 Sensors come in 3 different levels of functionality and scope (Sensor 1 has the smallest scopeand is the cheapest, while Sensor 3 has the widest scope and is the most expensive) Find and Fix Bug option fixes the bug temporarily - the bug could reappear
 Perform Predictive Maintenance option reduces chances of the bug reappearing
 Goal: Reach “bug-less” state (~less than 5% chance of bug reappearing)
While designing the mechanics, Squire and Barab’s study of urban underserved students learning
history via Civilization III (2004) informed our game design. Squire and Barab stated that “Learning
occurred through recursive cycles of failure and revising strategies, which led to frustration, engagement
and learning” (2004, p. 1). This connection between the cycles of strategies (which we considered asgame mechanics in this case) and the frustration and engagement (which we considered as game
dynamics) is a great way of anticipating strategies and implementations that other players might doduring the game. Just as how Squire and Barab had guided the students in their study through historicalsimulation and discussion about hypothetical history via the game Civilization III, we intended to add
mechanics that allow Synthesizing the Mesh to not only be a tool for understanding the underlying theconcept of Bayesian reasoning, but also to contain mechanics that affect interactions and anticipation
amongst players’ thoughts, players’ moves, and any further relationships players might think of while
identifying and fixing the “bugs” in the game to extend their fundamental conceptual understanding. This
is not an emulation of mechanics between Civilization III and Synthesizing the Mesh because they havegaming natures and methods that are inherently different from each other, but we tried to follow the
expansion of educational scope beyond the game simulation, which Squire and Barab had
demonstrated in their research study (2004, p. 5).
Below is how the current game board prototype looks, with an example of how a bug is found:This figure shows red sensors, meaning a bug is in the vicinity of the red sensors. These clues that the player
has to generate will point the player closer to identifying the bug. The blue squares on the grid indicate where
Player 3 can put a sensor next.
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This figure shows that the player (Player 3) has successfully identified this bug (in light green). The dashboard
also changes to read “Bug found!”Future Work
Future work includes completing building out the game aesthetics of Synthesizing the Mesh, to include
possible real-world settings in the game, in order to connect the player’s in-game experience torepresentations that would also be personally meaningful outside the game. Upon completion of
Synthesizing the Mesh, we have plans to implement this in an after-school supplementary program to
examine how students interact with the content and representations in the game. We are interested instudying how students gain intuition and understanding for traditionally complex and advanced topics
in machine learning concepts, specifically Bayesian reasoning in this case, thus we will perform a preand post-test outside the game, and collect in-game observational data from this research project.

References
Abrahamson, D., & Wilensky, U. (2003, February). The quest of the bell curve: A constructionist
approach to learning statistics through designing computer-based probability experiments. In
Proceedings of the Third Conference of the European Society for Research in Mathematics Education.
Blikstein, P., & Wilensky, U. (2009). An atom is known by the company it keeps: A constructionist
learning environment for materials science using agent-based modeling. International Journal ofComputers for Mathematical Learning, 14(2), 81-119.
Cheng, P. C. H. (2011). Probably good diagrams for learning: representational epistemic recodificationof probability theory. Topics in Cognitive Science, 3(3), 475-498.
D'Agostini, G. (2003). Bayesian reasoning in data analysis: A critical introduction. World Scientific.
Holbert, N. R., & Wilensky, U. (2014). Constructible authentic representations: Designing video games
that enable players to utilize knowledge developed in-game to reason about science. Technology,
Knowledge and Learning, 19(1-2), 53-79.
K–12 Computer Science Framework. (n.d.). Retrieved March 29, 2018, from https://k12cs.org/
Kafai, Y. B. (2012). Learning design by making games: Children's development of design strategies in
the creation of a complex computational artifact. In Constructionism in practice (pp. 87-112). Routledge.
Next Generation Science Standards. (2018, March 08). Retrieved March 29, 2018, fromhttps://www.nextgenscience.org/
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Satake, E., & Murray, A. V. (2014). Teaching an Application of Bayes' Rule for Legal Decision-Making:Measuring the Strength of Evidence. Journal of Statistics Education, 22(1).
Squire, K., & Barab, S. (2004, June). Replaying history: Engaging urban underserved students inlearning world history through computer simulation games. In Proceedings of the 6th international
conference on Learning sciences (pp. 505-512). International Society of the Learning Sciences.
Weintrop, D., Holbert, N., Horn, M. S., & Wilensky, U. (2016). Computational thinking in constructionist
video games. International Journal of Game-Based Learning (IJGBL), 6(1), 1-17.
Wilensky, U., & Papert, S. (2010). Restructurations: Reformulations of knowledge disciplines throughnew representational forms. Constructionism.

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Teaching Computational Thinking with Minecraft &Microsoft MakeCode
Stephen Howell, stephen.howell@microsoft.com
Microsoft Ireland

Abstract
In this demonstration, we present Microsoft MakeCode, an open-source visual programming languagethat can be used to teach Computational Thinking concepts with Minecraft. With MakeCode, we willshow how Minecraft can be used as the microworld for exploring constructionist ideas. Students candesign shapes and structures to be built algorithmically. Programmable agents can be instructed tonavigate, act and explore, and it all takes place in a virtual world that many students find comfortableand familiar because of the success of Minecraft as a game.

A house created in Minecraft with a snippet of code that builds the walls using a nested for-loop

Keywords
computational thinking; microworlds; visual programming languages

Demonstration
In Minecraft, players must survive by collecting resources and carefully utilising them to ensure safety
when the monsters, or ‘mobs’, come (they mostly come at night). Luckily for educators, Minecraft also
has a ‘creative mode’, where there are no monsters and teachers can bring students to play together
without the ability to injure each other. This version of Minecraft is called the ‘Education Edition’, andthis edition was the first to add the ability to use a visual programming language to build and program
agents. In this demonstration, we will show the agent and builder.

AgentsWhen MakeCode is enabled by the student or educator in Minecraft (by issuing the command ‘code’ ingame), the agent appears. Designed to be cute and friendly to younger players, the agent can be
programmed to protect the player, or find and return resources, or even to fight on behalf of the player.

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The agent and a code that make the agent ‘jump’ up and forward in an arc

Builder
Perhaps of more interest to educators wishing to explore constructionist concepts is the builder. The
builder is invisible but can be instructed to quickly build complex structures, such as castles with moats,
battlements, floors and drawbridges (O’Sullivan et al., 2017).

A castle from the inside with the code that generated the floor and the door

References
O’Sullivan, M., Robb, N., Howell, S., Marshall, K., Goodman, L. (2017). Designing inclusive learning fortwice exceptional students in Minecraft. International Journal of E-Learning & Distance Education, 32,
(2).

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Interpolating (and Extrapolating) 3D turtle Programs
in Beetle Blocks
Ken Kahn, toontalk@gmail.com
Department of Education, University of Oxford, UK

Abstract
Turtle programs can be treated as objects to manipulate. In this demo a program takes two turtle
programs as input and creates a new program that is the interpolation between the input programs. An
input of .25, for example, will behave like one-fourth of the first program and three-fourths of the second.
An input greater than 1 will extrapolate beyond the second program in the direction from the firstprogram. This idea was explored in (Kahn 2007) for two-dimensional turtle programs. Here we
generalise it for Beetle Blocks (Romagosa et al 2016), a 3D version of Snap! (Harvey & Mönig 2010).

Keywords
Program interpolation; Snap!; turtle programming; 3D turtles; Beetle Blocks; codification;

Interpolating 2D Logo turtle programs(Kahn 2007) describes a program interpolator that can create a new program from two Logo programs
that are defined using FORWARD, RIGHT, REPEAT, SETPENCOLOR, PENUP, and PENDOWN. As
the simplest example consider two programs that draw different length lines:

to short
forward 40
end

to long
forward 100
end

The interpolated program is:

to short_to_long :xforward interpolate :x 40 100
end

to interpolate :x :a :boutput :a + :x * (:b - :a)end

SHORT_TO_LONG 0 behaves just as SHORT, SHORT_TO_LONG 1 behaves as LONG,
SHORT_TO_LONG .5 averages them, and SHORT_TO_LONG 2 extrapolates beyond LONG startingfrom SHORT.
The difficult step is canonicalising input programs that repeat a sequence of turtle commands a different
number of times. (Kahn 2007) explains this in detail.

Moving to 3D
Beetle Blocks (Romagosa et al 2016) is a well-designed 3D version of Snap! (Harvey & Mönig 2010).
Before one can begin to create interpolations of Snap! or Beetle Blocks programs we need a way totreat a block program as a data structure that can be manipulated programmatically. Fortunately, Snap!
has a “codification” feature (Ball et all 2015; Harvey & Mönig 2018). Codification has been used to define
how Snap! blocks can be translated to Python, C, Smalltalk, JavaScript, or other languages. We used
this feature to translate Snap! blocks into JSON strings that are then converted into Snap! lists.
The problem of programming a Snap! program that constructs another program (the interpolation
program) was resolved by defining the constructed program as a list of closures that can be run to788

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execute a sequence of commands. The values or expressions in corresponding commands in the two
input programs are handled as variables closed over by functions.
FORWARD (or MOVE as it is called in Beetle Blocks) is treated in a similar manner to how the 2D turtle
interpolator worked. RIGHT (or ROTATE as it is called in Beetle Blocks) has an additional argumentthat specifies whether the rotation is around the x, y, or z axes. As long as both programs rotate in thesame dimension in the corresponding program locations it is straight-forward to generalise to 3D. To
make a more generic interpolator other Beetle Block commands are also supported including “go to x:
y: z:” and a list of “set” commands that change coordinates, hue, saturation, lightness, and opacity.
Beetle Block command for extruding curves and lines are also handled. Furthermore, arithmetic
expressions involving addition, subtraction, multiplication, and division are supported. Support for user
reporters can be easily added.

Figure 1. A simple example of interpolating a yellow triangle to green hexagon

Figure 2. A program to run the triangle to hexagon interpolation program with 21 values from 0 to 1

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Figure 123. The result of running 21 interpolations between the triangle and hexagon

Figure 4. Interpolating and extrapolating between circles, stars, and pentagons

A very nice feature of Beetle Blocks is that the output of 3D turtle programs can be created on 3D
printers. Figure 4, for example, is an attempt to make a vase. This program (and the interpolation
program generator) can be found at https://tinyurl.com/circle-star-pentagon.

Conclusion
A turtle program is more than a shape. A circle, for example, can be drawn clockwise, counter-clockwise,or multiple times. The consequences of how a shape is drawn can result in dramatically different
interpolations and extrapolations. Students tinkering with program interpolation and extrapolation are
entering a mathematically and computationally rich area. And the creative and aesthetic possibilities ofinterpolating and extrapolating between turtle programs are many.

References
Ball, M., Mock, L., McKinsey, J. Machardy, Z., Garcia, D., Titterton, N., Harvey, B. (2015) Oh, Snap!
Enabling and Encouraging Success in CS1. In: SIGCSE '15 Proceedings of the 46th ACM TechnicalSymposium on Computer Science Education. pp 691-691.
Harvey, B., Mönig, J., (2010) Bringing “No Ceiling” to Scratch: Can One Language Serve Kids andComputer Scientists? In Proceedings: Constructionism, Paris, France.
Harvey, B., Mönig, J., (2018) Snap! 4.1. Reference Manual. https://snap.berkeley.edu/SnapManual.pdf.
Kahn, K (2007) A Program to Interpolate (And Extrapolate) Between Turtle Programs. International
Journal of Computers for Mathematical Learning. December. Volume 12. Issue 3. pp 255–262.
Romagosa, B., Rosenbaum, E., Koschitz , D. (2016) From the Turtle to the Beetle - The Beetle Blocks
programming environment. http://hdl.handle.net/10609/52807. Universitat Oberta de Catalunya.
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Hedgehog: A Versatile Controller for Educational
Robotics
Markus Klein, klein@pria.at
Practical Robotics Institute, Austria

Clemens Koza, koza@pria.at
Practical Robotics Institute, Austria

Wilfried Lepuschitz, lepuschitz@pria.at
Practical Robotics Institute Austria
Gottfried Koppensteiner, koppensteiner@pria.at
Practical Robotics Institute Austria

Abstract
We describe Hedgehog, an educational robotics controller designed to foster interest in STEM subjects.
Hedgehog allows building robots out of common actuators and sensors, and can be combined with
Lego for a beginner-friendly experience. Through building robots, students can learn and apply
engineering, electronics, planning and teamwork skills. The controller facilitates learning programmingat different age levels through both textual and visual programming support. For advanced students,
Hedgehog's open source ecosystem allows delving into subjects such as microcontroller programmingor cooperative robots as well. Hedgehog has been used in numerous workshops and also in robotics
competitions with great success.

Keywords
Robotics; programming; visual programming; STEM

Hedgehog Controller
Hedgehog is a robotics controller: a computer aimed at controlling robotic components to allow building
and programming robots. It is general-purpose, meaning that it was designed not only for a single task
but to support whatever robotic project one might envision, and designed with educational use cases inmind. For example, Hedgehog controllers connect to a central WiFi, meaning that network congestionis less of a problem even in classrooms with multiple robots.
The Hedgehog Educational Robotics Controller was designed to offer learning capabilities for people ofdifferent ages and skill levels. As Hedgehog itself is only the controller, a lot of its features relate tosoftware development, but also the hardware was designed with this in mind. The Raspberry Pi utilized
by Hedgehog allows for experimentation, and connectors of the Pi and microcontroller not directly usedfor Hedgehog’s core functionality are accessible as well. The case exposes the Pi's USB, HDMI, SD
card slot, etc., and has holing compatible to Lego to allow children to build robots out of familiar
components. Although mechanically compatible with Lego, Lego's motors and sensors are notsupported due to their proprietary plugs. Instead, Hedgehog uses standard 0.1" pin headers and iscompatible to easily available and relatively cheap RC-Servos.

Software Platform
Hedgehog supports out-of-the-box text-based programming with Python as well as a visualprogramming environment based on Google’s open source library Blockly. Especially the latter

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Figure 1. Screenshot of the visual programming environment with the visual program on the left and thegenerated Python code on the right.

Figure 2. Hedgehog robotics controller in use at a robotics competition.

option allows beginners without any prior experience to dive into the world of robotics as it introduces
an easier entry and less error potential in comparison with a text-based language. The design conceptof the controller puts much emphasis on extendibility and versatility. Thus, adding new programming
languages or integrating third-party libraries is well supported.
In order to allow children to quickly and easily get started with programming the Hedgehog controller, a
web-based development environment called the Hedgehog IDE was implemented.
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Programs are stored on the controller and users simply use their computers or tablets to connect to thecontroller via a browser and do not need to install any software on their machines which is a big
advantage in workshop situations where time is always precious.

Real-World Usage
The Hedgehog controller has been used in a number of contexts, including workshops for middle school
aged students, extracurricular activities with minimal supervision, and robotics tournaments. The
controller was also used in the construction of an underwater robot, described in Grabler et al. (2018),
which was in turn used in multiple educational robotics activities. Anecdotal evidence from these
activities hints towards good acceptance of the Hedgehog system with regard to hardware compatibility(electronics connectors and mechanical connection points), usability of the development environment
described in the previous section, and programming means - the libraries and the Python and Blockly
languages.
For a subset of activities, in particular four workshops with 70 students aged 11 to 16, Koza et al. (2017)
analyzed quantitatively and compared them with four earlier workshops using different controllers. Alleight workshops were part of the same project and used a comparable style and setting. The reference
workshops’ controllers used the C programming language instead of Python.
The statements "working with robots was interesting/difficult/fun," and "overall, I would rank this coursewith ... stars," were looked at. Regarding difficulty, the results showed no significant difference. In theother categories (interesting, fun, overall stars), low ratings (1 or 2 stars) were almost absent from theHedgehog workshops' results, while they did show up more frequently in the reference workshops.
Although factors such as different instructors and students were not taken into account, it can beinterpreted that students were more engaged in the workshops using Hedgehog.

Conclusion
Hedgehog shows a lot of promise for the education domain and has already been used successfully in
different settings. We are confident that Hedgehog's ease of use and versatility makes it a good choicefor various educational use cases. Through this, the Hedgehog controller helps reaching diverse
audiences to foster their STEM interests.

Acknowledgements
The authors acknowledge the financial support from the European Union’s Horizon 2020 research andinnovation program under grant agreement no. 665972.

Demonstration
During this 30-minute demonstration, we will present the Hedgehog educational robotics controller,
including its hardware capabilities and the software platform. The demonstration will also introduce
means to facilitate constructionist learning by building and programming robots using the controller.

References
Koza, C., Wolff, M., Frank, D., Lepuschitz, W. and Koppensteiner, G. (2017) Architectural Overview andHedgehog in Use. In Proceedings: International Conference on Robotics and Education RiE 2017. p.
238-249.
Reinhard Grabler, Markus Klein, Thomas Fellner, and Gottfried Koppensteiner. (2018) Development ofa Low-Cost Maritime Educational Robotics Platform. International Journal of Materials, Mechanics andManufacturing, Vol. 6, no. 3, pp. 208-214.

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Working groups
WG1: Constructionist Approaches to Computational
Thinking
Bernhard Standl, bernhard.standl@ifs.tuwien.ac.at
Gerald Futschek, gerald.futschek@tuwien.ac.at
Vienna University of Technology, Austria

Jane Waite, jane.waite@computingatschool.org.uk
Queen Mary, University of London, UK

Andrew Paul Csizmadia, A.P.Csizmadia@staff.newman.ac.uk
Newman University Birmingham, UK

Lina Vinikienė, lina.vinikiene@mii.vu.lt
Vilnius University, Lithuania

Abstract
This working group elaborated an approach to evaluate computational thinking activities with learning
in a constructionist way. As widely known, Wing stated in her refined definition of Computational
Thinking (CT) (Wing 2008), that CT is an approach for solving problems that draws on concepts
fundamental to computing. Later, Aho (2012) described the term CT as including algorithm-design andproblem-solving techniques that can be used to solve common problems arising in computing. As Yadav
et. al. (2011, 2016) reminded Wing’s initial paper (2006) points out that CT involves three key elements
Algorithms, Abstraction, and Automation. The term CT has been grown since then to a variety ofinterpretations. Ackerman (2001) compared Piaget’s constructivism and Papert’s development of thisin a constructionist way and drew the two views together as learning in a constructionism way ‘as Piaget
and Papert do, that knowledge is actively constructed by a child in interaction with its world, then weare tempted to offer opportunities for kids to engage in hands-on explorations that fuel the constructive
process.’. Papert’s core message that the learner is ‘projecting out our inner feelings and ideas is a key
to learning. Expressing ideas makes them tangible and shareable which, in turn, informs, i.e., shapes
and sharpens these ideas, and helps us communicate with others through our expressions.’ Thismeans, that new insights are the sum of single experiences made by applying existing knowledge forenhancing it. Considering both parts discussed above, constructionism and computational thinking, thisworking group’s intentions are based on the combination of both for selecting and evaluating classroom
activities. Therefore, we designed a matrix, where aspects from both, computational thinking and aconstructionist learning approach, can be analysed. The matrix is designed to identify, categorize andevaluate such classroom activities and encompasses three parts: Computer Science Concepts,
Problem-Solving Concepts, Levels of Abstraction.

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This working group’s paper presents our approach of a systematic evaluation of classroom activities interms of constructionist learning and discusses first results of our evaluation process, where we codedsuch activities corresponding to the matrix.

Keywords
computational thinking; constructionist learning; classroom activities

1. Introduction
This working group elaborated an approach to evaluate computational thinking activities with learning
in a constructionist way. As widely known, Wing stated in her refined definition of Computational
Thinking (CT) (Wing, 2008), that CT is an approach for solving problems that draws on concepts
fundamental to computing. Later, Aho (Aho, 2012) described the term CT as including algorithm-design
and problem-solving techniques that can be used to solve common problems arising in computing. As
Yadav et. al. (Yadav, Gretter, Hambrusch, & Sands, 2017; Yadav, Zhou, Mayfield, Hambrusch, & Korb,
2011) reminded Wing’s initial paper (Wing, 2006) points out that CT involves three key elements
Algorithms, Abstraction, and Automation. The term CT has been grown since then to a variety ofinterpretations. Ackerman (Ackermann, 2001) compared Piaget’s constructivism (Piaget & Duckworth,1970) and Papert’s development of this in a constructionist way and drew the two views together as
learning in a constructionism way ‘as Piaget and Papert do, that knowledge is actively constructed by achild in interaction with its world, then we are tempted to offer opportunities for kids to engage in handson explorations that fuel the constructive process.’. Papert’s core message that the learner is ‘projectingout our inner feelings and ideas is a key to learning. Expressing ideas makes them tangible andshareable which, in turn, informs, i.e., shapes and sharpens these ideas, and helps us communicate
with others through our expressions.’ This means, that new insights are the sum of single experiences
made by applying existing knowledge for enhancing it. Considering both parts discussed above,
constructionism and computational thinking, this working group’s intentions are based on the
combination of both for selecting and evaluating classroom activities. Therefore, we designed a matrix,
where aspects from both, computational thinking and a constructionist learning approach, can beanalysed. The matrix is designed to identify, categorize and evaluate such classroom activities andencompasses three parts: Computer Science Concepts, Problem-Solving Concepts, Levels of
Abstraction. This working group’s paper presents our approach of a systematic evaluation of classroom
activities in terms of constructionist learning and discusses first results of our evaluation process, where
we coded such activities corresponding to the matrix.

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In our approach, first, we tried to identify a suitable research question to our paper’s topic. One of theearlier ones was: How could be CT learned in constructionist way in different academic disciplines /different tasks? Following further discussion, the group agreed on investigating existing classroom
activities as a starting point to start to formulate and evaluate a toolset which might prove useful ininvestigating constructionism and computational thinking in K-12 classrooms.
Leading by the research questions: In what way do K-12 classroom activities teach computational
thinking in a constructionist way? How useful is our model in evaluating CT in K-12 classroom activities?
How useful is our model in evaluating constructionism in K-12 classroom activities? we continued witha discussion about our ideas and how we could create a system for a classification of ConstructionistComputational-thinking Classroom Activities.
The idea was agreed to design a mapping tool, which we could use to record and code classroom
activities to start to investigate and compare activities for their computational thinking and constructionist
attributes.
This working group paper is structured as follows. First, we are describing the background of our workin literature and further describe an overview on computational thinking, constructionism and its
concepts respectively combination of both parts. In a further section, we will describe the methods weused to gather and evaluate the classroom activities. This includes a detailed description of the mapping
tool we have developed. This is followed by a presentation of the results and a final discussion of thisworking group paper.

2. Background
Computational Thinking (CT) has been widely discussed since Jeannette Wing published her article“Computational Thinking” in 2006 (Wing, 2006). There have been several attempts to define this concept
more precisely since then with the discussion converging to a handful of skills which characterize thethinking concepts associated with CT. These concepts include abstraction, decomposition, algorithmic
thinking, generalization and evaluation. Algorithmic thinking education has a long tradition inconstructionist education. Logo, Scratch and other programming tools invite creative learning of
programming and algorithmic thinking. But CT is considered very broad, it covers not only programming
and algorithmic skills, but also activities like problem formulation, system modelling and solution
evaluation.
An international group collaborated on this working group paper to consider computational thinking andconstructionism. In identifying an underpinning theory to our working group’s topic, we first investigate
the two parts of the title of our paper: Constructionism and Computational Thinking. Despite both topics
have their own deep background, of which a description would go far beyond this paper’s scope andword length, the next two subsections will give a short overview and in particular connections to ourwork.

2.1 Constructionism
Ackerman (Ackermann, 2001) in her comparison of Piaget’s constructivism and Papert’s developmentof constructivism in a constructionist way, drew the two views together describing learning in aconstructionism way as ‘that knowledge is actively constructed by the child in interaction with her world,
then we are tempted to offer opportunities for kids to engage in hands-on explorations that fuel theconstructive process.’ She further asserted Papert’s core message that the learner ‘projecting out our
inner feelings and ideas. is a key to learning. Expressing ideas makes them tangible and shareable
which, in turn, informs, i.e., shapes and sharpens these ideas, and helps us communicate with others
through our expressions.’. Similar to Piaget, Papert identifies learning by constructing andreconstructing knowledge through experience. In particular, Papert’s constructionism sets a focus fromlearning in situations ‘rather than looking at them from a distance, that connectedness rather thanseparation are powerful means of gaining understanding’ (page 8). This means, that new insights are
the sum of single experiences made by applying existing knowledge for enhancing it. Therefore, ‘handson activities are the best for the classroom applications of constructivism, critical thinking and learning.
(http://www.teach-nology.com/currenttrends/constructivism/classroom_applications/)
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We summarise the significant feature of constructionism for our study as being that learners makesomething in order to learn. However, what that something is, and what degree of autonomy isassociated with the process of making is a focus of our study along with the relationship ofconstructionism and computational thinking.

2.2 Computational Thinking
Computational thinking has become a popular term in computer science education with, definitions
varying depending on perspective (Tedre & Denning, 2016). Previous works present at least three
types of approaches to defining CT: it is a set of skills to help solve problems e.g. (Wing, 2006), it is athought process e.g. (Aho, 2012), or it is a problem-solving process e.g., (Voogt, Fisser, Good, Mishra,& Yadav, 2015). As widely known, Wing (Wing, 2008) stated in her refined definition of CT that it is anapproach for solving problems that draws on concepts fundamental to computing. Later, Aho (2012, p.
832) described the term CT as including “algorithm-design and problem-solving techniques that can beused to solve common problems arising in computing”. As Yadav (Yadav et al., 2017, 2011) reminded
Wing's initial paper (Wing, 2006) points out that CT involves three key elements Algorithms, Abstraction,
and Automation. The term CT has been grown since then to a variety of interpretations. Settle andPerkovic (Perković, Settle, Hwang, & Jones, 2010), who proposed seven principles for CT across thecurriculum, added that CT also involves computation, communication, coordination, recollection,
evaluation, and design. For Lee (Lee et al., 2011) CT involves defining, understanding, and abstraction.
Barr et al. (2011) suggested that CT involves the design of solutions, implementation of designs, testing,running analysing, reflecting, abstraction, creativity, and group problem solving. Grover et al.
(Grover2013; Grover & Pea2018) stated that CT should include among others abstraction, information
processing, structured problem-solving decomposition as modularization, iterative recursive thinking,and efficiency. Again, for Lee et al. (Lee, 2011) CT involves defining, understanding, and solving
problems, reasoning at multiple levels of abstraction, understanding and applying automation, andanalysing the appropriateness of the abstractions made.
According Brennan & Resnick (2012), CT involves three dimensions such as computational concepts(the concepts designers employ as they program), computational practices (the processes of
construction), and computational perspectives (the perspectives designers form about the world aroundthem and about themselves).

2.3 Constructionism in Computational Thinking
Considering both parts discussed above, constructionism and computational thinking, this working
group’s intentions are based on the combination of both for selecting and evaluating classroom
activities. Therefore, we designed a matrix, where aspects from both, computational thinking and aconstructionist learning approach, can be analysed. The matrix is designed to identify, categorize andevaluate such classroom activities and encompasses three parts:
1. Computer Science Concepts
2. Problem-Solving Concepts
3. Levels of Abstraction matrix
Below we are discussing these three dimensions in detail:a) Computer Science Concepts
From the viewpoint of computer science education, teaching and learning of computer science (CS)concepts is more important than learning how to use computer systems. Dagienė, Sentance andStupurienė (2017) categorized in their paper “Developing a Two-Dimensional Categorization System
for Educational Tasks in Informatics” the CS concepts in following 5 categories:
●
●
●
●
●

ALP: Algorithms and Programming
DDS: Data, Data Structures and Representation
CPH: Computer, Processes and Hardware
C&N: Communications and Networks
ISS: Interactions, systems and society797

Constructionism 2018, Vilnius, Lithuania

These categories are so designed that each concept fits in one of the categories. Since CS tasks often
involve more than one concept, a task may be assigned to more than one category.
b) Computational Thinking Concepts (CT)Computational Thinking Skills are independent from CS concepts. Computational thinking originatesfrom solving CS tasks but the essence of these thinking skills can be applied also in all other disciplines.
Based on the work of Selby and Woollard (2013), Dagiene et al. (2017) categorized also theComputational Thinking Skills into 5 categories:● ABS: Abstraction● ALT: Algorithmic Thinking● DEC: Decomposition● EVA: Evaluation● GEN: Generalisation
Simple definitions of each of these concepts are (Selby 2013, Standl 2017)●

Abstraction: Ignoring unnecessary detail. When abstracting a problem in a way that helps tosolve it. If we had to keep all the details in our heads, we could never get anything done. As wehave described above, abstraction is mentioned as a key-stone of CT and Grover and Pea(2018) identified it as a core element, which differs CT from other types of thinking.
● Algorithmic thinking: Considering the sequence of steps. This includes a design of an algorithm
to develop the step-by-step instructions for solving the problem. Starting from what already is
known and working outward from there by making a plan how to approach to solve the problem.
● Decomposition: breaking a problem down into parts. Decomposition involves finding structure in
the problem and determining how the various components will fit together in the final solution.
Doing decomposition well makes it easier to modify the solution later by changing individualcomponents, and also enables the reuse of components in solutions to other problems.
● Evaluation: comparing alternatives and how does the solution work in practice and are there
alternatives? Trying to give the problem different inputs to look how the solution works.
● Generalisation: Creating things that can be reused in more than one scenario. Is the solution tosimilar problems also applicable and what is needed to do so? How can the solution be
generalized? Which parts turned at the evaluation out to be not necessary?
c) Levels of abstraction (LOA) matrix
The degree of autonomy of learners to make choices about their constructed artefacts was viewed as
an important aspect to capture in order to evaluate the opportunity for learners to engage inconstructionist learning (reference needed) in computational thinking activities. Three frameworks havebeen combined and further developed to create a levels of abstraction matrix which provide an
opportunity to evaluate autonomy for the different ‘levels’ or ‘stages’ for a programming project. The
work that we build upon is the Levels Of Abstraction (LOA) framework (Perrenet et al. 2005; Perrenet& Kaasenbrood 2006), the Abstraction Transition (AT) Taxonomy (Cutts et al. 2012) and the Use Modify- Create approach (Lee et al. 2011).
A levels of abstraction (LOA) model (Perrenet et al. 2005; Perrenet & Kaasenbrood 2006) has beensuggested to support novice university students in thinking about algorithms in programming of:problem, object, program and execution. This model has been situated for high school learners byrenaming the object level as algorithm (Armoni 2013) and with younger K-5 learners by renaming levels
as: task for problem; design for object; code for program and running the code for execution (Waite etal 2016, Waite et al 2017).
Cutt’s et al. (2012) investigated novice undergraduate ‘talk’ of programming activities in response topeer instruction questions and proposed the three leveled Abstraction Transition Taxonomy of English,
CS speak and Code, where English is used to define the goal, ‘CS Speak’ for the technical description
and code to accomplishes the goal.
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Constructionism 2018, Vilnius, Lithuania

The Use, Modify, Create approach has been suggested as a learning framework, where learners firstuse products created by other which are not ‘theirs’, move on to modify products and finally create their‘own’ products (Lee et al. 2011).

3. Method
3.1 The Mapping Tool
A mapping tool was required to provide a means to classify and evaluate CT and constructionism. A
simple google doc spreadsheet was used to facilitate sharing of the mapping tool. Following email
discussion of a range of opportunities for classifying computational thinking a modification of the 2dimensional model of computational thinking skills and computer science concepts for individual tasks(Dagiene et al 2017) was agreed to be used to evaluate the intended teaching and learning objectives
related to computer science concepts and computational thinking(CT).

3.2 Computer Science (CS) Concepts
The working group adopted the computer science concept categorization of school computing as
proposed by Dagiene et al (2017) as that of:
1. Algorithms and Programming
2. Data, Data Structures and Representation
3. Computer Processes and Hardware
4. Communications and Networking
5. Interactions, Systems and Society
In addition, the working group adhered to the guidance from Dagiene and el (2017) that for practical
purposes, a precise definition of each category is required and this can be achieved by the usage of
keywords as shown in Table 1.
Table 1 Computer Science (CS) Concept Categories and Keywords (Adapted from Dagiene et al (2017))

CS Concept
Categories

Code

Keywords

Algorithms andProgramming

APL

Algorithm; Binary search; Boolean algebra; Breadth-first search;
Brute-force search; Bubble sort; Code; Coding; Computational
complexity; Constants; Constraints; Debugging; Depth-firstsearch; Dijkstra’s algorithm; Dynamic programming; Divide andconquer; Encapsulation; Function; Greedy algorithm; Heuristic; IF
conditions; Inheritance; Iteration; Kruskal’s algorithm; Logic
gates; Loops; Maximum flow problem; Objects; Operations AND,OR, NOT; Optimization; Parameters; Prim’s algorithm;
Procedure; Program; Programming Language; Program
execution; Quick sort; Recursion; RSA algorithm; Shortest path;
Selection; Sequence; Sorting; Steps; Traveling salesman
problem; Variables
Steps, sequence, algorithm, code, program

Data, Data
Structures andRepresentation

DDS

Array; Attributes; Biconnected graph; Binary and hexadecimal
representations; Binary tree; Character encoding; Databases;
Data; Data mining; Eulerian path; Finite-state machine;
Flowcharts; Fractals; Graph; Hash table; Integer; Information;
LInked list; List; Queue; Record; Stack; String799

Constructionism 2018, Vilnius, Lithuania

Computer Processes
and Hardware

CPH

Cloud computing; Deadlock; Fetch-execute cycle; Grid
computing; Image processing; Interpreter; Memory;
Multithreading; Operating system; Parallel processing;
Peripherals; Priorities; RAID array; Registers; Scheduling; Sound
processing; Translator; Turing machine

Communications
and Networking

C&N

Client/server; Computer network; Cryptography; Cryptology; Ecommerce; Encryption; Parity; Protocols; Security; Topologies

Interactions, Systems ISS
and Society

Classification; Computer use; Design; Ethics; Graphical User
Interface; Human Computer Interaction (HCI); Legal issues;
Robotics; Social issues;

Apart from assisting in the categorization of tasks, keywords are helpful to teachers who wish to findtasks that assist in introducing, teaching or formative assessing a specific computing topic (Dagieneand Sentance 2016; Yang and Park 2014).

3.3 Computational Thinking (CT): Skills Level
The working group adopted as its categorization of computational thinking which followed the work of
Selby and Woolard (2013) and has been adopted by Computing At School in the UK in developing
guidance for teachers on computational thinking (Csizmadia et al 2015). This approach was adopted byboth Giordano et al (2015) and Dagiene et al (2017) in design a framework for classifying computational
thinking skills and computing concepts. The following table summarizes the five categories ofcomputational thinking: Abstraction, Algorithmic Thinking, Decomposition, Evaluation andGeneralization, and describes aspects of computational thinking as exhibited by learners as they work
their way through a task:
Table 2 CT Skills and ways to identify them (adapted from Dagiene et al.(2017))

CT kills

Code

How to identify the use of this skill

Abstraction

ABS

Removing any unnecessary detail
Identifying key elements in the problem
Choosing an appropriate representation of asystemAlgorithmic Thinking

ALT

Thinking in terms of sequences
Thinking in terms of rules
Creating an algorithm
Executing an algorithm

Decomposition

DEC

Breaking down tasks into sub-tasks
Thinking about problems in terms of theircomponent parts
Making decisions about dividing into sub-tasks
with integration in mind
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Constructionism 2018, Vilnius, Lithuania

Evaluation

EVA

Find an appropriate solution
Finding the best solution
Deciding whether the solution is fit for purpose
Deciding whether the solution is the mostefficient one

Generalization

GEN

Identify patterns, similarities and connections
Solving new problems based on solutions tosimilar problems
Utilizing the general solution, i.e. induction

Classifiers need to be able to know how to identify how a particular skill might be used to solve a giventask (Table 2). One of the difficulties encountered by the members of the working group was that they
could only presume to know how an individual learner solves a specific task which may differ from theway that either the task setter or the classifier would solve the task. In practical terms, there may be
more than one computational thinking skill associated with each task. Therefore, the working group
followed the guidance suggested by Dagiene et al (2017) of recording a maximum of threecomputational thinking skills.

3.4 Developing the Constructionism Matrix
A similar discussion was undertaken to find a framework to evaluate classroom activities for their‘degree’ of constructionism features. A new framework was devised, which will be evaluated as part of
the working group activity.
Waite et al. (2016) suggested that the LOA model might be mapped to the AT taxonomy, with Problem
being matched to English, and object to CS speak and Program and Execution to Code. Similarly, we
have combined the Program and Execution level, as our focus is on the autonomy of learners at eachlevel, and they can have no influence on how the code runs to suggest a modified LOA suitable for K12 of:● Problem or task (English)● Object, algorithm or design (CS Speak)● Program or code and Execution or running the code (Code)This provides the first dimension of our constructionism matrix to which we added the dimension ofUse, Modify, Create (UMC) (Lee et al. 2011) to provide an indication of learner ownership and autonomy
for each level. Descriptions were added for each intersection of modified LOA and UMC matrix. An
iterative process was undertaken to develop these descriptions. As classroom activities were coded,
the scale was reviewed and adapted to enable differences between the activities to be highlighted andthen further subdivided to a 1 to 5 scale to describe a gradation of learner autonomy in the construction
process. Our final constructionism matrix is shown in Table 3.
Table 3: Constructionism Matrix

Adapted
LOA

Problem or Task
(English)Scale

No independence e.g.
the teacher or activity
states the problem, acopy task activity

1

Design/Object/Algorithm Code/Program/Running Use/Modify/(CS Speak)the code (Code)CreateNo independance using a
pre-existing design, or no
designNo independence,
copying pre-defined
code

Use

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Constructionism 2018, Vilnius, Lithuania

2

Limited independence,
can only change
superficial aspects ofthe problem e.g. adding
extra questions to aquiz, changing thecharacters of ananimation (broadly aminimal remix)

Limited independance,
can only change someelements of the modelledor pre-defined or
example code

Modify3Moderate independence,
Moderate
Moderate independence, can change a broader
independence, cancan change a broader
range of characteristics
change a broader range range of characteristics of of the code but limited toof characteristics of the the design but changes the genre and context ofproblem but limited to
are within the genre and the task and also to thethe genre and context ofcontext of the original
hardware and softwaretheexemplified design.
defined by the task orteacher.

Modify4

Increasing
Increasing
independence,limited byindependence, cannot Increasing independence,
type of hardware andchange the 'Genre' but cannot change the design
software to be used, but
has full control of theapproach to be used, but
could choose a different
context e.g. must be a
can adapt the objects,
input type or type of
quiz, or a physicalalgorithm etc to meetmicrocontroller orcomputing problem but
needs of the problem
different block based
can be any context.
programming language.

Modify5

Full pupil independance Full pupil independance
Full pupil independance
the pupil has full control the pupil has full control
the pupil has full control
over the problem to beover the design to beover the hardware,considered e.g can be a considered can choose
software andquiz, game, physical format and approach to be
implementation choices
product, unplugged etc
taken

Create6

Levels 1 and 2 seen in a Levels 1 and 2 seen in a Levels 1 and 2 seen in a
Use/Modify
unit of work
unit of work
unit of work

7

Levels 1, 2 and 3 seen
in a unit of work

9

Not Applicable

Limited independence,
can only change some
aspects of the design,
objects, algorithm, forexample reordering
sequence of events,changing the data used

Levels 1, 2 and 3 seen in Levels 1, 2 and 3 seen in
Use/Modify
a unit of worka unit of work

Not Applicable

Not ApplicableAs the mapping tool was used further attributes were added, these included:●

Type of artefact made (Physical computing, onscreen, unplugged, concept)
802Constructionism 2018, Vilnius, Lithuania

●
●
●
●

Type of resource (Lesson plan, game etc)
Cost (Paid for, free)
Activity/Approach URL and description
Target age range (see Table 4)
Table 4: Target Age Range

Years old

US grades

English year groups

5-6

K-1

Key Stage 1 Years 1-2

7-11

2-5

Key Stage 2 Years 3-6

12-14

6-8

Key Stage 3 Year 7-9

15-16

9-10

Key Stage 4 Years 10-11

17-18

11-12

Key Stage 5 Years 12-13

Finally, each activity was double blind coded by two members of the group.

3.5 Selection of activities.
Where information was available related to the popularity of classroom activities this data was used to
select these activities. In the UK, a review of computer science education (Royal Society, 2017) wasused to select the top 5 resource providers and a selection of their materials was included. To selectmaterial provided by one supplier of resources, we contacted them and asked which were the mostpopular and used these. For some providers material is only available with payment, here the materials
were not included as there would be no means to then share and compare the approach taken withreaders of the research. Our study is not rigorous in sampling, but our intention was to trial the approachfor review of materials to suggest next steps for a more complete review.
For Lithuania, a number of anecdotally popular resources were selected, one from Italy and the rest
from the US. One activity (CodeMonkey) was chosen because this game-based environment wasawarded as the Best Coding and Computational Thinking Solution77 in 2018. This classroom activity is
recommended in informatics educational content in primary education in Lithuania as a tool thatengages student to learn informatics concepts by practice not only during informatics lessons, but also
during other subjects such as math, etc. (firstly, students solve problem without computer (eg. measure
the distance) and then repeat the same task using CodeMonkey tool). Other activities are applied orrecommended as classroom activities in primary or middle school in Lithuania as tools to teachcomputer science concepts or programming. Several activities were the most popular during the Hourof code78 (Code Event) in 2015. In addition, some resources (Scratch, Khan Academy, Code.org) are
mentioned as the best free resources for teaching youngsters to code.79

77

https://www.playcodemonkey.com/blog/2018/06/20/codemonkey-awarded-best-coding-computational-thinking-solution/
https://blogs.sas.com/content/sascp/2015/12/07/our-favorite-hour-of-code-resources-for-csedweek15/79
https://codakid.com/top-5-free-kids-coding-websites-of-all-time/78803

Constructionism 2018, Vilnius, Lithuania

Figure 1. Students solve problem without computers and then solve it with computers, discusses about codingwith CodeMonkey (Taurage „Saltinis“ Progymnasium, Lithuania)

3.6 Inter-reliability of codings & quantitative analysis
As a resource was added by a researcher to our main spreadsheet, a second sheet had been set upthat automatically inherited the link to the resource. No other data was copied across. A second coder
could then pick up the activity and code it without having seen the prior codings of their peer. Therefor
we had two authors independently coding each resource. The results were copied into SPSS forinterrelidability evaluation using Cohen’s kappa (Cohen, 2011) where each activity was a ‘case’. In using
SPSS there is a known limitation, that if one coder only uses one value for a variable, then SPSS
evaluates this as a constant and will not report a reliability statistic. An documented acceptableworkaround is to add a distinct pseudo case with a very small weighting (0.0001) as opposed to all othercases which are allocated a weighting of 1 (UCLA, 2018). This technique was required to be used as
we were using dichotomous variables (yes/no), such as for the computer science concepts which had
not coded for any activity.
As we were working as a distant group SPSS syntax was used and a shared excel spreadsheet so that
authors could validate the quantitative data analysis techniques used. The syntax and input data isavailable on request from the first author.
Our results are predominantly non-parametric data; therefore, we have used the Mann-Whitney U test
to compare two independent groups such as concepts being selected as taught or not (Cohen, 2011).
To grade effect sizes, we used Cohen's classification: if r is 0.1 to 0.3 there is small effect, if r is 0.3 to
0.5 there is moderate effect and 0.5 and above is large effect. For variables with more than 2 possible
values, such as the activity type or the artefact type created, the independent Kruskal-Wallis test wasused to investigate any significant differences (Cohen, 2011). We report the χ2 statistic for this statistic
and include post-hoc analysis through the Bonferroni-correction for pairwise comparison of categories
For paired dependent testing, such as comparing trends of coders ratings of the constructionism matrix
across the problem, design and code dimensions we have used the Wilcoxon Signed-Rank test (Cohen,2011). For this statistic we report the number of cases tied and the increases or decreases of ranked
responses to report on the underlying data.

4. Results
We first report on descriptive statistics for the coding of activities, followed by intercoder-reliability
reporting and finally with inferential statistics as we look for relationships between the variables coded.
Twenty-one activities were coded as shown in Appendix 1. 57% (n=12) of the activities were for 7 to
11 year olds, 23% (n=5) for 5 to 6 year olds, 2 were for any age from 5 years, 1 was for any age
between 5 and 11 and 1 activity for students from 10 years onwards. Therefore, all activities were judged
to be suitable for some set of pupils pupils in the K-5 (primary age range in England).
Over 75% of the activities selected were lesson plans (n=16), the remainder were online activities (n=4)
except for one board game. Two thirds of the activities originated from England (n=14), 29% from the
US (n=6) and one from Italy. Nearly 30% of the activities employed Scratch (n=6), 15% ScratchJr (n=3),
there were 2 route based programming languages where the student moved an onscreen character or
programmable toy with direction keys and there was one activity for each of the programming languages
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Constructionism 2018, Vilnius, Lithuaniaof blockly, CoffeeScript, Python, Java and a physical computing software called Crumble. There wasalso one activity that used Google Forms and 6 activities which used no programming language or
specific software to make something.
In order to give a % for each concept type we have taken the average number of coded activities foreach concept across the two coders and present this as a percentage out of the 21 activities as shownin Table 5. On average across the coders, the most popular CS concept taught in the sample ofactivities codes was ALP (Algorithms and Programming) with 90% of the activities coded to this concept.
No activities were coded to Data, Data Structure and Representation concept. For CT concepts, themost popular coded concept was ALT (Algorithms) with, 83% of the activities coded as teaching thisconcept, the second most popular CT is DEC (decomposition) with 45% of activities coded for this. The
most frequent artefact type coded was concept at 79%, followed by 64% for OnScreen artefact types.
Table 5. CS concept, CT concept and artefact type counts by coder and % out of 21 activities

CS concepts

CT concepts

Artefact type

ALP DDS CPH C&N ISS ABS ALT DEC EVA GEN Physi OnS UnPl Concecal cree uggeptnd
Coder 1 19

2

0

2

3

4

19

9

9

1

2

13

10

16

Coder 2 19

1

0

2

4

5

16

10

7

3

2

14

7

17

Averag
e

19

1.5

3.5

4.5 17.5

9.5

2

2 13.5

8.5

16.5

90%

7%

0% 10% 17% 21% 83% 45% 10% 10%

10% 64%

40%

79%

%

0

2

8

Cohen’s κ was run to determine if there was agreement between the authors coding of n activities asshown in Table 6. There was a substantial agreement (κ = .778 , p<0.005) across all the groups ofvariables.
Table 6 Cohen’s κ for coding of activities.

Group ofvariables

Computerscience(CS)
concepts

Cohen’s
κ

p

Notes

ALP: Algorithm andProgramming

1

p<0.000

All coder agreed on every
case

DDS: Data, Data
Structures andRepresentation.644

p=0.002

Variable

CPH: Computer,
Processes and Hardware

notreported

No cases were recorded for
this

C&N: Communications
and Networks

1

p<0.000

ISS: Interactions, systems
and society.829

p<0.000

All coders agreed on every
case

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Constructionism 2018, Vilnius, Lithuania

Average κ for all CS
concepts.868

ABS: Abstraction.696

p=0.001

ALT: Algorithmic Thinking.877

p<0.000

DEC: Decomposition.704

p=0.001.690

p=0.001

CT concepts EVA: Evaluation

Constructio
nism scale

Artefact type

Almost perfect agreement

GEN: Generalisationnotreported

17 agreements 2
disagreements but 1 coder
only recorded one value

Average κ for all CT
concepts.74175

Substantial agreement

Problem or Task.535

p<0.000

Design/Object/Algorithm.563

p<0.000

Code/Running the code.432

p=0.001

Average κ for all
Constructionism Scale.51

Physical Artefact Created1

p<0.000

On-screen Artefact
Created0.901

p<0.000

Unplugged Artefact
Created0.807

p<0.000

Concept Artefact Created

0.604

p=0.002

Average κ for all Activity
Type

0.82

Almost perfect agreement.7784

Substantial agreement

Overall Average κModerate agreement

As shown in Table 7, the majority of activities were coded at level 1 scale for Problem or Task level at
52% and 61% for coders, followed by 24% to 29% at level 2 and 9% to 10% for level 3 with only 1
activity assigned a 4 or 5 level. The majority of designs where at scale 2 at 53% to 71%, with 14% to
24% at level 1 and only 5% at level 3. As with the design level, the majority of activities were rated at
a level 2 scale for the code dimension at 57% and 61% with between 5% and 9% at level 1, and one
activity assigned a level 5. For the coding dimension a quarter, 24% to 29% were not assigned a level(for example as they were unplugged activities).

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Constructionism 2018, Vilnius, Lithuania

Table7. Percentage and count (n) of coded activities by constructionism scale.
AdaptedLOA Scale

Problem or Task
(English)

Design/Object/Algorithm(CS Speak)Code/Program/Running thecode (Code)

Coder 1

Coder 2

Coder 1

Coder 2

Coder 1

Coder 2

1

61% (13)52% (11)24%(5)

14% (3)9%(2)5% (1)2

5% (1)

0

28%(6)

33% (7)52%(11)

48%(10)

1 to 2

19% (4)29% (6)

24% (5)

38%(8)

5% (1)

13%(3)

2 or 1 to 2

24% (5)29% (6)53% (11)71%(15)57% (12)61%(13)

3

5% (1)

0

5% (1)

5% (1)

5% (1)

0

1 to 3

5% (1)9%(2)0

5 (22%)

0

5% (1)

3 or 1 to 3

10% (2)9%(2)5% (1)

5%(1)

5%(1)

5% (1)

4

0

0

0

0

0

0

5

0

5% (1)JS000

5% (1)JS

4 or 50

5% (1)

0

0

0

5%(1)

coded asnotapplicable

5% (1)

5% (1)

19% (4)

10%(2)

29%(6)

24%(5)

n

21

21

21

21

21

21

Our data is predominantly nonparametric. We have dichotomous (yes/no) variables for the CS concept
and CT concepts and ordinal variables for the constructionism scales for each of the dimensions ofproblem, design and code.
Summary Dimensions: To simplify reporting of the constructionism scale we have combined the ‘1 to
2’ response with the 2 response and the ‘1 to 3 response’ with the 3 response for each of the dimensions.
We have also removed the 4 and 5 scale responses as there was only 1 activity coded at the 5 scaleby one coder, and the second coder placed this same activity as 3, so we have classified this as an
outlier and will further consider this case in discussion. In doing this we have created Summary
Dimensions with scales of 1, 2 and 3.
We have used the Mann-Whitney U statistic to investigate whether there was any statistically significant
relationships between the constructionism scale and its CS concept or CT concept. Here the cases,
rather than being an activity, were the codings of each activity by each researcher therefore themaximum population was 42 (n=42) for these tests.
The null hypothesis was that for each CT and CS concept there was no statistically significant difference
between whether an activity was coded with that concept and the constructionism scale.
The null hypothesis for all CS and CT concepts could not be rejected as all tests showed no statisticallysignificant difference. The test statistics are shown in Appendix 2. A cross tabulation of the data is shownin Table 8.

807

Constructionism 2018, Vilnius, Lithuania

Table 8 Cross Tabulation of CS & CT concepts by constructionism matrix dimensions for both coders showing% of type and (n) using Summary Dimensions, with most popular per concept & dimension highlighted.

Problem
1
CS ALP 55%(1co8)
nc
ept DDS 100%(
s
2)
CPH:

0

C&N: 100%(
4)
ISS:

57%(4
)

Design

2

3

1

33%(1
1)

12%(
4)

0

0

0

0

0

0

0

0

43%(3
)

0

2

Code
3

1

2

3

23%
(8)

71%(
24)

6%(34
)

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

100%
(5)

0

0

0

100%(
1)

23%( 71%(2 6%(28)4))
33%( 67%(21))

29%( 71%(52))

CT ABS: 86%(6co)
nc
ept ALT: 61%
s
(17)

0

14%(
1)

0

67%(2 33%(
)
1)

25%(7
)

14%(
4)

24%( 69%(2 7%(2
7)
0)
)

12%
(3)

80%(
20)

8%(2)

DEC: 43%(6
)

43%(6
)

14%(
2)

25%( 63%(1 12%(
4)
0)
2)

8%(
1)

75%(
10)

17%(2
)

EVA: 54%(7
)

15%(2
)

31%(
4)

14%( 72%(1 14%(
2)
0)
2)

0

82%(
9)

18%(2
)

GEN: 100%(
2)

0

0

50%
(1)

50%(
1)

0

50%( 50%(11))

0Similarly, we performed the same statistic on the artefact types reported by the coders, as there wasonly one board game we removed this from the test and used a Mann-Whitney U to compare those
activities which were lesson plans and those which were online student activities for each of thedimensions of the constructionism matrix, namely problem, design and code, for the assigned
constructionism scale of pupil autonomy. The null hypothesis for these tests was that there would be no
statistically significant differences in the gradings of the constructionism scale for lesson plan based
resources or online student resources. There were two statistically significant differences, as shown inAppendix 2, for the Design level (p=0.018, n=25, U-54.5, r=3.98) and the Code level (p=.048, n=29,
U=37. r=.398) both of medium effect size, meaning we do not accept the null hypothesis. A crosstabulation of the data is shown in Table 9 and shows that the scale of autonomy was recorded at ahigher level for lesson plans than for online resources. This may be significant in that it is may not the
CS or CT concept that is having a bearing on the constructionism aspect of a task, more what type ofactivity it was.

808Constructionism 2018, Vilnius, Lithuania

Table 9 Cross Tabulation of activity types by constructionism matrix dimensions for both coders showing % oftype and (n) using Summary Dimensions.

Problem

Ac
tivi
ty
Ty
pe

Design

Code

1

2

3

1

2

3

1

2

3

Lesso
n Plan

61%(
19)

29%(
9)

10%
(3)

14%(
4)

82%(
23)

4%(1)

4%
(1)

92%(
21)

4%(1)

OnlineStude
nt
Activit
y

71%(
5)

29%(
2)

0

57%(
4)

43%(
3)

0

33%(
2)

67%(
4)

0

Board
Game

0

0

100%(
1)

0

0

100%
(1)

0

0

100%(
1)

We also compared the constructionism matrix scales to different types of artefacts created by activities,
the results are shown in Appendix 2. The null hypothesis was that there would be no difference in thereported autonomy of students for whether the artefact was of a particular type or not. A Kruskal Wallis
with bonferroni correction pairwise test showed ‘unplugged compared to physical’ (p=0.013, n=17.
U=5.678, r=.694) and ‘on screen compared to physical’ (p=0.016, U=5.328,n=29,r=.517) had astatistically significant differences at the problem level both with large effect size, but no statisticallysignificant difference for the others. However, caution is urged as there were only 2 activities creating
this artefact type, each coded by a different coder. A cross tabulation (Table 10) showed that physicalactivities had a higher autonomy scale than non-physical for the problem dimension. This indicates thatthe type of artefact may be having a bearing on the degree of constructionism of a task, rather than the
CT or CS concepts.
Table 10 Cross Tabulation of artefact types by constructionism matrix dimensions for both coders.showing % oftype and (n) using Summary Dimensions

Problem

Artefact
type

Design

Code

1

2

3

1

2

3

1

2

3

Unplugged

75%
(9)

17%
(2)

8%
(1)

12.5
%(1)

75%
(6)

12.5%
(1)

25%
(1)

50%
(2)

25%
(1)

Onscreen

65%
(15)

31%
(7)

4%
(1)

29%
(7)

67%
(16)

4%
(1)

9%
(2)

86%
(19)

5%
(1)

Physical

0

50%
(2)

50%
(2)

0

4
(100%)

0

0

100%
(4)

0

Concept

65%
(19)

21%
(6)

14%
(4)

19%
(5)

73%
(19)

8%(2)

10%
(2)

80%
(16)

10%
(2)
809

Constructionism 2018, Vilnius, Lithuania

To investigate if there was any statistically significant difference in coders assignment of scales for eachof the constructionism dimensions we performed the pair statistic of the Wilcoxon Signed Rank test withresults shown in Table 11. The null hypothesis was that there would be no statistically significant
difference in scales across the problem and design, design and code and problem and code dimensions.
There wa statistically significant evidence to reject the null hypothesis, indicating that there was a
relationship between coders allocation of scales across the dimensions. In our population coders rated
the design scale as higher than the problem scale with moderate effect size, and the code scale higher
than the design and the code scale higher than the problem (see Table 11 for test results). This indicatesthat our coders rated activities as being more ‘constructionist in the dimensions’ of coding, then design
and then lowest in problem.
Table 11: Comparing the changes in coders rating of scale across the construnctionism matrix dimensions using
the Wilcoxon Signed Rank test.

Test statistics

Comparing
constructionism
scalesComparing
constructionism
scalesComparing
constructionism
scalesProblem to Design

Design to Code

Problem to Code

Increase inscale

12 (sum of ranks 90)

4 (sum of ranks 10)

14 (119)

Decrease inscale

2 (sum of ranks 15)

0 (sum of ranks 0)

2 (17)

Tied

20

26

14

n

34

30

30

p

p=0.008

p=0.046

p=.003Z-2.673

-2.00

-3.00

r

-.46

-.37

-.55

During review of activities, coders added notes to the mapping tool of their justification for their allocationof concepts and constructionism scale, these are shown in Appendix 1. We have not undertaken aqualitative analysis of this data, but draw upon theses notes in discussion. In future studies these notes
could be analysed more rigorously to inform & improve the suggested tool.

5. Discussion
An initial point to note was the difficulty in creating a method for evaluating the degree to whichconstructionism is incorporated in activities, a matrix has been suggested, but this requires further
validation as a useful approach. When creating the matrix there was discussion of what were the mostimportant features of a constructionist activity. According Papert (1980), important is learning
experience that engage students in constructive activities that are meaningful to them. Furthermore,
activities should be accessible to students with different styles of thinking and learning. Harel stressedthat “students become deeply involved and gain deeper understanding… through the process ofconstructing, programming, and explaining their own representations” (Kafai, 1994, p. 24). Our focus
led to the attribute of learner autonomy in creating artefacts at each of the main stages of a productdevelopment. Here an adapted levels of abstraction framework was suggested with dimensions of810

Constructionism 2018, Vilnius, Lithuania

problem, design and code. A scale of autonomy from levels 1 to 5 was suggested with 1 representing
the least amount of learner control of the artefact being created to 5 representing the learners having
complete choice over what to make and how.
During the process of review of activities, it appeared that the scale criteria description was not sufficient
to distinguish between the sample of activities. Some coders rated activities at ‘1 to 2’ or ‘1 to 3’, thiswas because the activities, rather than being a single small activity were a unit of work across several
lessons, or within a single lesson there was a ‘graduation’ of pupil autonomy. This ‘graduation’ may
have been over the course of the lesson as different ‘tasks’ occurred, such as an initial closely controlled
task, followed by a task where there was more student control or it could be that over several lessons
pupils were moved from use to modify (Lee et al. 2011a).
In order to deal with this added requirement, two extra levels were added to the scale of ranges of ‘1 to
2’ or ‘1 to 3’ as coding value options. However, a % or degree of each level may have been more useful.
Within the qualitative data of ‘Justification of codings’ variable’ one coder said “ I was loathe to allocate
a level 2, as this only occured in the final 20th challenge and up to this point there had been absolutely
no student autonomy at all “ (Coder 2)
However, during reporting on the data, these two extra ‘range’ scales were merged back in with theirrespective highest level, this was because of the small number of activities which were surveyed andthe need for broader groups for statistical analysis. In further work, a larger sample of activities is needed
and the more granular levels can then be used to draw out clearer distinctions between activities.
Turning our attention to the quantitative data reported, the coding of computer science concepts was
reliable. There was a high level of inter-reliability agreement reported as being almost perfect (κ= .868,
p< 0.005) in Table 5.
For CT concepts the agreement was less but was still a substantial agreement (κ= .74175, p< 0.005).
However, for the Constructionism matrix agreement was only moderate (.51, p<0.005). Agreement wasalmost perfect on artefact type (κ=0.82, p<0.005).
Selection of activities to sample was problematical. In the UK there are a large number of resources
available for educators to select from and recent surveys which have reported on the most popular(Royal Society, 2017). However, in other countries the task of finding resources to evaluate was not soeasy. In other countries teachers can use resources that are internationally available, such as thecode.org materials but working group members reported that in some countries teachers are more likely
to create their own activities and that these are not then shared. For these countries, a generic set of
activities were created to represent these countries resources. Whether or not these are representativeof what is being actually used in class in difficult to assess. Similarly, in the UK, it is not clear as towhether teachers are using resources in their published form or if they are adapting them, in a recent
survey of 207 teachers (data not yet published) 40% of teachers reported they created their own
resources, including adapting resources from over 70 specifically named resource sets. The mostpopular three resource mentioned in this survey was the Barefoot materials (Berry et al. 2015) with 34%of the teachers mentioning using this resources followed by and 16% mentioning CodeIt and 13%
Switched on, of which we have reviewed sample material in our survey here. Although, teachers choose
the most popular computer science or computational thinking activities in the world, but sometimes thediversity of activities depends on policies, curriculum, grades, etc. For example, robotics has become
very popular in secondary schools in Finland; computer science equipment is provided by the States
and the local communities in Germany, programming with Scratch is involved as activity in primaryschool in Ireland; learning objectives include programming skills and knowledge of computer hardwarein secondary schools in Lithuania; a course that includes programming with Scratch or Kodu is available
in Portugal computer science education (7th-8th grade) (in Passey, 2017). Teaching of computerscience integrated to other educational subjects is started in primary school in Lithuania in 2017.
In some cases, teachers use computer science activities as a way to demonstrate how the sameproblem or task of real life could be solved by using computers. For example, students have to solve
problem/task during mathematics lesson, they try to solve its own or in pairs and at the end of the lesson
solve the same problem using the particular tool, like Scratch, code.org, Codemonkey, etc. It gives
811

Constructionism 2018, Vilnius, Lithuania

opportunity for students to understand the problem or task deeper or practice in different ways, engage
discussion between students.
Similarly, there was debate as to whether to include activities in which no physical ‘artefact’ wasconstructed rather than learners ‘constructed conceptual understanding. For one activity, a board game,the learners constructed their learning, as the ‘used’ others games, but then went on to create their own
version, very much transitioning through a use, modify, create approach (Lee et al. 2011). This transition
from using to making seemed to be the salient point in which constructivism (Piaget 1970) switches toconstructionism (Papert 1970) and as long as activities included this transition then they could be
included. Therefore, activities such as a Bebras task (Dagienė &. Sentance 2016) would not be included,
as they support development of a CT concept but do not include an element of then making or
constructing of an adapted or brand new version of a task. This is a problematical issue as a teacher
could use a Bebras task as a starting point and then adapt this context into a constructionist activity.
How teachers are using activities in practice was not captured by our survey of materials. But ourapproach could be used as a starting point to further investigate teachers transition of resources fromconstrivist to constructionist activities.
In the case of creating Bebras tasks, Dagienė at al. (2016) mentioned a constructionist anddeconstructionist learning ways. Constructive way of learning is the creation of computer science task
and deconstructionist way of learning is that an computer science concept is analyzed anddeconstructed in its main aspects (discovering why it is computer science).
Our review was of single activities rather than of sets of activities sitting with a progression ofdevelopment of knowledge, skills and understanding. This is a limitation of our study. Similarly, somecoders had considered the use of a construct such as a procedure or function was sufficient to warrant
generalisation could be assigned, whereas for other coders, only if learners had themselves generalised
rather than copied or used someone else's generalisation could that attribute be assigned to an activity.
There are opportunities to review the progression of the constructionism matrix and CT concepts
through time. For example, the Solo taxonomy (Biggs, 1982) starts with the learner having noexperience of a concept and moves to them being able to apply the concept in new and novel scenarios.
There is opportunity to map this taxonomy, or other similar to the constructionism matrix.
Looking at each of the dimensions for our activities, the fact that 52% to 62% of the problems wereassigned a scale of 1
The majority of activities were coded at scale 1 for Problem or Task level at 52% and 61% for coders.
The majority of designs where at scale 2 at 53% to 71%. Similarly, the majority of activities were rated
at scale 2 for the code level at 57% and 61% as shown in Table 7.
Looking at the overall statistics for the constructionism matrix and its dimensions and autonomy scales,
as shown in Table 7, there is as an overall pattern whereby autonomy for learners is most restricted at
the problem stage with around half of activities coded at scale 1. Autonomy for learners is slightly better
during design and code where scale 2 was assigned for around half to three quarters of the time. What
this indicates, for our sample, is that learners have little control over the context of the activities they are
undertaking, they have some opportunity to start to modify the design and the code, but very few
opportunities to be involved in a more free form create where they have control over what they mightmake it and how. Therefore, the level of constructionism might be considered to be LOW if we measure
it based on student autonomy, learners are not moving to the point where they might feel ownership(Lee et al. 2011) as they are not yet making their own new artefacts.
Considering each of the computational thinking concepts and their relationship with constructionism andrelating this to our findings.

ALT- Algorithmic Thinking
Despite having no statistically significant statistics related to our data for the algorithmic thinking CT
concept, there are interesting features of the descriptive data. ALT was our most popular CT concept
coded for the 21 activities, 16 to 19 activities (Table 5 ) were coded as teaching this concept by by our2 coders. When analysing the activities that were matched by the coders to this concept, the majority
812

Constructionism 2018, Vilnius, Lithuaniaof these were related to level 1 problem activities, level 2 design and level 2 coding activities (Table
8). The inter-reliability for ALT was almost perfect agreement (κ=.877 p<0.05) (Table 6).
Considering whether there might be specific reasons why algorithmic thinking might be particularly
suited to constructionism then we turn our attention ideas on progression for this concept and the workby Rich, Strickland, Binkowski, Moran, & Franklin (2017) who suggested a learning trajectory fordeveloping sequence. Rich et al. grouped initial learning about sequence into everyday activities related
to ordering and precision, suggesting that learners transitioned from understanding the world aroundthem for a particular concept to then applying this in a programming context with an intermediate phaseof computational thinking. An example of a goal on this trajectory is “During this trajectory they need tolearn that programs are made by assembling instructions from a limited set’’(page 187). Whether thisobjective is more effectively met through CodeMonkey activities with no choice of the problem, thedesign or the code (as there is only one possible solution to a puzzle), or with Lightbot, with similar
restrictions or through a more open exploration of a ScratchJr activity to draw a square, which still is alevel 1 problem, but which allows learners to select the character that draws the square and the learnercan choose where exactly to start, how big the square might be and can embellish this activity in next
steps, so this becomes a level 2 design and code activity has not been evidenced by our study.

DEC - Decomposition
Just less than half of our activities were assigned to the decomposition concept (dependent on coder
per table 5 ).These were relatively evenly split between level 1 and level 2 on the problem dimension
with a couple of activities at level 3. For design most were at level 1 and most level 2 for the coding
activities (table 8). The inter-reliability was at a substantial agreement (κ=.704 p<0.05) (Table 6). Why
decomposition might have lower pupil autonomy at the design level is interesting, whether this isbecause design is lacking as a clear and identified step may be a contributing factor. A recent review
of resources for teaching computing, concluded that there was a lack of resources with design (Falkner& Vivian, 2015) and a study matching learning goals against research cited design as the most
unmatched goal (Rich, Strickland & Franklin, 2017). We did not specifically ask coders to reflect on this,however one coder noted that ‘Design element was STRONG’(coder 2) in activity 19 but nonexistentin the puzzle based activities which have a predetermined problem and solution algorithm. In
undertaking design students are required to break their problem down into parts (decompose it), theyneed to consider the level of detail that is appropriate (and so are also abstracting) and order these
items (and so are practising algorithmic thinking). Considering how students are experiencing design
they may be copy an existing design, or working in a more constructionism way by creating their own,
perhaps activities could be improved by increasing the element of independent design by learners inthe same way that they are required to do when they are undertaking other subjects such as whenlearning to write (Waite, Curzon, Marsh, Sentance, Hawden- Bennett, 2018).

ABS - Abstraction
Only 4 to 5 activities were identified as teaching abstraction and the inter reliability was moderate(κ=.696, p=0.001), yet abstraction is seen by many as the cornerstone of computational thinking. Wing
wrote “The abstraction process, deciding what details we need to highlight and what details we canignore, underlies computational thinking” (Wing 2008, p.3718). The activities which have abstraction
coded for them are predominantly at level 1 for problem and level 2 for design and code (see table 8).
In our study we have developed upon a particular use of abstraction through our constructionism matrix
and the levels of abstraction, where the dimensions of our matrix represent levels of detail for different
purposes of an activity, the problem, design and code, however that does not relate to how activities
engaged with the teaching and learning of abstraction.
In looking at our coded activities, as with decomposition, learners are using abstractions as they are
given a problem by their teacher or an online system, this is coded at scale 1 and what we saw for mostof our small sample. If learners then follow a predefined design (as with the puzzle activities) they areusing an abstraction and if they are copying or figuring out a pre-defined code solution, again they areusing an abstraction at scale 1.

813

Constructionism 2018, Vilnius, Lithuania

Despite learners using abstractions, few of our activities were coded with this concept. Perhaps this isbecause the concept is hidden from both the teacher and the learner, and only when a specific process
of abstraction is mentioned would it be coded? We have 2 activities coded level 2 for design and 1 forlevel 3 at code and design. This is for activity 19, a ScratchJr which the coder justifies her allocation forby saying‘The design element is STRONG. I wish I could have given this a much higher score - maybe
this needs to be reflected - but the genre was fixed and the language used. I allocated more CT
aspects as kids were deciding on their own designs so abstracting and decomposing as they
were designing. I am not sure they did generalisations - maybe because they were doing repeats... need to discuss more about how we allocated each of the CT concepts - need a clearer
definition.’ (Coder 2)
However, for this same activity coder 1 disagreed and only gave the activity a level 2 for design andcoding and did not allocate decomposition or abstraction as concepts taught.
The comments made by coder 2 implies a constructionism approach was being taken, as learners were
‘deciding on their own designs’ however, whether this means that learners will make more progress
rather than using someone else's is yet to be robustly evidenced for this age group of learners.

GEN - Generalisation
This concept was assigned the fewest times from all of the CT concepts, only 1 to 3 activities werecoded as GEN and the inter-reliability was not reported by SPSS as there were no agreements andnumbers were small. Even where it was assigned there was a lack of certainty of the allocation, one
coder remarked in the justification notes (Appendix 1) about Activity 21 (Lightbot) ‘‘Although thisteachers procedures - is this generalisation or is it decomposition??? As the purpose is not to reuse fora logical reason but to reduce code blocks used.’’ (Coder 2) The same coder raised the same question
about Activity 19’ I am not sure they did generalisations - maybe because they were doing repeats ...
need to discuss more about how we allocated each of the CT concepts - need a clearer definition.’’Coder 2)
Again this was raised for Activity 24:‘If we are considering repeats as decomposition then this is used, I am assuming then as it
moves to functions then we can say that generalisation is used HOWEVER the level of use of
these is very low. On solo it's really just first level as using what is provided - maybe unistructural,
whether we think this use of a procedure that someone else made is generalisation I am reallynot sure it will be interesting to see what the other coders thought and whether the learning ofgeneralisation here has been done in a constructionist manner at all.’’ (Coder 2)
Two issues are raised by these comments, firstly, is a function or procedure which is used solely to
reduce the number of lines of code for something that can be repeated a generalisation or adecomposition used within a repeat? Does generalisation need to be where we are “transferring aproblem solving process to a wide variety of problems” CSTA & ISTE , 2011))? Has this confusion arisen
as the term pattern has been associated with generalisation? This term may have been introduced tosimplify language and as an instructional approach in a progression of learning for generalisation to
encourage the spotting of patterns that might then become generalisations. However, during thisprogression reusing code to simplify code within a single solution, such as in the activity 19,21 and 24
is this generalisation?
Secondly, does using elements created by someone mean we are learning about the process involvedin creating those elements?

EVA - Evaluation
Evaluation according to Bloom is the highest order thinking skill (Bloom, 1957) which learners encounteras they gradually spiral through a succeeding progression of more complex material. In our review ofthe 21 activities, 7 to 9 activities were identified as teaching evaluation with substantial inter-rater
reliability (κ=0.69 p=0.001). Of those activities assigned this concept by the two coders, over half were814

Constructionism 2018, Vilnius, Lithuania

rated as level 1 (54%) and a third (31%) level 3 for the problem, nearly three quarters (72%) were rated
level 2 for design and 82% at level 2 for coding. This is quite a wide spread, with some tendency to thehigher levels for code and design. Perhaps implying that evaluation of the code was more prevalentthan evaluation of a design. But this is a broad assumption which needs more careful analysis
particularly around what we might mean by an activity having evaluation being assigned as teachingthat CT concept. In our definition of evaluation the following phrases were provided to help coders
decide if evaluation was a skill USED in the activity ‘Find an appropriate solution’ Finding the bestsolution’ ‘Deciding whether the solution is fit for purpose’ ‘Deciding whether the solution is the mostefficient one’. This list implies an order of progression. Surely the first of which must have been seen in
any activity that had some kind of solution. Therefor all activities, if they solved any kind of task or activity
should have had an element of evaluation. It may be that coders discounted this first example and only
assigned where there was a ‘best solution’ considered, or if there was a design to which the solution to
could be compared to decide if it was fit for purpose. There appears to be more work to be done in
defining the rationale for assigning a concept as being associated with an activity, perhaps as a gradingof association.
Our quantitative data should be viewed with much caution as our sample size was small at only 21
activities, and only 3 authors coded, each doing a mixture of 1st and 2nd coding. However, this small
number of activities became 42 cases to review researchers views of the attributes of activities. Our
approach for classification appears to have some reliability, as there was substantial agreement acrossthe variables and coders. However, for some activities there was very different allocation of theconstructionism scale, such as for the activity 25 the JavaScript onscreen activity which was the only
activity to be awarded more than a scale of 3. It was allocated a 1-2 by one coder for all three dimensions
but a 5 for just the problem and coding and a 1 for design by the other coder. The rationale for this may
be that the activity sat within an overall complete unit of work, which eventually might lead students tobe given a chance to create a new artefact using any product, in any context, but within the activity
reviewed this was not the case. This indicates an issue with how an instrument to measure theconstructionist aspect of activity might scope the boundaries of an activity.
Despite the limitations of our quantitative work, a number of interesting data results have been revealed,such as there being no statistically significant differences between activities with each of the CT and CS
concepts across the scales of each of our problem, design and code dimensions of our constructionism
matrix and yet there were differences for other aspects, including the activity type, such as lesson plancompared to onscreen student activity and also the artefact created such as a physical artefact
compared to an onscreen activity or an unplugged activity. A resulting suggestion might be that CT &CS concepts are merely the content that is being delivered by a constructionism approach, the successof this approach is less impacted by the underlying material being delivered but more by the techniquesapplied to deliver it, such as through human (teacher) mediated activity as opposed to a symbolic(system) mediated (Kozulin, 2003) event, or through the creation of a physical artefact compared to a
onscreen or unplugged artefact.
Our contribution to this field is to have suggested and trial a new mapping tool incorporating a newframework, called the constructionism matrix, for reviewing activities which are used in the teaching andlearning of computing in terms of constructionism and computational thinking. We have identified
limitations with our framework and with our trial, but from its development and using it with a small
sample of lesson plan and online student activities (n=21) targeted at K-5 learners, we have been able
to report the trial results of its first use, discuss problems encountered and suggest opportunities for
improvement of both the mapping tool and the matrix.
We suggest next steps should be to further refine the constructionism matrix, by adding % of use ofeach scale and more closely defining the scope of the activities reviewed, it should then be used tosurvey a larger population of resources. Similarly, more work is needed to reflect on the depth oflearning of CT & CS concepts perhaps in a similar scale as the student autonomy but perhaps using
Solo taxonomy or other more finely grained frameworks of a degree of learning. Our mapping tool willthen have a combined framework, which might provide more insight into the potential relationshipbetween constructionism and computational thinking.
815

Constructionism 2018, Vilnius, Lithuania

Acknowledgements
We would like thank you Janne Fagerlund, University of Jyväskylä for contributing to this working group
paper.

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Csizmadia, A., Curzon, P., Dorling, M., Humphreys, S., Ng, T., Selby, C. and Woollard, J., 2015.
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system for educational tasks in informatics. Informatica, 28(1), 23-44.
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Edition. Routledge.
Cutts et al., 2012. The abstraction transition taxonomy: developing desired learning outcomes throughthe lens of situated cognition. In Proceedings of the ninth annual international conference on
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Giordano, D., Maiorana, F., Csizmadia, A.P., Marsden, S., Riedesel, C., Mishra, S. and Vinikienė, L.,2015, July. New horizons in the assessment of computer science at school and beyond: Leveraging on
the viva platform. In Proceedings of the 2015 ITiCSE on Working Group Reports (pp. 117-147). ACM.
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Vladimir, and M. Miller, Suzanne, eds., Vygotsky’s educational theory in cultural context, Cambridge
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Papert, S. (1980). Mindstorms: Children, computers, and powerful ideas. Basic Books, Inc.
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Derived from Research Literature: Sequence, Repetition, Conditionals. Proceedings of the 2017 ACM
Conference on International Computing Education Research (pp. 182–190). ACM.
Kathryn Rich, Carla Strickland, and Diana Franklin. 2017. A Literature Review through the Lens ofComputer Science Learning Goals Theorized and Explored in Research. In Proceedings of the 2017
ACM SIGCSE Technical Symposium on Computer Science Education. ACM, 495–500.
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Selby, C.C., Woollard, J.: Computational thinking: the developing definition, pp. 5–8 (2013)
Settle, A., & Perkovic, L., 2010. Computational Thinking across the Curriculum: A Conceptual
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Standl, Bernhard, 2017. Solving Everyday Challenges in a Computational Way of Thinking. In:
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Jane Waite, Paul Curzon, D Marsh, Sue Sentance, and A Hawden-Bennett. 2018. Abstraction in action:
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in Education, 13(2), 323-332.

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Appendix 1 Coded activities & Qualitative notes
Acti Activity/
vity ApproachId
to Map1

Rationale forchoiceActivity Description/Justification
Description/Just
Type/Additional Comment(s) coder ification
1/Additional
Comment(s)coder 2

Activity:Co Developed as an
Gamedy Robyunplugged
resource for
URL:http://
teaching
codeweek.i
programming
t/codyroby/concepts, initiallyfor Code Week
Italy and has been
adopted by
CodeEU for Code
Week

This activity allows learners toeither play a predetermined
unplugged coding game ordesign/construct their own
unplugged coding games.

Many of theexample games
are algo rather
thandecomposition.
But you could
When learners play the gamecreate games thatthey have to think and abstract
also supported
their strategy (conceptual
decomp. There isartefact) and breaking down
this issue of the
moves into potential smaller'surface' levelmoves. Once a move is made,understanding
they a player will need toand depth that
evaluate when has occurred
could be brought
and think about their next move.
about byThis becomes an iterative
discussion, andprocess as the game is play toalso by creating
an outcome.
games. I am
When a learner designs a game finding it hard tothey create rules for playing that say anything isgames and may create new
NOT concept... ascards (physical artefact).
it is taking you to
1st Solo stage ofAcademic Reference(s): 2injust introducing a
Englishconcept by
Ferrari, F., Rabbone, A. andexperience. Also
Ruggiero, S., 2015, September. there is this whole
Experiences of the T4T group in problem of
primary schools. In The
whether this isProceedings of International
coding or an
Conference on Informatics inalgorithm. The
Schools: Situation, Evolution
information
and Perspectives-ISSEP.
processing agent
Klopfenstein, L., Fedosyeyev, A. is the person who
and Bogliolo, A., 2017. Bringing executes it... This
could be taken to
an Unplugged Coding Cardthe next level byGame to Augmented Reality.
actually
INTED PROCEEDINGS,
implementing
pp.9800-9805.
these in scratch...
or other block
based
programming
languages... itwould beprobably be
819

Constructionism 2018, Vilnius, Lithuania

harder than I
think.
2

Activity:
Top 10 of
LessonBarefootresources
plan
crazy
according to Royalcharacters - Society report.
unplugged Funded by DfE for
activity
resources forteaching
primary teachers toaboutteacher new
algorithms. computing
curriculum.
Selected as
specifically aboutteaching
algorithms using
an unplugged
approachThis activity introduces
algorithms by teachersmodelling the creation of asimple set of instructions(algorithm) to draw a character.
Pupils follow the teacher'salgorithm and then create theirown. The teacher models how
to evaluate the precision andcompleteness of the algorithm
to introduce the idea of
debugging. Children thenconstruct their own algorithm fortheir own crazy character. They
test out their friends algorithms
to help them debug them. One
could say that there isabstraction, as pupils have toconsider what is most importantto their character, and that they
think of terms which are an'abstraction' of how how to drawthat part of the object e.g. draw
a triangle. However, abstraction
is not the focus of the activity.
The focus is on introducing the
vocabulary and concepts ofalgorithm and debugging andmaking a link between theseterms and familiar instruction
writing in English and Art
lessons. Pupils aredecomposing the task as they
think of the individual steps andwhat to do next. They aresequencing their instructions,
therefore one could link to thisprogramming construct. But
again this explicit link is not
made. One could argue that the
introduction of the term
algorithm and debugging isconstructing an abstract
concept. However we need toconsider whether this is going
too far with the idea of
constructionism (which we mayneed to further explore in ourpaper). The process ofevaluation is explicitly built into
the lesson through a box on the820

Constructionism 2018, Vilnius, Lithuania

worksheet requiring anotherpupil to test the algorithm out so they are becoming thecomputational agent (could link
to Wing and Denningargument). A physical artefact iscreated by the learner when
they construct their algorithm.
3

Activity:
BarefootbeebotbasicsTop 10 of
Lessonresources
plan
according to Royal
Society report.
Funded by DfE forresources forprimary teachers toteacher newcomputingcurriculum.
Selected as
specifically aboutteaching
programming using
programmable toys

In this activity pupils construct
and solve programming
challenges for a simple
programmable toys. One could
say that learners areconstructing concepts(conceptual artefacts) in thatthey are constructing theconcept of an algorithm andthen constructing the concept ofa program as they implement
the algorithm as code. But asstated about I am not sure about
whether this is all learning.
There is a question here as towhether the algorithm is in factcode. As there is often a 1:1
mapping between the writtencommands and the buttons
pressed (commands) whichmake up the code. This issuggested to be a group activity,with children taking on different
roles of the algorithm creatorand the implementer of thealgorithm (the programmer), thisis an early introduction of pair
programming. The requirementof the pupil's to talk about theiralgorithm and whether it will or
will not work through the use of
a fake bot is significant. As they
are using a tangible
manipulative that represents the
programmable device. Only
when they have tested anddebugged their algorithm dothey then implement it as code.
The use of the fakebot is
important perhaps in interrupting
a trial and error (random)debugging approach and could
be argued to support thedevelopment of the 'conceptual
artefact' of an algorithm (but I
am not sure). The important821

Constructionism 2018, Vilnius, Lithuania

thing for constructionism is thatthey are creating their own
challenges - get the bot from thelight green square to the yellow
diamond avoiding all the circles.
The use of challenge cards isstart to develop further
algorithmic thinking concepts,
such as longest route, shortest
route. The pause on, avoid
encourage development of theskill of decomposition.
Evaluation is encouraged
through explicit use of the
fakebot and reference todebugging of the implemented
code. However, the amount of'freedom' that pupils have in thecontext of the task will dependon the choices made by theteacher. If they use pre-mademats and no challenge cards,
there is very limited pupil'choice', if they ask learners tocreate their own mats (assuggested in the extension)then learners can havepersonally meaningful contexts.
In terms of the design the
choice is limited as is theimplementation.
4

Activity
Barefoot
Viking Raid
Animation

Top 10 of
Lessonresources
plan
according to Royal
Society report.
Funded by DfE forresources forprimary teachers toteacher newcomputingcurriculum.
Selected as
specifically aboutteaching
programming using
onlineprogramming
language focusedon teachingsequence andrepetition

In this introductory programming
activity pupils create a simple
animation using sequence. The
algorithm is developed bylearners taking a predefinedbackground and set of spriteand deciding on their movement(action) and what they say(display) on a paper based
design. Therefor there is very
limited learner involvement in
the definition of the task, or thedesign as they are constrainedby the context and thecommands being used. The
code is developed by thelearners being shown acommand at a time by theteacher modelling its use. The
learners then tinker with thecommands that have beenmodelled. This is guided
exploration and then implement822

Constructionism 2018, Vilnius, Lithuania

their design. Again the learners
have limited choice on what
they implement beyond thebounds of the commands
introduced. For extension
further commands (repeats) areintroduced for learners to extend
their design and implementation
within the context.
5

Barefoot
Top 10 of
Lessonmaths quiz resources
plan
according to Royal
Society report.
Funded by DfE forresources forprimary teachers toteacher newcomputingcurriculum.
Selected as
specifically aboutteaching
programming using
onlineprogramming
language focusedon teaching
selection andvariables

In the introduction to selection
and variables activity learners
create the algorithm with apartner, using their knowledgeof how simple quizzes work.
The task is preset as a maths
quiz with the requirement
broadly to ask questions, checkthe answer and keep a score.
The context is pre-set. To
explore the commands thatmight be used to implement thealgorithm learners are provided
with a scratch file that has
useful commands in thescripting area. BUT they are notsnapped together. Learners
then explore these commands
to discover how they mightwork. Learners are 'constructing'their understanding as they
explore. However, this is guided
as they are given thecommands to start with. Again
one could say they are
constructing knowledge of theselection construct as aconceptual artefact. Learners
decided on the questions theywant to include in their quiz, but
the context and genre isconstrained. For extension abuggy program is provided witha repeat and use of randomvariable block.

6

Barefoot
one ofnetworkones

In this activity pupils create theinternet by physically enacting
the role of clients, routers andservers passing packets of dataaround. The concept of what is
the internet, packets of data andthe role of the components in a
network are being learned. So
here the pupils are constructing

Top 10 of
Lessonresources
plan
according to Royal
Society report.
Funded by DfE forresources forprimary teachers toteacher newcomputing823

Constructionism 2018, Vilnius, Lithuania

curriculum.
Selected as
specifically aboutteaching networks

an unplugged representation ofa network, but the objective isconstructing conceptual
understanding. The data that ispassed around is supplied by
the teacher as are thecomponents within the network.
Therefor the pupils have no
choice on what the context orgenre or data is. The planningstates that the lesson supports
the development of abstraction
as the learners are creating an
abstraction of the real internet.
However, they are broadly
acting out someone else's viewof an abstraction.

7

Code-IT I
am special
ScratchJr

Top 20 of
Lessonresources
plan
according to Royal
Society report.Very
popular resource
created by a CAS
master teacher,teacher trainer.
This item selected
as it is for
programming withyoungest age
groups with scratchjnr

In this introduction toprogramming activity learners
create a simple animation about
what makes them special. The
idea of an algorithm being thesequence of statements thatthey have written, or had
scribed for them on a simple
planner, with the order shownby numbering. The context for
the activity is set, as is thegenre, the planning approach
and what hardware andsoftware is being used. Thissimilar to other introductory
activity. However, because thecontext is about the children's
personal lives and somethingthat is relevant to them thisactivity may be seen as being
more constructivist? Is this true?
Therefore even though there is
no more/ or less freedom interms of choices available, therelevance may be the thing that
influences what isconstructivist?

8

Code-ITJam
Sandwich
Bot

Top 20 of
Lessonresources
plan
according to Royal
Society report.Very
popular resource
created by a CAS
master teacher,teacher trainer.
This item selected

In this activity the teacher
pretends to be a robot and is
instructed how to make a jam
sandwich by the pupils. The
teacher shows how instruction
must be precise for theenactment to be successful. The
task, genre, etc. are allpredetermined. The learners are824

Constructionism 2018, Vilnius, Lithuania

as it is for teachingalgorithms - very
popular.

constructing their understandingof an abstract concept - a
program for a computer tofollow.

9

Code-IT
Crumbleactivity

Top 20 of
Lessonresources
plan
according to Royal
Society report.Very
popular resource
created by a CAS
master teacher,teacher trainer.
This item selected
as it is for teaching
physical computing
with aprogrammablemicrocontroller

In this activity learners make asimple display for any scene
they are interested in andprogram the lights using asimple programmable controller
called the crumble. This is avery simple design andtechnology, science andcomputing cross curricularlesson (plus art). The design isrecorded as series of sentence
which a says what the program
will do in terms of controlling thelights. The teacher models thesentence - such as 'My program
will 'make lights flash byreplacing the stars in my picturewith programmable lights. The
user will see the stars twinklingin the sky and the moon lit up
with a bright white light'. The
teacher models each step of theaddition of the components tocreate the circuit, and help
cards are provided for pupils tofollow. An example script isprovided for pupils to predict
what the code will do, and thento run and then to investigate
before writing their own code.
The physical wiring, andprogramming is limited but thecontext is not.

10

Code-IT
How aninternet
searchworks

Top 20 of
Lessonresources
plan
according to Royal
Society report.Very
popular resource
created by a CAS
master teacher,teacher trainer.
This item selected
as it is for teaching
using search in an
unplugged way

In this activity pupils complete aworksheet that requires them tofind common classroom items
e.g. scissors and write down
where they find them. They thenrank the locations. This
unplugged activity introduces
the idea that search engines
present information in an order,a rank, that is theoreticallyuseful for the person wanting tofind things. It is constructing an
abstract concept, through afamiliar activity. The ranking istested by a pupil from anotherclass who is not familiar to the825Constructionism 2018, Vilnius, Lithuania

class environment. The pupils
have no control over thecontext, genre or objects they
are finding and the locations
they are ranking, but they dodecide on what the criteria forranking is. As with the network
activity you could argue that an
abstraction is being learned
about.
11

Code-IT
Magic
CarpetTop 20 of
Lessonresources
plan
according to Royal
Society report.Very
popular resource
created by a CAS
master teacher,teacher trainer.
This item selected
as it is for teaching
programming.

In this introductory scratch
activity an example game isprovided for a specific context,
but teachers could choose - orpossibly pupils too - to create
their game in different context(examples are provided). This
simple offering of a range ofother contexts may besignificant in terms of choicegiven to pupils for their design
and then build. 'Cross Curricular
Focus this can be adapted to fit
in with many topics or projects.
It could be a lost cave dweller
trying to find their way home
without falling from the rockwalkway, a bee that has topollinate each flower or aspaceship that has to visit every
planet. (See Travel Europe forGeography version)' http://codeit.co.uk/wpontent/uploads/2017/
01/MagicCarpetPlanning.pdf
Had the other products given
this simple option then theconstructivist opportunities
would have been increased.The
general approach for theplanning is to ask learners to
USE the example program andspot the main features which
they then list, this is adecomposition or abstraction
perhaps of the main features.
The teacher then models
creating each feature and pupils
then explore the commands
associated with each featureand implement this part and tickoff their list. The actual choicefor implementation is low, as is
the design. However, thecontext may be varied (if elected826

Constructionism 2018, Vilnius, Lithuania

by the teacher)
14

Switched on Top 10 of
LessonComputing resourcesPlan
We areaccording to Royal
Detectives Society report.
Funded by a
private education
publisher. A
popular and freesample.

In this activity pupils reademails, create emails andrecord information in a
spreadsheet to solve a mystery.
The emails are all pre-preparedby the resource creators, pupils
construct reply emails andsummarise data in a
spreadsheet. Teachers canadapt the context for the activity
but pupils are constrained by
the flow of the events of theactivity. They are constructing
an understanding of email
address formats and other ideas
on emails. No specific reference
is made to computational
thinking, but in extracting key
information one could say that
abstraction is being practised(tenuous), and also the order inwhich things are happening
could be sequence and CT(tenuous). This brings in play
the whole discussion about what
is CT and if it is thinking skills if
you are not then moving on to
use those skills in order toproduce a program. And if theskills are not revealed tolearners - are they CT. Else alllearning where you have somedependency of task could be
ALT.

15

Switched on Top 10 of
LessonComputing resourcesPlan
Learn toaccording to Royalcode
Society report.
sampleFunded by a
private education
publisher. A
popular and freesample.

In this introduction to Scratch
activity pupils follow step bystep instructions to create a
simple animation. At the end
pupils are asked to change
specific simple aspects and add
extra suggested commands. A
set of questions at the end ask aseries of yes/no questions toencourage thinking about the
task.

827

Constructionism 2018, Vilnius, Lithuania

16

Switched on Top 10 of
LessonComputing resourcesPlan
We areaccording to Royaladventure Society report.
gamersFunded by a
private education
publisher. A
popular and freesample.

In this introduction to Python
pupils create an a simple
interactive story which prints
statements then the user
answers a yes/no answer tomove onto the next statement.
The context for the story is open
to the teachers and pupils todecide upon but the structure of
the code that will be written isprovided. The pupils replace
displayed text in the example
code given, which is modelledby the teacher with their text
from their design. The conceptof text based programming in
introduced here by pupils being
given and then typing in python
commands. It is not quite copy
code, but it is close. Having theirown story context increases thedegree of personal choice.

18

https://www. This game isOnlineplaycodemo recommended for activity
nkey.comprimary school in
Lithuania
according the
project “Informatics
in primaryeducation“
https://informatika.
ugdome.lt/en/about-project/In this game students learn towrite code in programming
language called CoffeeScript.
The goal for student is to helpmonkey to catch bananas bywriting lines of code. The gamecover this computer science
concepts: loops, objects,
funcion calls, boolean
conditions, function, conditions(if, if else).

CodeMonkey
Awarded Best
Coding &Computational
Thinking Solution(https://www.playc
odemonkey.com/bl
og/2018/06/20/cod
emonkey-awardedbest-codingcomputationalthinking-solution/)

This actuallyteaches a
misconception interms of CT as it
encourages
learners NOT todecompose a
problem, as if you
want to try out 1
or 2 steps it saysthat is wrong. The
task is set there is
In the paid version teacher
NO mention of
could get the curriculum
design and the
explanation (teaching progresscode has to be
step-by-step explanation), track
more or lessstudent progress. In the free
identical to what
version only 30 challenges,
was required bycurriculum explanation are
the developer.
available. Curriculum has lesson
You may be outplans, workshops
by 2 or 3 steps,
but this is notparticularly about
choice. At
challenge 14 wehave the firstcode reading withcode that is in thewrong order.
Again this isteaching
sequence (flow of828

Constructionism 2018, Vilnius, Lithuania

control) but notmuch else. At
challenge 16 it
suddenly
introduces theterm argument - itdoes not explain
this AT all. At
challenge 21 they
introduce repeat
loops I don't know
if there is asandbox at any
point or when thisactivity stops
19

Animated
This tool is
LessonGenres:reccomended for plan
https://www. primary school inscratchjr.or Lithuania
g/curricula/a accroding thenimatedgen project “Informatics
res/full.pdf in primaryeducation“
https://informatika.
ugdome.lt/en/about-project/ In addition
I looked to thisarticle:https://ase.tufts.ed
u/devtech/publicati
ons/Portelance2015-ConstructingScratchJr.pdf

During the curriculum, students Lessons 1 is 1,1,1
created projects within three
but lesson 2‘‘animated genres:’’ collages,
already becomes
stories, and games During
2,2,2 but withlessons students get to know
very very close
about the main ScratchJr
confines. As
features and programming by
moves to 3 thenblocks. Students are able topupils can choose
create their own projects by
their characters applying concepts learned in
but still tightly
module lessons. In the firstcontrolled on
module during teacher andprogramming
students discussion, teacher
blocks. The
explain the meaning ofdesign is throughinstructions and sequences.
physicalAfter discussion students learn enactment.
to move or use blocks in the
Lovely use of
scripting area, select blocks
getting kids tocategory. In the second module predict what code
students learn to create
might be used tosequences using variety of
implement thedifferent motion blocks. Teacher running of thedemonstrate several example
code. Then therewith different sequences.
is a lovely open
Students learn to use the start challenge whereon green flag and end block,they make achoose new characters.
collage... an
Teacher demonstrate theanimation. Thensituation as unplugged activity next few lessons
and later ask student to write a introduce further
program.
concepts - guided
exploration andagain an open
project wherelearners havemore control tocreate a story
type animation.
The activities are829

Constructionism 2018, Vilnius, Lithuania

creative - evenwith the jungle
speed wherelearners haveopportunity tochoose an animal
and then makethem fast or slow
and then racethem. This
planning is sodifferent tocodemonkey, or
the khan java
script activity. The
design element is
STRONG. I wish I
could have given
this a muchhigher score maybe this needs
to be reflected but the genre was
fixed and thelanguage used. I
allocated moreCT aspects askids were
deciding on theirown designs so
abstracting anddecomposing as
they were
designing. I am
not sure they did
generalisations maybe because
they were doing
repeats ...
20

Learning
ScratchJr
via
PlaygroundGames

https://ieeexplore.i Lesson
eee.org/stamp/sta plan
mp.jsp?tp=&arnum
ber=8363498
https://dl.acm.org/c
itation.cfm?id=2532751

In this curriculum (lesson plans) Starts with very
teacher shows features ofsimple activities
ScratchJr and students create a 1,1,1 draw a
specific playground game using square, thenpresented features. Lessons
make the cat walkplans is based on the discussion diagonally and do
and practical programminga cartwheel
ScratchJr environment. Teacher 1,1,1/2 By lesson
can adapt prepared curriculum for it is slightly
or extend lessons.
less 'copy mydesign' - which isverging on copy
code to become
sing the Hokey
Pokey and a830

Constructionism 2018, Vilnius, Lithuania

dancing cat - thisis harder than it
looks - they could
have made it
have far morechoice by saying
any song. The
amount that iscovered in these
8 sessions isquite ridiculous. It
forces copy codeby having far too
much covered particularly as thisis for such very
young learners. I
feel as though thelack of creativity
until the later
lessons is not
being reflected in
the coding of theactivity for the
research. Thoseless able pupils
are very muchbeing restricted
here - andactually I don't
believe thatyoung learners
would not go ofand do their own
thing much earlier
on.
21LightBot is According
Onlinea puzzle
https://venturebeat. activity
gamecom/2014/06/03/12
based on-games-that-teachcoding.
kids-to-code/
URL:
andhttp://lightbo
https://dl.acm.org/ct.com/index.
itation.cfm?id=301
html
7728

Lightbot is an educational game There is NO
for kids that introduces several choice or
principles of programming.
creativity here theChildren will practice concepts problem, design
like sequence, conditions, and and code are allloops without typing or coding. predetermined. It
Use problem solving skills towill be interesting
complete the puzzles
to see what the(http://www.abcya.com/lightbot. other coder has
htm)
coded. Although
this teachers
procedures - isthis generalisation
or is itdecomposition??
? As the purpose
is not to reuse fora logical reason
831

Constructionism 2018, Vilnius, Lithuania

but to reduce
code blocks
used... it isdependent on you
knowing yourright and left andhaving good
spatial awareness- but I like themusic - the sprite
box is not inlightbot now - thishad will loops be
associated withdecompositionand procedures
withgeneralisation again justbecause I used
one for someone
else's problem
does that mean I
can generalise
the concept ofdecomposition
etc... we shouldhave used solowith CT.
23

ChooseYour Own
Adventure

It focuses on
Lessonexcellent
plan
instruction withgroup andindependentpractice activities
that build creativity,communication,and collaboration.
Their goal is toreach all students
and see computerscience become
part of a completeelementary
education (URL:
https://repositorio.g
rial.eu/bitstream/gri
al/1068/1/TEEM_2
017_paper_03_preprint.pdf)

Students tell a story by following This is interestinga sequence, using statement.
as theBrainstorm is used to find out
implementation is
the meaning of computer
onscreen but via
science and technology
google forms. The
connecting daily life. Students
focus is on
create an anchor chart with CS learning flow ofwords. Teacher introduce
control andconcept "conditions" by
selection. designcode.org video example andis strong - it's ausing decision tree on a an
shame that theanchor chart or board.
google form video
Discussion about story, teacher does not
introduce Kodable worldexemplify using
storyline and decision tree
the design. I think
template, present how to fill in
evaluation is usedthe decision tree graphic- but this isorganizer to guide their writing probably true now
process. Teacher present
I come to think ofgoogle forms.
it for any

https://ieeexplore.i
eee.org/stamp/sta
mp.jsp?tp=&arnum832Constructionism 2018, Vilnius, Lithuania

ber=7860198
Accordinghttps://www.scienc
edirect.com/scienc
e/article/pii/S22128
68917300338
24

FrozenCode.org provides Online
sequences ofactivity
videos and puzzles
where users
control charactersfrom populargames like Rovio’s
Angry Birds
or movies like
Disney’s Frozen
with drag-and-drop
programming.
Using
programming bydemonstration toseamlessly
integrate gameplay
and educational
content (citationfromhttps://dl.acm.org/c
itation.cfm?id=310
2106)

In this activity teacher introduce
these concepts: code,
debugging, program. "Using
programming by demonstration
to seamlessly integrate
gameplay

A pure copy code
activity, what isinteresting is theage groups ofFrozen andrequired reading
vocabulary andand educational content"
maths. If we are(https://dl.acm.org/citation.cfm?iconsideringd=3102106)
repeats as
decomposition
then this is used, I
am assuming
then as it moves
to functions thenwe can say thatgeneralisation isused HOWEVER
the level of use of
these is very low.
On solo it's reallyjust first level asusing what isprovided - maybe
unistructural,
whether we think
this use of aprocedure thatsomeone else
made isgeneralisation I
am really not sureit will beinteresting to see
what the othercoders thought
and whether thelearning ofgeneralisation
here has been
done in aconstructionist
manner at all. I
am not sure ...
this learning is so833

Constructionism 2018, Vilnius, Lithuania'hidden' as anexperience rather
than anything thatis noted or over.
The amount ofreading skill ishigh. It's such ashame there is
not more code
reading. YOU
HAVE TO DO 19
activities before
you are finallygiven a sandbox I was loathe toallocate a level 2,
as this only
occured in the
final 20thchallenge and up
to this point there
had been
absolutely no
student autonomy
at all , The design
really is none
existent and is not
at all exemplified
during theactivities 1 to 19
except fromdefining what you
have toreproduce. You
can't create yourown procedures...
missedopportunity
25

Intro to JS: According Khan
Drawing & Academy answer
Animation about the mostpopular course

OnlineactivityThis is JavaScript course.
During this course students
have to work through thetutorials, solve challenges, trysome projects (start with anexisting project or modify it).
Writing code canvas is updated
according written code. This
course runs through thefollowing sections: Intro to
Programming (which contains avideo explaining theirapproach), Drawing Basics,
Coloring, Variables, Animation
Basics, Interactive
Programs,Text and Strings,

There are very
tightly controlled
initial tasks where
you have toreproduce what
they want you tocode. It's notprecisely copy
code. But it isvery close. It doessuggest to create
your design of thedrawn image ongraph paper first.
You have no
choice on the834Constructionism 2018, Vilnius, Lithuania

Functions, Logic and If
problem and very
Statements, Debugging
little choice inPrograms, Looping, Writing
terms of the
Clean Code, Arrays, Objects,
design or the
Object-Oriented Design,code. Algorithms
Becoming a Better Programmer. are not discussed
explicitly but
sequence isbrought toattention of thelearner. At theend of thesequence youthen have acompletely free
option. It is very
boring watchingsomeone code. At
the end of thecopy code
activities it thengives you a set ofpredefined tasks
to choose fromand a starter
design and code
to then amend. I
chose 10
onwards for age
as the maths andreadingrequirement isquite high as it is
text based.I was
loathe to allocate
a level 2, as this
only occured in
the final
challenge.

Please note that not all coders completed the justification part.

835

Constructionism 2018, Vilnius, Lithuania

Appendix 2 Mann Whitney U statistics comparing concepts, artefact and activity
types to constructionism scale for each of the constructionism matrix
dimensions.
Note scales where a coder had rated as 1 to 2 this has been added to scale 2 and where a coder rated
1 to 3 this has been coded as 3. Scales 4 and 5 have been removed as only 1 activity was coded to thisscale by one coder, and the second coder rated this same activity as 3.

ProblemDesignCodep=.083 n=39 U= 144

p-.714 n=36 U=28p
wascomputednotp=.389 n=39 U=22

p-.665 n=36 U=41.5

p
wascomputednot

Not computed

not computed

not computed

C&N:

p=.179 n=39 U=40

not computed

not computedISS:

p=1. n=39 U=112

p=.611 n=36 U= 88.5

p=.914n=30 U= 65

p=.322 n=39 U=84.5

p=.199 n=36 U=72.5

p=.067
U=28.5p= .701 n=39 U=166.5

p=.876 n=36 U=97.5

p=.914 n=30 U=60

p=.149 n=39 U=225

p=.814 n=36 U=168

p=.439U=127

n=30

p=.268 n=39 U=206.5

p=.253 n=36 U= 190

p=.171
U=137

n=30

GEN:

p=.389 n=39 U=22

p=.495 n=36 U=23

p=.331 n=30 U=15

p=0.007 n=39 U=125 Z = p=.576 n=36 U=76
-2.952 r=.04 (medium
effect size

0=.930 n=30 U=54

Physical

p=.489 n=39 U=159

p=.280 n=36 U=111

p=.872
U=84.5

Unplugged p=.329 n=39 U=149

p=.327 n=36 U=174

p=.441 n=30 U=97

p=.692 n=39 U=132

p=.475 n=36 U=151

p=.713
U=109

ALP

DDS

Computerscience (CS)concepts
CPH:

ABS:
ALT:
CT concepts DEC:

EVA:

Artefact type
(Mann
Whitney U)

Onscreen

Concept

n=30

n=30

n=30

836

Constructionism 2018, Vilnius, Lithuania

Artefact type

Overall(Kruskal
Wallis with
Pairwise
Bonferroni
Correction
comparing
Physical
onscreen
andunplugged)

p=.012 n=39 χ 2=8.876 p=.367
d.f=2
d.f=2

n=36 χ 2.007 p=.968
n=30 χ
2=0.065 d.f=2

unplugged to physicalp=0.013 Z=2.862, n=17
U=5.678 r=.694 (large)
onscreen to physicalp=0.016Z=2.785 U=
5.328
n=29
r=.517(large)unplugged- on screen
p=1
Z=3.502
U=.402
Activity Type(only
comparing
Lesson plans
and Online
as there was
onlyoneboard gameactivity)

n=39

Lessonp=.535=38 U=94.5
Plan/
OnlineStudent
Activity

p=.018, n=35 U=54.5, p=.048,n=29
Z=-2.358
U=37,
Z=-1.978r=.398(medium)r=.367(medium)

Mann
Whitney U
Activity Type Overallwith Kruskal
Wallis with
Bonferroni
correction
OverallComparing
Pairwise
Lesson Plan,
Online and
Board
Game)

p=0.164 (χn=39, d.f =2

2=3.619, p=0.009 (χn=36, d.f =2

2=9.363, p=0.009(χ
2=9.373, n=30, d.f=2

Student Onscreen
Board game

- Student Onscreen Board game

p=0.021
Z=8.816 p=0.010 Z=6.166
U=2.690 n=36
U=2.946 n=30
lesson plan board game lesson plan board
p=.173Z=3.485 game
p=.061
U=2.234
Z=35.831 U=2..946
Student
lesson plan
p=.076
U=2.234

Onscreen- Student Onscreenlesson plan
Z=3.485 p=.228
U=1.775

Z=2.617837

Constructionism 2018, Vilnius, Lithuania

WG2: Developing Constructionism, or a New
Learning Concept, across the Ages
Don Passey, d.passey@lancaster.ac.uk
Lancaster University, Lancaster, UK

Loice Victorine Atieno, atienomunira04@gmail.com
Eötvös Loránd University, Budapest, Hungary

Wilfried Baumann, baumann@ocg.at
Austrian Computer Society, Vienna, Austria

Valentina Dagienė, valentina.dagiene@mii.vu.lt
Vilnius University, Vilnius, Lithuania

Abstract
Curricula in many countries are adopting computing (or informatics) as a subject or discipline.
Computing learning practices in these ‘new’ curricula can involve pupils from 5 years of age, either
within a discrete subject, or integrated into other subject topics across the curriculum. However, thelearning concept, framework or theory that such curricula are based on, is not clear in curriculum
documentation.
Our curriculum concepts of learning progression are largely based on Piaget’s research, who describedlearning as a form of cognitive constructivism, developing over the age span of young people, andprogressing through a series of stages: sensorimotor; preoperational; concrete operational; and formal
operational. The new computing curriculum and teaching and learning practices could well be placed
within this conceptual framework. However, Vygotsky’s research added a more social dimension oflearning, a concept of social constructivism.
Our contribution will review the constructionism approach by Papert that is based on uses of digital andcomputing-based resources within a constructivist approach to learning, and will investigate what
learning concept, framework or theory should underpin computing curricula to ensure that practices andoutcomes are as effective as possible.

Keywords
Constructivism, Activity theory (Lev Vygotsky), Learning Theories

Background
Curricula in many countries are adopting, or have recently adopted, computing as a subject or discipline(such as the national curriculum for computing in England, 2013), although the subject may be given
alternative names and have somewhat different concerns in different countries (computer science in the
United States, Canada, or New Zealand, or informatics in Germany, Poland, or Lithuania, for example).
Computing learning practices in these ‘new’ curricula can involve pupils from 5 years of age, perhaps
within a discrete subject, or perhaps integrated into other subject topics across the curriculum. The
learning concept, framework or theory that such curricula are based on, and therefore which canunderpin practices and outcomes across the age span of learners, is not clear in curriculum
documentation, however.
Western curriculum concepts of learning progression are largely based on Piaget’s research (1936),
who described learning as a form of cognitive constructivism, developing over the age span of young
people, and progressing through a series of stages or phases: sensorimotor; preoperational; concrete
operational; and formal operational. The new computing curriculum and teaching and learning practices
could well be considered and placed within this broad conceptual framework. However, Vygotsky’s
research (1978) added a more social dimension of learning, a concept of social constructivism. The
social constructivist approach to learning may be important to consider also when implementing a
838

Constructionism 2018, Vilnius, Lithuania

computing curriculum. With the advent of digital and computing-based resources, further concepts oflearning have been developed. From the perspective of computing, the most significant of these isperhaps the concept of constructionism developed from Papert’s research (1991). In summary, over theprevious 80 years, as digital resources have continued to be developed and used in education, so ourconcepts of learning have been reconsidered - from a cognitive individual perspective, to a social
perspective with others (including teachers and peers), and then with digital resources (Passey, 2013).
Our current contexts, however, are different from those previous times when researchers developed
their learning concepts or theories.
This paper will consider this issue: what learning concept, framework or theory should underpin
computing curricula to ensure that practices and outcomes are as effective as possible across the age
span of that curriculum.

Research questions
Can a concept such as constructionism still be reliably adopted and developed across the age
range from 5 to 18 years?

Do we need a new conception of learning that accommodates our current context?

Is this concept or theory something that we can create from a research perspective, or can we
do it from practical know-how and experience?

What should we do in the future to underpin learning development in computing?

Approach
In this paper, we start to explore the research problem by taking a systematic analytic approach:Initially, we describe and detail the skills and competencies that are required of a contemporary
computer programmer or software developer.



We then take a number of relevant learning theories, describing and detailing their sources, andtheir main features.

Using the features of each learning theory, we identify whether and to what extent there is amatch to the approaches of skills and competencies required of a contemporary softwaredeveloper, and whether certain skills or competencies would not be easily developed.



We critically map the findings from across all the learning theories we have explored, to indicate
where there are matches and where there are gaps, and consider whether a new learning theory
is required, and how this relates to previous learning theories.



From the outcomes of the mapping, we identify the rationale for, a possible name for, and detailthe nature of any new learning theory proposed.

This is clearly an ambitious project. Hence, we do not foresee that we will provide in this paper a picturethat cannot be further developed and questioned. It is more our intention to open up the researchquestions, which we see as being important strategically for the future of computing (informatics)
development in education. Learning theories continue to evolve in parallel with social constructs; we
see this paper as a contribution within that evolution.

Skills and competencies required of a contemporary softwaredeveloper or computer programmer
According to the United Kingdom (UK) National Careers Service (2018), a software developer(programmer) could:“work in a wide range of businesses and industries, public services, utilities, defence andresearch. …work closely with project managers, business analysts and graphic designers, to find839

Constructionism 2018, Vilnius, Lithuania

out what the client wants and the best way to achieve it. Usually …work in a team. …could workon a wide variety of projects, from financial databases to robotics to apps for phones and tablets.
...may use a number of programming languages or project management tools. …day-to-day tasks
may include: talking through requirements with the client and the development team; taking partin technical design and progress meetings; writing or amending computer code; testing softwareand fixing problems; keeping accurate records of the development process, changes and results;
carrying out trials and quality checks before release; maintaining and supporting systems once
they're up and running. As an experienced developer …supervise a programming team andprovide feedback on coding work.” (National Careers Service, 2018, p.n.p.)
There are clear links for an individual involved in this work to a computing (informatics) curriculum. The
skills and competencies of a contemporary software developer can be considered as an end product ofcomputing or computer science education. But, in terms of developing those skills and competencies,
they should adequately match or be enabled through learning activities that are consistent with any
appropriate learning theory or theories that traverse the age span of learner and learning development.
So, we need to detail these skills and competencies, within a contemporary rather than historic context.
The skills needed by a software developer can be analysed through nine different elements orprocesses:Conceiving – taking user, market, technical and end-product requirements into consideration,
producing ideas or drafts of a software process or product; Planning – exploring scope and defining specific user requirement, outlining and detailing theoverall process and time plan leading to a final outcome; Designing – generating an overview or high-level design of the outcome, identifying specificelements or modules of a program, how they integrate, and what language, operating systemand hardware components might be involved;
 Developing – prototyping to consider proof-of-concept or trials of possible alternatives, leading
to implementation that involves programming the code; Documenting – detailing the internal design, so that the software and process can be reviewed,
revised and maintained in the future; Debugging/testing – finding and resolving problems that stop efficient and effective use of thesoftware, and judging the quality of the outcome compared to initial requirements;
 Deploying – releasing the software for use, with additional concern for customisation;
 Evaluating/improving – beyond the initial deployment period, taking ongoing feedback into
account, and exploring how to improve, perhaps integrating further or new software facility; and Maintaining software – picking up on problems or issues over longer periods of time, andexploring ways to resolve these within contemporary situations.
Skills are developed through experience, and experiences can be divided according to the form ofinteraction taking place – whether from the individual’s interest or concerns, whether through formal
education routes, or whether through work and employment. Skills might arise, therefore, through(although specific skills are not necessarily associated with each of the following dimensions): Talent; such skills are difficult to ‘teach’ or ‘acquire’, but it is important that they are recognised
and discovered. Some people appear to naturally engage with certain processes of softwaredevelopment, exhibiting specific talents, while others do not naturally engage in the same way.
How such skills arise, whether from other experiences, or from role models, for example, is noteasy to identify, but it is important for educators to be able to identify these. But persistence andexperience can in many, but possibly not all, cases make up for differences in skills arising fromtalent.
 Education; those skills are abilities associated with a formal education.
 Practical experience; these skills are acquired on-the-job when working in the field. They not
only encompass technical skills, but also organisational skills, such as planning, structuring andemotional organisational intelligence.
840

Constructionism 2018, Vilnius, Lithuania

Skills can be categorised in a number of different ways. Lamb, Maire and Doecke (2017) categorised
21st century skills as: critical thinking; creativity; metacognition; problem solving; collaboration;
motivation; self-efficacy; conscientiousness; and grit or perseverance. Clearly, this categorisation
focuses on what might be regarded as ‘soft skills’, without emphasising skills that might be regarded as
operational or technical (manipulative and mechanical, for example). SkillScan (2012) takes analternative approach, categorising skills as: relationship; communication; management/leadership;
analytical; creative; and physical/technical. For this paper, the skills are broadly categorised into threedifferent groups that are formed from a coalescence of these sources: Technical skills; these involve having a good understanding of the theoretical background of the
field as well as of the common practices used and discussed by the community. As SkillScan(2012) state, these skills include those that enable effective interactions between the individualwith physical objects including machines and technological systems.
 Analytical skills; these include the abilities to collect and analyse information, problem-solve, andmake decisions. Such skills are concerned with logical thinking, mathematical reasoning, andstructural reasoning (planning). Critical thinking is an important basis for this category. People
with this skill see trouble-shooting as a challenge rather than a nuisance. The skill to have great
attention to detail and to consider overall context at the same time is important, but often founddifficult to master. People with good analytical skills need to quickly switch between abstract andpractical viewpoints. They must be able to master complexity. In particular, they should be able
to estimate project cost and understand the scope of projects.
 Social and emotional skills; software developers should have very good communication skills
when communicating with team members, customers, their team leader, etc. Writing andunderstanding documentation like user manuals, training materials, tutorials, and troubleshooting guides are also part of the overall and necessary communication effort. It has beenfound that effective developers are willing and able to share knowledge and expertise with fellow
team members. Depending on their work situation (whether a lone warrior, team player, etc.)
they need to be able to work either independently or within groups. They should be able toanalyse user needs and have a solid understanding of a company’s needs. They should be able
to scrutinise all the information they are given, anticipate events that are hard to predict, andhandle both pressure and failure.
Some of the skills listed above are more clearly obvious as critical requirements, and they are often
easier to assess. Some of these are more related to short-term success, and those would be expected
to be in high demand in job advertisements. Some skills are more subtle, and are more related to longterm success.
In summary, and taking the range of required skills that will be used in the next section of the paper, ourframework for analysis is shown in Table 1. Using this framework, we consider whether each learningtheory we have selected can underpin the development of learning activities required to develop eachof these skills. We answer the question: is it possible to understand how this learning theory relates toor explains or underpins the development of learning activities to support each of these skills?
Table 1. Framework of skills required by contemporary software developers
Elements or processes
Conceiving
Planning
Designing
Developing
Documenting
Debugging/testing
Deploying
Evaluating/improving
Maintaining

Technical skills

Analytical skills

Social and emotional skills

841

Constructionism 2018, Vilnius, Lithuania

Matching learning theories to skills and competencies ofcontemporary software developers
In this section, we take a number of learning theories, chosen because they either often underpin
educational curricula and practices, or are associated in certain ways with computing (informatics)
education and practices. For each learning theory, we consider their source(s), their main features, thematch to the development of learning activities that can support the skills and competencies of acontemporary software developer (taking the skills of a software developer identified in the previous
section), and highlight those skills and competencies not clearly supported through this learning theory
approach.

Cognitive constructivism
Source: Cognitive constructivism is a theory that emerges from Piaget’s studies on cognitive
development (Piaget, 1936; Piaget & Cook, 1952). Piaget did not examine the development of learningper se, and his theory focused only on early learners (up to 11 years, and from beyond 11 years of age,the development identified was considered to go across the lifespan). His evidence was gathered froma small, discrete sample (3 of his own children, and some of his colleague’s children in his later work).
Main features: His theory identifies three important components of development: schemata (building
blocks in memory that can develop arenas of knowledge or understanding); adaptation (how anindividual moves from one stage of development to another); and stages of cognitive development. He
described schemata as sequences of actions that can be held in memory, providing blocks for theretention of and building of knowledge, ideas and understanding. He described adaptation in threestages: assimilation (using an existing schema to explain or work with a new idea or piece ofknowledge); accommodation (realisation that an existing schema does not apply and needs to beadapted); equilibration (when a schema needs to be replaced, and the challenges associated with doingthis). He described the stages of development as: sensorimotor (up to 2 years, when mental
representations can be held in memory or mind); preoperational (2 to 7 years, when an object or word
can be symbolically associated with something else); concrete operational (7 to 11 years, when internal
thinking can occur without having to do this physically); and formal operation (from 11 years, when
thinking can be logical and about abstract concepts). His later work (Piaget, 1958) indicated that
adaptation required an active learning involvement, and that problem-solving needed to be experienced
rather than learnt. Whether stages do exist, and what the influences of social and cultural factors mightbe, were not explored within his research. The role of language, and the concept of schemata, haveboth been questioned by other researchers.
Match to the skills and competencies of a contemporary software developer: If a teacher used thislearning theory as an underpinning base, learning activities that could be devised that would support
the development of the skills in the framework are shown in Table 2 (at this stage, interpreted bymembers of the working group only).
Table 2. Framework of skills related to cognitive constructivism
Elements or processes

Technical skills

Analytical skills

Social and emotional
skillsConceiving√ from 2 years of age√ from 11 years of age?

Planning√ from 11 years of age√ from 11 years of age?

Designing√ from 7 years of age√ from 11 years of age?

Developing√ from 7 years of age√ from 11 years of age?

Documenting√ from 7 years of age√ from 11 years of age?

Debugging/testing√ from 11 years of age√ from 11 years of age?

Deploying√ from 7 years of age√ from 11 years of age?
842

Constructionism 2018, Vilnius, Lithuania

Evaluating/improving√ from 11 years of age√ from 11 years of age?

Maintaining√ from 11 years of age√ from 11 years of age?

Skills and competencies not covered: Taking Piaget’s stages of development as a guide, it is possible
to see how some technical skills could be developed from 2 years of age, while others could be
developed from 7 years of age. If all of the skills could not be developed until 11 years of age, then thisplaces a wide of learning activities out of the central context of contemporary practice. Additionally, thestages of development do not allow a clear understanding of how necessary social and emotional skills
might be developed.

Social constructivism
Source: Social constructivism is a theory of knowledge in sociology and communication theory thatexamines the development of jointly constructed understandings of the world that form the basis forshared assumptions about reality. Social constructivism has been studied by many educational
psychologists, who are concerned with its implications for teaching and learning. The social theory ofknowledge concerns human growth that is socially organised and knowledge created throughcooperation (McKinley, 2015). The approach has been influenced by Vygotsky’s work and is centred on
the social context of learning, in which he believes that knowledge is collectively created and assembled(Bodrova & Leong, 2012; Gaurain, 2008). Through interaction, learners get the chance to express theirthoughts, hence creating a common understanding associated with the idea (Kalpana, 2014). Gredler(2008) believes that as we move from the theory developed by Piaget towards that of Vygotsky, there
is a shift of ideas from individualism to collaboration, aided performance, social communication andsociocultural activity. According to this theory, learning is often socially constructed, perhaps as a model.
Constructing a model can constitute making a sequence of ideas that are public, for discussion andchange; this is similar to programming activity, where Papert indicated parallels between professional
programmer work and children’s programming actions.
Main features: Conversation is the most important means of maintaining, modifying and reconstructing
subjective reality (according to Berger & Luckmann, 1991). Vygotsky's theory is based on 3 major
themes namely: social interaction, the More Knowledgeable Other (MKO) and the Zone ofl Proximal
Development (ZDP). Social interaction enables the process of cognitive development where learners
exhibit both social and individualistic functions; learning through The More Knowledgeable Other (MKO)
concerns learning through someone more familiar with the subject under study. They may be a teacher
or coach or an older person, a peer, younger person or even a computer; and the Zone of Proximal
Development (ZPD) is the cognitive gap or difference between the learner's ability to perform a taskwith the help of another or through collaboration and the time the learner performs the task
independently.
Match to the skills and competencies of a contemporary software developer: If a teacher used thislearning theory as an underpinning base, learning activities that could be devised that would support
the development of the skills in the framework are shown in Table 3 (at this stage, interpreted bymembers of the working group only).
Table 3. Framework of skills related to social constructivism
Elements or processes

Technical skills

Analytical skills

Social and emotional
skills

All

√

√

√

Skills and competencies not covered: Whilst it is possible that all skills could be developed byconsidering this learning theory as a background, the roles of different social actors will clearly be
important here. How teachers and significant others are involved, including the extent and their forms
of interactions, could easily determine the feasibility of any of these forms of skill development.

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Constructionism 2018, Vilnius, Lithuania

Social learning
Source: This theory was introduced by Albert Bandura with the intention of clarifying how youngsters
learn in social conditions by watching and afterwards mimicking the conduct of others. The theory
postulates that individuals learn from each other, though observation, simulation, and demonstration.
The theory has frequently been termed as a link between behaviourist and cognitive learning theories
since it incorporates attention, memory, and motivation. Bandura believed that learning could not be
exhaustively clarified through reinforcement, but the existence of others played a big role. The outcomeof a learner’s conduct was seen more as a result of watched behaviour than one they had adopted
themselves. The results were gathered from a series of experiments where the youngsters watched as
the grown-ups attacked Bobo Dolls. As the doll was hit, it fell down and bounced up again. When theyoungsters were set free they imitated the forceful conduct of the grown-ups. He also observed that the
youngsters were less ready to mimic the grown-ups when they saw them being rebuffed after the act.
Main features: Social learning has four elements: observational learning, reciprocal determinism, selfregulation, and self-efficacy. Observational learning involves observing a model doing something andthen imitating the same. This can be achieved by observing someone, a description in a book or evena movie. For observation to take place successfully, it must be given attention, retention of what isobserved and motivation of learners to apply what has been learnt. Reciprocal determinism describes
the influence of the environment on the learners. It advocates for a positive learning environment if
learning is to take place. It is more concerned with social interaction between the learner and others in
the environment. The third element is self-regulation which involves setting of goals, discipline foractions and follow-through for learning to occur and performance to improve. Lastly, self-efficacy
involves the belief the learner has in their capability to learn and perform. Hence, creation of anenvironment that foster confidence in the learner should be encouraged and cultivated.
Match to the skills and competencies of a contemporary software developer: If a teacher used thislearning theory as an underpinning base, learning activities that could be devised that would support
the development of the skills in the framework are shown in Table 4 (at this stage, interpreted bymembers of the working group only).
Table 4. Framework of skills related to social learning
Elements or processes

Technical skills

Analytical skills

Social and emotional
skills

All

√

√

√

Skills and competencies not covered: Concerns with using this background learning theory are similar
to those posed in discussing social constructivism. Although all of these skills could be developed, it is
the order of these, and the importance of developing and subsequent reliance upon metacognitive skills
such as self-regulation, that could affect the relationship of the development of these skills.

Constructionism
Source: This is an educational development theory developed by Seymour Papert of the Massachusetts
Institute of Technology (MIT) and is based on Piaget’s theory of knowledge. Piaget believed thatknowledge is actively created, based on world experience, hence constructionism. He came up withseveral tasks and questions that revealed the different kinds of knowledge structure built by children at
different ages. Evidence was gathered, based on how children of different ages constructed theirknowledge, given the same task. Based on what Piaget had learnt from the children, Papert used it torethink educational practice. Constructivism is a well-known theory of learning indicating that learners
actively construct new knowledge by combining their experiences with what they already know. A similarsounding concept “constructionism”, developed by Seymour Papert, takes constructivist theory a stepfurther towards the points of action. Although the learning happens inside the learner’s mind, thishappens when the person is engaged in a personally meaningful activity outside of their mind. This canthen make the learning interesting, real and shareable. Papert defined constructionism as: “Fromconstructivist theories of psychology we take a view of learning as reconstruction rather than as atransmission of knowledge. Then we extend the idea of manipulative materials to the idea that learning
844

Constructionism 2018, Vilnius, Lithuania

is most effective when part of an activity the learner experiences as constructing a meaningful product”(Papert, 1986; Bruckman, 2006). Four aspects are essential to the design of constructionist learning
environments. These are: learning through designing, personalising, sharing, and reflecting.
Constructionism is grounded in the belief that the most effective learning experiences grow out of theactive construction of all types of things (Papert, 1980). Constructionism strongly resonates with thecurrent maker movement (Martinez & Stager, 2013). Constructionism as a theory symbolised a way of
thinking about learning, the most general and effective being: building a model, reflecting on it, testingit, and sharing with others. DiSessa and Cobb (2004) argued that constructionism is “learning bydesigning” and falls into a category they called “frameworks for action”; however, they do not provide
scientific ideas and structure for the design of learning environments. From that perspective, Noss andClayson (2015) suggested a constructionist agenda and defined six characteristic: (1) modelling, (2)accessibility to the modelling process, (3) layering of principles and abstraction, (4) tapping into youth
culture, (5) being represented in learner’s language, and (6) collaboration. Many educators andcognitive psychologists have applied constructivism to the development of learning environments.
Main features: Constructionism is built on the main idea that knowledge is not passively received either
through the senses or by way of communication – it is actively built up by the person during the learning
process. Another strong feature stressed by Glasersfeld (1988) and cited in various papers, is that thefunction of cognition is adaptive and serves the subject’s organisation of the experiential world, not thediscovery of an objective ontological reality.
Match to the skills and competencies of a contemporary software developer: If a teacher used thislearning theory as an underpinning base, learning activities that could be devised that would support
the development of the skills in the framework are shown in Table 5 (at this stage, interpreted bymembers of the working group only).
Table 5. Framework of skills related to constructionism
Elements or processes

Technical skills

Analytical skills

Social and emotional
skills

All

√

√

?

Skills and competencies not covered: Whilst it is feasible to consider that all skills could be developed
using this background learning theory, the importance of development of social and emotional skills,
and the need for these to be introduced at a young age, is stated more clearly in later definitions anddiscussions of constructionism. If there is a focus on interaction in learning activities with material
resources, at the expense of interactions with peers and others when doing this, then this could hinder
the important development of relevant and related social and emotional skills.

Situated learning
Source: This is an instructional approach developed by Jean Lave and Etienne Wenger in the early
1990s. The approach is based on the works of Dewey, Vygotsky and others who believed that learning
takes place more if learners are actively involved in the learning process (Clancey, 1995). It is believed,
however, that the idea of situated learning was first used by Brown, Collins and Duguid (1989) to develop
a proposal for an instructional model that has inferences to classroom practice. This was as a result of
the researchers observing successful learning situations. Examples of effective learning in any context
or culture were identified and key features of the models analysed (Herrington & Oliver, 1995). Situatedlearning is a theory about the nature of human knowledge, claiming that knowledge is dynamicallyconstructed as we conceive of what is happening to us, talk and move, but is concerned also with how
individuals acquire professional skills. Our action can be situated in our role as a member of acommunity. Situated learning focuses on the relationship between learning and the social situation inwhich it occurs. The theory of situated learning claims that knowledge is not a thing or set of descriptions
or collection of facts and rules.
Main features: According to Herrington and Oliver (1995), the principal theorists and critics of situatedlearning believe that learning environments that possess the following features elicit effective attainmentof usable knowledge: authentic context, authentic activities, expert performances and the modelling of845

Constructionism 2018, Vilnius, Lithuania

processes, multiple roles and perspectives, collaborative construction of knowledge, coaching andscaffolding at critical times, reflection to enable abstractions to be formed, articulation to enable tacitknowledge to be made explicit, and lastly, integrated assessment of learning within the tasks. Situatedlearning declares that learning: (1) should be always integrated with the individual's identity andparticipation; (2) is constituting an evolving membership and capability to participate in different forms;
and (3) is the means of reproduction and development of communities of practice.
Match to the skills and competencies of a contemporary software developer: If a teacher used thislearning theory as an underpinning base, learning activities that could be devised that would support
the development of the skills in the framework are shown in Table 6 (at this stage, interpreted bymembers of the working group only).
Table 6. Framework of skills related to situated learning
Elements or processes

Technical skills

Analytical skills

Social and emotional
skills

All

√

√

√

Skills and competencies not covered: While situated learning would appear to be an ideal backgroundlearning theory to frame learning activities to develop all of these skills, it is difficult to see how this might
be applied in practice for young people starting their involvement from, say, 5 years of age. Successwould be likely to depend on having access to teachers and resources that would be competent andconfident in terms of creating a situated learning environment.

Discovery learning
Source: Discovery learning established its base from theories developed by John Dewey (Dewey,1997), Jean Piaget (Piaget, 1973), and Lev Vygotsky (Rice & Wilson, 1999) that described learning as
active, process-based, and collaborative. It involves an instructional model and strategies that presents
learners with more active and practical learning opportunities (Dewey, 1997; Piaget, 1973). According
to Berding (2000), Dewey believed that children were naturally motivated to actively learn, and that
education only served to make more learning possible. He believed that mental development wasachieved through social interaction. Dewey saw children as participants in their learning rather thanreceivers of their learning. He established a Laboratory School at the University of Chicago wherelearners were encouraged to actively participate in collaborative learning activities such as students’building a playhouse to learn geometry and measurement principles. He believed that children shouldbe active, participatory learners who collaborate with others to better understand meaningful situations.
Piaget (1973), on the other hand, believed that understanding emanates from unearthing and thatwithout it invention and creativity are eliminated and one is caught in only duplication. He also observedthat children do not think with the same logic as grown-ups (Papert, 2001). He recognised that children
were not “empty vessels” to be filled with knowledge, but active developers of it. Piaget saw children as
active, participatory learners as they frequently generated and tested their understanding of the world.
Lev Vygotsky stressed the influence of cultural and social interactions on mental development, mainly
the interaction of children with other people (Rice & Wilson, 1999). Through the theoretical concept of
the Zone of Proximal Development (ZPD), he emphasised that there is a difference in what a child canaccomplish in isolation and what he or she can accomplish with assistance. Discovery learning is aninquiry-based, constructivist learning theory that takes place in problem-solving situations where thelearner draws on his or her own past experience and existing knowledge to discover facts andrelationships and new truths to be learned (Bruner, 1961). Models that are based upon discovery
learning model include: guided discovery, problem-based learning, simulation-based learning, casebased learning, and incidental learning.
Main features: Bicknell-Holmes and Hoffman (2000) describe the three main features of discovery
learning as exploring and problem solving (exploring and problem solving to create, integrate, andgeneralise knowledge); taking responsibility for learning (student driven, interest-based activities inwhich the student determines the sequence and frequency); and building new knowledge on existingknowledge (activities to encourage integration of new knowledge into the learner’s existing knowledge
846

Constructionism 2018, Vilnius, Lithuaniabase). There are several features: (1) encourages active engagement; (2) promotes motivation; (3)promotes autonomy, responsibility, independence; (4) develops creativity and problem solving skills;and (5) tailors learning experiences. From Wikipedia discovery learning is described as:“There are multiple essential components that are required for successful discovery-basedlearning which include the following: Teacher guidance where the emphasis is on building upon
students’ reasoning and connecting to their experiences; Classroom culture where there is ashared sense of purpose between teacher and students, where open-mindedness and dialogue
are encouraged; Students are encouraged to ask questions, inquire through exploration andcollaborate with teacher and peers.”
Match to the skills and competencies of a contemporary software developer: If a teacher used thislearning theory as an underpinning base, learning activities that could be devised that would support
the development of the skills in the framework are shown in Table 7 (at this stage, interpreted bymembers of the working group only).
Table 7. Framework of skills related to discovery learning
Elements or processes

Technical skills

Analytical skills

Social and emotional
skills

All

?

?

?

Skills and competencies not covered: While this background learning theory could be used to frame thedevelopment of all skills, the approach to discovery learning would need to be carefully considered.
Expecting discovery to result without appropriate and adequate support would be unlikely to enable aneffective development of these skills; misunderstandings and misinterpretations could too easily
develop and persist without sufficient monitoring and discussion.

Experiential learning
Source: The model of experiential learning was developed by Kolb (1984) and Rodgers (1969) and it
was based on John Dewey’s concept of ‘Learning by Doing’. Rodgers emphasised the importance ofexperiential learning as knowledge application and not mental learning. He believed that experiential
learning looked at the needs and wants of individuals and was connected to their change and growth.
Kolb, on the other hand, came up with a four-stage cyclical process, namely: concrete experience,reflection, abstract conceptualisation and active experimentation.
Main features: Kolb (1984) described learning as a process of knowledge creation based on thealteration of experience and the understanding of experience together with its transformation He further
proposed six key features of experiential learning: being perceived best as a process, rather thanoutcomes; an unceasing process based on experience; need for conflict resolution between dialecticallycontrasting ways of adaptation to the world; an all-inclusive process of adaptation to the world;
encompassing connections between the individual and the environment; and lastly involving creation ofknowledge as a result of the relationship between social and personal knowledge. Experiential learningis the process of learning through experience, and is more specifically defined as “learning throughreflection on doing” (Patrick, 2011, p.1003). A four-stage cyclical theory of learning, Kolb’s experiential
learning theory is a holistic perspective that combines experience, perception, cognition, and behaviour.
The four styles proposed are: (1) assimilators, who learn better when presented with sound logicaltheories to consider; (2) convergers, who learn better when provided with practical applications ofconcepts and theories; (3) accommodators, who learn better when provided with “hands-on”
experiences; and (4) divergers, who learn better when allowed to observe and collect a wide range of
information. Building upon earlier work by John Dewey and Kurt Levin, American educational theorist
David A. Kolb believed “learning is the process whereby knowledge is created through thetransformation of experience” (1984, p.38).
Match to the skills and competencies of a contemporary software developer: If a teacher used thislearning theory as an underpinning base, learning activities that could be devised that would support
847

Constructionism 2018, Vilnius, Lithuania

the development of the skills in the framework are shown in Table 8 (at this stage, interpreted bymembers of the working group only).
Table 8. Framework of skills related to experiential learning
Elements or processes

Technical skills

Analytical skills

Social and emotional
skills

All

√

?

?

Skills and competencies not covered: While all skills could be developed using this background learningtheory to frame learning activities, the basis of the learning theory and its focal elements were developed
from adult investigations and approaches, rather than from exploration of developmental approaches atyounger ages.

Problem-based learning
Source: Tse and Chan (2003) describe problem-based learning (PBL) as a learning approach where“the problem drives the learning”. The problem is given first and then the learners are presented withthe opportunities to discover solutions to it (Salas, Segundo, Álvarez, Arellano & Pérez, 2014). The“learning is student-centered” (Tse & Chan, 2003) and the teacher acts as a facilitator whose role is toguide the “self-directed learners” (Forcael, Gonzalez, Orozco, Opazo, Suazo & Aranguiz, 2015;
Stanford University Center for Teaching and Learning, 2001). Savery and Duffy (1995) assert that PBL
first emerged in the field of medical education in the 1950s. This was as a result of increasing
displeasure with the traditional medical education practice at McMaster University in Canada (Barrows,
1996). The PBL inventors critiqued traditional health science education due to its lecture approach, withmore prominence put on memorisation of fragmented biomedical information, overlooking theimportance of assisting learners cultivate the clinical problem-solving skills required for a lifetime ofpractice and learning (Barrows, 1996). Learners worked in small groups where they interacted withsimulated patients with complex and meaningful medical problems. Using patient interviews, records,
and selected laboratory results, they were able to identify learning issues and develop a diagnosis andtreatment plan (Torp & Sage, 1998). The learning process was reinforced by teachers whose role wasto facilitate discussion-based learning through questions, asking about and monitoring the problemsolving process (Hmelo, 1998). PBL is a constructionist method which allows students to learn about asubject by exposing them to multiple problems and asking them to construct their understanding of thesubject through these problems. This kind of learning can be very effective; for example, in mathematics
or computing classes, because students try to solve the problems in many different ways, stimulating
their minds (Hmelo-Silver & Barrows, 2006). Many studies have reported the utilisation of PBL in teachingsoftware engineering courses, especially in teaching Agile software development methods.
Main features: Popper (1994) believes that “Alles leben ist Problemlösen” (all life is problem solving);
hence, life is full of learning opportunities. For PBL to take place, the following key features should bepresent: problem-focused (learning begins by addressing simulations of an authentic, ill-structured
problem - what is to be learnt is built round a problem rather than creating a list of topics); studentcentred (the teacher does not command the learning activities, but rather acts as a facilitator in thewhole process); self-directed (students individually and collaboratively assume responsibility for
generating learning issues and processes through self-assessment and peer assessment and accesstheir own experiential knowledge and learning materials); self-reflective (learners monitor theirunderstanding and learn to adjust strategies for learning); and facilitative (instructors are facilitators andnot teachers). The following are some of the defining characteristics of PBL: (1) learning is driven bychallenging, open-ended problems or tasks; (2) problems are context specific; (3) group work is
common: learners work as self-directed, active investigators and problem-solvers in small collaborative
groups; (4) a key problem is identified and a solution is agreed upon and implemented; (5) teachers
adopt the role as facilitators of learning, guiding the learning process and promoting an environment ofinquiry.
Match to the skills and competencies of a contemporary software developer: If a teacher used thislearning theory as an underpinning base, learning activities that could be devised that would support
848

Constructionism 2018, Vilnius, Lithuania

the development of the skills in the framework are shown in Table 9 (at this stage, interpreted bymembers of the working group only).
Table 9: Framework of skills related to problem-based learning
Elements or processes

Technical skills

Analytical skills

Social and emotional
skills

All

√

√

√

Skills and competencies not covered: This approach could enable the framing of development of all
skills. Appropriateness to age would need to be considered, but, having said this, problem-based
approaches are used in effective ways across the age span of compulsory and higher education.

Connectivism
Source: Connectivism is a digital age theory developed by George Siemens and Stephen Downes whichcame about as a result of the two criticising restrictions of behaviourism, cognitivism, and constructivism
theories (Siemens & Downes, 2009). According to Siemens (2005), “Connectivism presents learning asa connection or network-forming process”. Siemens believes that learning today is too complex and weneed to rely on a network of people and technology to store, access, and retrieve knowledge andmotivate its use (Siemens, 2006). Learning is viewed as multi-faceted and particular tasks define whichapproach to learning is most appropriate to the learner (Siemens, 2003). Connectivism is a learningtheory that explains how Internet technologies have created new opportunities for people to learn andshare information across the World Wide Web and among themselves (Siemens, 2005).
Main features: According to Siemens (2004), key features of connectivism include: correct and current
knowledge (the main purpose of all connectivism learning); decision-making (important to learning andis also a learning process); ability to establish associations among multiple fields, ideas, and concepts
is essential; learning can exist in devices other than human beings (this includes networks, databases,
communities); knowledge acquisition is a process of linking varied devices, knowledge bases, oraccessing prevailing networks; knowledge acquisition relies on the varied thoughts and perspectives
within a network; and being able to acquire more knowledge more vital than the already acquired
knowledge. In addition to these, Siemens (n.d.) also affirms the following as key features ofconnectivism: the integration of thought and sentiments are vital to the creation of meaning;
maintenance and nurturing network linkages in order to sustain continual learning; development andutilisation of knowledge is essential in knowledge acquisition; and lastly learning takes place in morethan one way. A key feature of connectivism is that much learning can happen across peer networks
that take place online. In connectivist learning, a teacher will guide students to information and answer
key questions as needed, in order to support students’ learning and sharing on their own. Students arealso encouraged to seek out information on their own online and express what they find. A connected
community around this shared information often results. A lack of comparative literature reviews inconnectivism papers complicates evaluating how connectivism relates to prior theories.
Match to the skills and competencies of a contemporary software developer: If a teacher used thislearning theory as an underpinning base, learning activities that could be devised that would support
the development of the skills in the framework are shown in Table 10 (at this stage, interpreted bymembers of the working group only).
Table 10: Framework of skills related to connectivism
Elements or processes

Technical skills

Analytical skills

Social and emotional
skills

All

?

?

?

Skills and competencies not covered: It is not clear that this learning theory would enable a framing oflearning activities for use across the age range. Activities that would depend on uses and understandingof distributed resources and distributed knowledge might not easily support younger age groups.
849

Constructionism 2018, Vilnius, Lithuania

Mapping of learning theory features for the framing of skills andcompetencies of a contemporary software developer
The mapping exercise shows that a number of background learning theories could be used to frame thedevelopment of learning activities in computing, computer science or informatics education. However,there are a number of critical concerns that are raised by this mapping exercise:

If stages of cognitive development are used a frame for learning activity development, thencomputing, computer science or informatics learning will become decontextualised from acontemporary perspective. Certain skills will be emphasised earlier than others, and how these
are then contextualised later will need to be a key concern.



If social constructivist approaches are used as a background frame, then extents and forms ofsocial intervention and social interaction need to be carefully considered, as they will influence
outcomes and longer-term approaches to skills development and use.



If social learning is used as a background frame, then the importance of developing andsubsequently using metacognitive skills will need to be a focus for development from early ages.



If constructionism is used as a background frame, then the presence and roles of social
interaction will need to be as much a focus as material resource interaction, across ages, and in
all forms of activity (with those concerned with technical skills just as much as those focusing onsocial and emotional skills).



Situated learning could provide an ideal background frame, but then teachers and othersupporters will need to be highly competent and confident, in creating and using a situatedcontext for their learners.

Discovery learning could form a background frame, but independent and unsupported discovery
will not be likely to result in effective outcomes, considering the nature of the subject, and the
misconceptions and misunderstandings that could result.



Experiential learning could form a background frame, but its focus from investigating adultinteractions means that it will need to be trialled and tested extensively enough across age
ranges.

Problem-based learning will appear to provide a useful background frame, and has been used
across educational practice widely.

Connectivism as a background frame is likely to be difficult to apply for younger age ranges if
distributed resources and knowledge are involved in learning activities for those age groups.

Skills and competencies not covered by any learning theory
It is not possible to say that any of the skills could not be developed through learning activities framedby one or another learning theory. However, it is clear that background learning theories used to frame
the development of learning activities can have a major effect, and could easily influence (positively or
negatively) the forms of interaction and, hence, either enhance or limit developments of certain skills tolesser or greater extents. Table 11 shows, from the interpretations shown in previous tables, those skills
that are highlighted as being most uncertain to be developed, considering how appropriate forms oflearning activity might be created, taking cognitive constructivism, social constructivism, social learning,
constructionism, situated learning, experiential learning, and problem-based learning theories asbackground frames into account.

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Constructionism 2018, Vilnius, Lithuania

Table 11: Framework of skills related to interpreted limitations of background learning theories
Elements or processes
Conceiving
Planning
Designing
Developing
Documenting
Debugging/testing
Deploying
Evaluating/improving
Maintaining

Technical skills

?

?
?
?

Analytical skills
??
??
??
??
??
??
??
??
??

Social and emotional
skills
???
???
???
???
???
???
???
???
???

Rationale, proposed name and features of a new learning theory
Based on this analysis, whilst a completely new learning theory might not be shown as an absolutenecessity at this time, it is highlighted at the same time that any background learning theory used as aframe needs to be considered adequately and appropriately in terms of how analytical and social andemotional skills particularly will be developed through appropriate learning activities. Similarly, how skills
that might not be easy to develop at young ages are appropriately contextualised as early as possible,
also will need careful consideration. Investigating stages of development for an updated learning theory
approach, perhaps a learning theory that might be termed social creative constructivism, should bea focus of concern and action for future research.

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WG3: Creating and Looking at Art with Logo Eyes
Evgenia Sendova, jenny.sendova@gmail.com
Institute of Mathematics and Informatics at the Bulgarian Academy of Sciences, Bulgaria
Christos Chytas, christos.chytas@uni-oldenburg.de
Computing Education Research Group, University of Oldenburg, Germany
Katarzyna Olędzka, katarzyna.oledzka@oeiizk.waw.pl
Computer Assisted Education and Information Technology Centre in Warsaw, Poland
Ralf Romeike, ralf.romeike@fau.de
University of Erlangen-Nuremberg, Germany
Wolfgang Slany, wolfgang.slany@tugraz.at
Catrobat Team, Graz University of Technology, Austria

Abstract
Computation is an integral component in nearly every dimension of our lives and provides new tools for
self-expression even in areas that one would not dare to enter without the access to computers. In 1967,
Seymour Papert, Wally Feurzeig and Cynthia Solomon designed Logo to allow children and youth to
explore programming and mathematics concepts, while providing exciting opportunities for creativity
and experimentation. Computation provides an appropriate framework for dealing with notions of how
to and what if rather than with notions of what is, which already has an impact on the way we teach finearts, music, or literature. For Papert and his fellow constructionists the programming was not only atechnical skill for industry but also a mode of creative and personal expression. Today, the absence ofcomputation in mainstream art and science education is still apparent but the appearance of Logo andnew modern tools that inherit the ideas of the Logo educational philosophy (known as constructionism
today) promises to change that. Drawing on examples from experiences with IT teachers and children
in participatory design courses, we discuss how to harness the Logo culture of making things happen,
making things work, so as to open the eyes of young people to the beauty around them.

Keywords
art; creativity; coding; logo; visual programming; maths; stitching; embroidery

Prologue (in which the Turtle makes a point of the computer art)
Achilles and the Turtle (Fig. 1), characters we know not only from the famous Zenon paradox but also
from the essay of Luis Carol What the Tortoise said to Achilles [1] and the book Gödel, Escher, Bachby Douglas Hofstadter [2], in which the author promotes the idea that in the history of human civilization
the division between science and art is in fact superficial.

Figure 1. Achilles and the Turtle discussing the modern and the artificial art (cartoonist Yovko Kolarov)

Turtle: About 60 years ago I attended an exhibition where there were only black paintings looking
practically identical. The artist would address all the questions with a mysterious smile: Looking at art is855

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not as simple as it looks… But then would continue with a serious tone: Art is too serious to be taken
seriously [3].
Achilles: I know what you are talking about – in a recent exhibition I attended (Fig. 2) the largest
paintings in the modern art wing appeared to be solid black; but with sustained looking, shapes started
to emerge. I wonder what art critics would say if such paintings were generated by a computer.

Figure 2. An exhibition of Mark Rothko in MFA, Boston (2018).

Turtle: True! But the attempts to model abstract art do not intend to challenge the art critics. Maybe it
is for the first time today that the computer provides people with environments where they could
experimentally verify ideas, formulated by eminent representatives of the abstract art such as Kandinsky
and Mondrian. You might have heard that Mozart has composed music by combining randomly readymade 2-bar musical fragments. Randomness can also be seen in art, e.g. Hans Arp has thrown color
pieces of paper by leaving to the destiny to combine them. It is known that the only rule therepresentatives of Dadaism followed was randomness.
Achilles: You mean that the abstractionists do not accept any rules? Turtle: On the contrary. In his
book On the Spiritual in Art the founder of the abstract art, Kandinsky, writes: We are approaching thetime when a conscious, reasoned system of composition will be possible, when the painter will be proud
to be able to explain his works in constructional terms.
Achilles: Sounds like someone who would have found like-minded people among theconstructionists…
Turtle: In the field of the so called Artificial Art there are programs which can generate paintings on a
specific topic. An interesting example is Aaron, developed by Harold Cohen, (Fig. 3), which generates
figures resembling people dancing or playing with a ball in an environment looking like a forest, rocky
hills or beach. Due to the random choice of specific variables Aaron always generates different
paintings, obeying a specific style.

Figure 3. Detail from an untitled AARON drawing, 1980 (left); AARON image created at the Computer Museum,
Boston, MA, 1995 (right) [4].

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Achilles: I would rather think that it is more natural for an intelligent computer to draw a self-portrait,i.e. a computer which draws a portrait of itself, which draws a portrait of itself, etc.
Turtle: I know your passion about recursion. But you will see in the works of our authors and theirstudents that the computer could be used not only to play the role of an amateur-abstractionist, but also
as an environment for artistic experiments. In such an environment (Logo or Logo-like) one could
comment on what makes the abstract art in fact art and not just random collections of figures spread ona canvas. Besides, using a language-based computer environment it is easier to give working definitionsof abstract notions, such as harmony of colors, balance, tension, calmness, and verify experimentallythe validity of these definitions. Besides, computation provides new tools for self-expression even inareas that one would not dare to enter without the access to computers.
Achilles: They say that a picture is worth more than 1000 words. (Even if it is a computer work). Thus
– FORWARD to the paintings!
Turtle: Although I am just a little ahead of you, I don’t think you have chances of reaching me (and myrelatives for that matter) before infinity. My recently born cousin, the Logo turtle, is already a well-known
artist. Let us see what the authors of this article have to say about how they and their students look at
art with Logo eyes…

The Logo turtle as an eye-opener and as а tool for enriching theartists’ palette
Art or a Logo exercise?
The first two pictures in Fig. 4 are the simplest version of abstract art which could be modeled by meansof the basic primitives of the Logo turtle graphics. Thus, they could be included as an exercise not only
to implement students’ skills in controlling the turtle but also to open their eyes for the art of Max Billand his love of geometry [5].

Figure 4. Max Bill (lithographs from 1938) and their Logo variations.

An episode from a PD course for IT teachers
During a PD course for teachers in informatics at Sofia University a shy young woman handed to thelecturer a piece of an old calendar with a painting by Vasarely (Fig. 1, leftmost) and ask her if it could
be modeled by means of the Logo turtle [6].

Figure 5. Vasarely’s Los Angeles and Logo variations on it.
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Of course, there are different ways to create computer models of Los Angeles. After a short discussion
they figured out that the Four-bug problem could be used (modified for 6 turtles in this case) byconnecting the positions of the turtles at regular intervals of time alternating the color of the trace. From
there on variants by modifying the parameters in the initial setting of the problem (e.g. the strength of
the attraction, the number and the positions of the turtles) were created. A sufficient number ofexperiments produced realistic images (Fig. 5, rightmost). And variations on a theme are in fact the cruxof creativity. Furthermore, one can come to an understanding of such fundamental cognitive processes
as pattern perception, extrapolation and generalization only by modeling them in the most carefully
designed and restricted microdomain [7].

Episodes from pre-service course for math and CS teachers
Ideas of identifying what is essential in a specific painting, in the author’s style, or even – of a period inart, could be found in the wonderful book of James Clayson [8]. Good practices in the context of visualmodeling with students of different age and cultural background demonstrate the applicability ofClayson’s ideas. An inspirational synergy among mathematics, informatics and art has been achieved
within the pre-service and in-service teachers in mathematics and informatics at Sofia University. The
first stage of the modeling process was to identify fundamental elements in paintings characterized bytheir vibrant colors and sharply patterned geometric forms (e.g. de Still group, Kandinsky, Sonia
Delaunay, Escher) and then to create stylized versions of those by Logo procedures. The next stage
was to understand the balance (Mondrian) and the forces-tensions living in the painting (Kandinsky).
The students created working definitions of balance according to different criteria (e.g. determining theweight of an element by its size, complexity, color) and tested them in a number of iterations,concentrating on the phenomenon to be modeled. The students realized that the abstract paintings
contain a harmony whose laws could be revealed and their hypotheses about those laws could bematerialised in computational form to be verified experimentally. Here are some examples.
Modeling circles a la Kandinsky

Figure 6. Deepened Impulse (left); Circles in a Circle (right).

Various ideas of modeling the circles include using sequences of nuances to create the illusion of 3D
objects, to create a balance among their locations, to animate them (make them pulsate or moveaccording to different rules).

Figure 7. Creating visual effects by means of Comenius Logo.
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As seen from these pictures the work on specific elements of a painting being modeled requires
experiments of various ideas and techniques both from informatics and artistic point of view.
Variations on Vega by Vasarely
For modeling Vega (Fig. 8, leftmost) we used Lissajous curves describing a complex harmonic motion.
The graphs are the traces of a turtle whose Cartesian coordinates are obtained by the positions of twoother turtles moving along specific intervals on the coordinate axes of rectangular coordinate system.

Figure 8. Vasarely Vega (1957) and Comenius Logo variations.

Such curves could be described also by a single turtle by means of trigonometric functions. The reasonof using three turtles (apart from using a more modest mathematical apparatus) is to generalize themotion of two of the turtles by driving them along polygon lines while the position of the third is always
in the middle between the first two (Fig. 9).

Figure 9. Exploring further the effect of modifying the Logo models of Vega.

In our sessions we encourage students and teachers to not reduce their visual models to imitations
only. Instead, they are encouraged to use their own creativity and experiment with their own designideas.
This process contains (to use the phrase of Hofstadter) unpredictable predictability. In other works we
can expect the appearance of new stylistic attributes depending on the rank in which we vary the randomvariables. After leaving the frames of the strict imitation we could be inspired by new combination offorms and colors and get new insight leading to a new formalization – a process supported by theenormous potential of the computer to make various combinations in short time and by our humanpotential to interpret new abstract structures [9].
Recent programs in artificial art that can tweak photos to mimic the style of famous painters are alreadywidely available. There are even apps that do this, such as DeepArt [10]. But even more ambitious is a
new system producing images in unconventional styles from scratch [11].

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Episodes from our work with children
Looking with Logo eyes around us
Geometric shapes and regular patterns can be found practically everywhere, although they are often
unnoticed by an untrained eye. For example concepts like the golden ratio, Fibonacci sequence,symmetry can be found in nature (from the number and configuration of petals in flowers to the spiralpatterns of shells). Such concepts have intrigued the interest of artists and scientists for centuries andcomputation provides the means to express such concepts faster and more efficient than ever. Logo isa language suitable for dealing with phenomena which we could better understand by modeling them
… What is valuable in programs in a microworld tuned to a particular domain (art in this case) is thatthey could be used as glass-boxes. During online courses for primary school children [12] we start from
simple drawings to move to more complicated ones. By solving several tasks students build their own
knowledge. They combine finding recurrent elements with the ability to describe pictures by Logo
procedures. Moreover, children improve their understating of geometric shapes.

Figure 10. Tasks for finding repetition from miniLogia competition.

They need to integrate art, mathematics and computational thinking. Students learn how to efficiently
use the most important procedures of turtle graphics, apply iteration and recurrence and break down aproblem into sub-problems. Motives are made by simple and complicated structures, there are rhythms
in different dimensions, sometimes balance or even the tension. Mathematical knowledge is needed tosolve the problems: square and its diagonal, proportions, regular polygons like triangle, square,hexagon, angles: right angle, straight angle, angle of 360⁰, partition into equal parts.

Figure 11. The more complicated tasks from miniLogia competition.

The older the students are, the more advanced pictures they can draw. During the miniLogia competition
for primary school children [13] or the Logia competition for gymnasium students, [14] participants solve
different problems inspired by everyday life (Fig. 10-11).
Logo eyes help us understand better the world860

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Using different geometric shapes, we can build some pictures that are tricky. Sometimes our perceptionof objects is inadequate to their physical properties. Optical illusion can be explained from physiologicalpoint of view, but also can be examined by drawing.
The line segment forming the shaft of the arrow with two tails is perceived to be longer than that forming
the shaft of the arrow with two heads. When one compares the code for a turtle, we can realize thatthese two segments are equal length (line 3 and 13).

1. def arrow1(a):2.
for i in range(2):3.
fd(2*a)4.
rt(135)5.
fd(a);bk(a)6.
rt(90)
7.
fd(a);bk(a)8.
rt(135)9.
bk(2*a)10.
rt(180)11. def arrow2(a):12.
for i in range(2):13.
fd(2*a)14.
rt(45)
15.
fd(a);bk(a)16.
lt(90)
17.
fd(a);bk(a)18.
rt(45)
19.
bk(2*a)20.
rt(180)Figure 12. Two arrows and the code in Python with module turtle.

In another example two circles of identical size are placed near to each other and one is surrounded bysmall circles while the other- by a big one. The first central circle appears to be bigger than the second
one. While comparing the code in Python: the central circles are identical (line 4 and 11), but thedifference effect is due to the difference in the external circles (lines 6-8 and 13-15).
1. def jump(a):2.
pu();fd(a);pd()
3. def big_circles(d):4.
dot(d)
5.
for i in range(6):6.
jump(d)7.
dot(d/2)
8.
jump(-d)
9.
rt(60)
10. def small_circles(d):11.
dot(d)
12.
for i in range(6):13.
jump(4/2*d)14.
dot(3/2*d)15.
jump(-4/2*d)
16.
rt(60)

Figure 13. The Ebbinghaus illusion.

In the third example, there is a grid built by squares. On both pictures the squares are identical. Whileon the left they are forming straight columns, on the right – they are shifted. This creates the illusionthat the horizontal lines are not parallel.
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Figure 14. Grid built by squares

Playing with optical illusions made by a turtle, can lead to important discoveries. It helps the students tounderstand how human brain can be deceived and how to cope with situations when the perception isdifferent from the reality. There are artists (e.g. Maurits Cornelis Escher) who use it for entertainment
and fascination. For children, it is a field to meet challenges and be stimulated to develop their own
creativity.

Beyond Logo
In the early 80s, when personal computers started infiltrating mainstream education, Papert published
Mindstorms [15], a book on the potential of computers in children’s and youth’s education. In his
inspiring book, Papert introduces the idea of a Mathland as a place where learning mathematics would
be the equivalent of people learning their native language as when they were children. Logo (with its
educational philosophy known as constructionism today) consists of both an environment and a tool topromote such types of learning. It allows children and youth to participate actively in creating andunderstanding more complex concepts of mathematics and computation in more creative ways than theones provided in mainstream education in the past. Furthermore, innovations in educational technology
which inherit the ideas behind Logo provide accessible tools that can support the creation ofsophisticated projects through algorithmic or generative design and digital fabrication. With turtle
graphics or block-based commands children and youth can use powerful computational tools to create
projects ranging from small programs to physical artefacts (e.g. 3D printed models). The creation ofphysical artefacts with computational means can open new ways for creating and looking at art throughtangible interactions, where the artistic creations become ‘’objects-to-think-with’’ [15]. The term ‘’begreifbarkeit’’ as coined by Katterfeldt et al. in [16] describes an element in a situation where the learners
can grasp something physically (grab something tangible) but also grasp something as understanding
it (in our case computational models for art). A good example of ‘’graspable’’ art in the service ofmathematics and computing education is given in the work of [17]. In a school setting, the students
explored computational and mathematics concepts under the lens of 3D printing as a medium forcreativity. They used turtle graphics in Beetle blocks [18] to create artistic artefacts that worked as‘’objects-to-think-with’’. However, even though the results of the course were promising, the combinationof mathematics and programming concepts was a great challenge for the learners. Mathematics andcomputing science as subjects are considered difficult and not creative by many students. Therefore,
we need to provide the right conditions and challenges of computational design considering the diverse
experience, knowledge and interests of students. The intersection of mathematics, computational
design and art might be an efficient way to highlight the beauty of science through the creation of
personally meaningful artefacts.

Catrobat and TurtleStich
Catrobat is a free open source non-profit project that allows users to create and publish their own games
and interactive animations directly from their smartphones, without the need for any PC, laptop,
Chromebook, or tablet. Initiated in 2010, Catrobat as of July 2018 has more than 700,000 users from180 countries, is available in 50+ languages, and has been developed so far by almost 1,000 volunteers
from around the world. Catrobat’s apps and services have been immensely influenced by MIT’s Scratch
project, and Catrobat is considered to be Scratch’s little sister project for smartphones. Indeed,862

Constructionism 2018, Vilnius, Lithuania

Catrobat’s apps allow to import Scratch projects, thus giving access to more than 37 million projects onphones as of July 2018. Catrobat’s goal is to provide computational thinking skills for all worldwide
teens, especially teens from less developed areas where other computational devices such as PCs arealmost non-existent. Catrobat is a visual programming language like Scratch or Snap! and, like them,includes powerful Turtle graphics pioneered by Logo. Catrobat also allows to take advantage of themany sensors and high-resolution multi-touch screens of modern smartphones, allow to project one’s
smartphone’s screen to arbitrarily large screens, some of which being multi-touch enabled with a backchannel to the phone’s screen, thus allowing to serve as large interactive boards for games or largescale art installations. Catrobat’s apps also allow to seamlessly interact wirelessly with a plethora ofexternal devices such as Arduino and Raspberry Pi boards that are frequently used by artists ininteractive installations. Using Catrobat, creating the software for these interactive art installations is so
straightforward that regular visitors to an art exhibition can be empowered to not only control these
installations but also change or even create completely new objects of art in a few minutes without any
previous knowledge of coding, on their own smartphones.
Below find a few examples from the many interactive animations that have been created using Catrobat.
From left the right, “Psychedelic”80 allows to tap the screen anywhere to place arbitrary colored andsized off-centered turning discs; “Japanese Rock Garden” allows to use a virtual rake with one’s finger
to create patterns in the pebbles as known from traditional Japanese rock gardens such as in the worldfamous Ryōan-ji Zen Temple in Kyoto, where the creation of these patterns is used in traditional
meditation by Buddhist monks; “Ten Fingers Painting” which allows to draw with all ten fingers at thesame time with discs that constantly change their colors while one paints – all these interactive
animations have an almost hypnotic impact on the users and in particular are endlessly fascinating forsmall children.

Figure 15. Interactive animations with Catrobat.

In a newly funded multi-year project, the Catrobat team has started to implement a TurtleStitch
extension inspired by the TurtleStitch.org project based on Snap!. Catrobat’s TurtleStitch extension
allows to transfer the patterns users create on their phones to a programmable embroidery machine.
Using the concepts of Logo’s turtle graphics, users can create arbitrary patterns, including interactively
generated ones using live user input. These patterns can then be stitched on clothes, bags, etc.
Programmable stitching machines are increasingly becoming available in Fablabs, and recently havealso become available in selected shops from Ikea and Muji. The prices of these machines range from600 Euro for the smallest devices for private uses, up to hundreds of thousands of Euro for very large
industrial machines. A pattern created with a Catrobat program called Dream Catcher together withparts of the corresponding Catrobat code on the left is shown in Fig. 16.

All mentioned Catrobat projects can be found by searching for their names in the “Explore” section of Catrobat’s coding apps, which can be
downloaded from, e.g., Google Play via https://catrob.at/gp for Android smartphones (as of July 2018, the Catrobat app for iPhones is still aclosed beta on iTunes).
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Figure 16. The ‘’Dream Catcher’’ pattern.

On the right there is a photo of a stitching machine in the process of applying the pattern to a T-shirt(the photo is used with kind permission from Andrea Mayr-Stalder from the TurtleStitch.org project,since Catrobat’s stitching extension is still under development).

Logo-like constructions from everyday life objects

Figure 17. Adam Hillman (Witenry) is truly one of the most talented artists on Instagram(https://www.instagram.com/witenry/)Figure 18. Kasey Lund – physics professor, using a ruler (left);
A 3D bugs variation by George Karageorgiev, software developer864

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Conclusion
Thus, the study of the structure of knowledge and creative processes from procedural point of view has
emerged as opposed to a more declarative point of view typical for the traditional education.
Computation provides an appropriate framework for dealing with notions of how to and what if rather
than with notions of what is, which already has an impact on the way we teach art, fine arts, music orliterature. Both fine art and informatics have a specialised vocabulary of notions and use a specificsyntax. The abstract art speaks rather of things to be seen. Similarly, Logo is a language suitable for
dealing with phenomena which we could better understand by modeling them …
What is valuable in programs in a microworld tuned to a particular domain (art in this case) is that they
could be used as glass-boxes. In addition to provide an accessible enough formalised description of theprocess of generation, one can play with them long enough to check their flexibility. It is in this sensethat we talk about Logo as editor of ideas (the editing process including corrections, modifications,refinement and enrichment of the program).
And now, it is time to give the floor again to Achilles and the Turtle:

Epilogue (in which Achilles and the Turtle reflect: what is thepoint?)
Achilles: So, we are not arguing anymore if the computer can think but rather
if what it creates with our help is in fact art…
Turtle: What the computer generates could not be called art in the usual
meaning of it, but when we say this, we should be clear why not… What has
been formalised in terms of Logo procedures is in fact a materialised
conjecture about patterns noticed and reflected in a model of specific art
works. When experimenting with such a model we could add or reject somerules based on the results and thus achieve a certain balance between the
expected and unexpected.
Achilles: Would you please remind me what is a model in the context of creativity?
Turtle: The notion of model used here is in harmony with the definition of Marvin Minsky: To an observer
B, an object A* is a model of an object A to the extent that B can use A* to answer questions that interest
him about A.
Achilles: Entering the shoes of B, I can accept that programs generating works of fine art are models(A*) – while playing with them I started noticing things that I had not seen before, while looking at theoriginal paintings (A). Not to mention that while at school I hated the grammar, the music theory and thecritical analysis of poetry or fine art. I thought that I could enjoy the art without being an artist…
Turtle: The works presented by the authors of this article illustrate the goal behind the computer
generated art. Starting with relatively simple models, one can tackle much more complex ones basedon what one has learned. Thus we do not only learn to model but model to learn (to paraphrase my
spiritual father, Seymour Papert)…
Achilles: Can we look at the computer microworlds in a Logo sense as a means for exploring andediting ideas?
Turtle: Not only – the abstract sciences and the art alike work with imagination. Thus all the computer
environments providing means for exchanging and sharing ideas are enriching these fields.
Achilles: Interesting. Some poets think that the computers are drying the imagination of children. And
you are saying that computers help us seeing and even sharing our imagination.
Turtle: If we paraphrase Bernard Shaw: some look at abstract paintings and ask ‘’why?’’. Others
generate abstract art by computer and ask ‘’why not?’’.

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Achilles: Another interesting thing in the context of visual modelling is that the shortcomings in a model
are not fatal. On the one hand, we could easily figure them out provided that we sufficiently carry out
many experiments. On the other hand they could provide us some interesting artistic ideas. By the way,
I remember somebody saying that the debugging is the most powerful educational idea of 20th century!
But going back to the point of programming in the field of art, I believe that the goal is not to replace theartists but rather to give more people the chance of understanding what could be formalised...
Turtle: And to enter in spheres which they otherwise would touch only as passive consumers. The
enigma of the creativity will be left to the professional artists; they would start from a higher level, theone requiring an insight… It is in fact the creativity which is the common characteristics of the scienceand art.
Achilles: But do the computer works have the chance of being taken seriously?
Turtle: The main reason for computer-generated art to be treated as normal is that the culture of 20th
century broadened the spectrum of what is considered real art.
Achilles: So, you mean that a robot could not pretend to be a man, but if it is present at a Masqueradeball it would be taken for a man who pretends to be a robot…
Turtle: Yes, similarly some computer-generated works could pass for real art, especially if we don’tknow that their author is computer. This is the well-known Elisa effect.
Achilles: Does the opposite also hold – to look for shortcomings of art works just because we think they
are generated by a computer? I believe that people are prejudiced in both directions.
Turtle: The real challenge from the point of view of AI is to create a model, which not only doesthings but also knows that it does, i.e. it is aware of having its own style which could be developed. This,by the way, holds true for creativity in general – why do we consider an art work to be great is not trivial.
But even if we would model art works by means of computer just to distinguish between the great art
and the imitation, we would make a point…

References[1] Carroll, L. (2018). What the tortoise said to Achilles. In Thinking about Logic (pp. 3-7). Routledge.
[2] Hofstadter, D. R. (1980). Gödel, Escher, Bach (pp. 137-138). New York: Vintage Books.
[3] Barcio, P. (2018, July 26).How AD Reinhardt taught
http://www.ideelart.com/module/csblog/post/269-1-ad-reinhardt.html.

us

to

look

art,

from

[4] Cohen H. and AARON (2018, July 26). A 40-Year Collaboration,
http://www.computerhistory.org/atchm/harold-cohen-and-aaron-a-40-year-collaboration/

from[5] Fleischmann G. (2018, July 26). Max Bill and his love
https://www.mitpressjournals.org/doi/pdf/10.1162/07479360152383787.

from

for

at

geometry,[6] Sendova, E. (2001). Modelling creative processes in abstract art and music. In G. Futschek (Ed.)
EUROLOGO 2001 A Turtle Odyssey, Proceedings of the 8th European Logo Conference 21-25 August,Linz , Austria[7] Hofstadter, D. R. (1981). Metamagical themas. Scientific American, 244(1), 22-is16.
[8] Clayson, J. (1988). Visual modeling with Logo. MIT press.
[9] Sendova*, E., & Grkovska, S. (2005). Visual modeling as a motivation for studying mathematics andart. Educational Media International, 42(2), 173-180.
[10] DeepArt , a novel artistic painting tool. (2018, July 26). From https://deepart.io/.
[11] Elgammal, A., Liu, B., Elhoseiny, M., & Mazzone, M. (2017). CAN: Creative adversarial networks,
generating" art" by learning about styles and deviating from style norms. arXiv preprint
arXiv:1706.07068.
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Constructionism 2018, Vilnius, Lithuania[12] Konkursy - OEIiZK. (2018, July 26). From http://konkursy.oeiizk.edu.pl.
[13] Minilogia competition. (2018, July 26). From http://minilogia.oeiizk.waw.pl.
[14] Logia competition for gymnasium students. (2018, July 26). From http://logia.oeiizk.waw.pl.
[15] Papert, S. (1980). Mindstorms: Children, computers, and powerful ideas. Basic Books, Inc..
[16] Katterfeldt, E. S., Dittert, N., & Schelhowe, H. (2015). Designing digital fabrication learning
environments for Bildung: Implications from ten years of physical computing workshops. International
Journal of Child-Computer Interaction, 5, 3-10.
[17] Kastl, P., Krisch, O., & Romeike, R. (2017, November). 3D Printing as Medium for Motivation andCreativity in Computer Science Lessons. In International Conference on Informatics in Schools:
Situation, Evolution, and Perspectives (pp. 27-36). Springer, Cham.
[18] Beetle Blocks - Visual code for 3D design. (2018, July 26). From http://beetleblocks.com/.

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WG4: The Constructive Strategies in Teaching
Humanities with Films81
Jūratė Baranova, jurabara@gmail.com
Vilnius University Institute of Educational Sciences, Lithuania

Lilija Duoblienė, lilija.duobliene@gmail.com
Vilnius University Institute of Educational Sciences, Lithuania

Luc Anckaert, luc.anckaert@kuleuven.be
KULeuven and Vives, Department of Paramedical Sciences and Biotechnology,, Belgium

Wilfried Baumann, baumann@ocg.at
Austrian Computer Society, Wien, Austria

Abstract
Our contribution investigates the question whether is it possible to apply constructionist methods ofeducation in teaching humanities with fictional films. From a more sceptical viewpoint one could argue,that this is not possible for several reasons. The article starts the discussion, suggests some arguments
for justification of positive answer and reveals several different possible experiences of including
constructionism in teaching humanities with fictional films. The theoretical basis for these approaches
are the pragmatic pedagogy of John Dewey, the Deleuzean theory of cinema and Paul Ricouer’s theoryof personal identity. The article deals with the several different possible experiences of including
constructive strategies teaching humanities with films.

Keywords
Constructionism; humanities; teaching films; Deleuze; film theory; personal identity

1. Introduction: The Challenge of the Sceptic
Let’s start to analyse the problem from the sceptical challenge. One can say that teaching social issues
with fictional movies is not in line with constructionism for several reasons. First of all, the sceptic says:
An educational process implies there is a consensus on what has to be taught and especially in thiscase what are the commonly agreed on moral values. While this seems straightforward for manyquestions this will surely be controversial for others. Who decides those cases? The sceptic does nottrust in teacher’s or professor’s moral sense. Secondly, the film industry is just another industry, trying
to make money and under the control of people that follow their own personal interests and not theinterest of society as a whole. For any educational process we should ask if it is beneficial for society
as a whole. There is a thin line between education (which should benefit society as a whole) andmanipulation (which usually only benefits the manipulator).
Thirdly, Constructionist learning is when learners construct mental models to understand the world
around them and learning can happen most effectively when people are active in making tangible
objects in the real world. But there is hardly anything more passive, according to the sceptic, thanwatching a fictional movie. It tries to involve us emotionally, to incite feelings like anger, guilt, pity, pride,
love, fear, empathy, curiosity, etc. The fictional movies merely give the audience the feeling to be
involved at best, which is not in the sense of the active involvement required by constructionism.
Fourthly, the sceptic supposes the best what one can expect from the fictional movie is some sort of theinformation. Information that is presented visually is often easier to understand and more convincing
than textual information. The effect of visually acquired information is in some cases longer lasting thanother information because visual information can sometimes be recalled easier than other information.
The half of the article is based on the investigation included into the project “Philosophical Sources and Problems of Multimodal
Education” financed by the Research Council of Lithuania (No.S- MIP-17-37)81868

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Written text, like a novel, can just as well try to evoke powerful images but with a movie a much larger
group of people can be addressed. Many people lack the reading competence, the imagination or thepatience to read a novel. In a movie it is easily possible to add subtle, implicit, additional informationthat is registered and processed subconsciously at least partly. This somewhat subliminal content
passes the critical barrier every person is equipped with, much more easily because it is difficult to
scrutinize information whose acquisition someone is not aware of. This additions could be specific visualclues, for example like subtle properties of the clothes the hero or the villain is wearing, it could be thesound track or it could be generic visual effects like a fish eye view, a tint, a soft focus or a blurred view.
As a consequence from above, some people cannot question what is presented to them visually they
believe everything they see in a film is real. And this visual access to the information has nothing to dowith the student-centred, discovery learning.
Firstly, despite of these doubts the sceptic supposes that some movies, albeit usually not the ones
dealing with social issues, have large followings (Start Trek, Rocky Horror Picture Show) that attend fan
screenings where members from the audience dress up in costumes and re-enact scenes from themovie. Although this is an act of imitation rather than a learning process, it hints that active involvement
is possible and even desired by parts of the audience. How could such an active involvement look like?
There could be physical symbols for representing characters, important objects or locations from themovie, satisfying the haptic element of Constructionism. Participants could discuss and re-enact
alternative endings, additional scenes, sequels, turns of the stories that they didn't like, ethicalimplications etc. There could be a prepared list of tasks to perform or questions the group has to workon. Members of the group could change the story add new characters or change their role. The results
could be added to a database and the group could be able to compare their results to results from othergroups.
Also, participants could be encouraged to create a work of art inspired by the movie they watched. This
could be a drawing, a painting, a poem… This work of art can but not necessarily has to reference any
elements from the movie. This work of art could express different feelings that the viewer experienced.
But this work could also contradict, challenge or ridicule the movie.

2. Theoretical Basis: Addressing the Scepticism
The response to the challenge from the followers of using fictional movies in constructionist learning
would be following. First of all: to the challenge that educational process implies there is a consensus
on what has to be taught and especially in this case what are the commonly agreed on moral values wereply: Distrust for the teachers ability to discern by herself/himself what the moral values are is thefeature of totalitarian education, where all the rich variety of ethical life is reduced to several simplified
concepts. We consider that the educator’s diploma suppose the required ability in the incommensurable
alternatives of real life take the courage for the moral decisions, also in addition to it we are following
Immanuel Kant’s insight that rational agent is able to understand the essence of categorical imperative
for the reason of his rationality (even in the case she/he is Moon dweller).
Secondly, we do agree with the sceptic that film industry is something different from the process ofeducation. It has its own pitfalls, which were reflected by the film philosopher Gilles Deleuze, when hewrote: “Cinema is dying, then, from its quantitative mediocrity. But there is a still more important reason:
the mass-art, the treatment of masses, which should not have been separable from the accession of
the masses to the status of true subject, has degenerated into state propaganda and manipulation, intoa kind of fascism which brought together Hitler and Hollywood, Hollywood and Hitler” (Deleuze 1989:
159). But on the other side cinema is not the only one of the spheres of culture with the sin ofcollaboration with fascism. Yes, Leni Riefenstahl, the talented film director can be blamed for her
sympathy with National Socialism But the fact that such famous writers as Ezra Pound or T.S.Elliot orLouis-Ferdinand Celine also expressed antisemitic views does not stop from using literature in theprocess of education. The same case is with the fictional films or the other branches of art. The kitschin the painting or unsuccessful piece of the music do not exclude the power of talented works. The factthat some sort of film production is mediocratic, manipulative or express doubtful values does make thefilms as the real pieces of art less valuable for the purposes of education. How to find the distinction?
The suggestion of Deleuze would be very simple: just concentrate an attention to the good films. In his869

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philosophy of cinema he had found and discussed on about four hundred ‘good movies’. Of course, inthis case the educator should have some sort not only moral rationality, but also the developed aesthetic
taste to discern between mediocratic and good movies. In difference from the sceptic we treat fictional
movies first of all as the works of art. So we see deep need for using arts in the teacher’s education.
Thirdly, we oppose the sceptics’ opinion, that the films influence only the emotions of their spectators.
It would be very limited aesthetic theory. As we treat them as the pieces of art, we consider theirinfluence in regards to Kant understands of the nature of aesthetic taste, it means as the disinterested
game of imagination and intellectual abilities. Fourthly, so in contrast to the sceptic’s view, we do notmake emphasis on the process of the gaining information when using fictions films in humanitarian
education, but consider them as the challenge for creative mental learning and provoking critical abilitiesof the students. We are following Deleuze’s insight, that watching the good movies first of all is
encounter, which inspires a thought. But not the thought stemming from the sources of everyday
experience, but the special type of the thought, in Del;euze words “the identity of thought with choice as
determination of the indeterminable” (Deleuze 1989: 171). The spectator of serious movies faces the
problem: how to withstand something which is unbearable and unthinkable in reality. So firstly, we doagree that it is possible and useful to use methods of creating the different endings of films stories, thesame as it would be possible to play with the written texts. It can create much fun and amusement forthe students as well as for the educator. But the question remains about the value and purpose of suchrecreated endings. Do they have the value in itself as the source for the entertainment? We do not trust
the young people to take the life seriously and construct by the use of their imagination and mental skill
their own reading of serious cinema as a valuable piece of art?
Modern cinema is only indirect
representation of reality. But in evoking the special kind of thinking, it has the power to restore our belief
in the world, says Deleuze (see.Deleuze 1989: 166).

3. Methodology
The research is provided using and combining theoretical analysis and three case studies. The thought
experiment is used in justifying the possibility of applying the constructivist methods in teaching
humanities with films. The analytical review of critical literature is used to clarify the theoretical premisesof research. The qualitative experiment is used investigating the possible involvement of students. The
phenomenological method is used reflecting the analysis of the data. The comparative method is used
to investigate the distinction between associational and differentiational type of thinking. The
hermeneutical method is used link the Biblical texts with possible interpretations of the film.

4. Teaching with films: associated and differentiated access tothought’s construction
Looking on the experience of teaching with films in different countries, we can find more than few strong
traditions. We would like to analyze at least two of them. One, which is oriented toward teaching social
problems, is elaborated by William B. Russell in his book Teaching social issues with films (2009). The
other one, which is elaborated by Jasson Walin in A Deleuzean approach to curriculum. Essay on apedagogical life (2010) and Jan Jagodzinski Visual Art and Education in an Era of Designer Capitalism(2010), also their publications in the book Deleuze and Guattari, Politics and Education. For a Peopleyet-to-Come (2014), suggests new way of teaching with films for critical and in the same time creative
purposes. Both traditions are quite new, both are oriented toward teaching and learning to think in a
special constructional way and the second one, following Deleuze, operates not with association, butwith differentiation.
The two schools mentioned above demonstrate that different approaches to apply films for teaching andlearning can follow distinctively different methods. They also show what understanding of the world any
method constructs and what results we can expect. The first tradition of teaching with films, represented
by Russell teaches to understand, to recognize and to use associations, whereas the second one,
represented by Wallin and Jagodzinski, teaches to criticize, to experiment, to invent and to create.
Construction of concepts, thoughts and meanings - in other words - ways of thinking are very important
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for both of them. Learning by construction of knowledge forms the basis of constructivism (Phillips,
1995, von Glaserfeld, 1995, Piaget, 1955, Vygotsky, 1997), and constructionism (Papert, 1980; Berger,
Luckmann, 1967). Both traditions mentioned above are sometimes refered to with either of those terms
by scholars, depending on approach and arguments. We suggest the way which allows going beyond
constructionism.
Some of scholars, investigating constructionism and constructivism, and separating cognitive
constructivism from the metaphysical constructivism think, that “metaphysical issues are irrelevant to
the pedagogical enterprise except when explicit philosophical issues arise”. (Grandy, 1998, 415). Thisis the state which is applied by Grandy to science teaching. We are thinking about social and humanities
having in mind studies on pedagogical or teacher’s training courses, teaching as well as learning at
school. We think that metaphysics plays an important role for pedagogy and when teaching criticalthinking and agree with the idea of Rosi Braidotti about the purpose of humanities, expressed with thesewords: “We know by now that the field is richly endowed with an archive of multiple possibilities which
equip it with the methodological and theoretical resources to set up original and necessary debates withthe sciences and technologies and to meet other grand challenges of today” (Braidotti, 2013, 15). So in
which sense humanities and teaching to think constructively is important during the classes for film
analysis and why it is beyond constructionism?
In the books Teaching social issues with films Russell suggests methods, schemes, templates of survey,
describing and exemplifying the tools very precisely. His main focus is on teaching social problems,such as poverty, drugs, asocial/risk families, bullying, addiction, depression, aids marginalization ofcultures, care about animals and others sensitive topics (30 social issues). These problems aresuggested by Russell to analyze because of related to American social life, culture and pedagogy, but
not only; it is common for many countries. The theoretician formulates concrete questions to work withfilm material: questions for gathering information, analysis, interpretation and creation. Students canrecreate the end of film or even synopsis, to present their fabula (how they understand film with theirpersonal narration). They are inspired to discuss hot problems, to evaluate the actions of heroes andposition of film director. The teachers are instructed with the list of films, proper according to student’s
age and must to avoid the scenes of violence and sex.
What does Russell use for theoretical standpoint and methodology? It is mostly Driscoll and Engle
teaching of critical thinking, decision making and reflective thinking (Russell, 2009). That is also close
to the Deweyan tradition of problem solving, trying to find answer for the question one has. Dewey itcalls interest. Interest is the main vehicle. So films give great possibility to compare what is already instudents experience and to think following moving image. Dewey in Democracy and Education says:“Any activity with an aim implies a distinction between an earlier incomplete phase and later completing
phase; it implies also intermediate steps. To have an interest is to take things as entering into such acontinuously developing situation, instead of taking them in isolation”. (Dewey, 1997, 137) and adds
quite pragmatic note, that thinking “is the intentional endeavour to discover specific connections
between something which we do and the consequences which result, so that the two become
continuous” (ibid, 145). Theory of teaching with films, based on this standpoint, suggests constructionof world view and especially understanding of social field through critical and reflective thinking anddecision making. It deals with connection of various elements and involvement of new elements, whichappears in the process of learning, also reconstruction of presented ideas based on students own
understanding, values and experience. Russell states, that such tool for film analysis “increase students
interest in the material being studied, thus allowing it to become more meaningful and relevant to thestudent. Furthermore, authentic classroom activities help teachers achieve instructional goals such asretention, understanding, reasoning, and critical thinking”. (Russell, 2009, 2).
Russell presents a list of films, mostly well-known and popular (Schindler’s list, 1993, Trainspotting,
1996, The Terminal, 2004, Scarface, 1983, American Girl, 2002, The Virgin Suicides, 1999), which areawarded in international films’ festivals. They are very good and undoubtedly fit for analysis of social
issues, giving a lot of material for the interpretations. On the other hand, some films, when teaching toperceive situations associatively and to make decisions in accordance, give cliché instead of creativity.

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Wallin and Jagodzinski suggest bit different kind of films, probably not always good for teaching
teenagers, much more fitting for curriculum studies at the university level. Anyway, they expect to bringto the schools the new way of thinking. Wallin suggests film of Jim Jarmush Ghost Dog: the way of
Samurai (1999), also Quentino Tarantino Kill Bill (2003) and Todd Haynes I’m not there (2007). He
demonstrates how to work with the problem of time and atemporal person, how to link different
heterogeneous lines of actions, how newly to treat the absent of arguments for the agreement, and the
problem of multiply identity. In that way he expects to protect students’ thinking from cliché, stereotypes,
and to escape technics of repression during pedagogical activities. Teaching with films according to him
is step forward from banking education, criticized by Paul Freire. Wallin explores Deleuzean andGuattarian concepts and cinema theory about time-image, which interrupt into movement-image and inthat way brakes dialectical understanding of film actions, instead - open space for imagination andunexpected combinations of elements which fulfill the cracks, gaps, ruptures in films, which appear by
specific montage. Montage of such films is differing from classical montage, because is oriented toward
presentation of intervals rather than connections of shots.(Milerius, 2013). It can be called as
montrage82. Here is a lot of space for linkage of different heterogenous elements and planes, because
the main vehicle in film teaching is not the interest, but desire and affect. How they differ? If the interestis oriented toward concrete results, desire much more works for involvement into the process of creation
through the affect (Deleuze, 1995). That means active participation in creational process, which is not
personal, it means being a part of assemblage. So the construction is not personal and even not social,
much more mechanical , depended on combination of organic and artificial, real and imaginary, social
and natural. It is construction not of forms, but of forces. It is not about identity and individual, but about
individuation and becoming. As Deleuze states: “Cinema always narrates what the image's movements
and times make it narrate. If the motion’s governed by a sensory-motor scheme, if it shows a character
reacting to a situation, then you get a story. If, on the other hand, the sensory-motor scheme breaks
down to leave disoriented and discordant movements, then you get other patterns, becomings rather
than stories”.(Deleuze, 1995, 59). Breaking, crossing and displacing appear as main tools as well as
cracks and ruptures. Stemming from Jan-Luc Godard films Deleuze states: “This is not an operation of
association, but of differentiation, as mathematicians say, or of disappearance, as physicists say: given
one potential, another one has to be chosen, not any whatever, but in such a way that a difference ofpotential is established between the two, which will be productive of a third or of something new”
(Deleuze, 1989, 179,180). Such a method Deleuze calls “Between”: between two visual or soundimages, between two affectations, between sound and visual image et ctr. Wallin also uses Godard wayof montage for the interpretation of I’m not there, and especially Godard words: “It is not where you takethings from- it’s where you take them to” (Wallin, 2010, 194).
Working in the same tradition Jagodzinski analyzes films such as Sergei Eisenstein’s Battleship
Potemkin, (1925), Joaquino Fernandezo, Colino Gunno Indoctrination (2011) or Lana and Lilly
Wachowskis The Matrix (1999), when first one demonstrates dialectical move and spectator
involvement into action and third one – erases border between reality and hyper reality. Using theseand other films Jagodzinski pay attention onto the message of ideology, which is perfectly demonstrated
in the film of Eisenstein and from the other side – possibility of another construction of world view, which
is much more complicated and much more rich, integrating what is imaginary, virtual and only possible,
as it is in the film of Wachowskis. In both cases students are inspired to understand the construction of
their worlds, though access is different in both films cases: to construct one’s vision according toideology and concrete expectations (Battleship Potemkin) and to show how one can be constructed inmodern, much more complicated reality, through the erasing borders between real and imaginary,
natural and technological, human and not human (The Matrix). Jagodzinski criticizes construction ofpeople consciousness, especially of students, and following Deleuze outlines new way of presenting
image, which does not fit for narrow formation of thinking as well as marketization and selling of image
for the masses. Moreover new way of films montage, based on differentiation and intervals, helps todestroy usual way of thinking, which is easy going for manipulations, dominant policy and dominant
pedagogy.

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The other kind of films, which he chooses for the analysis in the classes – is of performansist Bill Viola,
who is famous for his video installations such as The Greetings (1995), Five Angels for the Millenium(2001), The Raft (2004). His installations allow appearing in different realities. Viola works with different
consistencies, especially water and absolutely different speed of movement in it. That is also related todifferent time perception, quite close to filming in style of slow mood. Jagodzinski’s examples of video
projects and fiction films allow understanding world as infinitive; more than one usually see and hear.
This method helps to open thinking for imperceptible and what is only possible. It is not the matter of
how film or performances directors express their view, rather how student are involved in film as
machine, working through affects, percepts and concepts. So the student during the humanities lessons
participating actively and creatively creates the thought and is created by thoughts. She or he becomes
one of the elements, integrated in the assemblage as creational machine. Not because of ideologicalconstruction, but because of creational event, this happens during the classes. Jagodzinski, who follows
Deleuze, thinks that such a films allows to be much more closer to virtual world, full of surprises, newcombinations, and in that way to expend teaching and learning possibilities. The aim of visual studies
according to Jagodzinski is to investigate paradoxes of “lived” life. “The power and force of the image
in an expanded sense (be it in performance, film, television installation, and so on) reside in its affect or
intensity in parallel with its contents. This means that semantically or semiotically ordered levels ofanalysis—representation as such—are no longer adequate for the task. A turn to philosophies of theunconscious that address the paradox of these two levels—the semantic and the affective—as theywork and twist with each other in different contexts becomes a necessity for VCAE’s83 advancement”(Jagodzinski, 2016, 104).
The author of this sub chapter experimented with many films, using different methods, working not withschool students, but with university students of pedagogical studies. The main result working with themis their huge interest in new paradigm, new way of thinking, constructing their inner and extrinsic world
not with very concrete separate elements of clear shape and content, but with elements of absolutely
different level, plane, from the different assemblages. They work on combining heterogeneous elements
and consequently inventing the world in the process of becoming. They watch and interpret but also
create practically during the course of Visual studies and education. Students already created their own
multimodal projects, trying to find proper images for their ideas, to combine them, to add any existed or
to create sound track. They presented wonderful examples of mixing elements of different levels orfields and producing very unique audio-visual constructs, for example: mixing videos from funeral of
President Kennedy and concerts of The Beatles. In this combination the visual images, expressing
different crowd’s emotions are absolutely mischievous. Additionally it was complicated with the soundtrack, which was created separately and was not diegetic - not coincided with visual image. The feelingof reality was mixed with fantasy of creator and spectator. The perception and understanding of separate
events were blundered and in that way created enigma of the film. The other students’ film used theimage of the legs of school children, their move under the tables, and special sound track. This
combination provoked to think the idea of film in many directions. The question is – why educational
programme students, who are ready to go to work in schools choose to experiment with sound andimage in unexpected way looking for new combinations in contrary to the creating projects in traditional
way? The same tendency is evident during the analysis of films in the classes, such as Peter Weir Dead
poet society (1989), Eric Toledano Intouchables (2011), Hal Hartley Unbelievable truth (1989) and Wim
Wenders Wrong move (1972), then huge attention was shown for the Wrong move, which mostly
expresses new way of thinking; thinking of infinity, experimenting, inventing and thinking in different
directions according to the unpredictable vectors of nomad. The result of teaching with films is notstudents’ thinking according to given new constructs of film, but thinking side by side with the inventionof constructs, experimentation and creativity. Such a methodology of teaching thinking is not as muchhumanistic, as it is post-humanistic, oriented toward link of all fields- human and technologies, naturaland artificial, actual and virtual, and it is beyond constructionism. Through the differentiation, cracks,
ruptures, inbetweenness, involving also new elements, it deals with broad scale of elements in the sametime is the part of much bigger creational and constructional process, which is more than human.
Coming back to the question about empathy, feelings, which are expressed in the films: will we skip
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them? New teaching thinking does not stress emotional field. It combines perceptions, sensations andthoughts. All are important, though thoughts are axis for the construction beyond already legitimatedconstructionism.

5. Constuctivism in Cinema: What Films are Suitable for
Humanitarian Education?
Matthew Kearney and David F. Treagust demonstrated how in teaching Physics the educational
research on constructionism can be united with the development and use of a multimedia computer
program. Using interactive digital video clips they presented sixteen real world demonstrations toPhysics students in order to elicit their pre-instructional conceptions of Force and Motion and encourage
discussion about these views. (Matthew Kearney, David F. Treagust 2001). We ask alternative question:
can the educational research on constructionism and multimedia be useful in teaching not only Physics,
but also and Metaphysics, having in mind not only Philosophy, but also the Humanities in the broader
sense. Is multimedia able to elicit students pre-instructional conceptions of Human reality andencourage discussion even about education itself? Can it be considered as the Copernicus turn in
Humanitarian education?
Our preliminary hypothesis is that some cases of multimedia really are able to meet this particularchallenge. We restrict this particular our research to fiction movies.
The history of fiction movies reveals the origin of the term ‘constructionism’ and its practice. It originatedin Russia at the beginning of the 20th century in the sphere of revolutionary architecture and design. It
was also used and reflected by Russian film director Sergej Eisenstein in the process of creation of therevolutionary movies. Eisenstein discovered not only the possibility of the dialectical editing, but also
reflected how to construct the fiction movie in order to impress the public and to impose the ideas thecreator intends to impose. Every piece of art, says Eisenstein, has an educational purpose (Eisenstein).
This educational purpose in film is reached by the dialectical construction of the film as organic whole,which leads to the pathos experienced by the spectators when the film reaches its climax. Eisensteinrealized the artistic essence of the cinematic image as “producing a shock to thought, communicating
vibrations to the cortex touching the nervous and cerebral system directly”‘ (Deleuze 1989: 151). In his
book Problems of Film Director Eisenstein step by step reveals the inner laboratory of the constructing
his the most popular movies, which imposed the belief of the inevitability of the Socialist October
Revolution in masses. Was this revolution in reality so inevitable? Or maybe just the result of thesuccessful conspiracy of the small group of Bolsheviks and Eisenstein with his films persuaded the
public of its historical meaning? After watching the Eisenstein’s film Battleship Potemkin Joseph
Goebbels, the Nazi propaganda minister, reflected; “anyone who had no firm political conviction could
become a Bolshevik after seeing the film.’ The most celebrated scene in Battleship Potemkin is themassacre on the Odessa Steps. The sequence is built from separate shots combined in a very dynamic,
rhythmic way. Eisenstein edited the film to produce the greatest emotional response, so that the viewer
would feel sympathy for the rebellious sailors of the Potemkin and hatred for their overlords. The casewith the Eisenstein’s constructivist approach used for the aims of destructive and dangerous
propaganda reveals the limitations and possible pitfalls of the constructivist approach. If the truth about
the reality is unattainable and we rely as on knowledge on our own world view constructed by ourselves,
why not to construct our own truth and by the means of personal genius not to impose on the rest of
population? Where are the limits indicating the film director have to stop? As a matter of fact Eisensteinin the history of film making as usual is praised for his genius findings in technique of film constructing
and the destructive impact of his movies to the history of his country as usual is skipped with silence
and tolerance (see Baranova 2017).
The opposition to Eisenstein’s constructivist theory of editing was anti-constructivist approach to film
editing suggested by the Russian film director Andrei Tarkovsky. Tarkovsky in his book Sculpting inTime opposing Eisenstein conception of montage wrote: “I feel that Eisenstein prevents the audiencefrom letting their feelings be influenced by their own reaction to what they see. When in October hejuxtaposes a balalaika with Kerensky, his method has become his aim, in the way that Valery meant.
The construction of the image becomes an end in itself, and the author proceeds to make a total
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onslaught on the audience, imposing upon them his own attitude to what is happening” (Tarkovsky
1987: 118). Tarkovsky rejects constructivist approach trying in films to catch the glimpses of reality
itself. The signs of reality coincide with the signs of time. Reality reveals itself through flowing time.
“Time in cinema becomes the basis of bases, like sound in music, colour in painting”, writes Deleuzereflecting Tarkovsky’s idea (Deleuze 1989: 288). Montage, says Deleuze, as following Tarkovsky, canbe only indirect representation of time. But how to reach the direct image of time? How to approach thereality itself? To our research the following question is also very important: has an educator moral rightto use films in teaching process in the manner of the constructionism practiced by Eisenstein (organic
model of cinema education, using Deleuze’s terms), or should an educator encourage the students to
be influenced by glimpses of flowing reality he is able to discern in hors-champ (beyond field) of thefilm (crystalline model of cinema education)? Can the films be used as ideological materials in educationor just as the source of more profound questions about human reality? If one chooses for the answer
the second alternative the questions still remains: how it is possible to create the movie without
ideological input? Do such type of a film have any educational meaning at all? What kind of film should
be used in humanitarian education?
Deleuze would had answered: good films. Not depending on genre or topic. Good films, according to
Deleuze, are those which are able to restore the link between man and the world which is recently
broken: “Only belief in the world can reconnect man to what he sees and hears”, concludes Deleuze(Deleuze 1989: 166). Deleuze does not state that the films will open what reality is in itself. He does
not join the position of intuitive realism. And in this respect he is closer to constructionism than to realism.
But emphasizing the need for belief in reality he becomes rather close to William James’ fideism. This
statement can be declared as the one of the main educational aims encouraging to include films in the
curriculums of the humanitarian education. “Something in the world forces us to think. This somethingis an object not of recognition but of fundamental encounter”, writes Deleuze in Difference and
Repetition (Deleuze 139). Watching films one encounters the glimpses of possibilities from the different
specter of perspectives one is not able to experience in everyday life. We will add: the selection of films
for humanitarian education needs to meet the requirement of hidden secret directly not expressed in
the image. The education process should presuppose the hidden encounter. As one of the first year
philosophy student wrote in essay about impression of watching Krzystof Kieslowski film Three Colors:
White Trois couleurs: Blanc: “Every minute of watching film and the feeling afterwards I experienced
strange feeling: I can define it as puzzlement, at the same time as the silent admiration, but also theinability to comprehend what is going on” (K.K.). The learning experiment with the film was provided inorder to discuss the concepts of optimism and pessimism, the will to belief, the will to die and the will to
live, stemming from William James pragmatism in the course of the philosophical ethics. The encounter
with the film as event leads twenty students who participated in this educational experiment to thedifferent conclusions. Some of them concluded that the moving stimulus for the main character Karolradically change his life was just the fact of his temperamental optimism, some of them – the obsession
with love for his lost wife, some of them – the need for revenge, one of the students refused to suggest
his own explanation saying it would be too oversimplified. All the views were expressed during theseminar in the form as a conference. The educator had no purpose to present one final and generalized
point of view pretending to the ‘truthful interpretation’. She only expressed her own point of view open
to criticism as well.
Kearney and Treagust in their already mentioned experiments with using digital multimedia in promoting
a student’s conceptual development in the domain of Physics discerned four methodical steps: a.
articulation and/or justification of the student’s own ideas; b. reflection on the viability of other students’ideas; c. critical reflection on the student’s own ideas; d. construction and/or negotiation of new ideas”
They also concluded that this “program provides students with an opportunity to engage in ‘sciencetalk’ … and a means of developing science discourse skills (exploration, justification, negotiation,
challenge etc.) (Kearney and Treagust 2001: 69).
The first two steps can be noticed in the experiment with the movie White provided in the course ofphilosophical ethics. Are students able to learn from these different points of view in this open discussion
and to modify their primary insights, as is presupposed in the pedagogy of social constructionism? Is
there a need for this modification mainly in humanitarian education? Or the educational outcome from875

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this teaching experience is the encounter with the inevitability of living of pluralistic social universe? The
educator also supposes that something in the process of pedagogical experiment should remain as thesecret also for the educator. On the other hand analysis of the essays reveal that the students are able
to change their opinions even during the process of the reflection in writing and keeping innermonologue with themselves. Student A.L. in essay starts the reflection of movie from sceptical tones.
He had read in advance and before that from all the trilogy of Kieslowski Three colours this particular
film White is the weakest at all. After the reflection in written form he ends with rather different
conclusion: “It is very difficult to summarize such a subtle and in many aspects ambiguous (from a pointof view of and moral posture) film White by Kieslowsky trying to reduce it to one or the other stimulus.
It would be an idiotic attempt. Kieslowsky is not sorcerer who pronounces how the things should be andhow it is necessary to behave. But the geniality of the film and certainly of Kieslowsky reveals itself
when the situations – hypothetic or realistic open the plan for the question: what is this, which forces us
to act and not to give up. And the complaints expressed in the beginning of essay do not seem any
more so justified (because of the unclear end and all other things). The meaningful is the opening up
the field for discussion” (A.L.). “It was very interesting for me to learn that it is possible to consider
Karol’s action as the revenge or obsession with love. It did not come to my mind when I watched themovie”, reflected one of the students K.B., who relied on the alternative of temperamental optimism.
So the films suitable for humanitarian education are the films which resist one straightforward
interpretation and create the field for possible multidimensional social constructionism in learning.
Deleuze in his philosophy of cinema discerned two kinds of constructionism in film making: the idea ofa vertical construction going right to the edge in both directions suggested by Eisenstein – and alreadydiscussed; “The whole was thus being continually made, in cinema, by internalizing the images andexternalizing itself in the images, following a double attraction. This was the process of an always open
totalization, which defined montage or the power of though” (Deleuze 1989: 193). On the other side
Deleuze mentions the constructionism of French director Jean Luc Godard who’s aim was not toconstruct the whole, but who supposed that 'the whole is the outside'. The point of constructionism ofGodard is quite different. In the first place, says Deleuze, the question is no longer that of the association
or attraction of images. “What counts is on the contrary the interstice between images, between two
images: a spacing which means that each image is plucked from the void and falls back into it” (Deleuze
1989: 193) From the point of view, of virtual constructionism, which tries to reach the whole Godard
experiments with the disconnected images which bring together Golda Meir and Hitler in lci et ailleurs,
Deleuze acknowledges, would be intolerable. We are also not ready to suggest the reading of Godard
films for bachelor students. But for the master students who took the course of Deleuze studies seminarthe experience of reading of Gardard’s films can be productive. Godards experiments with the Hitler
created the scandal. Deleuze is on the side of Godard: “But this is perhaps proof that we are not yet
ready for a true 'reading' of the visual image. For, in Godard's method, it is not a question of association.
Given one image, another image has to be chosen which will induce an interstice between the two. This
is not an operation of association, but of differentiation…” (Deleuze 1989: 193).

6. Film as an imagination for creating identity
Already Aristotle pointed out the mimetic character of the arts that lead to a catharsis. The catharsis hardly described by Aristotle in Poetics - can be understood as the therapeutic concept and insight into
one’s own emotional relationship to reality and the harmonization of emotion and reflection. The
catharsis is the result of the mimetic-narrative structure of the identity. In narrative mimetics, the idem
is challenged to follow the long path of emotional identifications that can lead to a cathartic insight into
the narrative constructed identity as Mimèsis II. The film Va, vis et deviens (Radu Mihaileanu) illustrates
this process. Within educational processes he forms an invitation and challenge to purify the own
mimetic processes by placing them in a broader metaphorical context. The film is the imaginativemetaphorical enactment of the identification process
The impressive film Va, vis et deviens paints the intriguing fates of an Ethiopian child who gets lost in
the misery of an African refugee camp in Sudan. As an adopted orphan, Solomon grew up in Israel. He
studied medicine in Paris as an adult and married the girl with whom he had been in love for a long time876

Constructionism 2018, Vilnius, Lithuania

in Jerusalem. Eventually the main character returns to Sudan as a Médicin sans frontières and finds his
old and shriveled mother in an emotional scene.
At the heart of the movie there is a kind of Talmudic discussion between the white Jewish boy Michael
and the black adopted son Solomon. The question of Adam’s skin color is at the heart of the discussion.
Like in a miniature, the central challenge is meticulously worked out.
The imposed question is: “What is color of Adam’s skin?” Solomon had asked the rabbi to participate in
the discussion in order to prove his Jewish identity. The white Michael is the first to be given the floor
and makes the following statement, skillfully supported by references to the Torah:Michael:Adam was created after the image of God. And the beautifully chosen color was white. We
were all this, in the beginning.
After the flood Noah and his sons left the ark. Noah cursed the descent of his son Cham, whose
grandson Canaan said “Cursed is Canaan: slave to his brothers he will be” (Genesis 9:25).
Kush, the eldest son of Cham, is the heir of another curse. Certain descendants of Cham aresaid to have black skin.
This is how it happened: Kush became black and from him were born the Kushim, the black
people of Africa. The descendants of Cham have become slaves and blacks.
Michael’s argumentation is built up in the three steps of the classical mythical narrative of the golden
origin, the decay and the restauration. First of all it is said that man was originally created after theimage of God. The objective reference is linked to a subjective interpretation from which the conclusion
is drawn that everyone originally belonged to the white race. The white skin is linked to the createdoriginal. Then social inequality is justified by the flood and by the curse of one human being by another.
The curse as a sanction leads to the slave status. Finally, the racial differences are also explained by acurse. Social inequality is linked to racial inequality.
Salomon initially wants to repeat the starting point of his opponent, as it fits within the genre of the
proposed discussion:
Salomon:Are Adam and God of the same color, white?
... Michael has said. ...No.
After the hesitation and interruption of the discourse, Salomon looks at the Qes Amhra, the Jewish black
religious leader who had fled Sudan with him. Solomon had come to know the man as the figure who
protested against the second-class position of the Falasha Jews by invoking their recognized Jewish
identity as legitimate descendants of the Biblical king Salomon and the African queen Sheba. The Qes
helped him write letters to his mother in Sudan, thus forming the symbolic link between Solomon andhis lost homeland. When Salomon wants to follow on from Michael’s argumentation, the Qes nods no.
Solomon remembers his words immediately preceding the discussion. When writing to his mother, hisletter was rejected as a mere repetition of pre-existing models and formulas:No, this is not good. Interpret, Schlomo, do not repeat like a parrot. Interpret. Insert Schlomo into thetext. Let us take this up again.
Salomon interrupts his discourse, which threatened to result in a mere repetition of the existingarguments. The duplicate can never have the qualities of the original. Pure repetition of actions andspeech leads to decay. It lacks the imagination of Mimèsis II that links the present with the past and thefuture. Ricoeur understands human identity in a non-substantialist way. Human identity is a fragile
phenomenon. It rests on a twofold dialectic. The identity can be understood as sameness (idem) andselfhood (ipse). Within this dialectic, identity is formed by a threefold mimèsis. Sameness expresses
the permanence in time, the structure of my identity, or what I’m. Selfhood expresses the changing
identity of who I am. Within this dialectic, there is an apodictic element. As sameness, identity expresses
the permanence in life, the constant element of identity. But this apodictic identity is not adequate. The
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discovery of the meaning of one’s own lidentity is a life-long adventure. Moments of joy and fear, lifeand death, self-loss, psychological traumas, erotic experiences and hospitality form the concrete
content of the person I am ultimately. This construction of identity is happening in a narrative way.
Understanding oneself takes place in the exploration of the meanings of the ever-changing ipse. Bymeans of narrative identifications, the subject constructs the diachronic self-constancy of theexperienced identity within the continuity of the identity (ipse). Therefore, identity is not a fact that canbe unfolded, but the long detour in which the identity gets a concrete meaning throughout the story oflife. The alterity is multifaceted here. This narrative aspect has a triple mimetic structure. The second,
central mimesis (Mimèsis II) interweaves the activity of storytelling with acting in its temporal character.
It is the textual configuration (emplotment) that links with the past (Mimèsis I) as prefiguration and withthe future (Mimèsis III) as refiguration. This has an imaginative structure. In the narrative mimetic
process, the identity construction takes place. The relationship to the triple absence of time isconstitutive. A metaphorical texture is woven through the mimèsis, in which the idem acquires meaning
as an ipse.
Salomon makes a radically different choice and takes a new start. The act of speaking is congruent withan alternative vision of creation. The contradiction between the two visions of creation summarizes thestake of the film in a very concentrated way. Unlike Michael, Solomon understands creation as acontinuous beginning and a call to responsibility. Alluding to a Midrash passage on Psalm 90:3 where
the word and the Torah are marked as preceding the creation of the world (“In the beginning, two
thousand years before the heaven and the earth, seven things were created, the Thora, written withblack fire and white fire”) and alluding to the opening of the Gospel of John (“In the beginning was theword”), Solomon takes a new beginning.
At first, there was the language, the word. God created the earth and life by giving breath to the word.
God has believed in Man. In everyone.
Creation begins anew in every human being and God believes in every human being. In every humanbeing there is a new beginning. In this vision, creation is not a one-off event in which every new person
is a repetition of the original, broken condition of existence that is marked by evil. As Franz Rosenzweig
elaborates in detail, creation is the continual re-creation of new forms of life, the abundance of beingborn over and over again. Nothing precedes this creation. This means that no determining or limiting
factor - or determining logic - precedes creation that would restrict free creativity. Every creation is aradically new beginning. In this context philosophers and theologians speak of a creation from nothing
or a creatio ex nihilo. We find this thought very richly expressed in several texts by Jean-Luc Nancy. In
his book Being Singular Plural, this French philosopher reflects on human existence as absolutely
irreducible and singular, but also as fundamentally committed to our fellow human beings:
Right away, then, there is the repetition of the touches of meaning, which meaning demands. This
incommensurable, absolutely heterogeneous repetition opens up an irreducible strangeness of eachone of these touches to the other. The other origin is incomparable or inassimilable, not because it issimply ”other”, but because it is an origin and touch of meaning. Or rather, the alterity of the other is its
originary contiguity with the ”proper” origin. You are absolutely strange because the world begins its
turn with you [le monde commence à son tour à toi].
The repetition referred to here is not the faithful copy, which is an imitation of the previous one. However,there is a repetition of constantly recurring sources of meaning that are mutually irreducible. Every
human being has its own individuality because the world in every human being miraculously begins
anew. Through this new beginning every human being is connected with his own origin. Nothing
precedes this origin. In other words, being human is not conditioned by the past, the body, the colour ofthe skin, the gender, etc. Nancy explicitly links this thought with the creatio ex nihilo: “Not only is thenihil nothing prior but there is also no longer a ‘nothing’ that pre-exists creation; it is the act of appearing[surgissement], it is the very origin – insofar as this is understood only as what is designated by the verb
to originate.”
The statement about the ever renewed creation is of course not a cosmological statement. It concerns
the human condition and the ethical responsibility of the human being. Creation takes place by878

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interpreting and giving meaning to life and existence over and over again. In order to bring this aboutfrom his own life and name, Solomon invokes the text:
He gave us the word so that we could all give it a personal breath, miraculous, different, deep, human.
By interpreting the word. As for Adam, his name is derived from ‘Adamah’, which means ‘earth’ inHebrew. God created Adam with the earth of clay and with water. He breathed his breath and breathedinto the miraculous, as he did with the word. That’s how Adam was born. Adam has the color of clay:
red. Like the Indians. Red in Hebrew is: ‘Adom’. You see, Adam is neither white nor black; he is red.
As is well known, Hebrew is written in the square-letters, which consists only of consonants. The firstword of the Torah looks non-vocalized as follows: ‫ב ר א ש י ת‬. In the first centuries of our era, in order toavoid any misunderstandings when reading, the Masorets added the vowels in the form of small dots
and signs. In his Hebrew grammar, Spinoza reiterates the Jewish-mystical intuition that vowels are thesoul of the letters. Without vowels, the consonants are a body without a soul. The consonants becomereadable when they are animated by the vowels. This inspiration is found in reading. Solomon speaks
of man from the same image. Being created is a spiritual event. As a creature, the individual humanbeing is not determined by the burden of the Flood with the racial differentiation, but every human being
is a double being: well-founded and rooted in the earth, but also spiritually brought to life. Humanexistence arises in the creative act: every time physicality is animated and breathed life into in all its
diversity. In other words, each person is his or her own origin, a creatio ex nihilo. This implies that thecolour of the skin does not determine identity. And if there were any doubt about that: if the first person
already has a colour - which is actually an irrelevant question - then it is red.
After the liberating perspective from creation, Solomon interprets the discussion from his own breath,his own personal process. Eroticism and an offspring have an important role here.
But, does he feel good, alone, red, in this new world?
Then God thought of Eve. But Adam did not understand what God wanted, what he asked him to do.
What was he supposed to be and do down here?
Salomon meets Sarah, a white Jewish woman with curly, red hair. She is the daughter of a politicallyand religiously highly conservative father. By choosing Solomon, she, like the wife of Biblical Abraham,
has to break all ties with her family. As a white Jewish ‘princess’ - which is the translation ‘Sarah’ - shewants to create a new generation by marrying the dark king Solomon. For Salomon, however, theintimacy of the sexual relationship means the physical revealing of his identity as a non-Jew. After all,he had not been circumcised. What is his responsibility here?
The question of identity is elaborated narratively in the film in the face of tests:
And what about all these tests?
The tests are represented by moments in which the main character looks at the moon with a dreamy
gaze. The moon depicts the mother who stayed behind in Sudan: the absent origin of his identity.
Salomon looks at the moon five times. Five times this points to an important turning point. The arrival in
Israel sealed his fate as a refugee in order to grow up in a foreign country far away from his mother. He
speaks to the moon and he reverses the stone that his mother had given him as a fetish. Then there isa conflict in the school where he is forced to eat and where he claims not to be guilty. The school
symbolizes the structural initiation into culture. In the conversation with his mother, he tells that he wants
to keep his own identity and that he refuses to adapt to the new culture. He takes off his shoes in a very
symbolic way and walks barefoot to the South. A third time Salomon looks at the moon after he hasread the opening sentence about creation in the Hebrew Bible. After the acquaintance with the Torah
he asks himself the question what it means to be Jewish: to become white, to speak Yiddish or as thewell insane Mrs. Silbermann who survived the camps, constantly saying ‘Oï, Oï Mein Gott’. After asking
this question, he was excluded from school and walked barefoot to the South again. A fourth time helooks at the moon when his ultimate proof of being a Jew - winning the Talmud discussion - is notconvincing. Previously he had become a bar mitza, fell in love with Sarah and was sent away by her
father. After the Talmud discussion, he went to his adoptive parents and told them not to be their son.
He leaves for a Kibbutz and tells Sarah that he has a new girlfriend, Mandela. However, the resounding
name of the African liberation hero is assigned to a bovine animal intended for slaughter. A fifth time879

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after seeing images of the famine in Africa, Solomon wants to return to Sudan. De Qes prevents himfrom doing so and tells his own life secret: he has lost wife and children and says he is condemned tolive. This condemnation consists of an ethical responsibility: to help those who have survived. Ethicalresponsibility is not a free choice of human beings, it is a condemnation. After this, the Qes shows the
only object he has left from his ‘homeland’: an old, damaged Thora scroll. In the Talmud we read a
strange story about the Torah (Shabbat, 88a):
Rav Abdimi bar Chama bar Chassa said: this teaches us that the Holy-Blessed-Be-He has bent themountain over them in the form of an inverted tub and he said to them: If you accept the Torah, thebetter; if not, it will be your tomb.
We remember that after the exodus from Egypt, the people were wandering through the desert for forty
years. At one point Moses received the Law from God on Mount Sinai. Rav Abdimi bar Chama bar
Chassa is said to have said that God, by the gift of the Law to Moses on Mount Sinai, had placed themountain like a tub above the people. When the people “listen and do” the Torah, life comes into being.
Otherwise the mountain remains a private tomb. Doing the Torah is an ethical task the human being
cannot escape. But the guidelines of the Torah (Rosenzweig translates the word as Weisung) are also
the only thing man is given: no country, no people, no religion, nothing. As in the story of the Second
Testament about Lazarus and Abraham the Qes says to Solomon: you have the Torah, that’s enough.
There is no more as an orientation in life, you have to do it with that: va-t-en!
The symbol of the moon is in tension with the Jewish identity: land, education, race, language, theShoah, a community ideal (Kibbutz), the Torah... The tension can be summed up in the topic of the lost
homeland. What is Salomon’s homeland? The Greek éthos’ means dwelling place. The material,
political, cultural, domestic identity of a concrete existence connected in an established people or the
ethical homeland of the Torah? During the struggle with his secret, Solomon did not find a house or‘home’ in Israel, but became ill from nostalgia for his country of origin. He wants to go south barefoot.
In the film, the moon symbolizes homesickness for the homeland: a concrete, historical reality or an
ethical homeland? In order to develop this, we start from a description of dwelling, inspired by Hannah
Arendt and Emmanuel Levinas. Identity is never an abstract given, but arises in the course of a lifetime.
One is not a human being, but becomes a human being. The course of life is always linked to a placeof dwelling, a home. A small phenomenology of dwelling shows that the identity is supported by athreefold attachment. This connectedness is always supported by a separation or an openness to theother. The time of living is characterized by three events: natality, eroticism and fertility. These events
form the connection between the past, the present and the future – the emplotment of Mimèsis II.
Natality or being born shows the passivity of human identity: no one is the origin of his own life. Natality
points to some irreducible dimensions of human existence. Man is the result of a relational event of
which he is not the source. This means that man is fundamentally intersubjective and is carried by apast that is unobtainable. The physicality of the birth refers to a transcendental origin. In addition, man
has been thrown into the world and physically by birth. The fact of the In-der-Welt-Sein - which, unlike
Heidegger, is understood from the perspective of natality and not death - shows the human openness
to life in the world. The closed intimacy of natality is through physicality the possibility of life in the world.
In eroticism man gives himself over to a rhythm of time that is not controllable. Life is physically sharedwith the other person. This interaction is not the sum of a mutual initiative, but the origin of a newexperience. As in creation, a new sentence springs from the eroticism. This takes place at thecrossroads of identity and connection. In eroticism one finds oneself through the connection with theother. This event creates a new meaning and future. In eroticism there is an ambiguous interplay ofsimultaneity and dissimilarity, presence and ‘coming’. The eroticism as a concrete time of ‘living
together’ indicates that man is not an original and pure identity (a monadic without windows or doors),
but grows in relationships with very concrete others.
Through parenthood, mankind experiences a new future in a child. The term future can be understoodin two ways. The French ‘futur’ refers to the future as the realization of a project or a plan that man setsout for himself. In this vision, every future moment is the extension of the present or the same. It is thetime in which man makes ‘projects’ that are realized through the development of strategies andmanipulations: future reality is calculated and manipulated. The French ‘avenir’, on the other hand,880

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points to this future that literally ‘arrives’ at man. It is the unexpected and the unplanned that can takeplace in a lifetime. This future rises above all project thinking. It is the time of the difference in which theother or the strange ‘arrives’ at man. Parenthood is the flesh-covered experience of the future: in thechild a time is opened that is irreducible for any systematic thinking.
The threefold structure of natality, eroticism and parenthood shows the dwelling place of mankind. Manis carried by a triple absence. The origin cannot be repeated (Mimèsis I: the stakes of the discussion
about the meaning of creation); the erotic connection puts identity at risk and forms her (Mimèsis II:
Solomon’s refusal to reveal the secret of his non-Jew in the eyes of Sarah); fertility is a concrete andphysical experience of a different future (Mimèsis III: the birth of the son coincides with the retrieval ofthe mother in Sudan).
The process of constructing identity can respond to this absence in a double way. Either the absent canbe recovered: the natality or the origin is then understood as the blood identity (the one-time creation of
the white man as an image of God); the erotic becomes profit and selfishness (the temptation scenethrough the whore and the beating through her pimps); parenthood becomes the insurance of the future(the vision of Sarah’s father and Salomon’s adoptive father). The double figure of Solomon, however, isthe rebuttal of this: his origin is always a lost origin and he goes in search of lost time. The flawlesserotic relationship with Sarah is impossible because Solomon conceals the secret of his non-Jewish
identity. Parenting is not the establishment of his Jewish identity, but the crisis of homesickness.
The identity is characterized by the tension between idem and ipse. Is the identity anchored in a
substantial and original presence? This is Michael’s thesis. When we approach the identity construction
from the outlined characteristics of dwelling, it can be seen as a search for the ipse. The place of dwellingof this ipse is marked by difference, which makes an ethical identity possible. Is identity an ethicalidentity? Solomon’s tests are the question of his responsibility. What am I doing here on earth? The
difficulties in exploring the relationship with Sarah show the problems of identity and solidarity.
In education, constructing identity is a search between the idem and the ipse. Mimetic narrativity canbe the royal way in this quest. The inscription of one’s own life story in a mimetic plot - for example,
provided by a film - leads to the question what is the place of identity: bound to a solid origin orchallenged by the fluidity of an increasingly absence. The answer to this question requires mimetic
imagination that leads to a catharsis.

8. Summary as Conclusion
The authors discovered and suggested the three possible constructive strategies in the process ofteaching with films: 1. Creation of the students their own multimodal projects, trying to find proper
images for their ideas, 2. Watching films alongside with reflection of some philosophical concepts,
afterwards writing essays and presenting them to the group in the discussion as the possibility toencounter the glimpses of reality from the different perspectives.3. Using film as a challenge to purify
student’s own mimetic processes by placing them in a broader metaphorical context. he film in this caseis the imaginative-metaphorical construction of the personal identification process. The authors discuss
the three strategies as parallel, not opposing or excluding each other. All three strategies lead to theconstructing students world view not with very concrete separate elements of clear shape and content,
but with elements of absolutely different level, plane, from the different assemblages and enlarge thecapacity of their critical and creative thinking. Also these approaches develop their social capacities –the ability of the understanding and communication with the different other.

References
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Auerbach, Erich (1946) Mimesis. Dargestellte Wirklichkeit in der abendländischen Literatur. Bern:Francke.
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Berger, P. L., Luckmann T. (1967), The Social Construction of Reality: A Treatise in the Sociology ofKnowledge. Anchor.
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Habberjam. Minneapolis: University of Minnesota Press.
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Deleuze, G.; Guattari, F. (1987). A Thousand Plateaus: Capitalism and Schizophrenia. Trans. B.
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IJsseling, Samuel (1997) Mimesis. On Appearing and Being. Kapen: Kok Pharos.
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Jagodzinski, J. (2010). Visual Art and Education in an Era of Designer Capitalism. Deconstructing theOral Eye. New York: Palgrave Macmillan.
Jagodzinski, J.(2014).On Cinema as Micropolitical Pedagogy: Is there an Elephant in the Classroom?
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New York, London: Bloomsbury, pp. 1-15.
Levinas, Emmanuel (1979) Totality and Infinity. An Essay on Exteriority. A. Lingis, Translator. Nijhoff:
The Hague.
Levinas, Emmanuel (1987) Time and the Other. R.A. Cohen, Translator. Duquesne: Duquesne
University Press.
Kearney, Matthew, Treagust David F. (2001) Constructivism as a Referent in the Design andDevelopment of a Computer Program Using Interactive Digital Video to Enhance Learning in Physics,
Australian Journal of Educational Technology, 2001, 17(1), 64-79.
Milerius, N. (2013) Montažas ir intervalas kine in Milerius [Montage and interval in film], N., Žukauskaitė,A., Baranova, J; Sabolius, K.; Brašiškis, L. (2013). Kinas ir filosofija [Film and Philosophy]. Vilnius:
Vilniaus universiteto leidykla.
Nancy, Jean-Luc (2000) Being Singular Plural. R. D. Richardson, Translator. Stanford, California:
Stanford University Press.
Nussbaum, Martha (1994) The Therapy of Desire. Theory and Practice in Hellenistic Ethics. Princeton:
Princeton University Press.
Papert, S. (1980) Mindstorms: Children, Computers, and Powerful Idea, Harvester Press.
Piaget, J. (1955) The Child's Construction of Reality. London: Routledge and Kegan Paul.
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Phillips, D.C. (1995) The Good, the Bad, and the Ugly: The many faces of Constructivism in Education
Researcher 24 (7), pp. 5-12.
Ricoeur, Paul (1969) Le conflit des interpretations. Essais d’herméneutique. Paris: Seuil.
Ricoeur, Paul (1975) La métaphore vive. Paris: Seuil.
Ricoeur, Paul (1983-1984-1985) Temps et récit. Paris: Seuil.
Ricoeur, Paul (1990) Soi-meme comme un autre. Paris: Seuil.
Russell, B. W. (2009) Teaching social issues with Films. Information age Publishing Inc.
Vygotsky L.S. Education Psychology. Boca Raton, Florida: St.Lucie Press, 1997.
Wallin, J. J. (2010). A Deleuzean approach to curriculum. Essay on a pedagogical life. New York:
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For a People-yet-to-Come. (Eds.) M. Carlin, J.Wallin. New York, London: Bloomsbury.pp.117-141.

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WG5: Constructionism in the Classroom: Creative
Learning Activities on Computational Thinking
Michael Weigend, mw@creative.informatics.deHolzkamp Gesamtschule Witten, University of Münster, Germany

Zsuzsa Pluhár, pluharzs@caesar.elte.hu
Eötvös Loránd University, Faculty of Informatics, Budapest, Hungary

Anita Juškevičienė, anita.juskeviciene@mii.vu.lt
Vilnius University Institute of Data Science and Digital Technologies, LithuaniaJiří Vaníček, vanicek@pf.jcu.cz
University of South Bohemia, Faculty of Education, Czech Republic

Kazunari Ito, kaz@si.aoyama.ac.jp
Aoyama Gakuin University, Japan

Igor Pesek, igor.pesek@um.si
University of Maribor, Faculty of Natural Sciences and Mathematics, Slovenia

Abstract
An essential assumption of Constructionism is the idea that students learn best when they constructartefacts that they consider to be relevant (Papert). Computational thinking (Wing) is made up of thoughtprocesses, such as abstraction, decomposition, algorithmic thinking, evaluation and generalization. This
contribution discusses learning activities without computer (“unplugged activities”) related tocomputational thinking, which challenge creativity. In more detail, these arrangements have thesecommon properties:

Creativity. The students create a product that can be shown around later. This may be a physicalartefact or a performance, which could be documented (photo, video). The concrete outcome isvery individual and may be surprising in contrast to analytical tasks with just one correct solution.

“Unplugged”. The activity is experience-based. It (ideally) demands all the senses andchallenges the whole person. The students do not use a computer (“unplugged”) and do notdevelop a program but may use Lego blocks, pencil and paper or other material found in theirenvironment.

Time. The activity can be performed without preparation ad hoc in one lesson in 5 to 40 minutes,
in contrast to projects that are carefully planned and require weeks of work. The focus is on
design ideas.

Based on Csikszentmihalyi’s system model and Margarete Boden’s psychological model, thiscontribution discusses the possibilities of being creative in the classroom. Extending Csikszentmihalyi’s
approach, the classroom situation is seen as a “local” creative system. Specifics of the domain“computational thinking” are discussed.
This contribution suggests to distinguish between four types of creative unplugged activities:
Type 1: Create an algorithm solving a given problem and present it.
Type 2: Create to a given algorithm or informatics concept a new situation, in which this algorithm orconcept can be used as well and present it.
Type 3: Create an algorithm with certain structural properties (like loops, recursion, functions callsetc.) and present it
Type 4: Create a visualisation for a given algorithm or concept of computer science and present it.
More than 300 computer science educators (school and university) from different countries were asked
about this classification scheme, about their experience with creative unplugged activities and the884Constructionism 2018, Vilnius, Lithuania

relevance and educational potential of the different types. Qualitative and quantitative analysis of theanswers suggest that the classification scheme is quite acceptable for the community of CS educators.
Creative unplugged activities are not often used in CS education but are still considered to be relevant.

1. Introduction
An essential assumption of Constructionism is the idea that students learn best when they constructartefacts that they consider to be relevant (Papert, 2000). Additionally, the process of creating has
educational value in itself, since creativity – the ability to generate new ideas – is an educational goalof high importance in a modern society (Resnick). Computational thinking (Wing) is made up of thoughtprocesses, such as abstraction, decomposition, algorithmic thinking, evaluation and generalization.
Creativity in computer science lessons often refers to creating digital artefacts with a computer(programming, visual design). Many students love computer science because they see opportunities tobe creative using computers (Romeike & Knobelsdorf, 2008). However, computer science (CS)education in schools is more than just programming. To develop computational thinking and get a deep
understanding of CS concepts a variety of different learning activities might be useful. This contribution
discusses activities without computer (“unplugged activities”) related to computational thinking andchallenging creativity. In more detail, these arrangements have three common properties:Creativity. The students create a product that can be shown around later. This may be a physicalartefact or a performance, which could be documented (photo, video). The concrete outcome isvery individual and may be surprising in contrast to analytical tasks with just one correct solution.
“Unplugged”. The activity is experience-based. It (ideally) demands all the senses and challenges
the whole person. The students do not use a computer (“unplugged”) and do not develop a
program but may use Lego blocks, pencil and paper or other material found in their environment.
Time. The activity can be performed without preparation ad hoc in one lesson in 5 to 40 minutes,
in contrast to for example software development projects that are carefully planned and require
weeks of work. The focus is on design ideas.

This article is structured in following way. First, we introduce a classification scheme for creative
unplugged learning activities Then we discuss theoretical approaches to creativity and computational
thinking. After that we present our international survey on creative unplugged activities in CS education.

Creative Unplugged Learning Activities
A creative task challenges creativity. In the context of Computational Thinking, creative skills may be
used for finding and representing a problem (that can be solved by an algorithm), an algorithm (solving
a problem) or a representation (visualisation) of an algorithm. An unplugged activity on computerscience is an activity without computer. There are wonderful unplugged activities on computational
thinking (Bell, McKenzie, Witten, Fellows, & Adams, 2015). But some of them are not creative. They areoften like puzzles with one correct solution. On the other hand, creative tasks have many correct
solutions and they always lead to a product that can be presented to an audience. We distinguishbetween four types of creative unplugged tasks related to computational thinking:Type 1: Create an algorithm solving a given problem and present it without a computer. Here, thechallenge is to find an algorithm with appropriate commands and data representations.
Example: Write a description how to walk from the entrance of this building to a certain room that canbe understood by a person, who has never been in this building before.
Example: Transmit a sequence of zeros and ones on the school yard from A to B via intermediate
stations using body language.
Type 2: Create to a given algorithm or informatics concept a new situation, in which this algorithm orconcept can be used as well and present it. In this case, the creative challenge to develop a story, aproblem from a different context, that can be solved with the algorithm. The student must understandthe given algorithm to be able to check whether the story is appropriate.

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Example: Describe a situation in which binary search is useful. Present the situation in a play using
things from this room.
Example: Find situation, which can be modelled by a list of lists. Draw a picture illustrating this situation.
Type 3: Create an algorithm with certain structural properties (like loops, recursion, functions calls etc.)
and present it in some way. Here, the creative challenge is both to find a problem and an algorithm.
This type of task is solved by teachers that create illustrating examples for programming concepts.
Example: Here is the definition of a function with one parameter in Python style:def make_a_ball(thing):
open your hand
put the thing into your hand.
while not thing has the shape of a ball:move your fingers
return the thing# main program
make_a_ball(paper sheet)
Create an algorithm in Python style and write it down. The program text should contain the definition ofa function with two parameters and a function call. Use things from this room as parameters.
Type 4: Create a visualisation for a given algorithm or concept of computer science and present it. Here,the creative challenge is to find a representation that the audience can understand. The students canuse different materials and expressive means: drawing, role play with dialogs and props, pantomime,
Lego etc. This kind of creative tasks solve designers who develop illustrations for a textbook.
Example: There is a collection of cards with informatics concepts. Each individual student or each teamdraws a card and visualizes the concept through a mime.

2. Creativity and Constructionism
The Creative System
Mihaly Czikszentmihalyi (Csikszentmihalyi, 2013) points out that »Creativity is a central source of
meaning in our lives« (p.8). People feel excited, joyful and »fully living«, when they are creative.
A person is creative, when she or he produces an idea or artefact that is new. Thus, creative persons
change our culture by adding new elements. According to Csikszentmihalyi’s system model, creativity
is not an individual ability but a system consisting of three parts (p.27 ff):


A domain, that is a system of symbols and rules (like mathematics, music, computer science)
A field that includes all persons that act as gatekeepers to the domain. They decide, whether anidea or product is accepted as new part of the domain.
An individual person, that is creative and creates new elements within the domain

The level of creativity is not only depended on a person, but different domains offer different
opportunities to be creative. Domains with clear structure, high centrality within the culture and goodaccessibility help to develop creativity.
How can a field (persons) affect creativity?


A field can be reactive or proactive. Proactive fields stimulate novelty. In school life, science fairs
and science slams promote novelty.
Narrow or broad filter when selecting novelty
A field can be connected well to the rest of the social system and channel support into its own
domain.
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Psychology of Creativity
Margarete A. Boden (Boden, 2007) distinguishes historical creativity (H-creativity) and psychologicalcreativity (P-creativity). H-creativity takes place, when people come up with ideas that are new and havenever been shared in the history of mankind. P-creativity happens, when a person comes up with an
idea that is subjectively new to this person, but has been shared by someone else before.
According to Boden there are three types of P-creativity: combinational, exploratory, andtransformational creativity.
Combinational creativity involves the generation of unfamiliar (statistical unusual) combinations offamiliar ideas. A typical example is a visual collage, made of found photos. Visualisations of CS
concepts (using body language, Lego, images) include combinational creativity. However, the CS
concepts might be less familiar than the elements that are used for visualisation. It also might be thatthe result of the process is an artefact of value and includes many design ideas.
“In exploratory creativity, the existing stylistic rules or conventions are used to generate novel structures(ideas), whose possibility may or may not have been realised before the exploration took place.” (Boden,2007). In CS, exploratory creativity may happen, when students experiment and explore a programming
language and discover new programming techniques that way.
Transformational creativity is the creation of ideas people had previously thought impossible and which
they still find counter intuitive. An example from the history of art is starting painting abstract pictures,
where previously the paintings had all been representational. In transformational creativity the new idea
is so radical that it is difficult to understand for the social environment.
Creative unplugged activities in the classroom clearly focus on combinational creativity. It is notforbidden that very bright high school students develop exploratory or even transformational creativity,
but such events are beyond the scope of general education.
In contrast to Csikszentmihalyi’s system theory, Boden’s concept of P-creativity is subjectivist andignores the social aspect of novelty completely. An idea is new, just if the subject has never thought
about it and never seen it. There are two implications: P-creativity might be an illusion and P-creativity
is dependent on how experienced a person is.
P-creativity might be an illusion, because people are not completely aware of their experience. Jenny
might have seen a solution to a problem and forgotten this later. But traces of the knowledge are still inthe sub consciousness. When she is in a similar situation later and the solution pops is her mind, shejust thinks it was her idea, but in fact she had taken it from someone else. Someone who is a novice ina domain (like children playing with Lego for the first time) probably discovers more frequently
subjectively new things than an experienced person.
Gerd E. Schäfer (Schäfer, 2001) points out that young children are “necessarily creative” because
almost everything in their environment is new to them. They reconstruct the world in their mind byobserving and experimenting and discover (or create) new knowledge all the time. Each day is full of
surprises.

Creativity in the Classroom
Local Domains and Local Fields
Csikszentmihalyi’s theory is based on interviews with exceptionally creative individuals from science,art and business. They know domains and fields very well. Creative scientists, working in research labs
and universities, are familiar with relevant literature and attend international conferences. Obviously,
the perspective of high school students is rather reduced. However, the classroom situation can beconsidered as a micro world representing the society in a small scale. The parts of domains the students
are familiar with are defined by the school curriculum, textbooks and learning media in the WWW. We
call this environment a “local domain”. All materials that are relevant in school life are monitored byuniversities and government administration, which are connected to international educational standards
in some way. Therefore, the local domain of computer science at an individual school reflects the global
domain of computer science. There is one fundamental difference between the local and the global
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domain related to persistency. The local domain might change rather quickly, whereas the global
domain is rather stable and expands relatively slowly.
When there are bright students and a supporting teacher in a CS class, in one year the local domain
might expand quickly. Students do unusual projects and share them in some way with the local
community. Examples of such sharing are posters in the hall of the science department or artefacts ina school exhibition. When these students or the teacher leave the school, this additional knowledge
might get lost. The next age cohort of students might be less interested in CS and the local CS domain
shrinks again.
In addition to the global field of CS, there is a local field of CS at each high school teaching this subject.
The local field mainly consists of CS teachers. They decide whether a student’s work is “creative” in themeaning of “something surprising” or “unexpected”. Many teachers that have filled our questionnaire
have stated that “creativity” is an important criterion for quality.
Also, students may be members of the field. They are experts and have specific knowledge about the(local) domain of CS and they act as gatekeepers. There are several filtering systems and gatekeeping
activities in school life that involve the students:
Programming teams must decide on design issues. “Which idea is new and should be implemented inthis project?
Scratch projects which are published in a studio may be commented by classmates. A comment might
include a statement on “how creative” a solution is.
The local field may be influenced by parents and students that are not in CS classes. They give a
feedback on work, shown in private, published in the school or presented in public exhibitions andscience slams. They are not part of the field because they are not familiar with the domain and because
they are not directly part of a filtering system.

The Domain Problem of Creative Computer Science
When creating a program, the programmer is not creative in the local domain of computer science
unless she or he develops a new programming technique, which is not mentioned in the textbook or
curriculum. However, she or he might have found this »new« technique in the internet. This would also
be a creative act related to the local domain, if this novelty is shared and accepted by the local field.
Although the idea is not new within the global domain, it is a local novelty and the local domain isextended by this idea.
A software project might be an act of creativity in a different (local) domain if the project implements a
new idea in this domain. Example:
Students have learned how to write interactive programs according to the simple structure »inputprocess data – output«. The creative task is: “Develop an interactive program that can be used to
calculate the concentration of methane in a room or building. “A version of this task which can be solved without a computer would be: “Develop an algorithm that canbe used to calculate the concentration of methane in a room or building and present this algorithm insome way.”
In this case the creative challenge is a) to find an explicit task within the context »concentration, gases,
methane« and b) to find a solution for this task.
a) The context is from the domain »high school chemistry«. The students have to solve problems within
this domain, when designing the task which can be solved by an algorithm:Which are typical rooms that might be contaminated by methane (e.g. school lab with Bunsen
burners)? Which situation is interesting and relevant (e.g. flames of Bunsen burners are
extinguished and gas is emitted)?
What kind of data are accessible and might be used for input (number of Bunsen burners, amountof gas that is emitted from a Bunsen burner per second, Volume of the room)?
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Constructionism 2018, Vilnius, Lithuaniab) In order to create the algorithm, they have to find out how to calculate the concentration of methanefrom the input data. This includes designing automatized dialogs using proper language and units.
This example illustrates that the application of computational thinking (in this case algorithmic thinking)may lead to creativity in other domains.

Unplugged Creative Tasks on Computational Thinking - Building Bridges
Between Domains
Creative tasks without a computer might be creative (in Czikszentmihalyi’s system model), if the symbol
system of the local domain is extended by fresh ideas how to represent a CS concept.
When students visualize a recursive algorithm in a role play they do not create a new algorithm. The
novelty is new metaphorical representations for calling a function, passing parameters, processing dataand so on. The task “create a role play” encourages to construct connections between domains. This
goes well if the students are experienced in role plays. Building connections between concepts fromdifferent domains (from a familiar source domain to a more unfamiliar target domain) is called
metaphoric thinking. New metaphors for CS concepts can be considered as an extension of the local
domain of CS, since affects the way how to understand these concepts and how to use them. Consider
an example from a different domain: The number line, visualising the set of real numbers, is part ofmathematics.
New examples for algorithmic idea can be considered as an enrichment of the domain of CS. Each new
example extends the already known area of application of the algorithm. This is also part of CS
knowledge. Abstraction is part of computational thinking.

Skills and Attributes of Creative Persons
According to Czikszentmihalyi, being creative is like being involved in a traffic accident. One may havea disposition to be creative but every act of creativity depends on several factors. One cannot say thata person starts a creative process. Creative people interviewed by Czikszentmihalyi said they were justlucky. They were at the right time at the right place.
Czikszentmihalyi mentions attributes and skills that increase the chance of a person to be creative:Creative people must internalize the creative system: know the domain very well.
They are curious and are interested in a domain.
Creative persons are playful and have discipline (both is important), they need endurance and selfconfidence to finish a project.
Creative people can alternate between fantasy and imagination and a sense of reality.

The Enjoyment of Being Creative
Czikszentmihalyi points out that it is joyful to be creative. The optimal experience is called flow, thefeeling, when things are “going well as an almost automatic, effortless, yet highly focused state ofconsciousness” (Csikszentmihalyi, 2013). The joy of being creative – the joy of novelty – is determinedby the human genes. Novelty is important for the development of a culture. It increases the fitness toadapt to changing environments. Creative unplugged tasks on computational thinking are an
educational method to motivate students to learn and reflect CS concepts.
Creative persons interviewed by Mihaly Czikszentmihalyi reported that situations that are experienced
as enjoyable (flow) contain these elements:









There are clear goals in every step of the creation process. In flow we know exactly what to do.
There is immediate feedback, which makes it easy to distinguish between good and bad ideas.
There is a balance between challenge and skills.
Action and awareness are merged. (I am thinking about what I am doing.)
Distractions are excluded.
There is no worry of failure, the subject is too involved to be concerned
The self-consciousness disappears.
The sense of time becomes distorted.
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Constructionism 2018, Vilnius, LithuaniaThe activity becomes autotelic. It is joyful per se.

Teachers designing creative tasks should do everything to keep the joy of the creation situation.

How to Encourage Creativity?
An important element of CS classroom education is designed activities. They consist of a task and somematerial which is used to solve the task. “Unplugged” activities use tangible material like blocks or any
material that is ad hoc available, pencil and paper or just own body, but not computers. The charm of“unplugged” activities is often the contrast between the material and the CS concepts that are
elaborated.
In research literature and educational programs one can find much advice how to encourage and foster
creativity. In this section we try to relate this advice to creative unplugged activities, considering

activity design, including the task and the used material,
scaffolding during the performance of the activity
presentation of the results including rules for feedback

Activity Design
Boden mentions different ways to encourage creativity depending on the type of creativity which isintended to be developed. Combinational creativity can be encouraged by enlarging the variety ofconcepts in a person’s mind. The more concepts I know the more “unusual combinations” I can find.
Additionally, a person can practice finding new associations between concepts and learn to judge thevalue and novelty of ideas. (This is internalizing the field in Czikszentmihalyi’s system theory.)
In task design, a simple method to enlarge the repertoire of concepts is to let the students work in teams
consisting of persons with different interests and knowledge. When people collaborate in diverse
groups, new and surprising ideas may evolve easier.
Secondly, the task should be surprising and raise curiosity and interest. Czikszentmihalyi suggests tocultivate curiosity and interest. If unusual results are expected, the task should be unusual too.

PerformanceCzikszentmihalyi suggests to cultivate flow in everyday life. People should wake up with a specific goal
they look forward to. Everything we do we should do well and joyful. An atmosphere of strength and joy
should be maintained during the performance of a creative activity. The learning environment shouldmake it easy to focus and prevent distractions. An empty school yard might be a good environment forsome creative activities. Classrooms should be designed to support the creative process, like thelaboratory in Dalton education.
Scaffolding during the process should support(Csikszentmihalyi, 2013):






and encourage the following

operations

clarify, analyse and re-define the problem or question to uncover new ways of looking at it,try to find connections between seemingly unrelated subject matter,challenge established wisdom by asking: how would I improve this?
recognise alternative possibilities,
look at things from different perspectives.

Presentation
Boden (Boden, 2007) points out that students should not be discouraged by dismissing new andsurprising ideas as mistakes. Creative persons need self-confidence. The logical consequence is thatencouraging creative task should always lead to a success. There is no right and wrong. Imagine thetask “Create a role play visualising the execution of a recursive function.” After some time, the team has
to come up with a role play and perform it. This role play must be a success, even if there is hardly avisible connection to recursion at first glimpse. The teacher must support a certain culture of reception,
which is friendly and appreciating. The audience must appreciate that watching all the little details of890

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the play and reflecting and discussing their relation to the CS concept of recursion helps to get a deeper
understanding.
During the presentation the field gets active. This is important for the creators since they must internalize
the field and must learn to distinguish between good and bad ideas, new and old ideas. The presentation
is a good opportunity to discuss novelty and values. Some ideas, developed in creative unplugged tasks
might get documented in some way and extend the local domain of high school CS. The challenge forthe school is to install opportunities for this documentation: science fairs, showcases, posters, wikis etc.

3. Computational Thinking and Creativity
In this section the view on creativity from computational thinking (CT) practitioners point of view ispresented. First the concept of CT is briefly analysed. Next the creativity concept in CT domain ispresented.
In 2006 Wing (Wing, 2006) presented the concept of CT: “ ... involves solving problems, designingsystems, and understanding human behaviour by drawing on the concepts fundamental to computerscience”. Based on this it can be concluded that CT involves three key components: algorithms,
abstraction, and automation. However, the rice of this concept can be related to Seymour Papert byintroducing it in the context of suggesting an alternative, computationally-based mathematics education(Papert, 1996). Hence the researchers are very interested in CT approach and its application in
education. However, it is still at an early stage of maturity (Lockwood, Mooney, 2017) and the steadydefinition of CT is not provided (Voogt et al., 2015). In order to explore CT approach (Voogt et al., 2015)
provided the discussion about the definition and core concepts of CT only in the Computer Science
domain (excluding other domains). They focus on finding similarities and relationships in the discussions
about CT. Similarly, the analysis made in (Kalelioglu et al, 2016) provided the word cloud of CT
definitions used in analysed papers. The generated ‘Wordle‘ by Kalelioglu and others was based on thedefinitions provided by analysed researchers and not included some core concepts of CT.
CT definition challenge was analysed also in others domains, such as mathematics and science by(Weintrop et al., 2016). They reviewed the literature on CT and proposed a definition of CT in the formof a taxonomy consisting of four main categories: data practices, modelling and simulation practices,computational problem solving practices, and systems thinking practices. Additionally, the set of ten CT
skills were proposed. This set was developed based on literature review on CT with a focus on
applications to mathematics and science.
Some researchers focus on CT skills in order to explain CT (Curzon et al, 2014; Atmatzidou &Demetriadis, 2016) or very similar named concepts (Catlin & Woollard, 2014), such us abstraction,
decomposition, generalization.
Others interpret CT based on its elements. Such us (Grover & Pea, 2013) identified nine elements:
Abstractions and pattern generalizations (including models and simulations); Systematic processing of
information; Symbol systems and representations; Algorithmic notions of flow of control; Structured
problem decomposition (modularizing); Iterative, recursive, and parallel thinking; Conditional logic;
Efficiency and performance constraints; Debugging and systematic error detection. Additionally, othersargue that CT is an activity, often associated with, but not limited to, problem solving (CSTA & ISTE,
2011; Beecher, 2017, p. 8; Haseski et al., 2018).
All these concepts (skills, elements) are widely used by researchers and educators with the aim to teachcomputational skills across the different curriculum. In the book by Williams (Williams, 2017) ISTE
developed concepts are used in K-5 curriculum. Author argues that CT can be incorporated into any
subject. Also, she gave the case studies with Bee-Bots, Code.org, Scratch and ARIS and presented
how CT concepts can be applied to these tools in detail.
CT can be seen as a fundamental skill for everyone, not just for computer scientists. It is applicable ineither a computerised or unplugged problem-solving process. CT has the potential for application in awide range of disciplines as the creative learning arrangements. Two main strategies are used for CT
skills development: unplugged activities (activities that891

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involve logic games, cards, puzzles, strings or physical movements to get in touch with computerscience concepts such as algorithms, data transmission or data representation) and computerized
activities (such as, programming in arrow-based visual environments, programming in block-based
visual environments, using textual programming languages, programing that is connected with thephysical world) (Moreno-León et al, 2018).
In this paper CT is defined as the skills of being able to develop creative solutions for the problem withan algorithmic approach by handling a problem by the individuals that could establish healthy
communication in a cooperative environment (Korkmaz et al., 2017).
CT is a set of skills that help to set up a problem in such a way that a computer can help you solve it(Krauss & Prottsman, 2017, p.47). However, computers are not necessarily. Solutions could be
represented as computational steps and algorithms (Aho, 2012). Main CT skills are as follows:






Abstraction is the process of creating something simple from something complicated, by leaving
out the irrelevant details, finding the relevant patterns, and separating ideas from tangible details(Atmatzidou & Demetriadis, 2016);
Algorithmic thinking is the ability to understand, execute, evaluate, and create computational
procedures (Lamanga, 2015);
Decomposition is the process of breaking down problems into smaller parts that may be moreeasily solved (Atmatzidou & Demetriadis, 2016);
Generalisation is transferring a problem-solving process to a wide variety of problems (Atmatzidou& Demetriadis, 2016);
Evaluation is the ability systematically (through criteria and heuristics) make substantiated value
judgements (Catlin & Woollard, 2014).

Moreover, researchers agree that CT is overlapping with many aspects of 21st century skills such as
creativity, critical thinking, and problem-solving (Lye, Koh, 2014). According to Korkmaz et al. (2017),
CT is the extension of the problem solving skills of a person and the development of the creativity andcritical thinking skills of the people by re-focusing. Thus creativity plays an important role in CT approach(Korkmaz, 2017). CT mainly focuses on the creation of algorithms by using problem solving skills.
Moreover, most of CT parts involve creativity, such us algorithmic design, generalisation and patternmatching (e.g., needs big creative leaps to see the links between apparently different situations),
evaluation (e.g. needs creativity in coming up with logical arguments or ways to explore situations)(Curzon, McOwan, 2017, p. 209). Creativity in abstraction occur, e. g. when students invent a new
representation for recursion - say in a role play. Additionally, Curzon and McOwan (2017) argue thatmost creative ideas come from groups not from individuals. Moreover, CT foster creativity in theclassroom due it allowances for student to move from technologies’ consumers to developers in order
to benefit society, also, creativity can be augmented by CT (Mishra et al., 2013). The College Board(2017) developed the CT framework for a Computer Science Principles course for high schools in theUSA. According to it computing is a creative discipline and student are engaged in creative aspects ofcomputing by designing and developing computational artefacts, also, by applying computing
techniques to solve problems (p. 9), such as, by employing non-traditional, non-prescribed techniques,
the use of novel combinations of artefacts, tools and techniques, as well as, exploration of personal
curiosities (p.11). Students could be developing artefacts for creative expressions and those can reflect
personal ideas or interests (p. 12). In such way, computing enables students to use creative
development processes.
Many creative unplugged activities examples are presented by Curzon and others (2014). Let's take,for example, such a type of creative unplugged activity that aims to create an algorithm for solving agiven task and present it without a computer. The task is to help person with locked-in syndrome (total
paralysis due to a stroke) to communicate. The solution could be the communication by blinking: one
person says the letters of the alphabet, and the other blinks when they get to the letter they wish tocommunicate. Thus the creative algorithmic thinking is demonstrated when the audience are asked tothink of improvements, the details of this algorithm that need further thought to make it work. As wellas, generalisation needs the creativity from audience for suggesting things like predictive texting and892

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frequency analysis as improvements. Likewise, evaluation needs creativity to discuss an algorithm’sfunctionality, its performance and its usability.
Creativity has its place in many areas, such as art, economy, psychology and science. In schools it isassociated with art, music, and writing classes (DeSchryver, Yadav, 2015). And it is differently definedin various fields. Moreover, such a complex concept is difficult to measure, although many approaches
to the measurement of creativity are developed (Jackson et al., 2012). Creativity in CS field usually aims
at producing new mechanisms, both hardware and software, that can provide solutions to practicalproblems. However, creativity involves the same kinds of cognitive processes that generate answers incomputing as well as in the natural sciences (Saunders & Thagard, 2005). Moreover, in CT, creativity
could be seen as an ability by applying imagination to develop a physical object or some mental oremotional construct (Korkmaz et al., 2017) that is judged to be novel and also to be appropriate, useful,or valuable by a suitably knowledgeable social group (DeSchryver, Yadav, 2015).

4. Questionnaire
In order to get the opinions from the computer science educators at all levels of educational system
about the creative unplugged activities in the classrooms and also their opinion about the categories
model of creative unplugged activities, an online survey was conducted. First the questionnaire in
English language was prepared with questions about the four categories of the creative unplugged tasks
and questions about the use of such type of tasks in their daily practice in the classrooms. Then thequestionnaire was translated to the national languages and distributed through different national
channels to the computer science educators. In the table 1 is the distribution of the educators based on
their country of origin and total number of answers which is 360.
Table 1. Country of origin of the respondents

Country

Number of respondents

Czech republic

133

Lithuania

8

Slovenia

25

Germany

153

Japan

14

Other countries

27

Together360

Types of Creative Unplugged Activities in Computer Science
The questionnaire first presented definitions and examples of four different types of creative unplugged
activities and asked the respondents how clear the definitions were. The responses were on scale fromunclear (1) to clear (5). In table 2 descriptive statistics are presented. It can be observed, that for
educators’ types 1 and 3 are clearer and more understandable than type 2 and 4. It seems that
educators feel more confident with activities that ask for specific structural features than with moregeneral concepts. One of the reasons might be that activities and tasks that fit in type 3 category are
more frequently used in CS teaching.

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Table 2. Clarity of the definitions of the four types of creative unplugged activities

Type of unplugged creative learning activity

Degree of clarity
Mean

Std. Deviation

Type 1: Create an algorithm
Invent an algorithm that solves a given task and present it
without a computer.

3.78

1.16

Type 2: Find an example
For a computer science algorithm or concept, find a newsituation in which to apply this algorithm or concept.

3.24

1.27

Type 3: Find an example algorithm (more open form)
Find an algorithm that has certain given structural features (e.g.,loops, recursion, function calls) and represent it in some way.

3.77

1.12

Type 4: Create a visualization
Invent a visualization for an algorithm or concept of computerscience.

3.33

1.25

Next we asked them if they know about creative unplugged activities that do not fit in presented model.
30% of the teachers claimed that they know other activities that do not fit. We asked them if they canprovide an example or comment.
107 from 360 respondents (more than ¼) claimed that they know other creative informatic activities
without computer out of our four categories. 38 of them described one or more activities, some
respondents gave general comments.
Only about one third from these 38 free text responses can be considered as examples for creative
unplugged activities according to the given definition. Most of the presented activities were unplugged
but not creative. They were typical problem-solving activities with only one possible solution (e.g.
ordering data, comparison of algorithms). Some activities were not from CS (e.g. painting). In somecases, it was impossible to decide whether this activity is creative or not, for example from the response“puzzles – Sudoku” one cannot not deduce whether the respondent meant solving sudoku or creatinga new one.
Some of the responses described concrete creative activities which can be related to one of the fourtypes e.g. “Representing the TCP/IP protocol stack through role play using envelopes as packets” which
is of type 4 (create a visualization).
However, a few answers from the 38 responses were valid creative unplugged activities that do not fit
to our model as it is. One teacher suggested this task: “Create a protocol for bidirectional communication
via one wire”. Since a protocol is not an algorithm, this activity is not of type 1 (create an algorithm).
Activities like this would be covered by the model, if we slightly extend the scope of type 1, like: “Create
an algorithm or part of an algorithm…” Part of an algorithm could be a protocol, a data structure, a classmodel etc.

Experiences with Creative Unplugged Activities in Computer Science
Next we asked the respondents about their experiences with creative tasks. On the question “How often
have you spent the last 12 months doing creative tasks without a computer in computer scienceeducation or a computer science course?”, 50% of the respondents said they used them once or twice,
27% used it more than twice and 23% said they never done them in the classroom. If we look at theresults of the next question about the reaction in the classroom where nearly 60% answered that thereaction is positive and combine them with the result from this question where 50% of educators use
unplugged creative tasks use them only once or twice in one year, the situation is rather disappointing.
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Maybe one reason lies in the fact, that there aren't that many available prepared creative unplugged
activities that educators could use or educators don't have time to use them in classrooms.
On the question "If you have engaged in creative tasks, what has been the reaction of the students?"
we got the following results presented in Table 3. Just 10% of all answers is negative or with no reaction,
which is good. Fact that almost 60% educators noticed positive reaction must be used to further push
unplugged activities in the classrooms.
Table 3. Student’s reaction to creative unplugged activities

Student’s reaction

Percentage

Positive

13.6%

Rather positive

45.8%

neutral

12.7%

Rather negative

6.6%

negative

0.5%

no reaction

3.6%

didn't answer

16.9%

Next, we asked educators, if they could tell us why and how they used creative unplugged tasks.
Respondents could check multiple options. The results are presented in table 4. Educators are using
creative unplugged tasks for the activation or as suggestion to think about CS concepts. Note that morethan one third of educators use unplugged activities also as entertainment element in the classrooms.
Table 4. Educators’ intentions for using creative unplugged activities

Intention

Percentage

As an activating entry into a new topic.

51%

As a suggestion to think about certain abstract concepts.

47%

To promote creativity.

26%

As training, e.g. to practice the correct use of technical terms. 25%
As a diagnostic tool to identify a prior understanding of a topic 16%
or misconceptions.
As an entertainment element to relax a course.

38%

The last question in this section was "Creative solutions differ from examples previously discussed inclass. They are novel and original. To what extent do you assess the aspect of creativity in the work of895

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your students?". The responses are in Table 5 and show that almost all educators in some way asses
creativity of their students' work. Nevertheless, more than 50% don't assess creativity as official criteria.
One of the reasons lie in the fact that educators must comply to national rules for assessment (e.g. in
Slovenia it is very complicated to assess creativity) and educators sometimes avoid the risks with justnot assess creativity. Other reason lies in the fact that is already hard to describe and define creativity
in CS and therefore educators don't feel confident, that they know how to assess creativity of theirstudents.
Table 5. Relevance of assessing creativity

Assessment of Creativity

Percentage

I have practically never assessed the creativity of my students'
work.

8%

I have occasionally evaluated the creativity of a solution, but
creativity has not been an official quality criterion for myassessment of the work of students.

48%

Creativity is one of my official quality criteria for evaluating thework of students. However, creativity has only a subordinate
rank over other criteria (such as correctness of the solution).

35%

Creativity is one of my most important official quality criteria forevaluating the work of students. Among my evaluation criteria,
creativity has a similar importance to that of correctness.

9%

Educational Potential of Creative Tasks
In this section we asked the teachers about their opinion on the educational potential of creative tasks.
More precisely, we asked them about specific potential and which type of creative tasks can be usedinstead. More answers could be selected at each question. Results for different purposes are in Table
6. As a replacement for programming tasks most educators think that activities that fit into Type 1(Create an algorithm) are most useful and appropriate. The reason for such results lies in the fact, thatType 1 activities are in conceptual way very similar to the programming tasks, only allowed tools aredifferent. For other three type it's not that easy to see, how such activities can replace programming
tasks. It's interesting to notice, that educators don't feel that Type 3 activities can replace programing
tasks, although such activities develop understanding of some concept similar as programming tasks
do. Most of educators think that all types of creative tasks, except Type 3 tasks, are suitable for use in
the classrooms as enrichment. Analysing the answers more in depth, we see, that almost 90% of
respondents that checked Type 3 also checked tasks of Type 1 and Type 2. Educators think that tasks
of Type 2 are most appropriate to use them as encouragement, although other types of activities arealso used in classrooms. We can conclude that educators think that finding a new situation encourages
students to think about CS and also helps students to use their CS knowledge in different areas of theirlife. Most educators think that tasks of Type 1 and 2 help students develop such skills that can be usedin other scientific fields. The reason lies in the fact, that educators think that first two types help todevelop computational thinking which can be later applied in various fields.

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Table 6. Educational potential of creative unplugged activities

Educational Potential

Type 1Create analgorithm

Type 2
Find anexampleType 3
Examplealgorithm

Type 4Visualisation

A replacement for programming
tasks providing a comparable
learning experience in less time.

72%

50%

44%

46%

An enrichment to a lesson or lecture
to make the content more relevant
and attractive to the participants.

68%

58%

39%

62%

As a encouragement for students tothink about computer science
concepts.

61%

67%

54%

60%

As a help to develop transferable
skills.

62%

71%

49%

56%

Professional Experience in Informatics Education
In last section we asked them about their background in Computer science education. Results are in
Table 7; more than answer was possible. We can observe that our respondents were from all levels of
education and some of them had experience with authoring a book or being involved in research
activities. One quarter of respondents were also teacher trainers. We can conclude that such spread ofexperiences gives this survey reliability and important view in how educators think and view creative
unplugged tasks in CS education.
Table 7. Respondents’ professional experience

Experience

Percentage

Teaching computer science-related content at a primary school

28 %Teaching computer science related content at a secondary school

88 %Teaching computer science related content at a university

14 %Authoring or co-authoring a textbook in the field of computer science

12 %Research in the field of computer science

13 %Teacher training in the field of computer science27 %5. Discussion
The survey has shown that most teachers use creative unplugged activities in CS classes, but not veryfrequently. One of the reasons we can conclude from the responses is that computer science educators
consider unplugged creative tasks as not very relevant, since respondents suggested that they usually897

Constructionism 2018, Vilnius, Lithuania

use them in the initial parts of a lessons, as introduction to some topic or as initial phase of problem
solving tasks. They also suggested that these activities could be used for group work.
From curricula point of view, teachers saw application of these tasks as an introduction to understanding
how computer works, in media education and painting art activities and in creating criteria by ordering
data. This might indicate that some teachers connect creativity more to using ICT than to computerscience.
The questionnaire explained four types of creative unplugged task and gave examples. For manyteachers this might be the first contact with this area and they might have problems to understand thisshort introduction. This could explain some strange answers.
Teachers gave some attitudes to using creative informatics non-computer tasks. Some attitudes wereagainst leaving programming and coding, some of them expressed conviction that they could replace
programming in some parts of curricula (without specification) and some of them apologize respondents
for not doing enough for it (because of lack of time in compulsory education that leads teachers to teachthis as extracurricular activities, e.g children clubs).
A possible reason that teachers do not use creative unplugged tasks in CS education so much might
be that focussing on computational thinking is not established so well in CS education. Many CS classes
are mainly programming classes or teach how to use ICT as a tool.
How to assess creative work seems to be unclear for many teachers, which might partly be caused by
the fact that the term “creativity” doesn't have one single definition. On other hand assessing creativity
can be very subjective (if the teacher dominates the field) and teachers don't feel confident enough toassess what is creative.

6. Conclusion
We need teachers who are able to support creativity. Designing and conducting creative unplugged
activities should be part of teacher training. The question is, how to do this. We need concepts forworkshops inspiring teachers and future teachers.
Students can only be creative in domains they are familiar with. (For example, visualizing a CS concept
by mime works, if the students are experienced with mime.) Therefore, teachers creating new creative
unplugged arrangements should involve their students.
The four types of creative unplugged activities that we have suggested, are just a start. In future
research we can use the open text answers of the questionnaire to improve and extend the classification
scheme.

References
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Teacher’s Getting-Started Guide. Corwin Press Inc.
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WG6: Learning to Program in a Constructionist Way
Mattia Monga, mattia.monga@unimi.it
Università degli Studi di Milano, Milan, Italy

Michael Lodi, michael.lodi@unibo.it
Alma Mater Studiorum - Università di Bologna & INRIA Focus, Italy

Dario Malchiodi, dario.malchiodi@unimi.it
Anna Morpurgo, anna.morpurgo@unimi.it
Università degli Studi di Milano, Milan, Italy

Bernadette Spieler, bernadette.spieler@ist.tugraz.at
Technische Universität Graz, Austria

Abstract
Although programming is often seen as a key element of constructionist approaches, the research on
learning to program through a constructionist strategy is somewhat limited, mostly focusing on how tobring the abstract and formal nature of programming languages into “concrete” or even tangible objects,
graspable even by children with limited abstraction power. However, in order to enable constructionism
in programming several challenges must be addressed. One of the crucial difficulties for novice
programmers is to understand the complex relationship between the program itself (the text of the code)and the actions that take place when the program is run by the interpreter. A good command of thenotional machine is a necessary condition to build programming skills, as is recognizing how a relatively
low number of abstract patterns can be applied to a potentially infinite spectrum of specific conditions.
Programming languages and environments can either help or distract novices, thus the choice is notneutral and their characteristics should be analyzed carefully to foster a good learning context. The
mastery of the notional machine, however, is just the beginning of the game: to develop a realcompetence one must be able to think about problems in a way suitable to automatic elaboration; todevise, analyse, and compare solutions, being able to adapt them to unexpected hurdles and needs.
Moreover, it is important to learn to work productively in a team, in an “organized” way: agile methods
seem based on common philosophical grounds with constructionism.

1. Introduction
Educators, generals, dieticians, psychologists, and parents program. Armies, students, andsome societies are programmed. [Alan Perlis]

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Constructionism [Papert and Harel 1991] is a strategy of education which has its roots in Piaget’s
constructivist theory of learning as an active process, in which people actively construct knowledge fromtheir personal experience of the world. In general, students do not just receive pre-built ideas fromteachers: they have to make them up by engaging themselves with problems, projects, and other people(instructors, but also peers). Papert’s constructionism indeed emphasizes the importance of having
personally-meaningful goals and “public artifacts” (not necessarily concrete ones: either “a sand castle
on the beach or a theory of the universe” [Papert and Harel 1991]] that can be shared and discussedwith others interested in the same (learning) enterprise [Resnick 1996]. This is sometimes summarizedwith the four P-words: Projects, Peers, Passion, Play and this motto indeed inspired successful
educational initiatives such as the Scratch programming language [Resnick 2014].

1.1 Constructionism and programming
However, while programming is often seen as a key element of constructionist approaches (startingfrom Papert’s LOGO, a programming language designed to enable the learning of geometry), theresearch on learning to program through a constructionist strategy is somewhat limited, mostly focusing
on how to bring the abstract and formal nature of programming languages into “concrete” or eventangible objects, graspable even by children with limited abstraction power [Resnick et al. 2009; Hornand Jacob 2007; Hauswirth, Adamoli, and Azadmanesh 2017]. Notwithstanding this, constructionist
ideas are floating around mainstream programming practice and they are even codified in some
software engineering approaches: agile methods like eXtreme Programming [Beck and Andres 2004],
for example, suggest several techniques that can be easily connected to the constructionist word ofadvice about discussing, sharing, and productively collaborating to successfully build knowledge
together [Resnick 1996]; moreover the incremental and iterative process of creative thinking andlearning [Resnick 2007] fits well with the agile preference to “responding to change over following aplan” [Beck et al. 2001].
The iterative process described by Resnick in [Resnick 2007] originated by observing how
kindergarteners learn, and is now called "creative learning spiral" (Figure 1), that describes MIT’s viewon how to learn creatively [Resnick 2017]. When you learn by creating something (e.g. a computer
program) you imagine what you want to do, create a project based on this idea, play with your creation,
share your idea and your creation with others, reflect on the experience and feedback received fromothers, and all this leads you to imagine new ideas, new functionalities, new improvements for yourproject, or new projects. The process is iterated many times.

Figure 1: Creative learning spiral (Resnick 2017)902

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This spiral describes an iterative process, highly overlapping with iterative software development cycle(see 5.1).

2. What does it mean to learn programming?
The basic assumption/premise behind programming — i.e., producing a precise description of how tocarry out a task or to solve a problem — is that an interpreter, different from the producer of thedescription, can understand it and effectively carry out the task as described. There are thus two distinct
but tightly tied aspects in programming:
1. the program itself (the text of the code),
2. the actions that take place when the program is run by the interpreter.
We thus need to know the interpreter in order to program, in particular we need to know:the set of basic actions it is able to perform,

a language it is able to understand, with rules on how to compose basic actions,the relation between syntax and semantics, that is what actions it will perform given a
description, and, conversely, how to describe a given sequence of actions so that it will performthem.

The first aspect, that is the program source code, is explicit, visible. The second one instead, that is theactions that take place when the program is run, is somewhat implicit, hidden in the execution timeworld, and not so immediate to grasp for novices. Moreover, this aspect is sometimes underestimatedby both teachers and learners: teachers, as experts, give it for granted; learners tend to constructpersonal intuitive, not necessarily coherent, ideas of what will happen.
This dichotomy of programming — its static visible code and its implicit dynamics — emerges as a
critical issue when learning to program, as shown by studies from different perspectives. To cite afew [Sorva 2013]:
Phenomenography studies show how novice programmers tend to perceive programming as no
more than the production of code, missing to relating instructions in the program to what happens
when the program is executed.

Studies on programming misconceptions point out how most of programming misconceptions
have to do with aspects that are not readily visible in the code but are related to the execution
time, both in term of what will happen and of what will not unless explicitly specified in the code.

Threshold concept theory identifies program dynamics as a candidate threshold concept inprogramming as it has many of the features that characterise threshold concepts; among others:
it is a troublesome barrier to student understanding, it transforms how the student perceives the
subject, it marks a boundary between programmers and end users.

To help novice programmers take into account also the dynamic side of programming, the concept ofnotional machine has been proposed. A notional machine is a characterisation of the computer in its
role as executor of programs in a particular language (or set of languages, or even a subset of alanguage) for didactic purposes. It thus gives a convenient description of the association syntaxsemantics. The following learning outcomes should therefore be considered when teaching to program:the development by students of a perception of programming that does not reduce to productionof code, but includes relating instructions to what will happen when the program is executed,
and eventually comes to include producing applications for use and seeing it as a way to solve
problems;the development of a mental model of a notional machine that allows them to make theassociation (static) syntax - (dynamic) semantics and to trace program execution correctly andcoherently.
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In particular this latter outcome goal will include the development of the following skills:•

given a program (typically one’s own) and an observed behaviour:◦

identify when debugging is needed,◦

identify where a bug has occurred,◦be able to correct the code;•

given a program and its specifications, be able to test it;•

understand that there can be multiple correct ways to program a solution.

If this is a crucial point in learning to write executable descriptions, however, programming is indeed amultifaceted competence, and the knowledge to construct and the skills to develop span over several
dimensions, besides predicting concrete semantics of abstract descriptions given via programming
languages:•

understanding general properties of automatic interpreters able to manipulate digital information;•

thinking about problems in a way suitable to automatic elaboration;•

devising, analysing, comparing solutions;•

adapting solutions to emerging hurdles and needs;•organizing team work and productively eliciting, organizing, and sharing the abstract knowledge
related to a software project.

2.1 Constructionism and learning to program
In some sense programming is intrinsically constructionist as it always involves the production of an
artifact that can be shown and shared. Of course when teaching programming, this aspect can be
stressed or attenuated.

Failure rates and dropout percentages in traditional programming courses and the urge to introduce
programming early in school curricula have fostered new approaches to teaching programming, where
this aspect has gained importance. Indeed the following points are given particular consideration:•

motivation: programming tasks should be engaging to keep pupils’ motivation high;•syntax: novices should be introduced first to the logical aspects of programming and only at alater stage to the syntax;•

a constructivist approach: the construction of knowledge is to be fostered, for example throughunplugged activities that are more suitable to group work and shared meta-cognition;•

constructionism: the production of personal projects and artifacts must be encouraged.

In this perspective, for educational purposes visual programming languages (Section 3), have beendeveloped and unplugged activities have been designed. In particular visual programming languages
allow novices to concentrate on the logical aspects of programming without having to strive withunnatural textual syntactic rules. Moreover, they make it possible to realise small but meaningfulprojects, keeping students motivated, and support a constructionist approach where students areencouraged to develop and share their projects — video games, animated stories, or simulations ofsimple real world phenomena.

2.2 Computers Unplugged
Offline or unplugged programming activities were often used to explain important concepts or
vocabulary to students without actually using a PC, laptop, or smartphone, e.g., x/y coordinates, theneed for precise instructions for computers/robots, or variables and lists. Examples are to program aclassmate like a robot, paint instructions, pack a rucksack, or send “broadcast messages” to colleagues.
Unplugged activities in small groups have become popular over the years to introduce basic computerscience concepts in non vocational contexts. They offer a number of advantages:904

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inexpensive set up: they usually require very basic and inexpensive materials, so they can beeasily proposed in different contexts;
no technological hurdles: they do not involve the use of technology, with which not all teachers
are at ease;a constructivist environment: indeed•by manipulating real objects or dramatising processes, pupils can observe what happens,
formulate hypotheses, validate them through experiments, i.e. develop a scientific approach tothe construction of their knowledge;•

by working in group, pupils are encouraged to participate, share ideas, verbalise and uphold
their deductions.

As the results of the activities, be they the execution of a procedure, the design of a solution or theconstruction of an object, are always shared in the class, unplugged activities also have a constructionist
flavour and can be the first phase of a more structured constructionist proposal.
The following two examples, taken from CSUnplugged84 and ALaDDIn85, illustrate typical unplugged
approaches to introduce children to programming.
In CSUnplugged “Rescue Mission”, pupils are given by the teacher a very simple language with only
three commands: 1 step forward, 90 degrees left, 90 degrees right. The task is to compose a sequenceof instructions to move a robot from one given cell on a grid to a given other cell. Pupils are divided into
groups of three where each one has a role: either programmer, bot, or tester. This division of roles isdone to emphasise the fact that programs cannot be adjusted on the fly; they must be first planned,
then implemented, then tested and debugged until they work correctly. So the programmer must write
down the instructions for the task, then pass them to the tester, who will pass them on to the bot andwill observe what happens; its role is to underline what doesn’t work and hand them back to theprogrammer, who can then find the bug and fix it.
ALaDDIn “Algomotricity and Mazes” is an activity designed according to a strategy called
algomotricity [Lonati et al. 2011; Bellettini et al. 2012, 2013, 2014], where pupils are exposed to an
informatic concept/process by playful activities which involve a mix of tangible and abstract object
manipulations; they can investigate it firsthand, make hypotheses that can then be tested in a guided
context during the activity, and eventually construct viable mental models. Algomotricity starts“unplugged” [Bell, Rosamond, and Casey 2012] and ends with a computer-based phase to close theloop with pupils’ previous acquaintance with applications [Taub, Armoni, and Ben-Ari 2012].
“Algomotricity and Mazes” focuses on primitives and control structures. The task is that of verbally
guiding a “human robot”(a blindfolded person) through a simple path. Working in groups, initially pupils
are allowed to freely interact with the “robot”, then they are requested to propose a very limited set ofprimitives to be written each on a sticky note, and to compose them into a program to be executed by
the “robot”. Also, they have the possibility of exploiting three basic control structures besides sequence(if, repeat-until, repeat-n-times). Groups may try their solutions as they wish and, when they are ready,each group is asked to execute its own program. Then the conductor may decide to swap some
programs, so that a program is executed by the “robot” of another group. This allows the instructor toemphasise the ambiguity of some instructions or the dependency of programs on special features of
the “robot” (e.g., step/foot size). In the last phase, students are given computers and a slightly modified
version of Scratch. They are requested to write programs that guide a sprite through mazes of increasing
complexity and foster the use of loop control structures86.

2.3 Notional machines
Alan Perlis, in his foreword to “Structure and Interpretation of Computer Programs” (SICP) [Abelson,
Sussman, and Sussman 1996], sets the stage for learning to program: “Our traffic with the subject
84

https://csunplugged.org/
http://aladdin.di.unimi.it/
86
The pictures shown in the first page show the first and last phase of the “Algomotricity and Mazes” activity, respectively.
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matter of this book involves us with three foci of phenomena: the human mind, collections of computer
programs, and the computer. Every computer program is a model, hatched in the mind, of a real ormental process.” And then: “The source of the exhilaration associated with computer programming is
the continual unfolding within the mind and on the computer of mechanisms expressed as programsand the explosion of perception they generate. If art interprets our dreams, the computer executes themin the guise of programs!”.
This seems an important intuition for approaching programming from a constructionist perspective:
programs are a join point between our mind and the computer, the interpreter of the formal descriptionof what we have in mind. Thus, programs appeal to (or even exhilarate) our curiosity and ingenuity andare wonderful artifacts to share and discuss with other active minds. Such a sharing, however, assumes
that the interpreter is a “common ground” among peers. When a group of people program the same
‘machine’, a shared semantics is in fact given, but unfortunately people, especially novices, do notnecessarily write their programs for the formal interpreter they use, rather for the notional
machine [Sorva 2013; Berry and Kölling 2014] they actually have in their minds.
A notional machine is an abstract computer responsible for executing programs of a particularkind [Sorva 2013] and its grasping refers to all the general properties of the machine that one is learning
to control [Boulay 1986]. The purpose of a notional machine is to explain, to give intuitive meaning to
the code a programmer writes. It normally encompasses an idealized version of the interpreter andother aspects of the development and run-time environment; moreover it should bring also acomplementary intuition of what the notional machine cannot do, at least without specific directions of
the programmer.
To introduce a notional machine to the students is often the initial role of the instructors. Ideally thisshould be somewhat incremental in complexity, but not all programming languages are suitable forincremental models: in fact most of the success for introductory courses of visual languages (see 3.5)
or lisp dialects (see 3.4 and 3.1) is that they allow shallow presentations of syntax, thus focusing thelearners on the more relevant parts of their notional machines 87.
An explicit reference to the notional machine can foster meta-cognition and during team work it can help
in identifying misconceptions (see 2.5). But how can the notional machine be made explicit? The
discussion with novice programmers should be guided by an effort of tracing the computational process
and visualizing the execution, in order to make as clear as possible what one expects the notional
machine will do and what it actually does.

2.4 Abstract programming patterns
One of the most relevant competencies to be mastered when learning computer programming is that ofrecognizing how a relatively low number of abstract patterns can be applied to a potentially infinite
spectrum of specific conditions. This is often a challenge for novices, given that most of the times thediscipline is taught using a two-step procedure based on: (i) introducing one or more primitive tools (sayfunctions, variables, or control flow statements), and (ii) showing some (small number of) examples
highlighting how these tools can be combined together in order to solve specific problems. This mightlead to the rise of misconceptions of pupils w.r.t. the above mentioned tools (see 2.5 for more details).

87

On the other hand, several languages of widespread use by experienced programmers are in this sense more complex to handle in thecontext of introductory courses on programming. This is due to the fact that they force the novice to perform true leaps of faith in accepting an
intricate syntax required even to write the simplest programs, and definitely obfuscating the underlying notional machine. Just to state an
example, implementing in Java a canonical “hello, world!” requires the programmer to define a class, add to the latter a public, static, void
method, provide a contract for this method, written in terms of an array of strings, and actually write the only relevant line of code, yet invoking
a static method on a class variable of a system class.

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Figure 2: Roles of variables, organized in a constructionist-like hierarchy where
the predecessor of an arrow is a prerequisitefor learning the corresponding
successor (source: [Sajaniemi 2002])

The concept of role of variables [Sajaniemi 2002; Proulx 2000] has been proposed in order to suggest
a more constructionist-like learning path in which knowledge about variables is built exploiting someconcepts at an intermediate level between those of the operational definition of a variable as the holder
of a mutable value of a given type (say, of a float variable storing numeric quantities) and its specificuse cases in order to solve given problems (for instance, that of computing the maximal value within asequence of numeric quantities). Here, the key concept is related to an abstract use ofvariables — regardless of their type — following a small number of roles (namely: fixed value, stepper,
follower, most-recent holder, most-wanted holder, gatherer, transformation, one-way flag, temporary,and organizer). Just to state a couple of examples,•most-wanted holder identifies the role of a variable storing the most appropriate value found at
any intermediate execution time in order to solve a problem (e.g., the variable typically containing
the maximal encountered value while scanning a sequence), and•the follower role applies to all variables whose values is always copied from other variables when
the latter are about to be changed (like in the naive algorithm used in order to teach how to
generate the Fibonacci sequence without introducing recursion or the golden ratio).

Keeping up with the example of finding a maximal value, instead of jumping right away to the algorithmic
solution, there is a great opportunity in letting pupils reason about how this problem is in fact a special
case of the more general quest for an optimal value which can be found exploiting a greedy strategy.
This strategy consists in using a most-wanted holder to be compared each time with a new element of
the sequence, possibly updating the former if the new element allows us to find a more precise solution.
This general-purpose method easily fits the search of the maximal value, as well as the minimal one,but it allows us to efficiently handle less obvious cases such as that of finding the distinct vowels
occurring in a sentence.
The roles themselves fit the constructionist approach also because they can be gradually introduced
following the hierarchy illustrated in Figure 2, starting from the concept of literal (e.g., an integer value
or a string) and building knowledge about one role on the top of the knowledge of already understoodroles.
Loop patterns [Astrachan and Wallingford 1998] are an analogous interesting teaching subject when
the concept to be learned identifies with iterations. For instance, the loop and a half pattern is introduced
as an efficient way of implementing a processing strategy to be applied to a sequence of elements907

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while True:
value = input('insert a positive, odd value')if value > 0 and value %breakprint('the value is not valid')

Figure 3: A typical loop and a half pattern applied to the repeated validation of external input to aprocedure

whose end can be detected only after having accessed at least one of the elements themselves. The
pattern here resorts to rebuilding the almost extinguished repeat/until iterative structure [Roberts 1995]
using an infinite loop whose body accesses the next sequence element and subsequently checks
whether or not it is the last one. If it is, the loop is escaped through a controlled jump, otherwise somespecial actions (such as printing a warning) are executed before proceeding to the next iteration. In thisway, there is no need of duplicating code lines (typically accessing the first element in the sequence
outside the loop), and the check concerns the end of sequence rather than its logical negation (which
could be harder to grasp if it involves non-trivial logical connectives). Figure 3 shows one of thecanonical incarnations of this pattern, namely the possibly repeated check of a value given as input via
keyboard, detecting and ignoring invalid entries; other incarnations concern in general the handling of
external inputs (for instance when reading from a file), or the serialization of sequences which havebeen generated computationally. It is worth noting that a loop might encompass more than one pattern
simultaneously: for instance the code in Figure 3 is also an example of polling loop.Loop patterns fit wellwithin a constructionist-based learning path also because they naturally arise when critically analyzing
a less efficient implementation of a loop: for instance, the previous polling loop could be the point ofarrival of a reasoning scheme which started from the detection of a duplicated line of code in a quickand-dirty implementation proposed by pupils.
In general, abstract programming patterns are provided in a short number, so as to be easy to fully
cover this subject within a standard introductory course in computer programming; moreover, the related
concepts are easily and rapidly grasped by experienced computer science teachers [Ben-Ari and
Sajaniemi 2004], thus they can be embedded in already existing curricula with low effort.

2.5 Misconceptions
Sorva defines a misconceptions as understandings that are deficient or inadequate for many practicalprogramming contexts [Sorva 2013].
Some authors [Ben-Ari 2001] believe that computer science has an exceptional position inconstructivist’s view of knowledge constructed by individuals or groups rather than a copy of an
ontological reality: in fact, the computer forms an "accessible ontological reality" and programming
features many concepts that are precisely defined and implemented within technical systems [...]
sometimes a novice programmer “doesn’t get” a concept or “gets it wrong” in a way that is not a harmless(or desirable) alternative interpretation. Incorrect and incomplete understandings of programming
concepts result in unproductive programming behavior and dysfunctional programs [Sorva 2013].
According to Clancy [Clancy 2004] there are two macro-causes of misconceptions: over- or undergeneralizing and a confused computational model. High-level languages provide an abstraction oncontrol and data, making programming simpler and more powerful, but, by contrast, hiding details of theexecutor to the user, who can consequently find mysterious some constructs and behaviors.
Much literature about misconceptions in CSEd can be found: we list some of the most important causesof misconceptions, experienced especially by novices, divided into different areas, found mainly
in [Clancy 2004; Sirkiä 2012; Sorva 2013] and in works they reference. For a complete review see forexample [Qian and Lehman 2017].
English Keywords of a language do not have the same meaning in English and programming. For
example, the word while in English indicates a constantly active test, while the construct while cantest the condition again only at the beginning of the next iteration. Some students believe that the908

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loop ends at the precise moment the condition is falsified. Similarly, some of them think of the ifconstruct as a test continuously active and awaiting the occurrence of a condition, others believedthat the then branch is executed as soon as the condition becomes true.
Syntax Although one may think the syntax is one of the biggest sources of misconception, studies showthat it is a problem only in the very early stages. In particular, some students were able to write
syntactically valid programs, which, however, were not useful for solving the given problem, or were
semantically incorrect.
Mathematical notation Reported by many authors, classical is the confusion that generates theassignment with the = symbol (for example, seen as an equation or as a swap of values between
variables) or the increment (a = a + 1) thought of as an impossible equation.
Examples of over-generalization Some authors found a series of non-existent constraints (e.g.,
methods in different classes that must have different names, arguments that can only be numbers,“dot” operator usable just in methods) dictated by the fact that the students had not seen any
counterexample for such situations.
Similarities The analogy “a variable is like a box” can foster the idea that - like a box - it can containmore elements at the same time. The analogy “programming with the computer is like conversing
with it” can bring to attribute intentionality to the computer and therefore to think that it:•

has a hidden intelligence that understands the intentions of the programmer and helps him
achieve his goal (the so-called “superbug”);•

has a general vision, knowing also what will happen in lines of code that it is not currently running.

Some aspects of programming are particular carriers of misconceptions.
Sequence Many misconceptions are due to lack of understanding of the program flow: all lines activeat the same time “magic” parallelism, order of instructions not important, difficulty in understanding
the branches.
Passing parameters Students present difficulties in this area, for example by confusing the types ofpassing (by value, by reference ...), making confusion with the return value or with parameters scope.
Input Input statements are particularly problematic. Students do not understand where the input data
come from, how they are stored and made available to the program. Some of them believe that a
program remembers all the values associated with a variable (its “history”).
Memory allocation There are considerable difficulties in understanding the memory model oflanguages where allocation happens implicitly.

3. Programming languages for learning to program
Ptydepe, as you know, is a synthetic language, built on a strictly scientific basis. […] There are many
months of intensive study ahead of you, which can be crowned by success only if it is accompanied bydiligence, perseverance, discipline, talent and a good memory. And, of course, by faith. [Václav Havel]
From a constructionist viewpoint of learning, programming languages have a major role: they are a key
means for sharing artifacts and expressing one’s theories of world. The crucial part is that artifacts canbe executed independently from the creator: someone’s (coded) mental process can become part ofthe experience of others, and thus criticized, improved, or adapted to a new project. In fact, the originof the notion itself of constructionism goes back to Papert’s experiments with a programming
environment (LOGO, see 3.1) designed exactly to let pupils tinker with math and geometry (a similar
approach applied to physics had a smaller impact) [Papert 1980]. Does this strategy work even when
the learning objective is the programming activity itself? Can a generic programming language be used
to give a concrete reification of the computational thinking of a novice programmer? Or do we needsomething specifically designed for this activity? Alan Kay says that programming languages can be
categorized in two classes: “agglutination of features” or “crystalization of style” [Kay 1993]. What ismore important for learning effectively in a constructivist way? Features or style?
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In the last decade, a number of block-based visual programming tools have been introduced whichshould help students to have an easier time when first practicing programming. These tools, often basedon web-based technologies like Adobe Flash and later JavaScript, CSS, and HTML5, as well as anincrease in the number of modern smartphones and tablets, opened up new ways for innovative coding
concepts [Kahn 2017]. In general, they focus on younger learners, support novices in their firstprogramming steps, can be used in informal learning situations, and provide a visual/block-based
programming language which allows students to recognize blocks instead of recalling syntax [Tumlin
2017]. Many popular efforts for spreading computer science in schools, like [Goode, Chapman, andMargolis 2012] or the teaching material from Code.org [Code.org 2018] rely on the use of such blockbased programming environments. In addition, such tools are broadly integrated in primary throughsecondary schools, and even at universities, thus they have been adopted into many computing classes
all over the world [Meerbaum-Salant, Armoni, and Ben-Ari 2010].

3.1 LOGO
LOGO was designed (since 1967) for (constructionst) educational purposes by Wally Feurzeig,
Seymour Papert and Cynthia Solomon [Papert 1980]. Its syntax was heavily influenced by Lisp (at thetime the standard language for Artificial Intelligence research) and LOGO featured a graphical (at leastin principle) environment: the instructions the programmer writes are directed to a “turtle” (a small
isosceles triangle in which the acutest angle marks the head) who moves around the screen, possibly
leaving a colored trace. The turtle should help learners (typically 6th-8th graders) with a sort of selfidentification: its movements have a clear correspondence with their movements in the real world,(although the turtle moves in a 2D space). The patterns drawn by the turtle can be the way the learners
build their understanding of 2D geometry, discovering in the process even deep mathematical truths as
the fact that a circle can be approximated by a high number of straight segments [Abelson and DiSessa
1986] (see Figure 4).
TO CIRCLEREPEAT FOREVER[
FORWARD 1
RIGHT 1]Figure 4: A procedure to draw a circle in LOGO

LOGO was conceived to empower learners of geometry and kinematics, not programming.
Programming is just a means of expression, but one with a great epistemic potential. According toPapert: “in teaching the computer how to think, children embark on an exploration about how they
themselves think. The experience can be heady: Thinking about thinking turns every child into an
epistemologist, an experience not even shared by most adults” [Papert 1980]. Also, by expressing
something in a way even the LOGO turtle can “understand” can be fruitful even for real world activities.
Juggling, for example, a complex motoric activity which requires dedicated training, can be analyzed
with LOGO: the identification of proper sub-activities (i.e., sub-routines like TOP-RIGHT to recognize
when one juggling ball is at the top of its trajectory going to the right, or TOSS-LEFT to throw the ballwith the left hand) may shorten significantly the time for acquiring juggling skills (from days to hours,
according to [Papert 1980]). And here ‘proper’ should be understood as appropriate to the task, but also
as “fitting properly with the programming language idiomatic way of describing computational
processes”. LOGO had many independent implementations and its approach is still very popular, even
Python has a turtle package in its standard library.

3.2 Smalltalk
Smalltalk [Goldberg and Kay 1976] also has its roots in constructionist learning. Back in the earlyseventies, at the Learning Research Group within the Xerox Parc Research Center, people wereenvisioning a world of personal computing devices: they should have intuitive user interfaces and an
explicit “programmability”. Smalltalk, with whose lineage traces clearly to LOGO and Lisp, was designed
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with a general audience in mind, since everyone should be comfortable with programming andcomputing devices should become ubiquitous in learning environments “along the lines of Montessori
and Bruner” [Kay 1993]. Thus, although clearly designed to foster a personal learning experience,
Smalltalk was not directed specifically to children and it has conquered a wide professional audience.
In Smalltalk everything is an ‘object’ able to react to ‘messages’. RìThere follows a highly consistentobject-oriented approach and code can be factored out by inheritance and dynamic binding. Smalltalk
introduces also the idea that everything in the system is programmable: in fact, the tool-chain itself andthe application a programmer is writing are indistinguishable and available for modifications, even at
run-time. By design, such a dynamic environment encourages a trial-and-error approach. A common
practice for Smalltalk developers is programming with the constant support of the debugger: instead ofcreating a method before its call, one sends a message that an object “does not understand”, then they
use the debugger to catch the exception and write the code that is needed. A specific Smalltalk systemfor children was designed later as an evolution of Squeak Smalltalk: E-toys [Kay et al. 1997] provided aworld of “sprites”, funny characters that can be moved (concurrently) around the screen by programmingthem in Smalltalk. E-toys then evolved in Scratch (see 3.5), where the programming part was replacedby visual blocks.

3.3 BASIC, Pascal
The search for programming languages suitable for students in fields other than hard sciences andmathematics was in fact started in the sixties. In 1964 at Dartmouth College rose BASIC (Beginner’s
All-purpose Symbolic Instruction Code) [Kurtz 1978]. It seems legit to mention BASIC in a paper on
constructionism and programming: for years BASIC has been the elective language for personal
projects and even before widespread Internet connectivity, several communities shared BASIC
programs in Bulletin Board Systems and magazines. Its popularity among self-taught programmers,
however, was due mainly to its availability on personal and home computing devices. Moreover, thelanguage was typically implemented using an interpreter, thus naturally fostering the trial-and-error andincremental learning styles typical of a constructionist setting. A generation grown with BASIC still thinks
it is a wonderful approach to get children hooked on programming (see for example [Brin 2016]).
However, many believe BASIC is not able to foster good abstractions and fear that BASIC programmers
will bring bad habits to all their future computational activities88.
In 1970 Niklaus Wirth published Pascal [Wirth 1993], a small, efficient ALGOL-like language intended
to encourage good programming practices using structured programming and data structuring. For
about 25 years, Pascal (and its successors like TurboPascal or Modula-2) was the most popular choicefor undergraduate courses and a whole generation of computer scientist learned to program through its
discipline of well-structured programs popularized by Wirth in his book “Algorithms + Data Structures =Programs”. Only Java had a similar success in undergraduate courses. However, while Java popularity
was (and is) influenced by trends in software industry, Pascal was appealing mainly for its intrinsic
discipline, which matched the academic sentiment of the time. Alan Perlis, in his foreword to “Structure
and Interpretation of Computer Programs” (see 3.4), says: “Pascal is for building pyramids—imposing,breathtaking, static structures built by armies pushing heavy blocks into place. Lisp is for building
organisms—imposing, breathtaking, dynamic structures built by squads fitting fluctuating myriads ofsimpler organisms into place. The organizing principles used are the same in both cases, except for
one extraordinarily important difference: The discretionary exportable functionality entrusted to theindividual Lisp programmer is more than an order of magnitude greater than that to be found within
Pascal enterprises. Lisp programs inflate libraries with functions whose utility transcends the applicationthat produced them. The list, Lisp’s native data structure, is largely responsible for such growth of utility.
The simple structure and natural applicability of lists are reflected in functions that are amazingly
nonidiosyncratic. In Pascal the plethora of declarable data structures induces a specialization withinfunctions that inhibits and penalizes casual cooperation. It is better to have 100 functions operate on

A recent anecdote: Brian Kernighan — one of the designers of C — called a book written by a BASIC programmer “the worst C programming
textbook ever written”! See https://wozniak.ca/blog/2018/06/25/Massacring-C-Pointers/index.html for the full story.
88

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one data structure than to have 10 functions operate on 10 data structures. As a result the pyramid muststand unchanged for a millennium; the organism must evolve or perish.”
Structured programming had also some LOGO-like descendants, the most famous one is probably
Karel [Pattis 1981], in which the programmer controls a simple robot that moves in a grid of streets (leftright) and avenues (up-down). A programmer can create additional instructions by defining them interms of the five basic instructions, and by using conditional control flow statements with environment
queries.

3.4 Scheme, Racket
Scheme [Abelson et al. 1998] is a language originally aimed at bringing structured programming in thelands of Lisp (mainly by adding lexical scoping). The language has been standardized by IEEE in 1999
and nowadays it has a wide and energetic community of users. Its importance in education, however,is chiefly related to a book, “Structure and Interpretation of Computer Programs” (SICP) [Abelson,
Sussman, and Sussman 1996], which had a tremendous impact on the practice of programming
education. The book derived from a semester course taught at MIT. It has the peculiarity to present
programming as a way of organizing thinking and problem solving. Every detail of the Scheme (which,being a Lisp dialect, has lightweight syntax) notional machine is worked out in the book: in fact at theend, the reader should be able to understand the mechanics of a Scheme interpreter and to program
one by her/himself (in Scheme). The book, which enjoyed widespread adoption, was originally directed
to MIT undergraduates and it is certainly not suitable either for children or even adults without a scientificbackground: examples are often taken from college-level mathematics and physics. Its emphasis on“organized abstraction” and “procedural epistemology” makes it a fundamental reading for anyone
reflecting on teaching and learning how to build complex systems.
A spin-off of SICP explicitly directed to learning is Racket. Born as ‘PLT Scheme’, one of its strength isthe programming environment DrScheme [Findler et al. 2002] (now DrRacket): it supports educational
scaffolding, it suggests proper documentation, and it can use different flavours of the language, startingfrom a very basic one (Beginning Student Language, it includes only notation for function definitions,function applications, and conditional expressions) to multi-paradigm dialects; this flexibility is relatively
easy in a Lisp-like world, since most of the “syntax” is in fact provided by macros, that can be active or
not89. The DrRacket approach is supported by an online book “How to design programs” (HTDP) 90 and
it has been adapted to other mainstream languages, like Java [Allen, Cartwright, and Stoler 2002] andPython. The availability of different languages directed to the progression of learning should help in
overcoming what the DrRacket proponents identify as “the crucial problem” in the interaction between
the learner and the programming environment: beginners make mistakes before they know much of thelanguage, but development tools yet diagnose these errors as if the programmer already knew thewhole notional machine. Moreover, DrRacket has a minimal interface aimed at not confusing novices,with just two simple interactive panes: a definitions area, and an interactions area, which allows a
programmer to ask for the evaluation of expressions that may refer to the definitions. Similarly to what
happens in visual languages, Racket allows for direct manipulation of sprites, see an example in
Figure 5.

89

A small syntactic improvement of Racket over Lisp, very useful for beginners, is that nested parentheses can be matched easily by using
different bracket families: for example, (- {/ [* (+ 2 3) 4] 2 } 1)
90
Current version: http://www.htdp.org/2018-01-06/Book/index.html

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Figure 5: Racket code for “landing a rocket”

The authors of HTDP claim that “program design — but not programming — deserves the same role ina liberal-arts education as mathematics and language skills.” They aim at systematically designed
programs thanks to systematic thought, planning, and understanding from the very beginning, at every
stage, and for every step. To this end the HTDP approach is to present “design recipes”, supported bypredefined scaffolding that should be iteratively refined to match the problem at hand. This is indeed
very close to the idea of micropatterns discussed in 2.4.

3.5 Scratch, Snap!, Alice, and others
EToys worlds (see 3.2) with pre-defined — although programmable — objects, evolved in a genericenvironment in which everything can be defined in terms of ‘statement’ blocks. Scratch [Resnick et al.
2009], originally written in Smalltalk (but this is hidden to most users: only a “secret” key combination
can bring the Smalltalk environment alive), is the most popular and successful visual block based
programming environment. Launched in 2007 by the MIT Media Lab, the Scratch site has grown tomore than 25 million registered members with over 29 million Scratch projects shared programs.
Unlike traditional programming languages, which require code statements and complex syntax rules,
here graphical programming blocks are used that automatically snap together like Lego bricks when
they make syntactical sense [Ford 2009]. In visual programming languages, a block represents a
command or action and they are arranged in scripts. The composition of individual scripts equals theconstruction of an algorithm. The building blocks offer the possibility, e.g., to animate different objects
on the stage, thus defining the behavior of the objects. In addition to the basic control structures, there
are event-triggering building blocks/conditions for event-driven programming [Georgios and Kiriaki2009]. Familiar concepts such as variables, variable lists, Boolean logic, user interface design, etc. areprovided as well. Furthermore, most visual programming environments offer the possibility to integrate
graphics, animations, music, and sound to create video games, movies, and interactive stories. In thatway, creative and artistic talents of the students are displayed in their games, stories, and applications.
Thereby, these visual languages offer the same programming logic and concepts as other (text-based)
programming languages.
Some characteristics of the Scratch environment [Maloney et al. 2010] are particularly relevant in theconstructionist approach.
Liveness The code is constantly executed and can be changed on the fly, immediately seeing
the runtime effects of the change; this encourages users to tinker with the code.
No error messages When you play with Lego bricks, they stack together or they don’t - thesame happens in Scratch; program always run: syntax errors are prevented from the block
shapes and connections, and also runtime errors are avoided by doing something“reasonable” (e.g., in the case of an out-of-range value); this is particularly important not tofrustrate kids and to keep them iterating and developing: “A program that runs, even if it is
not correct, feels closer to working than a program that does not run (or compile) atall” [Maloney et al. 2010].
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Other characteristics are useful to help novices avoiding misconceptions that often arise when starting
to learn to program.
Execution made visible A glowing yellow border surrounds running scripts; moreover version
1.4 (and Snap!, for example) provides a “single-stepping” mode, where each block ishighlighted when it is executed; this is very helpful in program reading and debugging, andhelps students form a correct mental model of the notional machine underlying the program
execution.
Making data concrete You can see in a variable box, automatically shown, its current value:
again, this is helpful for making the underlying machine model visible.
Finally, other characteristics introduce important software engineering and development concepts.
Open source Each shared project has a “see inside” button that brings you to the projectsource; you can read and edit the blocks to see what happens.
Remixing If you edit someone else’s project, you create a remix: you are the author, but thesystem automatically gives credits to the original author (at any depth, keeping track ofmultiple remixes in a tree) and suggests you to explicitly declare what changes you made.
The main limitation of Scratch programs is that they do not scale well from the abstraction point of view:
only since version 2 you can “make a new block” that is, a procedure with optional parameters. These
blocks have no possibility to return a value (like a number or a boolean) and so can’t be nested inside
other blocks, forcing you to modify global variables if needed.
Snap!91 (originally BYOB, Build Your Own Blocks) is an extended reimplementation of Scratch withfunctions and continuations. These added capabilities make it suitable for a serious introduction tocomputer science for high school or college students: in fact, Snap! is used as the basis for an Advanced
Placement CS course at Berkeley92.
The Scratch approach was also ported to mainstream programming languages: in Alice [Dann, Cooper,
and Pausch 2008] visual blocks are in fact Java instructions. Alice worlds are 3D: this choice makes itvery attractive and appealing to pupils, who can program amazing 3D animations. It also adds many
complexities, since moving objects in a 3D space is not trivial.
Recently, several block-based development environments for web-browsers were published (even thelast version of Scratch is web based, thus abandoning its Smalltalk inner soul). The most popular is
probably Google’s Blockly93, which allows for programming both with blocks and textual programming
languages (Javascript and Python): the programmer can see the source code in different
interchangeable ways. MIT has developed a version of Blockly that can even be used to create Android
applications, to be executed on mobile phones and that can take advantage of sensors and Google
services like maps (App Inventor94). Even Apple recently proposed a block-based interface for its Swiftprogramming language95 and other Android-based visual programming language environments exist.
For example, Pocket Code allows the creation of games, stories, animations, and many types of otherapps directly on phones or tablets, thereby teaching fundamental programming skills [Slany 2014]. The
free and open source project Catrobat 96 was initiated 2010 in Austria at TU Graz. This team develops
free educational apps for teenagers with the aim to introduce young people to programming. With aplayful approach, young people can be engaged and game development can be promoted with a focus
on design and creativity. The drag and drop interface provides a variety of bricks that can be joined
together to develop fully fledged programs. The app is freely available for Android at the Google’s PlayStore 97 and soon it will be available on the Apple App Store for iOS. The target audience for Pocket

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https://snap.berkeley.edu/
https://bjc.berkeley.edu/
93
https://developers.google.com/blockly/
94
http://appinventor.mit.edu
95
https://www.apple.com/swift/playgrounds/
96
https://www.catrobat.org/
97
https://catrob.at/pc
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Code are teenagers between the age of 11 and 17 years old who have access to or own an Android
smartphone.
Since its first versions, Scratch had blocks able to connect and program external (physical) robots. In
fact most of the mentioned environments can connect to physical devices and sensors, with the goal ofincreasing the constructionist appeal of block programming, and opening to the world of “tinkering” withelectronics. Resources like the ‘Makey Makey’ 98 tool became popular for activities during coding
workshops with innovative forms of production and do-it-yourself work [Schön, Ebner, and Kumar 2014;
Sheth, Bell, and Kaiser 2012].
All in all, visual programming languages seem to provide an easier start and a more engaging
experience for learners. The ease of use, simplicity, and desirability of new visual programming
environments enables young people to imagine complex goals. A study which compared three classes
that used either block-based (Scratch), text-based (Java), or hybrid blocks/text (Snap!/JavaScript)
programming languages showed that students generally found block-based programming to be easierthan the text-based environments [Weintrop and Wilensky 2015]. Some researchers, however, argue
that students are not fully convinced that a visual language can help them learn other programming
languages [Lewis et al. 2014].

3.6 Common features
The above short survey of programming languages for education shows they have some recurrent traits
that link them to the themes discussed in Sect. 2.
Personification The interpreter becomes a “persona”, computation is then carried out throughanthropomorphic (or, better, zoomorphic, since animals are very common) actions. This
seems to contradict a famous piece of advice coming from no less thanE. W. Dijkstra [Dijkstra 1985]. Speaking of anthropomorphism in computer science, henoted: “The trouble with the metaphor is, firstly, that it invites you to identify yourself with thecomputational processes going on in system components and, secondly, that we see
ourselves as existing in time. Consequently the use of the metaphor forces one to what wecall ‘operational reasoning’, that is reasoning in terms of the computational processes that
could take place. From a methodological point of view this is a well-identified and welldocumented mistake: it induces a combinatorial explosion of the number of cases toconsider and designs thus conceived are as a result full of bugs.” The reasoning in terms ofthe computational processes, however, is what is probably needed for a novice in order tofamiliarize with the notional machine. Some sort of operational reasoning seems also
important to foster the basic intuition needed by a constructivist approach, in which concrete,public actions are key.
Visualization and tracking Computational processes that evolve in time are described bystatic texts: the mapping between the two is not trivial and it requires an understanding of
the notional machine. Educational programming environments often try to make themapping more explicit with some visualization of the ongoing process: the trace left by the
LOGO turtle, or some other exposition of the changing state of the interpreter.
Appeal Engagement of learners is crucial: to this end it is important to give learners powerfullibraries and building blocks. It is not clear, however, how to properly balance amazing
effects in order to avoid they become a major distraction: sometimes children may spend
their (limited) time in changing the colors of the sprites, instead of trying to solve problems.
While they surely learn something even in these trivial activities, they also risk to lose theiropportunity of recognizing the thrill which comes when solving computational problems bythemselves.

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https://makeymakey.com/

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4 Learning to think computationally
The expression “Computational Thinking” (CT) has a very special relationship with Papert’s
constructionisim: the first ascertained attestation is in Papert’s seminal book Mindstorms [Papert 1980].
As already stated in 3.1, Papert was not focused on teaching computer science concepts, but on theuse of computation as a means of self expression and as a tool for learning ideas and concepts of otherdisciplines.
The expression almost completely disappeared (except for a Papert’s work about math [Papert 1996])
till 2006.
In 2006 Jeannette Wing brought the expression “Computational Thinking” back to the discussion [Wing2006], gaining a massive attention99. In that seminal article, Wing didn’t give a definition, but tightened
the concept to the computer science discipline, stating “Computational thinking involves solving
problems, designing systems, and understanding human behavior, by drawing on the concepts
fundamental to computer science” or that “thinking like computer scientists” is a fundamental skill foreveryone.
In the following years she proposed (along with Cuny, Snyder and Aho) a formal definition: CT is “thethought processes involved in formulating problems and their solutions so that the solutions are
represented in a form that can be effectively carried out by an information-processing agent” [Wing2010]. Wing argues that children who are introduced to CS learn more than just programming and that
this opens a new way of thinking [Wing 2008]. Moreover, students should think first about possible
solutions to a given problem (problem solving skills), and second implement their ideas by using acomputing device (programming skills [Lye and Koh 2014]). In order to successfully implement theirown solutions, students have to apply different programming concepts, such as loops and conditions,as well as practices, such as abstraction and debugging [Kafai and Burke 2013]. For teachers, however,developing computational thinking skills in students is actually a challenging task [Sentance andCsizmadia 2015]. After Wing’s articles, the discussion about the nature of CT and how to teach it in fact
exploded.
In [Corradini, Lodi, and Nardelli 2017] authors analysed different CT definitions and frameworks, findingout that all definitions agreed on the fact that CT is a way of thinking (thought process) for problem
solving but specifying that it is not just problem solving: the formulation and the solution of the problem
must be expressed in a way that allows a processing agent (a human or a machine) to carry it out.
Moreover, authors in [Corradini, Lodi, and Nardelli 2017] noticed that each definition listed somecharacteristics of CT, from thinking habits to specific programming concepts. They classified them infour categories. We quote here their findings.
Mental processes: Mental strategies useful to solve problems.
•

Algorithmic thinking: use algorithmic thinking to design a sequence of ordered steps(instructions) to solve a problem, achieve a goal or perform a task.

•

Logical thinking: use logical thinking and reasoning to make sense of things, establish andcheck facts.

•

Problem decomposition: split a complex problem in simpler subproblems to solve it moreeasily; modularize; use compositional reasoning.

•

Abstraction: get rid of useless details to focus on relevant information / ideas.

•

Pattern recognition: discover and use regularities in data, problems.

•

Generalization: use discovered similarities to make predictions or to solve more general
problems.
Methods: Operational approaches widely used by computer scientists.

99

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Constructionism 2018, Vilnius, Lithuania•

Automation: automate the solutions; use a computer or a machine to do repetitive tasks.

•

Data collection, analysis and representation: gather information/data, make sense of themby finding patterns, represent them properly; store, retrieve and update values.

•

Parallelization: carry out tasks simultaneously to reach a common goal, use parallel thinking.

•

Simulation: represent data and (real world) processes through models, run experiments onmodels.

•

Evaluation: implement and analyze solutions to judge them, in particular for what concerns
effectiveness, efficiency in terms of time and resources.

•

Programming: use some common concepts in programming (eg. loops, events,
conditionals, mathematical and logical operators).

Practices: Typical practices used in the implementation of computing machinery based
solutions.
• Experimenting, iterating, tinkering: in iterative and incremental software development, one
develops a project with repeated iterations of a design-build-test cycle, incrementally building
the final result; tinkering means trying things out using a trial and error process, learning byplaying, exploring, and experimenting.
•

Test and debug: verify that solutions work by trying them out; find and solve problems (bugs)
in a solution/ program.
•

Reuse and remix: build your solution on existing code, projects, ideas.

Transversal skills: General ways of seeing and operating in the world; useful life skills
enhanced by thinking like a computer scientist.
•

Create: design and build things, use computation to be creative and express yourself.

•

Communicate and collaborate: connect with others and work together to create somethingwith a common goal and to ensure a better solution.

•

Reflect, learn, meta-reflect: use computation to reflect and understand computational
aspects of the world.

•

Be tolerant for ambiguity: deal with non-well specified and open-ended real-world problems.

•

Be persistent when dealing with complex problems: be confident in working with difficult orcomplex problems, persevering, being determined, resilient and tenacious.

As you may notice, a lot of concepts are involved. Sometimes this led to critiques [Hemmendinger 2010]:
some of these concepts are not exclusively associated with CS, but taught in other disciplines (e.g.,math) or are general skills that children have been learning for a long time before the birth of CS. Anyway
CS brings to the discussion some characteristic problem solving methods (e.g., the possibility toeffectively execute a solution/a model/an abstraction by running an implementation of its
algorithm [Martini 2012]). Constructionist’s spirit emerges mainly in practices and transversal skills, but
we should pay attention not to forget mental processes and CS methods: loosing this connection may
lead to misconceptions about the nature of computational thinking, de-empowering Wing’s idea of theimportance of “thinking like computer scientists” in many human activities.
The most used technique to teach CT is teaching to program (with languages suitable for the learner’s
age and capacity). Programming is in fact the main way computer scientists express their solutions andso the main way they learn to think computationally. Teaching to program leads to problems andopportunities discussed in the previous sections. Nonetheless, unplugged activities (see 2.2) have been
proposed as well, as they help kids to act like the computer, becoming aware of the underlying notional
machine (see 2.3).

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5 Learning to program in teams
Working in teams requires new skills, especially because software products (even the ones in the reachof novices) are often tangled with many dependencies and division of labour is hard: it inevitably requires
appropriate communication and coordination. Therefore, it is important that novice programmers learn
to program in an “organized” way, thus discovering that as a group they are able to solve morechallenging and open-ended problems, maybe with interdisciplinary contributions.
To this end, agile methodologies seem to fit well with the needs of a constructionist team of learners
and they are increasingly exploited in educational settings (see for example [Kastl, Kiesmüller, andRomeike 2016; Missiroli, Russo, and Ciancarini 2016]):•

agile teams are typically small groups of 4–8 co-workers;•

agile values [Beck et al. 2001] (individuals and interactions over processes and tools; customer
collaboration over contract negotiation; responding to change over following a plan; working
software over comprehensive documentation) relate well with constructivist philosophies;•

agile teams are self-organizing and emphasize the need of reflecting regularly on how to become
more effective, and tune and adjust their behavior accordingly;•

typical agile techniques like pair programming, test driven development, iterative softwaredevelopment, continuous integration are very attractive for a learning context.

5.1 Iterative software development
For program development cycles, concepts of agile and iterative software development can be used toleverage this process and to see first results very quickly [DeMarco-Brown 2013; Davies and Sedley2009]. Figure 6 visualizes the process of game design in reference to agile methods. As the scope of
this paper is limited, only a simplified life cycle is visualized to describe the process of how a team works
in iterations to deliver or release software.

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Figure 6: Agile Development Cycle (DeMarco-Brown 2013; Davies and Sedley 2009)

The first step, Research, includes the development of the core idea by producing a simple game concept
or a storyboard. In this phase the story, title, genre, and theme of the game should be selected, as well
as a rough concept about the structure and gameplay.
In the second step, during the Design, the artwork, game content and other elements (characters,
assets, avatars, etc.), and the whole gaming word is produced.
The Development phase, includes all of the actual programming, followed by Testing the code and thesoftware (playtesting). Several iterations between testing and bug fixing are possible.
As a final step, the Release phase is planned. This could include several beta releases or a final releasefor end users. The agile model required to get started with the project works to bring customer
satisfaction by rapid, continuous delivery of useful software.
“In an iterative methodology, a rough version of the game is rapidly prototyped as early in thedesign process as possible. This prototype has none of the aesthetic trappings of the final
game, but begins to define its fundamental rules and core mechanics.” [Salen and Zimmerman
2003, p. 11]
To phrase it differently, before focusing on every detail of the project, focus on the smallest step thegames needs for playing it, e.g., limited interface control but basic game functionality.
Collaborative work and the connection with a real world problem makes their learning valuable [Jaime
and Ruby 2011]. Project work is always student-centered and task oriented. The final artefacts of thisproject work can be shared with a community, thus fostering ownership. The benefits of learning throughprojects include enhanced students’ participation within active learning and self-learning, enhanced
communication skills, addressing of a wider set of learning styles, and improved critical thinking skills.
This collaboration is needed for developing the game idea, communicating, sharing, and managing
assets and codes, playtesting and documentation.
Teachers have to consider how to support collaboration and communication during the whole gameproduction process [Ferreira et al. 2008]. They must therefore stick to certain design patterns anditerative cycles (e.g., agile) and explain game elements and rules. The project in general should foster
the teamwork in producing game assets and software. Teachers have to take into account the different
preferences of students, e.g., if they feel more confident in the role of developers or artists, or that919

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students do not shy away from switching roles. Finally, the teacher has to ensure that the ideas for the
project are simple and clear, as well as reduce the size and complexity of the game projects.

5.2 Test-driven development and debugging
Within the agile framework, a special constructivist emphasis is provided by test-driven development,
or TDD for short [Beck 2003]. This technique reverses the classical temporal order between theimplementation of code and the test of its correctness. Namely, the specification of the programmingtask at hand is actually provided with a test the defines correct behavior. The development cycle is thenbased on the iteration of the following three-step procedure:
1. write a test known to fail according to the current stage of the implementation,
2. perform the smallest code update which satisfies all considered tests, including the one
introduced in previous point, and3. optionally refactor the produced code.
TDD makes testing the engine driving the overall development process, and suggesting a good next
test is one of the hardest-to-find contributions that a facilitator might give in an active programming
learning context. Such suggestion has indeed the role of letting pupils aware that their belief at a broad
level (“the program works”) is false, thus an analogous belief at a smaller scale (for instance, “thisfunction always returns the correct result”) should be false, too. This amounts to the destruction ofknowledge necessary to build new knowledge (aka a working program) in a constructivist setting. Even
the refactoring step corresponds well to the constructivist re-organization of knowledge that follows thediscovery of more viable solutions. In fact, most of the developing activities consist in realizing that amore or less complex system which was thought to correctly work actually it is not able to cope with a
new arising test case. This applies of course also to the simplest coding tasks faced by students
engaged in learning the basis of computer programming.
Once pupils are convinced that their implementation is flawed, the localization of the code lines to bereconsidered is the other pillar of an active learning setting. Again, a paramount contribution for a
successful learning process should be provided by a facilitator suggesting suitable debugging
techniques (e.g., proposing critical input values to the wrong program, hinting specific points in theexecution flow to be verified, or giving advice about variables to be tracked during the next run).

6 Conclusions
Literature on learning to program through a constructionist strategy mostly focuses on how to bring theabstract and formal nature of programming languages into manipulation of more concrete (or eventangible) “objects”. Many proposals aim at overcoming the (initial) hurdles which textual rules of syntax
may pose to children. Also, several environments have been designed in order to increase the appeal
of programming by connecting this activity to real world devices or providing fancy libraries. Instead,more work is probably needed to make educators and learners more aware of the so-called notional
machine behind the programming language. Programming environments could be more explicit about
the complex relationship between the code one writes and the actions that take place when the programis executed. Moreover, micro-patterns should be exploited in order to enhance problem solving skills of
novice programmers, such that they become able to think about the solution of problems in the typical
way that make the former suitable to automatic elaboration. Agile methodologies, now also common in
professional settings, seem to fit well with constructionist learning. Besides the stress on team working,particularly useful seems the agile emphasis on having running artifacts through all the development
cycle and the common practice of driving development with explicit or even executable “definitions ofdone”.

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WG7: Constructionism in Upper Secondary andTertiary Levels
Ana Isabel Sacristán, asacrist@cinvestav.mx
Center for Research and Advanced Studies (Cinvestav), Mexico

Lina Kaminskienė, l.kaminskiene@pvt.vdu.lt
Vytautas Magnus University, Lithuania

Mihaela Sabin, Mihaela.sabin@unh.edu
University of New Hampshire, USA

Richard Akrofi Kwabena Baafi, richbaafius@yahoo.com
Eotvos Lorand University, Hungary

Abstract
In the constructionist paradigm, the fundamental premise is to create student-centred learning situations
for students to consciously engage in constructing shareable, tangible objects, through meaningfulprojects. In Papert’s vision, one particularly valuable means of doing that is in programming thecomputer because, in doing that, the student “establishes an intimate contact with some of the deepest
ideas from science, from mathematics, and from the art of intellectual model building” (Papert, 1980, p.
5). Since the 1980s, there have been countless experiences and studies exploring and documenting
the use of the constructionist paradigm, many of the first ones using Logo computer programming, but
mostly with young students at primary or middle-school levels. However, experiences in upper
secondary and university levels are scarcer. Laurillard (2002), nonetheless, advocates forconstructionist and collaborative technology-based learning environments in higher education; she says(p. 42): “the aim of university teaching is to make student learning possible […] not simply impartdecontextualised knowledge, but must emulate the success of everyday learning by situating knowledge
in real-world activity” helping students reflect on their experience of the world and ways of representingit.
The purpose of this working group was to share constructionist experiences in upper secondary andtertiary educational levels, particularly those involving computer programming and/or computational
thinking and environments; and to reflect on the challenges, needs and differences of constructionist
technology-based implementations in the various educational levels, and on how to promote such
implementations in upper levels.
The guiding questions for the working group were: 1. What are the characteristics of constructionist
implementations in upper educational levels? In upper secondary school? At university level? How are
they different from lower levels? What are the particular challenges? 2. How is, or what could be, therole of digital technologies and computer programming in such implementations? 3. How canconstructionist implementations be integrated and promoted in higher education? What is required forthat? 4. Can real-life data, phenomena and problems be harnessed for developing such
implementations?

Keywords
Constructionism; upper secondary level; tertiary level; learning environments; project-based learning;
computer science or technology-enhanced learning

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Introduction
Despite countless experiences and studies exploring and documenting the use of the constructionist
paradigm, most of them have been with young students at primary or middle-school levels. However,experiences in upper secondary and university levels are scarcer. But there are advocates (e.g.
Laurillard, 2002) for constructionist and collaborative technology-based learning environments in higher
education taking into account how students learn. Laurillard (2002) considers that “the aim of university
teaching is to make student learning possible [...] not simply impart decontextualised knowledge, butmust emulate the success of everyday learning by situating knowledge in real-world activity” (p. 42)
helping students reflect on their experience of the world and ways of representing it. However, anotherissue is that implementing constructionist exploratory learning environments in school cultures (at any
level) is problematic and complex, as has been discussed also by Laurillard (2002) and others.
The members of this group have all been involved with constructionist or innovative learning
experiences and environments in secondary school, university level, and even postgraduate level. We
are interested in contributing and sharing with others, practices and models of applying constructionism
in different educational contexts and levels, as well as help expand the foundation and background of
this area for research.
We all range from very different backgrounds and fields. Some of us are involved only in high-school
and upper secondary level, while others’ work is more centred at university level, and even graduatelevel and even adult education. Some of us work in STEM education (mathematics, science andengineering), others in business and language education, while others are more specifically involved incomputer science education. And some of us come from developing countries (Ghana or Mexico) andothers from developed countries (USA or Lithuania), and it is clear that there is a technological gap
between these. Nevertheless, as is exemplified in this paper, we all have commonalities working inconstructionist environments with mostly project-based and student-centred learning; also, most of us
are interested in issues such as the affective aspect, for instance in how constructionist tools in postsecondary computing education shape learners' dispositions to engage with peers on complex andcollaborative projects.

Key elements of constructionism
One task that we did in the group, was to compile some of the key elements of constructionism that we
consider as fundamental or that we have used in the learning environments with which we have been
involved.
As I expressed in the plenary paper for this conference (Sacristán, 2018), the fundamental premise of
the constructionist paradigm, as stated by Papert and Harel (1991), is that it shares “constructivism's
connotation of learning as ‘building knowledge structures’ […] then [adds] the idea that this happens
especially felicitously in a context where the learner is consciously engaged in constructing a public
entity” (p. 1), that is, something shareable.
Papert (1980) also placed emphasis on a different kind of learning culture, where:

educators act as support for learners to build their own intellectual structures;
create conditions for construction and invention (rather than providing ready-made knowledge),
give students objects-to-think and materials drawn from the surrounding culture,
there is an “emotionally supportive working conditions [that] encourage them to keep going” (p.
197), with students having creative and personally defined end-products that they can genuinely be excitedabout.
This latter point is the affective dimension which is a fundamental component of Papert’s constructionist
paradigm. Papert considered that affect had an indispensable role in creating and sustaining learners'
intellectual engagement (Papert, 1980/1993). And as we will see further below, this affective dimension
is an important motivation for most of us in the working group.

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Papert (1980) also proposed to use the versatility of computers to create microworlds which he defined
as incubators for knowledge and powerful ideas; that is, “self-contained world[s], […] each with its own
set of assumptions and constraints”, where learners “get to know what it is like to explore the propertiesof a chosen microworld undisturbed by extraneous questions [… and] learn to transfer habits of
exploration from their personal lives to the formal domain of scientific theory construction” (p.117).
“places” where these ideas can easily hatch and grow.
In particular, Papert (1980) promoted computer programming as a means for learning, explaining that“in teaching the computer how to think, [students] embark on an exploration about how they themselves
think” (p.19). He also emphasized the value of debugging, stating that errors are of benefit because
they lead to the need to understand what went wrong, and through that understanding, to fix them
(Papert, 1980).
Analysing some of the main descriptions of constructionism, in that paper (Sacristán, 2018), I summarised
there that I felt a constructionist learning environments could be defined as:







student-centred learning situations
where students consciously engage in constructing (e.g., program) shareable, tangible objects,
through creative personally meaningful projects (e.g. computer-based)
where they have objects-to-think-with, access/develop powerful ideas,
and have opportunities for explorations,and thinking about their own thinking (analysing) through debugging (fixing).

In that paper (Sacristán, 2018) added, that in my opinion, ideally,

there should be a medium (e.g. digital tools) for an exploratory and expressive activity (such as
computer programming/coding, or building/describing models or structures using an expressive
medium or software).
Students need to be actively involved and mostly in charge of their constructions and explorations.
Activities should take place within a structured of learning environments (microworlds) andcollaborative learning environment with the characteristics described above.

As described further below, the projects that the members of the working group have engaged in,most of them have promoted:
 Project-based activities and learning Engaging in authentic or real-life based problems. Some cases have included experiences or work
aspects related to future professions or workplace-bound and relevant aspects of work , including
professional types of digital tools
 Engaging in making personally-relevant products (such as programming computational artifacts or“apps”, or innovative projects)
 Flexibility (e.g. flexible content) Developing personal strategies
 Sharing the resulting products
 Using communication and collaboration platforms (including those that professionals use)
 A guiding engagement of teachers and even future employers providing expert mentorship
In the following sections, we provide examples of the projects and experiences that each of the teammembers have been involved with and that have fulfilled most of the above characteristics. Although
not all the examples are from upper secondary school and tertiary levels (some are from lower
secondary schools) we hope that we can learn lessons from those examples.

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Examples of constructionist experiences in secondary schools
and tertiary level
Focussing on the school culture and learning environment in high-schools in
Ghana
A first example, by the co-author of this paper Baafi, is an experience of constructionism in upper
secondary school, which focuses of how the lessons, learning environment and classroom culture areorganized. It is an example that doesn’t necessarily focus on the use of computers or digitaltechnologies, but rather on a constructionist culture in the classroom
Baafi has been teaching and researching the impact of school learning environments on students’performance in senior high schools in Ghana. Taking into account that the various types of instructional
strategies that teachers use in the classroom influence significantly students’ learning (Kaya et al., 2011;
Onweh & Akpan, 2014), it is important for the teacher to organize the lesson (and accordingly selectappropriate teaching materials) in a manner that enhances students’ learning and understanding. Thus,
Baafi considers the teacher to be an “architect” in selecting the best ways for helping students learn andgenerate knowledge through the teaching-learning process. He also considers that an important way inwhich constructionism can be implemented at the upper secondary level is through project-basedlearning and computer applications, in an activity designed to help develop innovative skills in learners.
In this sense, in the high-schools in Ghana where Baafi has been teaching, constructionism is used asa teaching and learning strategy to provide useful learning experiences for students, and help them todevelop and demonstrate their abilities in solving dilemmas in complex tasks (Kynigos, 2015). Most
students share knowledge through project-based learning. This offers them the opportunity to practicallyconnect classroom learning to a given activity where they implement what they learn. The content of
the lesson is prepared addressing students’ learning needs in order to select appropriately relevantteaching techniques, so that students can engage and be motivated by the tasks and find innovative
solutions. Thus, teachers create a learning environment which employs different teaching-learning
methods such as presentations, discovery, inquiry, discussions and team-teaching in order to help
students construct creative learning skills in solving problems (see Figures 1-3). Students may also be
arranged in groups, to encourage collaborative learning. Generally, students enjoy generating sharedideas with their colleagues during learning activities (Olsen et al., 2018). Teachers may also facilitate
collaborative discussions by having students ask questions to bring about debates on ideas, which also
helps build a learning relationship between the teacher and the learners. In this way, students develop
collaborative learning strategies through sharing and constructing ideas.

Figure 1. A classroom discussion session in ahigh-school in Ghana

Figure 2. A team collaborative project
presentation on ICT

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Figure 3. Team-teaching in the high-school in Ghana

Emphasis is thus placed on a learning environment that motivates students. For instance, some ofBaafi’s high-school students were able to solve a task that appeared difficult initially, after a field-trip
has offered the students new ideas where new ideas emerged and which allowed them to collaborate
in finishing their term’s project. Thus, the growth of students’ capabilities allows them to manage theirlearning environment in order to realise their potentials fully (Viray, 2017). Likewise, the teacher can be
an agent of motivation by engaging students in tasks that can test their potentials, helping them become
conscious of their capabilities and thus facilitating them to develop their cognitive capacity.

Technology-enhanced personalisation strategies for learning
Just as Baafi emphasizes the learning culture and environment, designing scenarios that empower
students, so is Kaminskienė’s case, whose experience with the constructionist approach has been
mainly in technology-enhanced learning using personalisation strategies in tertiary level and adulteducation (e.g. in business organisations) (Kaminskienė, Trepulė, Rutkienė, Arbutavičius, 2014;
Teresevičienė et al., 2015; Kaminskienė, Rutkienė, & Trepulė, 2015); and to a lesser extent insecondary level. Kaminskienė defines personalisation as something that allows developing different
learning scenarios with the facilitation of the teacher; however, it is most important that the learning
scenario be constructed mainly by the learner. The tools for the implementation of personalized learning
should address the core personalized learning components and characteristics described by Johns andWolking (2015):
 Student reflection and ownership (scenarios), Flexible content (restructured learning/teaching material),
 Tools, and learning environments.
These characteristics are important for the implementation of constructivist implementations at
secondary or university level.
Personalisation is usually based on several criteria: the most typical ones are relate to the current levelof knowledge, a learner‘s motivation and learning aims, learning styles, digital competences and others.
These criteria relate to the important aspect that Papert (1980) emphasized of student-centred learning,and of designing a learning culture where learners can carry out their own meaningful projects. They
also relate to the extended version of the concept of microworld by Hoyles and Noss (1987) in which
the student’s knowledge and component is central along with the other components: the technologicalone, the pedagogical design and a new role of teachers, and a social environment fosteringcollaboration and where products can be shared and collaboratively discussed.
Kaminskienė explains that as the education level changes (secondary or tertiary), learners grow inautonomy. and the skills required in personalisation can be systematically developed. Learning
autonomy should not be understood as learning alone, nor that collaborative learning is excluded. On
the contrary, personalisation can improve roles and responsibilities in group work.
In a research case study that Kaminskienė and colleagues carried out, fifteen, 8th grade, students in a
Lithuanian secondary school engaged in digital storytelling, during English language lessons, as a
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technology based pedagogical tool, used to provide student-centered learning strategies. Digitalstorytelling is project-based learning that enables learners to organize and present their individual
stories, bringing more personal essence and improving learners’ engagement. Digital storytelling is aconstructivist approach in the classroom, that increases peer-to-peer teaching and collaboration. It gives
students a chance to develop their English language and investigate various e-tools that can help them
express and digitalise their stories.
The research indicated that levels of student engagement fluctuated between moderate and high. The
use of software and conducting searches for digital media, took these levels to very high. In all cases
students liked using technology, searching the internet, and watching other’s digital stories. Data fromfeedback questionnaires indicated that 80% of the students consider this type of learning different fromtraditional classroom.
Throughout the study, students worked collaboratively and engaged with digital content. They did morework while working in groups and directly using applications and digital resources. This research also
observed collaboration between groups where different groups helped each other with technical issues.
This increased their levels of communication. Students have stated that they much enjoyed working
within their groups and asked for more group-work on each lesson. The findings revealed, however,that students could better understand their individual contribution to the team work if they are wellinstructed by teachers and have flexibility to work with the tools and content.
Results demonstrate (see 0), that 50% of the group consider this type of learning very interesting, 80%
think it differs from other lessons and they outline that learning process through Digital Storytelling wasinteractive and funny. 71% of children outlined that they have improved ICT skills while working on theirdigital stories, while 50% confirmed the improvement of communication skills. 36% thinks team-working
skills were also improved throughout this interaction. All children mentioned that they totally enjoyedworking within their teams, as it helped them to collaborate better.

Figure 4. Data from secondary students engaging in digital storytelling for English learning

A summary of some of the succesful aspects of the above experience, include:More engaged and motivated students (80% of students indicated that working collaboratively
while developing digital stories was an engaging experience).
Broader possibilities to personalise learning (technologies allow to differentiate curriculum
according to different characteristics of learners yet striving at the end achieving the plannedlearning outcomes).
Developing multiple competences (in the above case, in the English language); skills in using
technology-enhanced tools; and engaging in teamwork.
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On the other hand, some challenging aspects included: More focus on the form, rather than on the content. For example, the emphasis on digitalstorytelling could create a misunderstanding that the form (the e-tools and digital materials used
for the development of digital stories are more important than the content (the problem, idea, etc.).
In fact, 71% of students indicated that they improved their ICT skills vs. 36% who commented thatthey had improved in their English language skills..
 More regular feedback is required in cases of personalised learning, project-based learning, which
is a challenge for teachers.

Constructionist implementations related to STEM fields in higher education
Sacristán has been involved also in several constructionist projects in higher education in Mexico andCanada, where university students engage in computer programming and/or expressive activities(involving topics and data related to real-life phenomena, meaningful for their area of study) formathematical exploration or learning that usually include sharing, collaboration and discussion. These
are described in more detail in Sacristán (2017a) as well as in the plenary paper for this conference(Sacristán, 2018).
One project involved a distance-learning environment (a virtual laboratory) where university andcontinuing education students engaged in exploratory modelling tasks of various types of real-lifemathematical problems students (Olivera, Sacristán & Pretelín-Ricárdez, 2013). The tasks, carried out
through various digital means and interactive tools, involved building models through collaboration;
sharing, discussing and reflecting through the online platform; and even proposing new explorations.
The online exchanges constituted additional means for learning, since they forced students to express
their ideas, thus helping them clarify their own understandings. Although this project of a virtualmathematics laboratory was successful, after a couple of years it could not be continued due to thedifferences with the established curricula, which points to the challenges of implementing constructionist
approaches in institutional settings.
Another experience in which Sacristán was involved, was one where university engineering students
engaged in producing working models of certain real-life behaviours (e.g. of water behaviour or
simulated mechanical systems) within videogames (Sacristán & Pretelín-Ricárdez, 2017). One of theaims of this project was for students to develop know-how for their future profession as engineers on
how to apply mathematical knowledge and modelling. The videogame constructions involved several
stages of model-building tasks that included some collaborative programming work and whole classdiscussions. The projects were meaningful personal projects, so were highly motivating for the students.
This experience was so successful that it is became a regular course in the engineering program.
In a third project (Mascaró, Sacristán & Rufino, 2014), undergraduate and post-graduate environmental
sciences and biology students engage in sequences of constructionist and collaborative, computerprogramming tasks in the R programming language, for the learning of probability, statistics andexperimental analysis concepts. These tasks, presented through R-code “worksheets” with guidelines
and examples, use data adapted from real research situations, so that they are meaningful to students.
Research findings point in particular to improvements in the affective dimension, with many students
losing their fear of statistics, and being motivated to engage in the tasks; moreover, many students
appropriated themselves of the software (e.g. building their own R scripts) for their own research with
an apparent clearer understanding of statistical concepts. This statistics programme has been
integrated into regular courses for several years but only by the one teacher who designed thesequences of tasks.
A final project in which Sacristán has been involved, is the Mathematics Integrated with Computers andApplications (MICA) program at a Canadian university (see Muller, Buteau, & Sacristán, 2015; Buteau,
Sacristán & Muller, 2018). In the MICA program, university students, in addition to traditional
mathematics courses, have non-traditional courses where they engage in programming, using VB.net,
their own interactive mathematical digital environments, also called Exploratory Objects (or EOs),
through which they explore their own stated mathematical conjectures, theorems, or real-world
situations, and need to engage in various types of modelling of the mathematical ideas they explore
and program. This is an example of a constructionist program that has been sustained successfully as
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part of that university’s curriculum for over 15 years, although some conservative faculty members havechallenged it.

Students adapting and building apps for Computer Science learning
Sabin’s experiences are in computer science education mainly at university and post-graduate levels in
the USA, but also with high-school girls. In particular she has been involved in developing constructionist
learning environments in which CS students adapt or build apps, such as with a constructionist tool,
QuizPower, described further below.
Sabin considers that constructionist learning environments should use innovative tools that areaccessible to beginners with no prior disciplinary knowledge (low floors), but are powerful enough to beof interest and meaningful to expert users too (high ceiling). Another premise is that these tools should
facilitate connections to personal knowledge and experiences of all students, who bring in very diverse
cultural and life backgrounds (wide walls). In CS/IT degree programs at university level, constructionist
implementations involve computing tools and platforms that professionals use in the workplace. There
are also tools specifically designed to engage learners from a low ceiling entry point to a high ceiling ofcompellingly complex problems and projects (e.g., pythontutor.com for Python programming, App
Inventor for app development, Raspberry Pi for physical computing, or Tableau for dashboards) Thereis some prerequisite knowledge that is needed for using these tools, such as run-time environment,
execution flow, stack frame, object encapsulation, event-driven programming.
In any case, it is important that the innovative tools should make ideas and abstractions learnable bybuilding computational artifacts from which students transfer familiar intellectual constructs into
understanding new powerful ideas and more formal computing concepts. Block-based programming
platforms, such as Scratch and App Inventor, are constructionist tools that turn programming andcomputational artifacts into construction materials with which students learn abstractions throughdiscovery with concrete objects. Thus, learners follow Papert's call to "make something new" with
programing, "play with it, build with it" (Papert 1993, p. 120). QuizPower (see Figures 5 & 6) is an
example of a constructionist tool built with App Inventor (Meehan and Sabin, 2013). The app provides
beginner students or non CS majors a way to learn computing fundamentals in an engaging andeffective manner. More advanced students can adapt the app to create quizzes for other courses bydesigning a new web data store and gain more practice and deeper understanding of client-server
programming and databases. Students in a general education, introductory mobile app development
course for all majors played a vital role in giving feedback and informing the design of the quiz questions
and their organization. Computing students in more advanced CS courses can use the app to learn
more about web service development and deployment using App Inventor, PHP, and MySQL and theintegration of App Inventor development and packaging service with full-stack execution environments,
such as XAMPP.
Sabin’s experiences in post-secondary education includes computing curriculum development, projectbased learning with client-driven open source software, assessing collaborative and experiential
learning, and creating the constructionist tool described above, QuizPower.
Constructionism as a teaching practice in computing education situates learners and teachers together
in a project-based learning environment in which students make and share computational artifacts withthe help of innovating computing tools and modes of media for the purpose of learning and masteringcomputing concepts and professional practices.

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Figure 5. Activity diagram for the QuizPower
Figure 6. QuizPower user experience example
user interface

The importance of situating learning activities in supportive contexts is taken into account in the
ACM/IEEE Task Group on Information Technology Curricula (2017) report that defined curricular IT
guidelines for undergraduate programs. The report proposes an operational definition of IT
competencies as a triad of three interplaying dimensions, knowledge, skills, and dispositions, that do
not manifest in a vacuum or, what Papert calls "instructionist" environment, where formal andimpersonal learning is imparted through lecturing and tutorials. Instead, developing and demonstrating
IT competencies require an intentional context that is personally meaningful and conducive of directinteractions between learners and concrete things or "objects-to-think-with" (Papert 1993, p.168). Key
characteristics of the context in which students form IT competencies are described by Sabin, Alrumaih
and Impagliazzo (2018) and include:●

Workplace-bound experiences and relevant IT aspects of work●

Active involvement of employers to support internship and co-op programs and to provide expert
mentorship●

Authentic problems and engagement of diverse teams in relevant IT aspects of work●

Collaborative and project-based activities leveraged by using professional tools●

Deliberate and critical reflection on practice to participate effectively in decision-making and tostay engaged in a process of continuous learning.

Some examples of IT learning situated in constructionist contexts are practicum and internshipexperiences, projects with real clients, reflective journals of individual contributions to a complex teamproject, technical presentations judged by external partners, and capstone projects with external
evaluators.
In a framework proposed and discussed by MacKellar, Sabin, and Tucker (2013), client-driven open
source projects have the quality of representing the "source of concepts to think new ideas" that933

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constructionist projects, in Papert's view, should have (Papert, 1993, p. 168). They also afford students
the opportunity to experience new, state-of-the-art technologies from the vibrant and invested free andopen source software community, which creates excitement and forms a sense of belonging to thecomputing profession (Sabin, 2011). With real clients who interact closely and regularly with thestudents during the project development process, a culture of sharing, reflection, critique, anddiscussion is almost inescapable. In implementing the proposed framework by MacKellar, Sabin &Tucker (2013), by three different higher education institutions, an advantage was that the clients were
non-profit organizations who accommodated learners' "false theory building" through experimentation,risk taking, and failures. Two institutions used an an open source codebase and architecture that had
been previously developed in an academic course at another institution, which helped with striking adesirable balance between relying on a traditional self-contained toy project and engaging students in
an industrial-strength full-scale open source project. The experience report recommends that schools
develop and share exemplary projects that could be added to a collection of client-driven open sources
projects. Client-driven open source software projects create opportunities for tackling complex
computing problems with higher level of engagement among diverse students.

Figure 7. Students engaged in designing and building apps

At the secondary level, Sabin has studied how to increase confidence and interest in computing for girls.
According to a report by Google (2014), encouragement to study computer science and connecting
personal interests to computing areas influence women to pursue degree in computing. Guided by theseobservations, a one-week summer camp for girls in grades 7-9 was designed to improve girls'
perceptions of computer science (Sabin, Deloge, Smith & DuBow, 2017). The camp curriculum
incorporated inquiry and equity teaching practices and purposefully combined teaching of computing
content with culturally responsive pedagogies (Margolis et al., 2014). By using App Inventor to build
mobile apps, the girls experimented with component-based design and event-driven implementation;
shared among themselves app designs through gallery walks; worked in pairs to test and refine theirapps and media assets; and expressed their own interests in personally meaningful apps that told
stories about the girls' favorite places or hobbies. The analysis of a pre- and post-survey found that thecamp appeared to have positively influenced the girls, primarily in terms of improving computing
confidence and positive perceptions of computing careers (Sabin et al., 2017).

Commonalities of the above experiences
As can be seen in the above examples, there are many commonalities to the above experiences, as
are already described in the final list of key characteristic of constructionism given in the first section ofthis paper, such as:







Project-based learning: Almost all the above experiences involve this, whether it is projects in
Ghana, or digital story telling, or constructions, or programming videogames, EOs or apps.
These products or artefacts imply making personally-relevant products, which is motivating anddeals with the affective aspect. This affective component is all of the examples discussed above.
It also implies creating learning environments and using personalised learning strategies
with emphasis on the importance of the classroom culture, wherestudents are empowered and teachers are more guides and mentors rather than “instructors”. And
wherestudents engage in collaborative work934

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With many also involving using authentic or workplace-bound problems (such as the authentic
tasks in the virtual mathematical laboratory, or for some of the videogame designs, of for the EOs,
or as client-driven project) or work bound tools (such as R, VB.net and others mentioned by Sabin)

Addressing some of the guiding questions and someconclusions
The guiding questions for the working group were:

What are the characteristics of constructionist implementations in upper educational levels? In
upper secondary school? At university level? How are they different from lower levels? What are
the particular challenges?
 How is, or what could be, the role of digital technologies and computer programming in such
implementations?
 How can constructionist implementations be integrated and promoted in higher education? Whatis required for that?
 Can real-life data, phenomena and problems be harnessed for developing such implementations?
In terms of the first question, at the beginning of the paper, we have already stated some of what we
consider are key characteristics of constructionism. But an additional aspect that was addressed by theworking group members, was that as the educational level increases, so does the autonomy of thestudents, and also that at university level learners are more self-regulated. This may help when
implementing student-centred and project-based learning.
In terms of the last question, many of the above projects integrate authentic tools, problems or data, as
described in the previous section.
As to the other questions, these are somewhat addressed in the following section.

Some detected challenges and possible strategies for more successful
constructionist implementations in higher educational levels
Constructionism is still an innovative approach in the education, and many typical barriers still exist,
including those related to the integration of technologies in general (see Sacristán, 2017b), and otherssuch as:















Regulatory and institutional requirements;
The barrier to change ways of working, which is one of the greatest barriers to innovation, andwhich Sacristán calls the inertia of school cultures
Instructor's role as mentor and learner is counter learners' common expectation that instructors
impart knowledge and have all the answers.
Course evaluations are designed with a preset course curriculum in mind (course objectives) anda 'sage on the stage' instructor model (answered questions effectively)
Fear of using technologies; fear of technological risk.
Increasing digital gaps between the digital and networked society and the traditional curriculum ofeducational systems. As well as in terms of social, economic and geographical stratification.
Lack of personal leadership;
Lack of institutional support.
Technocentrism: focus on the technology, rather than on the subject matter and content
More regular feedback is required in cases of personalised learning, project-based learning, which
is a challenge for teachers.
The difficulty of transference (e.g. of “constructionist methodologies or projects”)
Collaborative projects need to mediate diverse personal interests
Assessing student learning in constructionist learning environments
Another barrier to implementing constructionist learning environments that use project-basedlearning in university upper-level courses relates to deemphasizing grading and evaluation ofcomputational artifacts. If computational artifacts contribute to a high-stakes real-world project,935

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evaluating student performance is predominantly determined by the quality of work products rather
than participatory activities and collaborative processes.
In order to integrate constructionist implementations be integrated and promoted in higher education, it
is clear we have to develop an appropriate learning culture in higher education institutions. University
degree programs as a whole should enjoy a constructionist culture. They have to be integral part of the
program curricula. So there needs to be a shift in the culture.
Innovations in how school cultures work may be needed, but also it may be that compromises need bemade between the utopic constructionist visions (such as those of Papert) and institutional
requirements. For example, in Mascaró & Sacristán (2018), in these conference proceedings, we givean example of “constructionist-compatible” ways that were developed to assess students’ learning, inorder to grade them for the university courses in which they are enrolled.
There may also need to be considerable investments (time, human resources training) andwork/preparation of students. The implementation of constructivist approaches in tertiary level may berelated to technology enhanced learning. Nevertheless, as has been noted by researchers (e.g.
Schneckenberg, 2010), technology-driven innovation in universities depends much on active
involvement of the faculty in organisational change. Schneckenberg (2010) speaks of the need of
institutions to develop a so-called eCompetence model, which is understood as a complex unit of
personal and institutional dimensions (covering micro, meso and macro levels); the eCompetencemodel is composed of competence, academic staff, competence development measures, eStrategy inuniversities, and pervasive potential of ICT in educational contexts.
In the case of computing education in higher levels, Sabin points out that Psenka and colleagues (2017)
discussed the applicability of constructionism to create learning environments for engineering designand offered a set of observations to be considered by the engineering design education. All observations
point to conditions and strategies that are instrumental to designing constructionist learning
environments for computing education in higher education. For example, project-based learning andinclusion of real-world problems have been largely adopted in the undergraduate curricula in the USA.
The literature is vast and findings are compelling to continue on this path. In the USA, the ABET
Computing Accreditation Commission updated the Computer Science program accreditation criteria toreflect curriculum guidelines for undergraduate computer science programs and includes a student
outcome for learning to "apply design and development principles in the construction of softwaresystems of varying complexity" (ABET, 2018), supporting the guideline that all CS graduates be
involved in at least one substantial project.
Sabin also points out, that in the case of Computer Science degrees, there are tools used widely bycomputing professionals (e.g. GitHub, a version control platform: Slack,a communication tool; andAmazon Web Services, a cloud deployment service); Sabin sees all three as constructionist tools thata CS/IT degree program should integrate across program curricula.
In terms of collaborative projects of significant scope and scale and possibly involving real clients, there
are, however, considerable challenges. Such projects constrain the playfulness of more relaxedenvironments in which students have conditions to be "let loose", and unplanned, emergent learning ismore likely to happen. There is also the tension between giving students the freedom to select their own
projects and having adequate mentoring capacity in large classes with too many individual projects.
We end with some questions of interest to implementing constructionism in higher education:How to let undergraduate students loose to create computational artefacts as learning materials
when constrained by grades and highly specialized curriculum in upper level courses?
What curricular changes of undergraduate courses are needed to facilitate natural andspontaneous ways of making ideas concrete in the classroom?
How to design or adapt innovative computing tools with which students can have multiple ways ofknowing through senses, movement, and other interactions to make concepts and theories "simple
and concrete" (Papert 1980/1993, p.7)?

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What preparation and learning do teachers need to change the learning culture in the classroom
and take into account the learners' affect and their dispositions to tinker, embrace persistent
struggle, and reflect upon their learning experiences?

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Teachers’ Day

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The beauty in science and the science in beauty ormathematics, informatics and science teachingas an eye-opener of the beauty of ideas
Evgenia Sendova, jenny.sendova@gmail.com
Institute of Mathematics and Informatics at the Bulgarian Academy of Sciences, Bulgaria

Abstract
In this talk I will discuss my involvement in different roles in some educational practices in Bulgarian andinternational context of opening students’ eyes for the beauty of science in general with an emphasison mathematics and informatics.
I had the chance of being involved in education relatively late in my professional carrier in the frames of
the Research Group on Education experiment (1979-1991) where the leading principles embraced
learning by doing and exploring, integration of school subjects, making the computer a naturalcomponent of the curriculum and enhancing the creativity of both teachers and students. The researchteam developing the educational resources in a newly designed subject Language and Informatics(integrating three natural languages – Bulgarian, Russian and English, with two artificial ones –mathematics and Logo) was given the freedom to implement the Logo educational philosophy(constructionism) in an attempt to make the children love the school in general (and math and sciencein particular). The experiment lasted for 12 years and what our team tried to transfer in the next
educational projects in national and international setting was that mathematics and science could beviewed and taught as a game of ideas, as painting with ideas. Thus, we got involved with the design
and implementation of educational resources – textbooks and dynamic scenarios in mathematics, IT,
and informatics (5th-12th grade, Figure 1) as well as in the development of computer microworlds, tuned
to specific domains (e.g. Geomland, a language-based mathematics laboratory for explorations in
Euclidean geometry, Figure 2). Such microworlds were launched within various Educational national
and European projects and used in numerous PD courses for teachers as platforms for testing andediting various ideas in math, science and arts allowing the participants to discover the beauty ofmathematics and science in terms of ideas – an experience they would convey further to theirstudents.
It is the very ideas behind some projects developed by the participants in PD courses at Sofia University
on implementing IT in math classes on the theme The beauty in mathematics and the mathematics inthe beautiful that I will present (Figure 3). Furthermore, I will discuss how educational resources
developed in the Institute of Mathematics and Informatics at the Bulgarian Academy of Sciences (IMIBAS) in support of the inquiry based mathematics and science education (IBMSE) are being
implemented in in-service teacher education (Figure 4).
The experience gained confirms our belief that the learners construct new knowledge with particulareffectiveness when they are engaged in modeling and constructing something that is meaningful tothemselves and to others around them (Figures 5 and 6).

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Figure 1. Textbooks of the Research Group on Education (4th, 5th and 6th grade)

Figure 2. Different hypocycloids generated in Geomland(http://sunsite.univie.ac.at/elica/PGS/INDEX.HTM)

Figure 3. In search for the golden angle in nature – a PD course for IT teachers at Sofia University942

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Figure 4. Educational resources developed by the IMI-BAS team within the projects VirMathLab(www.cabinet.bg) – left; Mascil (www.math.bas.bg/omi/mascil) – middle, and Scientix (http://www.scientix.eu/) right

Figure 5. Celebrating the STEM Discovery week (April, 2018, http://www.scientix.eu/stem-discovery-week) at the
IMI-BAS - the Scientix Bulgarian contact point, with the event Mathematics in the world around me

Figure 6. A visit to a Math and Science Fair in Varna, Bulgaria, within an international workshop of the STEM PD
Net project (http://stem-pd-net.eu/en/)

Keywords/phrases
Beauty; mathematics, informatics, IT and science education; creativity; Logo, Inquiry based learning943

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Ateities švietimas: naujos galimybės Lietuvai?
Rimantas Želvys rimantas.zelvys@fsf.vu.lt
Vilnius University, Lithuania
Viena iš trijų Lietuvos šimtmečio proga paskelbtų idėjų Lietuvai – „Mokytojas – prestižinė profesija iki
2025 metų“. Ar ši misija įmanoma? Teigiama užsienio patirtis rodo, kad iš principo tokios idėjos
įgyvendinimas įmanomas. Tačiau kyla kitas ne mažiau svarbus klausimas: ar mes ją įgyvendinsime?
Deja, tokia galimybė labai abejotina, jeigu įgyvendinimo sieksime tokiais būdais, kaip siekėme iki šiol.
Šiuo metu matome pastangas įgyvendinti pokyčius švietime, tačiau vyrauja kiti prioritetai: klasių
krepšelio įdiegimas, etatinis mokytojų darbo apmokėjimas, universitetų jungimas ir pedagogų rengimo
centrų steigimas. Nė vienas iš šių prioritetų tiesiogiai neprisideda prie mokytojo prestižo kėlimo aršviesesnės švietimo ateities. Mūsų manymu, tam, kad idėja Lietuvai būtų įgyvendinta, švietimo
prioritetai turėtų būti kiti:
1. Aiškių švietimo tikslų ir ateities vizijos suformulavimas. „Pradėkite kampaniją, kuri padėtų iš
naujo sukurti švietimo viziją, nes, jei nežinosite, kokios mokyklos norite, neverta nieko netpradėti“, sako vienas iš labiausiai pasaulyje žinomų švietimo ekspertų P. Salbergas. Ar mokyklai
lemta būti pramonininkų ir verslininkų poreikius tenkinančiu ekonomikos priedeliu, ar mūsų siekis– pagal tarptautinio PISA tyrimo rezultatus pavyti ir pralenkti Estiją, ar ir toliau vadovaujamės
Lietuvos švietimo koncepcijos nuostata, kad pagrindinė švietimo misija – ugdyti aukštos
kultūros, pilietiškai orientuotą ir socialiai atsakingą jaunąją kartą? Kiekvienas iš šių siekių
reikalauja skirtingų įgyvendinimo priemonių, o mokytojo prestižas bus aukštas tuomet, kaisuvoksime mokytoją visų pirma kaip kultūros nešėją, o ne kaip paslaugos teikėją.
2. Aukštesnis ir geresnis išsilavinimas. Daugelyje Europos Sąjungos šalių manoma, kad mokytojo
darbui būtinas magistro išsilavinimas. Kodėl mūsų šalyje galvojama, kad, norint tapti vaikų
gydytoju, reikia mokytis daugelį metų, o vaikų mokytojui tai nėra būtina? Ar pažinti jaunožmogaus sielą yra lengvesnis uždavinys, negu pažinti jauno žmogaus kūną? Profesijosprestižas tiesiogiai susijęs su išsilavinimu – vargu ar žemesnio išsilavinimo reikalaujanti profesija
gali būti prestižine.
3. Švietimas turi būti grįstas mokslu. Visuomenė, nepaisant visų pastarųjų dešimtmečių pokyčių,vis dar gerbia mokslininkus ir pasitiki mokslo žiniomis. Deja, politikai, priimdami sprendimus, vis
rečiau remiasi moksliniais tyrimais, o pati edukologija žeminama ir abejojama, ar tai iš viso
mokslas. Jei netinka pavadinimas – keiskime, jei netenkina mūsų mokslininkų lygis – kvieskime
iš užsienio pripažintus autoritetus, bet negalima mokslo aplamai ignoruoti. Mokytojas turės
didesnį prestižą tuomet, kai visuomenė žinos, jog savo darbe jis vadovaujasi naujausiais mokslopasiekimais.
4. Turi būti aktyviai kuriamas teigiamas mokytojo įvaizdis. Savaime jis tikrai nesusiklostys toks,
kokio norime. Jei pageidaujame mus tenkinančio įvaizdžio, turime aktyviai ir kryptingai dirbti :bendrauti su žiniasklaida, plėtoti ryšius su visuomene, tinkamai propaguoti mokytojo profesiją.
Pavyzdžių toli ieškoti nereikia – pakanka prisiminti, kiek pastangų pastaraisiais metais buvo
dedama, gerinant profesinio rengimo įvaizdį. Kryptinga veikla šioje srityje jau duoda
apčiuopiamų rezultatų. Beje, ir patys mokytojai turi saugoti savo įvaizdį ir jo negadinti
neapgalvotais lozungais ir akcijomis.
5. Prestižas tiesiogiai susijęs su atlyginimu už atliekamą darbą. Egzistuoja neprestižinės, bet gerai
apmokamos profesijos: yra nemalonių darbų, kurių niekas nenori imtis, todėl bandoma surasti
norinčių juos atlikti už gerus pinigus. Tačiau nėra prestižinių, bet prastai apmokamų profesijų.
Juk žmonės mąsto paprastai – jei atlygis menkas, tai ir darbas, matyt, nėra nei būtinas, nei
reikšmingas.
Taigi, šiandien Lietuvoje inicijuojami pokyčiai sudaro potencialias galimybes sėkmingai kurti ateitiesšvietimą. Tačiau tam, kad šios galimybės būtų realizuotos, būtinas kitoks mąstymas ir kiti prioritetai.
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WS1: Dynamic Teaching Ideas for teaching Music
TheoryJudith Bell, jbell@chisnallwood.school.nz
Chisnallwood Intermediate School, Christchurch, New Zealand

Abstract
In this workshop you will try out a range of effective, outside-the-square, tried-and-true theory activities
based on Judith’s creative work with the very popular Chisnallwood School Theory Club. You will learn
about music theory games are suitable for a range of ages and easily adaptable for different levels andmusic abilities, but ideally from ages 7 – 12. Some activities use digital technology, while others arecompletely unplugged. They are suitable for use in groups – even of mixed levels – and you’ll be amazedat the learning that comes out as well as the amount of fun.

WS2: Computer Science Unplugged for Teachers
Tim Bell, tim.bell@canterbury.ac.nz
University of Canterbury, New Zealand

Abstract
This workshop will demonstrate ideas from the "Unplugged" approach to teaching Computational
Thinking topics. This approach provides opportunities for students to encounter the great ideas incomputer science away from computers, and is a useful complement to classes that focus on "coding".
We will look at how you can engage your own students with the material, and also "plugged in" activities,
which connect the activities to programming. You will learn about free resources that you can use inyour classroom immediately, and also how you can integrate these activities with other school subjects.
The workshop will be suitable for both primary and secondary educators.

WS3: Developing Algebraic Habits of Mind inStudents
Paul Goldenberg, pgoldenberg@edc.org
Education Development Center, Waltham, MA, USA

Cynthia J. Carter, CCarter@rashi.org
The Rashi School, USA

Abstract
Participants will see one puzzle-centric approach (elaborating on the work of mathematician W.W.
Sawyer) aimed at developing the language and logic of algebra in grades 5 and up. Most of the focusof the workshop will be on the beginnings of that algebraic development, but we will also spend sometime looking at activities - from paper-ripping to factorials of negative numbers - that help build students’
comfort with the mathematical idea of extension, extending ideas that have “natural” meanings in thenatural numbers and extending them to “unnatural” places, like negative numbers and fractions.
Participants will be encouraged to pose problems of their own, extending problems presented in theworkshop, and will receive a packet of materials they can adapt and use with their classes.

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WS4: Puzzles & Programming to Develop
Mathematical Habits of Mind in 6–10 year Olds
Paul Goldenberg, pgoldenberg@edc.org
Education Development Center, Waltham, MA, USA

Cynthia J. Carter, CCarter@rashi.org
The Rashi School, USA

Abstract
This workshop will focus on tested work using a puzzle-centric primary mathematics curriculum with analgebraic focus(inspired by mathematician W.W. Sawyer), and new work we are doing now to infuse
that curriculum with programming (inspired by work by Jenny Sendova and others in Bulgaria and by
the ScratchMaths work done by Richard Noss and Celia Hoyles of the UK and Ivan Kalaš of Slovakia).
Puzzles help develop children’s logic; programming provides an extra language to help them express
that logic. Expressing and experimenting with one’s mathematical ideas helps develop them further.
Participants will solve and create puzzles that are suitable for young students and will see how computer
programming by young students can support their mathematical learning. All materials that will be usedor described are available free.

WS5: Powerful Ideas in Lower Primary
Programming: High Time to Recognize Them
Ivan Kalaš, kalas@fmph.uniba.sk
Comenius University, Bratislava, Slovakia

Abstract
We think that using any of the dozens of existing programming environments in regular primary school
setting, with general primary teacher does not work well, mostly because of the following reasons: (a)these environments are not designed for collaborative constructivist teaching where the teacher has tosupport everybody in the class, and (b) these environments often neglect basic, simple, but key
important powerful ideas that pupils should discover and adopt before computational constructs that areconsidered in general as introductory (sequence, selection, repetition, variables...)In the workshop we will use our latest development of Emil, a new programming environment and its
systematic pedagogy constructed and trialled in a group of design primary schools, with pupils aged 8.
Together with the participants we will experience, identify and discuss several powerful ideas that pupils
should experience before all other powerful ideas that we have exploited in our ScratchMaths
curriculum.

946Constructionism 2018, Vilnius, Lithuania

WS6: Snap! - Beauty & Joy of Computing (visually)
Witek Kranas, witek@oeiizk.waw.pl
OEIiZK, Poland

Abstract
It is worth noting that Snap! is not a toy for children. It was created to help you learn programmingunderstood as creating and implementing algorithms. And although the floor, like in Scratch, is quite
low, the ceiling is much higher. In our (Poland) realities, it can be reasonably used at the level of grades
7-8, although one can imagine an IT course for a high school based on Snap!, as the main programming
environment.
Prepared for a Teachers Day workshop during Constructionism 2018 conference in Vilnius.
Based on my articles published in a magazine “W cyfrowej szkole” and conference “Informatyka
w edukacji” materials, 2018.

About Snap! (snap.berkeley.edu)
Snap! is a Scratch clone created by Brian Harvey and Jens Mönig. In the first version, the environment
was named BYOB (Build Your Own Blocks). This name contained the main reason for the creation of
the environment - the possibility of creating own blocks by the user because in the version of Scratch
1.4 it was not possible. The creators decided to change name due to the other popular use of thisabbreviation – bring your own bottle, added on party invitations. They write about it in the textbook: “Thename of the program has been changed, because some teachers have no sense of humour.”
Snap! is programmed in HTML5 and can be run in almost any system, on any device. Creating new
blocks is much more complex than in Scratch. You can choose a palette for a new block and specify
whether it will be a command (fragment of a puzzle with a tab), a function (a rounded block) or apredicate (a hexagonal block). It is also possible to insert prefix parameters. In addition to a set of blocks
almost identical to Scratch, Snap! contains several extensions - additional block libraries. There is also
the possibility of switching on step work to trace scripts. The palette of blocks supporting lists has been
greatly expanded, which means that it is possible to use full capabilities of this data structure (list as avariable, lists of lists ...).
Seemingly Snap! is a Scratch clone (version 1.4). The set of blocks is almost identical to Scratch, theirappearance is very similar. So, is it worth to deal with the Snap! I will try to convince you that it is worthit.


The first reason - running on tablets.
Snap! has been programmed in HTML5, so it will work on the vast majority of mobile devices
basically regardless of the system. Installation of the offline version is also very simple, it does not
require any additions. This reason may disappear at the end of 2018, if Scratch's team will launcha new version 3.0 using Google Blockly blocks.

Second reason – easy localization (if you have local version of Scrtach).



Third reason - a new block can be a function (return value).

Fourth reason - additional blocks of libraries.
In addition to the standard set of blocks - almost identical to Scratch, Snap! offers several
extensions, additional block libraries. We add them to the palettes by selecting from the menu File/ Library.



Fifth reason - lists like in Logo.



Sixth reason - visualization of script execution (step work).
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Seventh reason - easy preparation of materials.



Last (but first for the authors) – first class procedures
From Brian Harvey letter: …most important to me: first class procedures. The FOR block, … iswritten in Snap!, not a primitive, and that's possible only because we can capture the script in the
C-slot as an input value, and the FOR block can then RUN it repeatedly. And of course MAP, KEEP,
and COMBINE require this feature.

About BJC (bjc.berkeley.edu)
The American computer science course "The Beauty and Joy of Computing", called here in brief BJC
is intended for students aged 16-18, what means the level of high school (lyceum). It belongs to
Advanced Placement courses, it finishes with an exam and gives you the opportunity to get points foruniversity admissions.

The course contains detailed materials in a form that allows the student to work independently.
The course consists of six compulsory parts:
Unit 1: Introduction to Programming
Unit 2: Abstraction
Unit 3: Data Processing and Lists
Unit 4: How the Internet Works

AP Explore Task

Unit 5: Algorithms and Simulations


AP Create Task

Unit 6: How Computers Workand two additional parts to be processed after the exam:
Unit 7: Fractals and Recursion
Unit 8: Recursive Functions

The authors write: “In this course, you will create programs using the Snap programming
language, you will learn some of the most powerful ideas of computer science, you will be
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creative, and you will discuss the social implications of computing, thinking deeply about how
you can be personally active in promoting the benefits and reducing the possible harms.”

Signalist (vee)https://snap.berkeley.edu/snapsource/snap.html#present:Username=witek&ProjectName=sygnalistaAng

This example was prepared by the authors of Snap! to show environments strength. We start with a few
new blocks drawing simple figures (triang, star, …).

Now we create variable shapes and give it the value of an empty list.
Then, on this list, we place the created blocks so that they can be
executed, i.e. in the grey envelope, (located in the Operators palette).
You can do this by using the add ... to ... block, as in the green flag
script in the picture.
It's time to create a main block (sign) that will draw a signal composedof two flags with random shapes. Running this block will draw two
flags. But how can you get a picture like the top picture, where there
are many more flags? Just put a few sign blocks on the list in grey
envelopes. Add and delete them, for example, using the scripts
shown in the last pictures.

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Well, now we have recursion without a base case and thus without a stop condition!

Polygon, pinwheel and for loop
https://snap.berkeley.edu/snapsource/snap.html#present:Username=witek&ProjectName=BJC3dlaAng
The second example refers to programming in Logo. How to draw a polygon? We can create a new
block in the Motion palette and in the block editor build a command script (i.e. the new block will havea blue colour and a tab layout). Let's add to the standard polygon (with a number of brunches and size)
the third parameter out and the block, which will cause the section of its length to stick out from one
side. Let's call this creation a pinwheel. The polygon is obtained in the extreme case, when theparameter out will be equal to zero. And what figure will we get when it's equal to size?

Now import the tools from the File menu. At the bottom of the Control palette
there will be a block for – a convenient iteration loop. In the same palette wewill find a grey block warp that does not appear in Scratch, and it will executethe turbo mode only for the blocks contained in it. We can already build a scriptwith animation, showing changes in the drawing when the value of theparameter out changes.
Many interesting drawings can be made with the help of for loop. Here are two
examples to try.

Which images will be done by scripts?
One more example of using for loop – countdown from 0 to 99. In the script, we use the function if,
which allows you to not write zeros at the beginning of the countdown.
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Symmetric encryption
https://snap.berkeley.edu/snapsource/snap.html#present:Username=witek&ProjectName=BJC43CCAng
In part 4 of the BJC course "How the Internet Works" in the laboratory "Cybersecurity" there are topics
related to cryptography, among them Caesar Cipher Project. Encryption is done by shifting letters in the(Latin) alphabet by a given number (1-26). This can be illustrated with the help of two independently
rotating wheels containing all letters of the alphabet around the circumference. You can see it in the
picture, which is a screenshot of a fragment of students BJC materials.

We start project in Snap! creating three global variables named: input text, shift and output text. In thescript run by pressing the spacebar, we ask for the text to be encrypted, the letter’s shift and ask tochoose whether to encrypt (1) or decrypt (2).

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The last part of this script starts encryption. It is executed by the encode block, which must be defined.
The new block will belong to the reporter category (i.e. function, rounded shape) and will have twoparameters: the text to be coded and the letter shift in the alphabet.
Building this block requires a for loop,
located in the tools library. So you have toimport the tools using the File menu. In
the block we create a local variable(codedText), which will store the coded
text, and then for all letters of the input
text we call the encodeLetter block,joining them into one encoded text.
You still need to create this last block.
A simple solution is to replace the letterwith its ASCII code, add shift to it andchange it back to the letter. Let's see how
this block codes the letter Z with shift 1.

You can see that we have not closed the wheel. We
have to modify this block by moving codes modulo 26(the number of letters in the Latin alphabet). It's doneby two blocks if identifying uppercase and lowercase
letters. Now only the letters are coded but with cyclicalbehaviour.
And how to get decoding? Look closely at the green
flag script.

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Sierpiński gasket
https://snap.berkeley.edu/snapsource/snap.html#present:Username=witek&ProjectName=BJC72SGAng
The last example is a fractal - a gasket (triangle) bySierpiński. In the BJC course, recursion is introduced very
slowly in additional chapters (7 and 8). Let's look at the
introductory script for the Sierpiński triangle recursive
construction. We have here, three times repeated, script
drawing a triangle. It is worth analysing this script first,
imagine how the next smaller and smaller triangles will bedrawn, and then start. This is not a typical Sierpiński
triangle, but let's try to draw it using recursion. You have todecide when to finish the duplication of triangles. The
simplest way is to stop drawing when the side length issmall enough. This implements a block if in Triangle. We
draw only triangles with a side longer than 5, and in the
middle we have a recursive call. Now let's consider the
position of this call in the script. It can be moved to thebeginning or the end. Try.
But you can also have fun inserting it inside the rotation, if
you swap the block turn (right) 120 degrees for two, e.g.
turn 30 degrees and turn 90 degrees, you can insert a recursive call between these two blocks.
Let's look at the block StriangleA, whose last parameter (turn1) is the angle of the first rotation (thesecond is easy to calculate) and the result of its operation.

Using blocks for and warp like in the second project, we can get a nice animation showing the change
in the drawing when the angle of the first rotation changes.

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WS7: ViLLE – E. Learning Path for Mathematics andProgramming
Mikko-Jussi Laakso, milaak@utu.fi, Petra Enges-Pyykönen, phenge@utu.fi
University of Turku, Finland

Abstract
ViLLE is an exercise-based education environment that enables easy learning and teaching of mathematics,
programming and other topics. The development is research-based, and the features and the methodology
utilized have been thoroughly studied with various setups in the Centre for Learning Analytics at Universityof Turku. For students, ViLLE offers more than 15 000 carefully designed, motivating and activating exercisesfor learning mathematics and programming. All exercises are automatically assessed and provide immediate
feedback. For teachers, ViLLE provides comprehensive learning analytics that visualize everything you needto know about your students’ learning process – including automatic detection of misconceptions and realtime analysis of students’ progress. ViLLE is used to transfer one lesson a week into an electronic learning
experience. There are existing exercises and materials for all nine grades of primary and middle school.
Moreover, there are programming exercises integrated into all levels to give students a head start in learning
computational thinking and basics of computer science.

Using ViLLE provides evidence-based, scientifically proven results for all grades. In the studies
conducted in Finland – the country that excels the Pisa assessments each year – it was confirmed thatthe students using ViLLE improve their learning significantly more than the control group learning
mathematics with traditional pen and paper method. With matching skill levels before the experience,groups using ViLLE achieved at least 20 percent higher scores in the exams conducted at end of theschool year. Students using ViLLE also make 70 % less errors than students in a control group.
Moreover, the students find ViLLE as highly motivating and fun tool to use.

WS8: Constructionism in Action: Do we Need toStart from Scratch?
Evgenia Sendova, jenny.sendova@gmail.com
Institute of Mathematics and Informatics, Bulgarian Academy of Sciences, Bulgaria

Nikolina Nikolova, nnikolova@fmi.uni-sofia.bg
Faculty of Mathematics and Informatics, Sofia University “St. Kliment Ohridski”, Bulgaria

Abstract
Children in the digital era are surrounded by information and communication technologies. The
development of digital competences and more specifically –the ability to express their creativity throughcomputational thinking, is evaluated by the society as vital for the contemporary society. This makes it
natural to introduce programming courses for students of younger age, new curriculum and even a newschool subject – computer modeling.
Should the programming be taught per se though? Does the introduction of new syllabus put a thresholdand ceiling on the performance of the teachers? What about programming languages with no thresholdand no ceiling (the ceiling being only the user’s imagination)? Bulgaria has a long-term experience inteaching programming, and even better – in learning through programming. The Logo philosophy
promotes the programming as a means for learning and creative self-expression. It is in harmony withthe family of contemporary programming languages, successors to Logo and developed specially forchildren.
There will be no threshold for the participants. However, this does not mean that we would start fromscratch. Rather, we will start with the traditions of the Logo philosophy and Logo culture in internationalsetting, we’ll present the potential of their development through Scratch and of course, we’ll work, createand have fun together! Most importantly, we’ll rely on high enough ceiling!
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WS9: Teaching Coding and Physical Computing
Gary S. Stager, gary@stager.org
Constructing Modern Knowledge, USA

Abstract
Learn how a project-approach to computer programming, robotics, and physical computing can serve a
diverse student population while developing your own skills. This workshop will explore powerful ideas
from computer science and engineering that may be employed in the solving of problems across thecurriculum. A review of software and hardware options will be explored in addition to two focusedprogramming and robotics activities. Participants will also have experience with Hummingbird Robotics,
the BBC Micro:bit and other low-cost "microcontroller development boards" offering great potential forlearning through making, tinkering, and engineering in the classroom.

WS10: The Essence of Programming at School –Logo in a Spiral Curriculum
Jacqueline Staub, Jacqueline.staub@inf.ethz.ch
ETH Zürich, Pädagogische Hochschule Graubünden, Chur, Switzerland

Abstract
School is responsible for priming and preparing pupils such that they develop a deep understanding oftechnology. Computer science education serves a vital role in fostering algorithmic thinking and problem
solving skills, as exemplified by programming. This form of learning is constructive, enriches creativity
and teaches precision. We have been introducing primary school pupils and their teachers toprogramming in Logo for more than a decade and thousands of children across Switzerland havelearned to program using our curriculum and purpose-built programming environment. In this workshop,
we give insights into how our curriculum guides pupils to progress individually and how we make pupils
building up competence by recovering from their programming errors autonomously. This workshop
caters towards educators and people interested in how to introduce computer science to novices.
Participants gain practical insights into our curriculum and discuss its didactic structure.

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WS11: How to Create and Sustain a Progressive
Pedagogy in a Traditional Setting (Roundtable
Discussion)
Carol Sperry Suziedelis, Carol.Sperry@millersville.edu
Millersville University, USA

Abstract
Format of the workshop: We will encourage a vibrant discussion of ideas, efforts, questions, and fears
about creating dynamic and engaging projects as well as the nurturing of an atmosphere of free
expression and exploration in the classroom. Without the support of each other, it may be difficult torelax and let go of the “controls” many think are necessary for a workable classroom. However, students
and teachers must have the latitude to pursue the construction of their own knowledge, the freedom tomake mistakes and “recalibrate,” and the time to discuss the powerful effects of the process. We willconsider ways to do it all within the constraints of the average classroom setting.
Along the way, we will revisit some of Seymour Papert’s profound insights into the remarkableendeavour of teaching and learning.
I believe the prediction of “expected outputs” is usually precarious for a constructionist teacher. To“expect” certain outputs is, to my mind, a way of limiting them. So, I would say, I hope that we have a
lively discussion that inspires ideas, creates new relationships, and bolsters the courage needed tofollow a joyful and innovative path.

WS12: Joyful Learning of Geometry in Cultural
Context. Analysis and Construction of Geometric
Ornaments
Igor Verner, ttrigor@technion.ac.il
Technion – Israel Institute of Technology, Israel

Khayriah Massarwe, massarwe@technion.ac.il
Technion – Israel Institute of Technology, The Arab Academic College for Education, Israel

Daoud Bshouty, daoud@technion.ac.il
Technion – Israel Institute of Technology, Israel

Abstract
Mathematics education seeks to accommodate pedagogical approaches that enhance learning
mathematics and make it relevant to today's students who prefer hands-on visual and joyful activities,
and are socially inclined. The challenge of teaching is to expose students to the interconnection between
real world practices and culturally rooted mathematical ideas. The subject Geometry is unique in its
combination of intuitively rooted figural concepts and abstract logical statements. The geometry teacher
should facilitate the learner to acquire the language of geometric terms and the ability to use thislanguage correctly when communicating geometric ideas. We have been developed two courses that
introduce prospective and in-service teachers to teaching and learning geometry in cultural context
using the ethnomathematical approach. The teachers analyzed and constructed, by compass andstraightedge, geometric ornaments from different cultures, posed and solved geometric problems
related to the ornaments. In this workshop, we aim to engage the participants in constructing andanalyzing of culturally meaningful geometric ornaments. The participants will solve geometric problems
related to the ornaments and develop new problem by themselves. Then we will discuss the arguments
for introducing activities with geometric ornaments in school geometry learning.
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Author IndexA

D

Abee, Kazuhiro · 745

Dabholkar, Sugat · 554

Adiguzel, Tufan · 777

Dagienė, Valentina · 4, 169, 180, 305, 344, 731, 838

Agatolio, Francesca · 498

Davey, Caitlin · 561

Akcay, Nevin · 777

Diethelm, Ira · 547

Angel-Fernandez, Julian · 150, 506, 686, 690

Dolgopolovas, Vladimiras · 180

Angulo, Carol · 514

Du, Xiaoxue · 193

Anton, Gabriella · 380, 554

Duca, Annalise · 686, 690, 773

Asiain, Alfredo · 498
Aslan, Ümit · 125

E

Atieno, Loice Victorine · 838
Avci, Hulya · 777

Enges-Pyykönen Petra · 954

BF

Baafi, Richard Akrofi Kwabena · 925

Fields, Deborah A. · 203, 214, 601, 754

Barendsen, Erik · 570

Foss, Jonathan · 437

Baumann, Wilfried · 838

Futschek, Gerald · 38, 678, 794

Bell, Judith · 520, 945
Bell, Tim · 21, 520, 945

G

Berger, Neeltje · 767
Beynon, Meurig · 437

Girvan, Carina · 506, 649, 686, 690

Blikstein, Paulo · 29, 203, 472, 481, 754

Giuliano, Angele · 686, 773

Boran, Ian · 449

Göcking, Fenja · 672

Brady, Corey · 263, 761, 769

Goldenberg, Paul · 39, 945, 946

Bray, Aibhín · 449

Goodman, Lizbeth · 759

Broll, Brian · 769

Greka, Xristina · 690

Bshouty, Daoud · 657, 956

Grgurina, Natasa · 570

Buteau, Chantal · 528

Griffin, Jean M. · 225

Byrne, Jake Rowan · 138

Grizioti, Marianthi · 357, 369, 649, 686
Gueorguiev, Ivaylo · 649, 686, 690

CGungor, Ali · 777
Guo, Yu · 238

Cabibihan, John-John · 150
Campos, Flavio · 536

H

Cañas, Alberto J. · 514
Carter, J. Cynthia · 945, 946

Harada, Yasunori · 741

Castro, Ana Gabriela · 514

Harvey, Brain · 53

Catlin, Dave · 150

Heldens, Peter · 767

Černochová, Miroslava · 543

Hickmott, Daniel · 251, 577

Chan, Monica · 781

Hjorth, Arthur · 68, 263, 274

Chukhnov, Anton · 160

Holbert, Nathan · 460, 561, 754

Chytas, Christos · 547, 855

Holmquist, Stephanie · 150

Clayson, James · 30

Horn, Michael · 392

Csizmadia, Andrew Paul · 150, 794

Horváth, Győző · 736

Čuma, Radek · 543

Howell, Stephen · 759, 767, 786

957

Constructionism 2018, Vilnius, Lithuania

Hoyles, Celia · 69, 754

M
Mäkiaho, Pekka · 698

I
Igwe, Kay Chioma · 193
Ito, Kazunari · 585, 593, 745, 884
Ivanović, Mirjana · 608

Malchiodi, Dario · 901
Marshall, Kevin · 767
Martin, Kit · 380, 392
Martinez, Sylvia · 121, 755
Mascaró, Maite · 615
Massarwe, Khayriah · 657, 956

JMatsuzawa, Yoshiaki · 703

Jasutė, Eglė · 180

Mayerová, Karolína · 623

Jatzlau, Sven · 285
Jevsikova, Tatjana · 180
Jung, Ungyeol · 295

Maytarattanakhon, Athit · 160
Michaeli, Tilman · 405
Monga, Mattia · 901

Jūratė Baranova · 868

Mönig, Jens · 82

Juškevičienė, Anita · 305, 884

Moro, Michele · 498
Morpurgo, Anna · 901
Mueller, Matthias · 104

K

Muller, Eric · 528

Kafai, Yasmin B. · 214, 601, 754

Muñoz, Leda · 514

Kahn, Ken · 315, 765, 788
Kalaš, Ivan · 71, 946

N

Kaminskienė, Lina · 925
Kandlhofer, Martin · 150
Kishi, Nobuko · 325
Klašnja-Milićević, Aleksandra · 608
Klein, Markus · 791
Klimeková, Eva · 334
Knox, Tony · 449
Knuth, Alexander · 672

Nakano, Issei · 703
Nakayama, Yuriko · 741
Niemelä, Pia · 415
Nikitopoulou, Sofia · 773
Nikolova, Nikolina · 954
Noguchi, Misako · 703
Noss, Richard · 69, 754

Koppensteiner, Gottfried · 791
Koza, Clemens · 791

O

Kranas, Witek · 947
Kryvoruchka, Liudmyla · 694
Kumar Luhana, Kirshan · 104
Kuno, Yasushi · 741
Kurvinen, Einari · 344

O’Sullivan, Katriona · 138
Oie, Yuichi · 593
Olędzka, Katarzyna · 855
Ortega Torres, Enric · 707

Kynigos, Chronis · 81, 357, 369, 506, 686, 773
P
L
Laakso, Mikko-Jussi · 344, 954
Lee, Gary C. F. · 781
Lee, Young-jun · 295
Lepuschitz, Wilfried · 686, 791
Lilija Duoblienė · 868
Lodi, Michael · 901
Luc Anckaert · 868

Partanen, Tiina · 415
Passey, Don · 838
Patarakin, Evgeny · 426
Peppler, Kylie · 754
Pereira de Souza, Elmara · 631
Pereira, Patrick Pais · 672
Pesek, Igor · 884
Petrosino, Tony · 761
Pina, Alfredo · 498
Pluhár, Zsuzsa · 884
Pope, Nicolas · 437

958

Constructionism 2018, Vilnius, Lithuania

Poranen, Timo · 415, 698

Thanapornsangsuth, Sawaros · 460, 561, 718

Posov, Ilya · 160

Todorova, Christina · 649, 686, 690

Poviliūnas, Arūnas · 4

Tohyama, Sayaka · 723

Pozdniakov, Sergei · 160

Tutiyaphuengprasert, Nalin · 727

Prieto-Rodriguez, Elena · 251, 577
V
R
Valente, José Armando · 472, 481
Riley, Clare · 767

van Veen, Klaas · 570

Romeike, Ralf · 285, 405, 855

Vaníček, Jiří · 488, 884

Rubio, Gabriel · 498

Varbanov, Pavel · 649, 690
Vartiainen, Katriina · 698

S

Verner, Igor · 657, 956
Veselovská, Michaela · 623

Sabin, Mihaela · 925

Vincze, Markus · 506, 686

Sabitzer, Barbara · 711

Vinikienė, Lina · 731, 794

Sacristán, Ana Isabel · 83, 528, 615, 925

Visnovitz, Márton · 736

Sanjosé López, Vicent · 707

Vittori, Lisa · 690

Schindler, Christian · 104
Schmidt, Laura · 672
Seegerer, Stefan · 405

W

Selcuk, Hasan · 543

Waite, Jan · 794

Sendova, Evgenia · 94, 855, 941, 954

Watanabe, Takeshi · 741

Sharkov, George · 649, 690

Weigend, Michael · 664, 672, 884

Shaw, Mia S. · 214

Wetzinger, Elisabeth · 678

Slany, Wolfgang · 104, 637, 855

Wilensky, Uri · 123, 125, 238, 263, 274, 392, 554, 761

Solaz Portolés, Joan-Josep · 707

Wilfried Baumann · 868

Souza Moura, Luísa · 631

Winters, Niall · 315

Spieler, Bernadette · 104, 637, 901
Stager, Gary S. · 120, 121, 955

YStandl, Bernhard · 678, 794
Staub, Jacqueline · 756, 955

Yiannoutsou, Nikoleta · 506, 649, 690, 773

Stroup, Walter · 761

Yoneda, Takashi · 593

Stupurienė, Gabrielė · 169

Yoshida, Aoi · 325, 593, 745

Suhre, Cor · 570

Yoshida, Mari · 325

Sullivan, Kevin · 138

Yoshizawa, Minori · 325

Suziedelis, Carol Sperrry · 122, 956
Z
TZamora, Natalia · 514

Takeuchi, Yugo · 723

Želvys, Rimantas · 20, 944

Tan, Michael · 714

Zhang, Jinbao · 750

Tangney, Brendan · 449

Zwaneveld, Bert · 570

959