the iafor

journal of
education
Volume 6 – Issue 3 – Winter 2018
Editor: Yvonne Masters

ISSN: 2187-0594

iafor
IAFOR Journal of Education
Volume 6 – Issue 3 – Winter 2018

IAFOR Publications

The International Academic ForumIAFOR Journal of Education
EditorYvonne Masters, University of New England, Australia
Associate Editors
Lynda Leavitt, Lindonwood University, USA
Massoud Moslehpour, Asia University, Taiwan
Raimond Selke, Goethe-Institut Jakarta, Indonesia

Published by The International Academic Forum (IAFOR), Japan
IAFOR Publications, Sakae 1-16-26-201, Naka-ward, Aichi, Japan 460-0008

Executive Editor: Joseph Haldane
Managing Editor: Elena Mishieva
Publications Manager: Nick Potts

IAFOR Journal of Education
Volume 6 – Issue 3 – Winter 2018
Published December 1, 2018

IAFOR Publications © Copyright 2018
ISSN: 2187-0594
ije.iafor.org
Image: Comfreak, pixabay.com

IAFOR Journal of Education
Volume 6 – Issue 3 – Winter 2018
Edited by Yvonne Masters

Table of Contents
From the Editor
Yvonne Masters

1

Notes on Contributors

2

Mentoring Experiences, Issues, and Concerns in the Student-Teaching
Program: Towards a Proposed Mentoring Program in Teacher Education
Susan Galamay-Cachola
Ma. Cristeta M. Aduca
Federicia C. Calauagan

7Work-Related Factors as Determinants of Self-efficacy and Resilienceamong Selected Filipino Child Development Workers
Katherine del Rosario
Nephtaly Joel Botor
Jewely Jean Padilla
Aida Escobin
Daisy Pelegrina

25

Role of Educational Technologies Utilizing the TPACK Frameworkand 21st Century Pedagogies: Academics’ Perspectives
Tirtha Goradia

43

Artificial Intelligence and the Student Experience:
An Institutional Perspective
Kriti Khare
Brian Stewart
Anshuman Khare63

Teaching Them before We Teach: The Effectiveness of Conducting
Classroom Experiments before Teaching the Underlying Theory
Raymond Li
Tina Wong

79

Learner-Generated Digital Media (LGDM) as an Assessment Tool in
Tertiary Science Education: A Review of Literature
Jorge Reyna
Peter Meier

93

Towards a Cross-Cultural Conceptual Framework for Researching
Social and Emotional Education
Edurne Scott Loinaz

111

The Effects of Using Representations in Elementary Mathematics:
Meta-Analysis of ResearchAndrzej Sokolowski

129

Reviewers

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IAFOR Journal of Education

Volume 6 – Issue 3 – Winter 2018

From the Editor
Dear Readers,This is the third and final issue of the IAFOR Journal of Education for 2018. The eight articles
again display the research being undertaken from a range of locations: the Philippines,
Australia, Canada, Hong Kong, England, and the United States. While the contexts aredifferent, the themes are relevant to educators globally.
The themes of the articles in this issue are as diverse as the countries from which they came.
The research reported covers TPACK, artificial intelligence, social and emotional education,mathematics, child development workers, mentoring, learner-generated digital media, andconducting experiments prior to teaching of concepts.
We hope that you enjoy these articles, find application for them in your own contexts, andconsider sharing your own research and experiences in the journal. The next issue is due to be
published on June 1st, 2019.
A thank you to all who have submitted articles in 2018. The huge response we have had to calls
for papers demonstrates the many research projects being conducted in the field of education.
Thanks go to all the reviewers throughout the year, many of whom have willingly accepted therole for more than one issue. Special thanks also to the associate editors, Lynda Leavitt,
Massoud Moslehpour and Raimond Selke, who have made my job easier and to the IAFOR
publications team for their hard work and dedication.
I look forward to building on our strengths throughout 2019.
Yvonne Masters
Editor, IAFOR Journal of Education,
ije.iafor.org

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Volume 6 – Issue 3 – Winter 2018

Notes on Contributors
Article 1: Mentoring Experiences, Issues, and Concerns in the Student-Teaching
Program: Towards a Proposed Mentoring Program in Teacher Education
Dr Susan Galamay-Cachola has a PhD majoring in Educational Management. She is an
Associate Professor / Faculty Researcher at Saint Mary’s University, Bayombong, Nueva
Vizcaya, Philippines. Her fields of expertise are Curriculum, Instruction, and Assessment;
Action Research in Education; and Religious and Values Education.
Email: susangcachola@gmail.com
Dr Ma. Cristeta M. Aduca has a PhD majoring in Educational Management. She is Professor/ Principal of the Grade School Department, Saint Mary’s University, Bayombong, Nueva
Vizcaya, Philippines. Her fields of expertise are Curriculum, Instruction, and Assessment andReligious and Values Education.
Email: tetaaduca@yahoo.com
Miss Federicia C. Calauagan has a Master of Arts in Education. She is an Associate Professor/ Student Teaching Supervisor at Saint Mary’s University, Bayombong, Nueva Vizcaya,
Philippines. Her fields of expertise are Reading education and Professional education.
Email: lizac22@yahoo.com
Article 2: Work-Related Factors as Determinants of Self-efficacy and Resilience among
Selected Filipino Child Development Workers
Ms Katherine del Rosario holds a Bachelor’s degree in Human Ecology from the Universityof the Philippines at Los Baños, Laguna. She is also a graduate of Master of Family Life andChild Development from the University of the Philippines, Diliman. Her specialization is on
early childhood development and education which includes management of preschool centers,
teacher training and coordinating various activities. She is currently an Assistant Professor at
the Department of Human and Family Development Studies, College of Human Ecology,
UPLB where she handles courses on human development across the lifespan, conducts training
for Child Development Workers and engages in research and extension programs of DHFDS.
Email: kedelrosario@up.edu.ph
Mr Nephtaly Botor holds a Bachelor’s degree in Psychology and is presently serving as an
instructor of the Department of Human and Family Development Studies, College of Human
Ecology, University of the Philippines at Los Baños, Laguna. He is also a licensed
psychometrician and a professional facilitator and trainer specializing in values education. Mr
Botor is currently in the final stage of his Master of Education, specializing in Guidance.
Ms Jewely Jean Varca-Padilla is a graduate of BS Human Ecology from the University of
the Philippines, Los Baños. She also earned her Master’s degree on Family Resource
Management from the same university. At present, she serves as a University Extension
Associate at the Department of Human and Family Development Studies, College of Human
Ecology, UPLB. She works as teacher-in-charge at the UPLB Child Development Laboratory(CDL), handling the nursery and pre-kindergarten classes. She develops and implements
learning modules for the children and coordinates the activities and events at the CDL. She
also assists in the research and extension activities of DHFDS.

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Volume 6 – Issue 3 – Winter 2018

Ms Aida Escobin holds a Master’s degree in Family Resource Management from the
University of the Philippines at Los Baños, Laguna. In 1990, she became a part of theCommunity and Family Life Education Laboratory (COMFA-LAB) under the Department of
Human and Family Development Studies, College of Human Ecology, UPLB, as a Family
Development Worker. Her duties include teaching preschoolers and coordinating activities andprojects with parents. From 1995 to present, Ms. Escobin serves as University Extension
Associate in the same department. She has over 25 years of work experience in the areas ofteaching, research and administration. She also facilitates training for Child Development
Workers in the Southern Tagalog Region.
Ms Daisy Pelegrina is a graduate of BS Development Communication from the University of
the Philippines at Los Baños, Laguna and is currently in the process of writing her thesis
leading to a Master of Science in Development Communication in the same university. At
present, she serves as University Research Associate and Development Communication
Specialist at the Department of Human and Family Development Studies, College of Human
Ecology, UPLB. She works as a language editor, writer, and coordinator of various researchand extension activities at both university and provincial (LGU) level which help fulfill
DHFDS’ vision to promote human development across the lifespan.
Article 3: Role of Educational Technologies Utilizing the TPACK Framework and 21stCentury Pedagogies: Academics’ Perspectives
Mrs Tirtha Goradia has a Bachelor’s degree in complementary medicine from India and a
Master of Education degree from Murdoch University (Perth). She also holds a Graduate
Certificate in Tertiary and Adult Education from Murdoch University. Her thesis for her Masterof education degree was “Strategies to enhance student engagement in online learning”. Tirtha
is currently a Senior Lecturer and e-learning specialist in the Biosciences department at
Endeavour College of Natural Health, a leading complementary medicine provider in Australia.
Tirtha’s contributions to publication, conferences and webinars have been around higher
education sector. Her research work explores concepts of self-regulated learning, online
learning, technologies and pedagogies, evidence based practice and higher education. She has
made various contributions in the teaching and learning space and worked on projects for firstyear students, online and blended learning and pathology and clinical science curriculum
development.
Email: Tirtha.goradia@endeavour.edu.au
Article 4: Artificial Intelligence and the Student Experience: An Institutional
Perspective
Ms Kriti Khare has a Master in Computing Science from University of Alberta and a Masterin Education, specializing in technology in education. While pursuing higher education as apreservice teacher, she is involved with data analytics related to university services at theInformation Services and Technology Department, University of Alberta. Her researchinterests include computing science education, inquiry-based learning, use of technology in
education, and data mining.
Email: kriti@ualberta.ca
Mr Brian Stewart is the Deputy CIO at the University of Alberta. His role is to provide
strategic leadership, vision, and direction for information services and technology and to directbusiness transformation through project and change management while also leading a lean

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Volume 6 – Issue 3 – Winter 2018

initiative for continuous operational improvement. Brian’s background is in strategic,
operational and technology management in the printing industry and higher education sector,
and he has written and spoken widely on these topics. Brian has a Master of Arts in Economics
from University College, Cork, and a Master of Business Administration from Athabasca
University. He is currently a PhD student at the University of Eastern Finland.
Email: brianstewart3@gmailcom
Dr Anshuman Khare is Professor in Operations Management at Athabasca University,
Canada. He is an Alexander von Humboldt fellow and a former Monbusho scholar. His
research focuses on environmental regulation impacts on industry, just-in-time manufacturing,
supply chain management, sustainability, cities and climate change, online business education,and so on. He is passionate about online business education and has won several awards as atestament to his commitment to exceptional online education. Anshuman serves as the editorof IAFOR Journal of Business and Management and associate editor of International Journalof Sustainability in Higher Education published by Emerald and is on the Editorial Board ofInternational Journal of Applied Management and Technology. In 2017 Anshuman co-edited
a book On the Line: Business Education in the Digital Age which was published by Springer.
Email: anshuman.khare@fb.athabascau.ca
Article 5: Teaching Them before We Teach: The Effectiveness of Conducting Classroom
Experiments before Teaching the Underlying Theory
Dr Raymond Li is currently a teaching-track faculty member at the Hong Kong Polytechnic
University. Dr Li received his formal training in Economics from Macquarie University inAustralia and obtained his Bachelor of Economics with first class honours. He also obtained
his PhD in Economics from Macquarie University. His primary research interests include
applied econometrics, energy economics and energy policy. His research outputs appear in arange of international journals like the Energy Journal, Energy Policy and Applied Energy,
amongst some others. Ray is an editorial board member of two international academic journals
and he serves as ad hoc reviewer of research articles for various academic journals.
Email: ray.li@polyu.edu.hk
Dr Tina Wong is a teaching fellow at the Hong Kong Polytechnic University. She obtained
her Bachelor of Social Science (Economics) from the Chinese University of Hong Kong and
the Postgraduate Certificate of Education (Economics) from the University of Hong Kong. She
had her further studies in the Hong Kong Polytechnic University (Master of Finance) and the
University of Newcastle (Doctor of Business Administration). She has over 10 years’
experience in teaching Economics and Finance.
Email: aftina.wong@polyu.edu.hk
Article 6: Learner-Generated Digital Media (LGDM) as an Assessment Tool in Tertiary
Science Education: A Review of Literature
Mr Jorge Reyna is a Lecturer in Higher Education, Learning Design at the University ofTechnology, Sydney, Australia. He has vast experience applying pedagogies, instructional
strategies, visual design, aesthetics, usability, and accessibility in online and blended learning
environments, as well as teaching digital media to communicate science. Jorge’s areas ofexpertise are in digital media production such as animations, screencast, podcasts, video
scripting, filming, editing and production, multimedia learning, web design, graphics, and4

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Volume 6 – Issue 3 – Winter 2018

photography. His current research includes the study of self-regulation and motivational
processes in Learner-Generated Digital Media Assignments (LGDM) in science education.
Email: jorge.reyna@uts.edu.au
Associate Professor Peter Charles Meier is Associate Dean of Teaching and Learning at the
University of Technology, Sydney, Australia. He is responsible for the implementation of newlearning technologies and approaches across the science curriculum. Peter is a specialist in
clinical learning and competency assessment, including virtual clinic environments. He iscurrently leading a nationally recognised lighthouse project in Work Integrated Learning in
Science.
Email: peter.meier@uts.edu.au
Article 7: Towards a Cross-Cultural Conceptual Framework for Researching Social andEmotional Education
Dr Edurne Scott Loinaz is co-founder of HAHA Academy and completed her PhD at UCL's
Institute of Education. Edurne's research interests lie in the area of social and emotional
education, comparative education and the decentralisation of knowledge. She has collaborated
actively with researchers in several other disciplines, particularly educational psychology. Her
writing and research to date can be viewed on her blog: http://emo.world
Article 8: The Effects of Using Representations in Elementary Mathematics: MetaAnalysis of Research
Dr Andrzej Sokolowski is a researcher and Mathematics and Physics Professor at Lone Star
College in Houston. He is an author of several papers on mathematical modeling and STEM
education. He recently published a book Scientific Inquiry in Mathematics, Theory andPractice.
Email: Andrzej.Sokolowski@lonestar.edu

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Volume 6 – Issue 3 – Winter 2018

Mentoring Experiences, Issues, and Concerns in the Student-Teaching Program:
Towards a Proposed Mentoring Program in Teacher Education

Susan Galamay-CacholaSaint Mary’s University, Philippines
Ma. Cristeta M. Aduca
Saint Mary’s University, Philippines
Federicia C. Calauagan
Saint Mary’s University, Philippines

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Volume 6 – Issue 3 – Winter 2018

Abstract
Mentoring involves the process of experienced teachers teaching and guiding student-teachers
on the different aspects of the teaching-learning process. This study aimed to determine thementoring experiences of cooperating teachers and student-teachers using quantitativequalitative design. Survey questionnaires based on Hudson’s model were distributed andinterviews were conducted among cooperating teachers and student-teachers. Means,standard deviations, t-test for independent samples and paired samples t-test were used toanalyze the data. Qualitative responses were analyzed and categorized thematically. Findings
indicate that the cooperating teachers perceived they greatly mentored student-teachers interms of personal attributes, system requirements, pedagogical knowledge, modeling, andfeedback which were validated by the student-teachers except in the area of system
requirements wherein they were mentored moderately. The study concluded that thecooperating teachers mentored to a great extent the student-teachers. Provision of continuing
professional education for cooperating teachers to enrich their skills on mentoring studentteachers and more time for post-conference were recommended.
Keywords: feedback, pedagogical knowledge, personal attributes, modeling, system
requirements

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Volume 6 – Issue 3 – Winter 2018

Teaching is a complex process which necessitates that teacher preparation programs provide
intensive training such as mentoring by expert teachers. Through mentoring, student teachers
learn about the teaching process specifically the acquisition of the required basic skills andprofessional knowledge (Mena, Hennissen, & Loughran, 2017). Student teachers are fieldedto laboratory schools as part of the student teachers’ training. Student teaching placements areimportant in preparing student teachers since these field involvements provide authentic andrelevant teaching experiences. In addition, these teaching experiences provide student
teachers opportunities to learn instructional and class management strategies from mentor
teachers (Chizhik, Chizhik, Close, & Gallego, 2017).
However, the realities of classroom teaching present problematic areas in the student teaching
program. On one hand, student teachers are under-prepared for actual classroom teaching.
The study of Soslau and Raths (2017) presented some problematic aspects of student teachingsupervision such as giving feedback, specifically on planning, assessment, and relationshipwith pupils, among others. On the other hand, some supervisors or cooperating teachers finddifficulty in providing evaluative feedback to student teachers, keeping communication
channels open, and maintaining positive daily interactions inasmuch as they perform bothformative and summative evaluations.
Mentoring has become a crucial component of pre-service field experiences such as student
teaching (Bird & Hudson, 2017). Proper and adequate mentoring of student-teachers is vitalin the student teaching program inasmuch as experienced mentors provide career andpsychosocial support to relatively less experienced protégés – the student-teachers (Menges,
2016; Cakir & Kocabas, 2016). Accordingly, it is mandatory that the cooperating teachers
who are tasked to mentor students demonstrate expertise in content and pedagogy, effective
communication skills, possession of a positive attitude and a professional demeanor,manifestation of genuine interest in preparing and supporting aspiring teachers, ability toeffectively prepare and support aspiring teachers, and willingness to work with other teacher
preparation professionals (Gareis & Grant, 2014).
Cooperating teachers provide inspiration to their student-teachers through their dedication to
uphold quality instruction which is made possible through relevant teacher education training
and programs (Gorozidis & Papaioannou, 2014) to improve their skills and become more
proficient. Cooperating teachers who manifest greater efficacy in their role as mentors tostudent-teachers become more effective instructional models and inspire stronger
performances by student teachers. As mentors, cooperating teachers also believed that they
benefited from reflecting on their teaching (Aspfors & Fransson, 2015) and sharing theirexperiences with colleagues (Clarke, Killeavy, & Moloney, 2013).
Crucial to the success of mentoring is the mentor-mentee relationship. Several studies have
explored the mentoring experiences that take place in the student teaching program. The
studies of Hudson (2016) and Ulvik and Sunde (2013) indicated that a positive mentor–
mentee relationship is essential for the mentee’s development of teaching practices. Findings
revealed that positive relationships required the achievement of trust and respect by sharing
information, resources, and expectations and by being professional, enthusiastic, andsupportive with collaborative problem-solving. For a positive mentoring relationship toprosper, certain attributes are desired for both mentors and mentees. The study of Hudson andHudson (2014) indicated that mentors’ desirable attributes included enthusiasm, commitment,
and resilience and mentors’ essential practices comprised planning, preparation, and building
a teaching repertoire for mentees. In addition, Hudson (2013) investigated the mentor

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Volume 6 – Issue 3 – Winter 2018teachers' expectations of desirable attributes and practices for mentees. Mentees are expected
to manifest desirable attributes such as being enthusiastic, personable, committed to children,
love learning, open/reflective to feedback, resilient, and taking responsibility for theirlearning. In terms of desirable practices mentees are expected to plan and prepare for teaching,reflect on their teaching practices, understand school and university policies, know students
for differentiated learning, and build a teaching repertoire such as teaching strategies,
behavior management, content knowledge, and questioning skills.
Mentor-mentee relationship is also founded on articulation of expectations at the beginningof the mentee’s school experiences. Mentees have high expectations of their mentors in termsof supervision and support (Kemmis, Heikkinen, Fransson, Aspfors, & Edwards-Groves,2014), pedagogical knowledge practices, and meeting teaching standards (Yirci, Karakose,
Uygun, & Ozdemir, 2016). Mentors expected their mentees to be risk takers with high levelsof professionalism that have students at the center of learning how to teach. As presented by
Hudson (2013), the development and provision of positive mentoring relationships are
essential to student-teachers’ learning. Trust and respect build and sustain mentor-mentee
relationship along with mentors’ professionalism, open communication, attentive listening
and friendly dispositions (Hudson, 2013; Straus, Johnson, Marquez, & Feldman, 2013).
Support provided by mentors consisted of providing information for planning, access toresources, and two-way dialoguing with feedback and reflections. Other forms of mentor
support also entailed encouraging mentees to get out of their comfort zone and explore andlearn new teaching practices.
This study was conceptualized in view of the clamor of student teachers for quality
supervision in the student teaching program and the mandate to provide quality education.
This study explored the mentoring experiences of the cooperating teachers and student
teachers in the areas of personal attributes, systems requirements, pedagogical knowledge,
modeling, and feedback as well as their issues and concerns in terms of supervision. It also
investigated whether there was significant difference in the extent of mentoring experiences
cooperating teachers provided and student-teachers received. Further, the study also exploredaspects of mentoring that need to be addressed and program in teacher education that can be
crafted to provide support for the professional development and training of cooperatingteachers in preparation for more effective mentoring practices towards the student teachers.
ProfileA. Teacher-respondents
1. In-campus
cooperatingteachers
2. Off-campus
cooperatingteachers
B. Student-teachers

Hudson’s Five Factor
Model
A. Personal
attributes
B. Systems
requirements
C. Pedagogical
Knowledge
D. Modelling
E. Feedback
Figure 1. Research paradigm

10

Recommendations
that will enhanceteacher educationin the graduate
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Volume 6 – Issue 3 – Winter 2018

The model of mentoring suggested by Bird and Hudson (2015) indicates five factors that arelinked to mentoring attributes and practices. The first factor refers to mentors’ personal
attributes which consist of mentors’ support of the mentees, ease of communication especiallydiscussion of teaching practices, and active listening to the mentees. The personal attributesof the mentors encourage the mentees to reflect on their pedagogical practices, inspire selfconfidence and positive attitudes.
The second factor in mentoring is on systems requirements. Mentors need to communicatethat educational systems have requirements such as aims, policies, and curricula. The
complexities for executing system requirements may be indicated in the pedagogicalknowledge mentors must articulate for effective teaching (Bird & Hudson, 2015).
Pedagogical knowledge, the third factor, indicates that mentors articulate making learningplans for teaching. Mentors need to discuss aspects of the preparation such as use of resources,
appropriate teaching strategies, and content knowledge of the mentee. The mentor can assist
the mentee in case incidental problems arise during lessons such as managing student behavior
inasmuch as the mentor has gained experience on how to deal with various student personality
types and behavior traits. The mentor can also assist the mentee on the art of questioning suchas formulating question that are of low order thinking or high order thinking. Learning plans
follow a certain structure and mentors can discuss the different parts and how these parts are
implemented. Mentors can also provide pedagogical knowledge about evaluation of students’learning and explain how it is linked to curriculum, pedagogy, and assessment (Bird &Hudson, 2015).
Modeling as the fourth factor indicates that the mentor’s readiness as a teacher can nurturethe development of desirable teaching traits in the mentee. Significantly, the teacher-student
relationship is vital to the teaching-learning process and establishing a positive relationshipwith students can demonstrate to the mentee how these behaviors can facilitate learning. The
mentor also needs to model proper classroom language appropriate for student learning,
instruction (what to do and what not to do), effective teaching, classroom management, handson lessons, and well-designed lessons (Bird & Hudson, 2015).
The fifth factor indicates the importance of feedback. Effective mentors communicate
expectations and provide guidance to the mentee in terms of reviewing lesson plans, observing
the mentee’s teaching performance, providing oral and written feedback, and giving further
advice on the mentee’s evaluation of their teaching and how the mentees establish a learning
environment (Bird & Hudson, 2015).
Hudson’s model indicates that effective mentoring of practice teachers can reinforce andenhance teaching practices that will contribute to improved student learning. Skillful analysis
of practice teachers’ teaching performance can have a profound effect on the learning that
occurs in the classroom. Because student learning is the primary function of the schools,
effective supervision of instruction is very critical. Thus, the pre-service teaching curriculum
should include a variety of teaching strategies designed to meet the diverse needs of all
students in our complex society.
In these contexts, the mentoring experiences of cooperating teachers and student-teachers
were investigated through a survey and structured interview guide questions. The study was
conducted to provide insights into cooperating teachers’ role as mentors. Likewise, areas fordevelopment in terms of supervision and instruction were also determined. Findings served

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Volume 6 – Issue 3 – Winter 2018as basis in drafting a program that addressed the needs of cooperating teachers and enhanced
the teacher education program. These experiences provided insights on how mentors canmake the practice teaching experience of student-teachers meaningful and help them acquireand develop pedagogical knowledge, skills and values essential in their formation as futureteachers in accordance with the requirements of the National Competency-Based Teacher
Standards (NCBTS).
Methodology
This study used quantitative-qualitative design. The quantitative design was used to determine
the extent of mentoring cooperating teachers provided to their student-teachers. It also
investigated the extent of mentoring received by student-teachers from their cooperatingteachers. In the quantitative design, survey questionnaires were distributed to the respondents.
This method was used to find existing realities that can provide essential information for the
study. Qualitative design, specifically the use of interview, was also employed to surface
responses that enriched the numerical data gathered in the survey. The study was conducted
at Saint Mary’s University, Bayombong, Nueva Vizcaya (SMU), and the public schools in
Nueva Vizcaya where the student-teachers were deployed in theiro-campus teachingexperience. SMU is one of the five Congregatio Immaculati Cordis Mariae (CICM) schools
in the Philippines founded in 1928 and earned its university status in 1994.
The tools that were used in gathering the data were a researcher-designed questionnaire basedon Hudson’s five factor model and structured interview guide questions constructed by the
researchers. There were two sets of questionnaire and interview guide questions answered by
the respondents. The first set was for cooperating teachers and the second set was for the
student teachers. The Cronbach alpha of .956 indicated that the survey questionnaire had very
high reliability. The Likert scale used in the interpretation of the data was as follows: 1.001.49 (Not at all); 1.50-2.49 (Little extent); 2.50-3.49 (Moderate extent); 3.50-4.00 (Great
extent).
The study used population sampling wherein all of the 71 student-teachers who were enrolled
in the Student Teaching Program in the school year 2016-2017 were respondents in order toobtain a holistic picture of the study. Teacher-respondents were those who were assigned
student teachers to mentor. There were 61 Off-campus cooperating teachers and 30 Oncampus cooperating teachers.
To determine the extent of mentoring provided by the cooperating teachers and received by
the student-teachers, means, medians and standard deviations were used. To determine thesignificant difference in the extent of mentoring provided by the off-campus and in-campus
teachers, t-test for independent samples was used. To determine the significant difference inthe extent of mentoring received by the student-teachers, paired samples t-test was used.
Qualitative responses from the written interview were used to support the qualitative data.
Qualitative responses were also analyzed and categorized thematically.
Results and Discussion
Extent of Mentoring Practices Cooperating Teachers Provide to Student Teachers
Table 1 presents the extent of mentoring practices cooperating teachers provided to student
teachers in terms of personal attributes. As indicated by the overall mean (Off-campus=3.81;
In-campus= 3.85), the cooperating teachers perceived that they greatly mentored the student-

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Volume 6 – Issue 3 – Winter 2018teachers in terms of their personal attributes. The off-campus cooperating teachers
demonstrated high willingness to model positive values while the cooperating teachers in theon-campus displayed commitment to mentor the student-teachers and passion for teaching toa large extent. Verbatim responses from the cooperating teachers indicated that they had“always been positive in teaching”. The cooperating teachers also indicated the need for theprovision of “orientation about good personal attitude” such as “always be on time; beflexible” and making themselves “available for student-teachers” and “promote comfort” and“help them gain confidence in teaching” by “treating them with respect, mentoring by
coaching, lending, motivating, inspiring them to teach”. In addition, an in-campus cooperatingteacher indicated that she used Appreciation, Time and Encouragement (ATE). She pointed
that “student teachers learn best when they feel that their cooperating teachers help them havetheir knowledge, skills and values as future teachers”.
Table 1. Extent of mentoring practices cooperating teachers provide to student teachers interms of personal attributes
In my mentoring with student-teachers, I
demonstrate that I …

Cooperating Teachers
Off-campus (N=61)

Cooperating Teachers
On-campus (N=30)

Mean

SD

QD

Mean

SD

QD

1. am flexible

3.74

0.44

Great

3.67

0.54

Great

2. am open-minded

3.84

0.37

Great

3.77

0.43

Great

3.74

0.44

Great

3.77

0.43

Great

3.85

0.35

Great

3.97

0.18

Great

3.84

0.37

Great

3.90

0.30

Great

3. foster confidence by providing
opportunities for friendship4. promote comfort and confidence and help
student teachers feel comfortable in teaching
5. make myself available for my studentteachers
6. show willingness to model positive values

3.92

0.27

Great

3.83

0.37

Great

7.am committed to mentor my studentteaches
8. am passionate in teaching

3.74

0.44

Great

3.97

0.18

Great

3.85

0.35

Great

3.97

0.18

Great

Overall Mean

3.81

0.28

Great

3.85

0.23

Great

Table 2. Extent of mentoring practices cooperating teachers provided to student teachers interms of system requirements

In my mentoring with student-teachers, I…
1.orient student teachers about school
requirements
2.provide guidelines for theaccomplishment of school records/forms
3.give instruction about grading systems
and school policies
Overall Mean

Cooperating Teachers
Off-campus (N=61)
Mean
SD
QD

Cooperating Teachers
On-campus (N=30)
Mean
SD
QD

3.56

0.53

Great

3.63

0.66

Great

3.51

0.56

Great

3.17

1.05

Moderate

3.59

0.49

Great

3.33

0.84

Moderate

3.55

0.43

Great

3.38

0.76

Moderate

Table 2 shows the extent of mentoring practices cooperating teachers provided to student
teachers in terms of system requirements. The overall mean (Off-campus=3.55; Incampus=3.38) indicates that the off-campus cooperating teachers greatly mentored thestudent-teachers while the in-campus teachers only to a moderate extent. The off-campus
teachers greatly mentored student-teachers on orienting student-teachers about school13

IAFOR Journal of Education

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requirements, provided guidelines for the accomplishment of school records/forms and gave
instruction about grading systems and school policies while the in-campus teachers did these
only to a moderate extent. The interview conducted with cooperating teachers showedverbatim comments indicating that they mentored the student-teachers in matters concerning“orientation on school requirements” and “…the policies of the school” to a high degree.
Table 3.Extent of mentoring practices cooperating teachers provided to student teachers interms of pedagogical knowledge
Cooperating Teachers
Off-campus (N=61)

Cooperating Teachers
On-campus (N=30)

In my mentoring with student-teachers, I …

Mean

SD

QD

Mean

SD

QD

1.coach via sharing ideas or telling information

3.69

0.46

Great

3.80

0.40

Great

2.plan collaboratively with my student-teachers onlearning plans
3.provide essential resources for teaching

3.7

0.61

Great

3.73

0.45

Great

3.51

0.59

Great

3.73

0.45

Great

4.share my vision/ principles of teaching

3.57

0.59

Great

3.77

0.50

Great

5.share my knowledge about

3.66

0.60

Great

3.67

0.54

Great

a. Problem-solving

3.61

0.66

Great

3.63

0.55

Great

b. Timetabling

3.72

0.55

Great

3.70

0.46

Great

c. Assessment

3.77

0.61

Great

3.83

0.37

Great

3.65

0.44

Great

3.73

0.4

Great

Overall Mean

Table 3 presents the extent of mentoring practices cooperating teachers provided to student
teachers in terms of pedagogical knowledge. The overall mean (Off-campus =3.65; Incampus= 3.73) shows that the in-campus teachers mentored more the student-teachers in their
sharing their pedagogical knowledge. The cooperating teachers also greatly mentored thestudent-teachers in their sharing of knowledge about assessment. Cooperating teachers
believed in the importance of mentoring student teachers on matters concerning pedagogicalknowledge stating that “if student-teachers’ schedule will be given earlier, they will be given
more time for close supervision”. Moreover, the cooperating teachers believed that studentteachers should be given “ample time to prepare and teach them to have time table” and that“a schedule that will work should be given so that student-teachers and cooperating teachers
can really collaborate”.
Table 4 shows the extent of mentoring practices cooperating teachers provided to student
teachers in terms of modeling. The overall mean (Off-campus= 3.74; In-campus=3.93)
indicates that the off-campus and in-campus teachers greatly mentored the student-teachers
in terms of modeling. The off-campus showed high enthusiasm in teaching (Mean=3.80)
while the in-campus teachers greatly demonstrated enthusiasm (Mean=3.97) and effectiveclassroom management. Verbatim comments from cooperating teachers pointed out that they
did “coach and share ideas in choosing appropriate strategy”, “share their vision of teaching”
because they believed that “experiential teaching is lasting and more meaningful”. In addition,a cooperating teacher also emphasized to her student-teacher that “all were provided to moldhim as good teacher – important information and best strategies”. Still, another cooperatingteacher shared that teaching is a vocation as indicated in her statement, “I believe that teaching
is not only a job, it’s a ministry”. They indicated that they modeled to student-teachers their“… adoption of reflective teaching approach”.

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Table 4. Extent of mentoring practices cooperating teachers provided to student teachers interms of modeling
Cooperating Teachers
Off-campus (N=61)

Cooperating Teachers
On-campus (N=30)

In my mentoring with student-teachers, I …

Mean

SD

QD

Mean

SD

QD

1.demonstrate how to teach the subject matter

3.67

0.65

Great

3.93

0.25

Great

2.show enthusiasm

3.80

0.60

Great

3.97

0.18

Great

3.demonstrate effective classroom management

3.75

0.62

Great

3.97

0.18

Great

4.demonstrate rapport with students

3.74

0.63

Great

3.87

0.34

Great

Overall Mean

3.74

0.59

Great

3.93

0.18

Great

Table 5. Extent of mentoring practices cooperating teachers provided to student teachers interms of feedback
Cooperating Teachers
Off-campus (N=61)

Cooperating Teachers
On-campus (N=30)

In my mentoring with student-teachers, I …

Mean

SD

QD

Mean

SD

QD

1.provide positive feedback

3.64

0.65

Great

3.87

0.34

Great

2.encourage students to practice reflective teaching

3.61

0.69

Great

3.83

0.37

Great

Overall Mean

3.62

0.66

Great

3.85

0.35

Great

Table 5 presents the extent of mentoring practices cooperating teachers provided to student
teachers in terms of feedback. The overall mean (Off-campus=3.62; In-campus=0.662)
indicates that the cooperating teachers greatly mentored the student-teachers in terms ofproviding feedback and encouraging students to practice reflective teaching. The cooperatingteachers believed that they have mentored the student teachers and that they have provided“…positive feedback and suggestions for improvement” which “…provides actionable
information” “…to develop the confidence of student-teachers”.
Table 6 shows the difference in the extent of mentoring practices cooperating teachers
provided to student teachers. Among the five factors, modeling (t=-2.31; p=0.023)
andfeedback (t=-2.136; p=0.035) yielded significant results. This indicates that the in-campus
teachers perceived that they mentored greatly the student-teachers in terms of modeling andgiving feedback than the off-campus teachers and the difference is significant.
Table 6. Difference in the Extent of Mentoring Practices Cooperating Teachers Provided toStudent Teachers
Independent Samples Test
Levene's Test for Equality of Variances
F
Sig.
t
df
Sig. (2-tailed)
1. Personal Attributes

Equal variances assumed

0.302

0.584

-0.68389

0.496

2.System Requirements
3. Pedagogical
Knowledge

Equal variances not assumed

21.856

0.000

1.156

38.4020.255

Equal variances assumed

0.041

0.84

-0.82589

0.412

4. Modeling

Equal variances not assumed

7.568

0.007

-2.31

79.702

0.023

5. Feedback

Equal variances not assumed

9.556

0.003

-2.13688.427

0.035

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Volume 6 – Issue 3 – Winter 2018

Extent of mentoring practices student–teachers received from cooperating teachers
Table 7 presents the extent of mentoring practices student–teachers received from cooperatingteachers in terms of personal attributes. As indicated by the overall mean (Off-campus=3.60;
In-campus=3.72), the student-teachers were mentored greatly by the cooperating teachers interms of their display of personal attributes. The off-campus teachers greatly mentored on
showing passion in teaching (Mean=3.70) while the in-campus teachers demonstrated
willingness to model positive values (Mean=3.83). The student teachers found their mentors
conscientious in their task of mentoring them through their own personal witnessing as
expressed in their verbatim comments to “… approach them and talk about their performancein teaching” to “share experiences that they can adopt”. Moreover, the student-teachers werementored greatly by cooperating teachers who extended their “moral support”, who were“kind enough to guide us in every teaching” and were “very kind and willing to share theirexperiences and knowledge” and showed “enthusiasm” in their teaching.
Table 7. Extent of mentoring practices student–teachers received from cooperating teachers
in terms of personal attributes
Cooperating Teachers
Off-campus
Statements
1.My cooperating teacher demonstrates
flexibility
2. My cooperating teacher demonstrates
open-mindedness
3. My cooperating teacher fosters confidence
by providing opportunities for friendship
4. My cooperating teacher promotes comfort
and confidence and help student teachers feel
comfortable in teaching
5. My cooperating teacher makes herself
available for mentoring
6. My cooperating teacher demonstrates
willingness to model positive values
7. My cooperating teacher demonstrates
commitment to mentoring
8. My cooperating teacher shows passion inteaching.

Mean

SD

3.54

0.62

3.68

0.55

3.56

0.60

3.55

0.65

3.52

0.67

3.73

0.53

3.58

0.69

3.70

0.57

Overall Mean

3.60

0.47

QD
Great
Great
Great
Great
Great
Great
Great
Great
Great

Cooperating Teachers
On-campus
Mean

SD

QD

3.58

0.62

3.76

0.52

3.70

0.61

3.73

0.58

3.73

0.53

3.83

0.37

3.69

0.57

3.75

0.52

3.72

0.46

Great
Great
Great
Great
Great
Great
Great
Great
Great

Table 8. Extent of mentoring practices student–teachers received from cooperating teachers
in terms of system requirements
Cooperating Teachers
Off-campus
My cooperating teacher
1.orient us about school requirements
2.provide guidelines for the accomplishmentof school records/forms
3.give instruction about grading systems andschool policies
Overall Mean

Mean

SD

3.32

0.67

3.27

0.69

3.18

0.76

3.26

0.63

16

QD
Moderate
Moderate
Moderate
Moderate

Cooperating Teachers
On-campus
Mean

SD

3.35

0.65

3.41

0.68

3.29

0.74

3.35

0.63

QD
Moderate
Moderate
Moderate
Moderate

IAFOR Journal of Education

Volume 6 – Issue 3 – Winter 2018

Table 8 presents the extent of mentoring practices student – teachers received fromcooperating teachers in terms of system requirements. As shown in the overall mean (Offcampus=3.26; In-campus=3.35), student teachers believed they were mentored on system
requirements only to a moderate extent. These system requirements consisted of orientation
about school requirements, provision of guidelines for the accomplishment of school
records/forms, and giving of instruction about grading systems and school policies. Verbatim
comments by the student-teachers stated they were mentored on “how to make standard lesson
log of SMU-HS” and “how to make Department of Education Learning Plans”.
Table 9. Extent of mentoring practices student–teachers received from cooperating teachers
in terms of pedagogical knowledge
Cooperating Teachers
Off-campus
QD
Mean SD

Cooperating Teachers
On-campus
QD
Mean SD

3.56

0.60

Great

3.65

0.65

Great

3.52

0.60

Great

3.55

0.71

Great

3.35

0.61

Moderate

3.46

0.69

Moderate

3.34

0.75

Moderate

3.46

0.73

Moderatea. Problem-solving

3.17

0.77

Moderate

3.48

0.69

Moderateb. Timetabling

3.42

0.66

Moderate

3.62

0.57

Great

c. Assessment

3.55

0.60

Great

3.61

0.64

Great

3.39

0.51

Moderate

3.53

0.57

Great

My cooperating teacher
1. coaches via sharing ideas or tellinginformation
2. plan collaboratively with student-teachers
on learning plans
3. provide essential resources for teaching
4. share his/her vision and principles ofteaching
5. share his/her knowledge about:

Overall Mean

Table 9 shows the extent of mentoring practices student-teachers received from cooperatingteachers in terms of pedagogical knowledge. The overall mean shows that the student-teachers
were mentored on pedagogical knowledge by the in-campus teachers to a great extent(Mean=3.53) and only to a moderate extent by the off-campus teachers (Mean= 3.39). The
student-teachers considered they were greatly mentored by the off-campus and in-campus
teachers through their sharing of ideas or telling information. However, they were least
mentored by the off-campus teachers on solving problems and least mentored by the incampus teachers on provision of essential resources for teaching and sharing their vision andprinciples of teaching, although still to a moderate extent. The student-teachers pointed outthat they were greatly mentored by their cooperating teachers through their assistance andsupport on how to “make lesson logs from syllabus”. The student-teachers were also helpedby their cooperating teachers who “gave advice and suggestions in teaching strategies andclassroom management”, demonstrated “questioning techniques”, provided “lists of strategies
in summarizing the lesson and ways in purposeful closure”, extended “materials needed,
comfort and ideas in delivering lesson” shared “…video clips on how to teach 21st century
learners” and “introduced several methods in conducting activities”.
Table 10 shows the extent of mentoring practices student–teachers received from cooperatingteachers in terms of modeling. The overall mean (Off-campus=3.50; In-campus = 3.59)
indicates that the student-teachers were mentored to a great extent by the cooperating teachers
in terms of modeling. The student-teachers were greatly mentored by the cooperating teachers
in terms of modeling enthusiasm in teaching and the least area of mentoring was on how to17

IAFOR Journal of Education

Volume 6 – Issue 3 – Winter 2018teach the subject matter, although still to a moderate extent. The student-teachers believed
they were greatly mentored by their cooperating teachers who demonstrated “how to
introduce lesson in an engaging way”, “how to teach subject matter”, “how to establish rapport
with students”, “how to handle class when there is group activity” and “how to conclude thelesson”.
Table 10. Extent of mentoring practices student-teachers received from cooperating teachers
in terms of modeling
Cooperating Teachers
Off-campus
My cooperating teacher
1.demonstrates how to teach the subject matter

Mean

SD

3.42

Cooperating Teachers
On-campus
Mean

SD

0.64

QD
Moderate

3.54

0.75

QD
Great

3.65

0.61

Great

2.shows enthusiasm

3.59

0.64

Great

3.demonstrate effective classroom management

3.52

0.62

Great

3.59

0.64

Great

4.demonstrate rapport with students

3.48

0.67

Moderate

3.62

0.64

Great

Overall Mean

3.50

0.53

Great

3.59

0.56

Great

Table 11. Extent of mentoring practices student-teachers received from cooperating teachers
in terms of feedback
Cooperating Teachers
Off-campus
My cooperating teacher
1.provides positive feedback

Mean

SD

3.59

0.57

2.encourages students to practice reflective
teaching

3.59

0.62

Overall Mean

3.59

0.56

QD
Great
Great
Great

Cooperating Teachers
On-campus
Mean

SD

3.63

0.56

3.62

0.64

3.62

0.55

QD
Great
Great
Great

Table 11 shows the extent of mentoring practices student-teachers received from cooperatingteachers in terms of feedback. As presented in the overall mean (Off-campus=3.59; In-campus= 3.62), the student-teachers were mentored by the cooperating teachers in both campuses interms of providing feedback and encouraging students to practice reflective teaching to a great
extent. The student-teachers agreed they were greatly mentored by their cooperating teachers
who “finds time to tell feedback about strengths and quality of my teaching” during the “postconference” wherein they were provided “feedback if their strategy is effective”. The studentteachers were greatly helped by the cooperating teachers because “in the post conference, they
told us about our weaknesses and how to improve our teaching methods”. The studentteachers were also mentored on the importance of “providing nice and proper feedback tostudents” and the “use of appropriate activities and instructional materials” in teaching.
Table 12 shows the difference in the extent of mentoring practices student teachers received
from cooperating teachers. The results yielded no significant difference. This indicates thatthe mentoring the student-teachers received from the cooperating teachers were statisticallythe same.

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Table 12. Difference in the extent of mentoring practices student-teachers received fromcooperating teachers
Paired Samples Statistics

1. Personal attributes
2. System requirements
3. Pedagogicalknowledge
4. Modeling
5. Feedback

Off-campus
In-campus
Off-campus
In-campus
Off-campus
In-campus
Off-campus
In-campus
Off-campus
In-campus

Mean
3.60
3.72
3.26
3.35
3.39
3.53
3.50
3.59
3.59
3.62

SD
0.479
0.462
0.632
0.639
0.519
0.571
0.537
0.562
0.568
0.552

t
-1.466df
70

Sig. (2-tailed)
0.147

-0.89

70

0.377

-1.58370

0.118-1.04370

0.301-0.37170

0.711

Discussion and Limitations of the Study
The mentors’ task of providing feedback to student teachers is crucial to the mentoringprocess. Mentors have the responsibility to assist their mentee in terms of their career andprovide advice, support, and feedback and “be a sounding board for the mentee” (Straus et al,2013). The cooperating teachers had done a great job in mentoring the student-teachers
especially in their witnessing of personal qualities that student teachers should imbibe as
future teachers. Effective mentors must be altruistic, honest, trustworthy, and active listeners.
The mentoring cooperating teachers provided to student teachers was indeed significant
inasmuch as student teachers found difficulty in accomplishing school system requirements.
The importance of an advisor or a mentor in guiding inexperienced teachers especially in theirperformance of bureaucratic duties and management of educational activity should not be
underestimated (Yirci et al, 2016). This implies that cooperating teachers realize theimportance of mentoring students and sharing their expertise on content knowledge as well
as strategies. It also indicates that cooperating teachers need to practice reflective approach inteaching so that they could better mentor the student teachers on what teaching strategies work
for a more effective teaching learning situation (Aspfors & Fransson, 2015).
Similarly, the cooperating teachers demonstrated commitment to their mentoring task ofteaching through practice. Cooperating teachers who demonstrated and modeled content
knowledge and strategies enhanced the professional development of student teachers as wellas improved their student teachers’ teaching methods and skills (Liu, Tsai, & Huang, 2015).
The findings imply that the cooperating teachers had provided more mentoring to student
teachers in terms of modeling specifically demonstrating how to teach the subject matter,showing enthusiasm, demonstrating effective classroom management, and establishingrapport with students as well as providing positive feedback and encouraging students topractice reflective teaching.
The study of Sempowicz and Hudson (2012) also affirmed that mentor-mentee’s personal
attributes had significant impact on their mentoring relationship which affected theeffectiveness of the mentors’ feedback and the mentees’ abilities to critically reflect on theirpractices. Student teachers who have positive relationships with their mentors are more likely

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to employ university-taught methods in their classrooms, take their supervisors’ advice, andview their supervisor as very knowledgeable regarding content, methods, and students in the“real” classroom (Asplin & Marks, 2013).
As mentees, student teachers need to learn the teaching strategy of timing within the lesson
structure to promote student interest in learning. Mentors emphasized the need to prepare andmanage resources and aids as well as the ability to solve problems in the classroom such aschanging strategies whenever necessary (Hudson, 2013). In addition, student teachers need to
be provided with feedback and assessment on their student teaching. Student teachers, inreturn, should be open to feedback, be active listeners, and be respectful of their mentor’s
input and time (Straus et al, 2013).
This study is limited to determining the mentoring experiences of the cooperating teachersand student teachers as indicated in the five aspects of mentoring presented by Hudson. The
study focused only on surfacing the mentoring experiences in general and did not explore thecultural practices between mentor and mentees. Web-based learning and e-mentoring may
also be explored in future studies to strengthen the mentoring process.
Aspects Needing to be Addressed
Reinforcing personal attributes through open communication, active listening, andself-learning
The responses of the participants to the structured interview guide questions yielded five
major themes relative to the need for mentoring. The first theme is reinforcing personal
attributes through open communication, active listening and self-learning which concurs withexisting studies on the beneficial effects of mentoring in the respondents’ formation as
prospective teachers. The quality of the cooperating teachers’ relationship with practiceteachers has direct impact on how they enact principles of practice. The positive relationshipand open communication with their cooperating teachers inspires them to reflect on theirpedagogical practices and develop their self-confidence and positive attitudes. Mentormentee’s personal attributes had significant impact on their mentoring relationship which
affected the effectiveness of the mentors’ feedback and the mentees’ abilities to criticallyreflect on their practices. In this theme, mentoring is the process which supports learning
development and improves performance of an individual. It manifests through the mentors’
treating the practice teachers with respect, having an open and motivational consultation,
showing moral and sometimes financial support, sharing ideas and experiences, providing
comfort and boosting one’s confidence and providing time to listen (Hudson, 2016; Hudson& Hudson, 2014; Kemmis et al, 2014; Hudson, 2013; Straus et al, 2013; Ulvik & Sunde, 2013;
Sempowicz & Hudson, 2012).
Providing intensive orientation on educational goals, aims, policies and curricula
The second theme is focused on providing intensive orientation on educational goals, aims,
policies, and curricula. Pre-service teachers need to acquire an understanding of the
expectations, components, goals, and challenges of the student teaching experience. Mentors
need to communicate that educational system has requirements such as aims, policies, andcurricula. The complexities for executing system requirements may be indicated in thepedagogical knowledge mentors must articulate for effective teaching (Aspfors & Fransson,
2015; Asplin & Marks, 2013; Gareis & Grant, 2014; Gorozidis & Papaioannou, 2014;
Hobson, Harris, Buckley Manley, & Smith, 2012). Most of the respondents acquired

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Volume 6 – Issue 3 – Winter 2018knowledge about the school assignments and policies; Department of Education Orders,
policies and requirements, and the what, why and how of the K to 12 curriculum.
Enhancing pedagogical knowledge in teaching
Enhancing pedagogical knowledge in teaching is another theme that emerged from the study.
Mentoring is a deliberate pairing of a more skilled or experienced person with a lesser skilled
or experienced one with agreed-upon goals. The lesser skilled person is assisted to grow anddevelop specific competencies (Menges, 2016; Cakir & Kocabas, 2016). This includes thedevelopment of pedagogical knowledge practices such as planning, preparation, teachingstrategies, questioning skills, assessment and how these practices influence the mentee’s
practice teaching. Willing, capable, and compatible mentors who possess varied expertiseprovide richer and more dynamic mentoring experiences (Gareis & Grant, 2014; Hudson,2012).
Nurturing the development of desirable teaching practices through modeling
The next theme is nurturing the development of desirable teaching practices throughmodeling. Experienced teachers can provide help by providing strategies they developed.
Moreover, student teachers can be guided in the acquisition of knowledge and skills byassigned mentors who model pedagogical practices Hudson’s (2012). Hudson’s study (2007)
revealed that mentors modeled teaching and classroom management, had a good rapport withstudents, and enthusiasm. The theme underscores the importance of mentoring of cooperatingteachers who have a repertoire of effective pedagogical practices and up-to-date curriculum
and professional knowledge to better assist student teachers in pre-service education (Yirci etal, 2016; Kemmis et al, 2014).
Communicating achievable expectations and providing constructive feedbacks
The last theme, which is communicating achievable expectations and providing constructive

feedback, reveals similar insights from the participants. This supports one of the findings of
Sempowicz and Hudson (2012) that “mentors expressed expectations for teaching, modeledreflective practices to their mentees, and provided time and opportunities for mentoring which
would influence the mentees’ reflective practices and their pedagogical development.” The
importance of communicating expectations and giving regular feedback on student teachers’
assessment in their practice teaching are also important concerns cooperating teachers should
provide. Providing professional development for the cooperating or mentor teacher in
preparation for accommodating a student intern is significant. Student interns must havementors who are skilled and experienced in mentoring and who can nurture positive
development toward becoming an effective teacher. Moreover, Teacher Education
Institutions need to ensure that mentor teachers are adequately prepared to model effective
strategies to facilitate the practice teaching experience. The study also reiterated that mentor
teachers understand their role in facilitating the internship experience because their roles are
critical to the development of the student intern (Gareis & Grant, 2014).
Conclusions
In the light of the findings, the following conclusions are drawn:
1. The cooperating teachers mentored to a great extent the student teachers in terms of
personal attributes, pedagogical knowledge, modelling, and feedback.
2. The student teachers need more intensive mentoring on the area of system
requirements.

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Recommendations
The following recommendations are presented:
1. Cooperating teachers should undergo continuing professional education specificallyin terms of mentoring student-teachers along the domains mentioned. A course
specifically on supervision and instruction be included in the curriculum to further
strengthen the cooperating teachers’ mentoring practices and skills.
2. More time for post-conference between the cooperating teachers and student-teachers
be provided to discuss supervisory concerns and address further mentoring needs.

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and mentoring for effective teaching. Australian Journal of Teacher Education, 37(7),70–84. https://doi.org/10.14221/ajte.2012v37n7.1 Hudson, P. B., & Hudson, S. M.
(2014). Mentors’ and mentees’ views about desirable attributes and practices forpreservice teachers. In 5th World Conference on Learning, Teaching and Educational
Leadership, 29 - 31 October 2014, Prague, Czech Republic. (Unpublished)

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Kahraman, M., & Kuzu, A. (2016). E-mentoring for professional development of pre-service
teachers: A case study. Turkish Online Journal of Distance Education, 17(3), 76–89.
Kemmis, S., Heikkinen, H. L., Fransson, G., Aspfors, J., & Edwards-Groves, C. (2014).
Mentoring of new teachers as a contested practice: Supervision, support andcollaborative self-development. Teaching and Teacher Education, 43, 154–164.
https://doi.org/10.1016/j.tate.2014.07.001
Koedel, C., Parsons, E., Podgursky, M., & Ehlert, M. (2015). Teacher preparation programs andteacher quality: Are there real differences across programs? Education Finance andPolicy, 10(4), 508–534. https://doi.org/10.1162/EDFP_a_00172
Langdon, F. J. (2014). Evidence of mentor learning and development: An analysis of New
Zealand mentor/mentee professional conversations. Professional Development in
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Mena, J., Hennissen, P., & Loughran, J. (2017). Developing pre-service teachers' professional
knowledge of teaching: The influence of mentoring. Teaching and Teacher
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Savage, S. L., Cannon, D., & Sutters, J. P. (2015). The yellow brick road to licensure:
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27. https://doi.org/10.1080/00043125.2015.11519326
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https://doi.org/10.1080/13540602.2015.1023031
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https://doi.org/10.12973/eurasia.2016.1440a

Corresponding Author: Susan Galamay-Cachola
Contact email: susangcachola@gmail.com

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Work-Related Factors as Determinants of Self-efficacy and Resilience among Selected
Filipino Child Development Workers

Katherine del Rosario
University of the Philippines Los Baños, Philippines
Nephtaly Joel Botor
University of the Philippines Los Baños, Philippines
Jewely Jean Padilla
University of the Philippines Los Baños, Philippines
Aida Escobin
University of the Philippines Los Baños, Philippines
Daisy Pelegrina
University of the Philippines Los Baños, Philippines

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Abstract
This descriptive associative study focused on work-related factors as determinants of selfefficacy and resilience among 58 purposively-sampled Child Development Workers (CDWs).
It specifically aims to profile the CDWs based on various work-related variables, to determine
their psychosocial needs, resources, levels of self-efficacy and resilience, and to verify the
association and relationship of these variables in relation to self-efficacy and resilience. A
questionnaire, comprised of questions on perceived needs and resources and work-related
aspects, a modified version of Bandura’s (2006) Teacher Self-Efficacy Scale, and Smith et al.’s(2008) Brief Resilience Scale, were group administered to the participants. Thereafter, results
were analyzed using descriptive and correlation statistics. Results showed that respondents
were divided almost equally between the low- and high-scoring groups in both self-efficacy
and resilience. Notably, respondents’ resilience and self-efficacy mean scores were relatively
higher than the expected average score. Correlation further revealed moderate positive
relationship between self-efficacy and resilience (r = 0.333). In terms of work-related factors,
many of those with high self-efficacy and high resilience scores reported also having: (a)supportive barangay officials, (b) cooperative day care children’s parents, (c) satisfaction withpupils, (d) job satisfaction, and (e) life satisfaction in general. As a conclusion, the majority of
the 58 selected CDWs reported satisfaction in work-related factors related to the children theycater to, and also work and life in general. In addition, CDWs who had high scores in selfefficacy and resilience reported having support, cooperation and satisfaction.
Keywords: self-efficacy, resilience, child development worker, early childhood care anddevelopment

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Introduction
Early childhood is a crucial stage primarily because of the rapid development in major
developmental domains: physical, cognitive, and socio-emotional, among others. It is asensitive period of limited duration in which the brain is highly plastic and greatly influenced
by experience (Knudsen, 2004). It is also believed to be at this stage that stimulation provided
to children within this period will have long lasting effects on learning and behavior.
The crucial nature of early childhood underscores the role played by significant persons
involved in child rearing. Among these are the Child Development Workers (CDWs). Morecommonly known as Day Care Workers (DCWs), CDWs play a significant role in the deliveryof early childhood care and development services. They are tasked to supervise the Day Care
Centers (DCCs) in every barangay, while being also called to assist in other community affairs,e.g., disaster response (Quismorio, 2014).
Early childhood care and development (ECCD) service providers, particularly Day Care
Workers, aptly referred to as Child Development Workers as promulgated in the Early Years
Act of 2013 (Philippine Congress, 2012), are key players in holistically addressing the needsof the Filipino child, especially the very young. It is, therefore, necessary to understand theissues relevant to their life and work as child care providers so that support and interventions
can be designed and implemented, geared towards enabling them to optimally function in ademanding working environment and empowering them to develop as individuals andprofessionals in the childcare profession. Unfortunately, there has not been extensive research
about CDWs in the Philippines. Empirical investigations on the plight of Filipino CDWs areavailable, albeit scant, including Abulon’s (2013) survey on the status of barangay Day Care
Centers in the country, Cadosales’ (2011) study on teaching needs of Day Care Workers), andReyes’ (1996) analysis of variables influencing the implementation of day care policy in one
of Philippine regions.
Day care centers in the Philippines, unlike in Western countries, have a quite different identity.
While in most countries the day care is nothing more than a venue in which working parents
can temporarily enroll their children to be supervised while away for work, in the Philippines,
the increasing number of day care centers manifests the country’s commitment to promoting
early childhood education (Abulon, 2013). There are almost 33,000 day care centers in the
country (Quismorio, 2014), under the supervision of the Department of Social Welfare andDevelopment (DSWD), which are manned by CDWs. Apart from their identity as learning
venues for very young children, the DCCs are also a platform for other social services as health,nutrition and safety. This only underscores the multiple and quintessential responsibilities
taken by the CDW.
Unfortunately, while the role of CDWs in the community is apparent and significant, there are
only a few available studies involving or pertaining them (Abulon, 2013; Cadosales, 2011;
Elarco, 2014). The present study envisioned contributing to the discussion on the issues andconcerns faced by CDWs in the country. It desired to contribute to the literature on childdevelopment profession in the Philippines by exploring dimensions of CDWs’ experiences in
the work place. On one hand, it recognized the necessity to look into factors that may promoteor hinder CDWs’ tasks as ECCD service providers. On the other hand, it found meaning inseeing how child care professionals perceive their ability to influence their working
environment, more so, how capable they are in responding to adversities in the workplace.

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With these premises, the study paid attention to two pivotal constructs: self-efficacy andresilience.
Self-efficacy, which pertains to “beliefs in one’s capabilities to organize and execute thecourses of action required to produce given attainments” (Bandura, 1977, p. 3), is deemed as aformidable predictor of behavior (Bandura, 1997, as cited in Henson, Kogan & Vacha-Haase,2001). It has been widely researched in the field of education, more specifically as the contextspecific construct teacher self-efficacy. Defined as “the degree to which teachers believed theenvironment could be controlled” (Gibson & Dembo, 1984, p. 570), teacher self-efficacy isdeemed as “self-regulatory” and as influential to teacher behavior and performance (Gavora,
2010, p. 17). Pre-school teachers who reported high self-efficacy are found to have planningskills, be more enthusiasm in their teaching endeavors, and to be highly innovative, and creative(Kihoro1i & Bunyi, 2017).
In the context of early childhood care and development, self-efficacy has been found toassociate with variables such as child care providers’ desire to stay in the profession, especiallywhen paired with job satisfaction and supervisor support (Chen & Scannapieco, 2010) and job
satisfaction and burnout (Skaalvik, E.M. and Skaalvik, S., 2010). It has also been associatedwith children’s learning outcomes as they learn language (Guo, Piasta, Justice, & Kaderavek,
2010) and with mothers’ psychological outcomes as they balance responsibilities at work andin child rearing (Ozer, 1995). Reciprocally, self-efficacy among childcare providers is
influenced by professional experience, perception of collaboration, and children’s engagement(Guo, Justice, Sawyer, & Tompkins, 2011), as well as professional development (Muñez,
Bautista, Khiu, Keh, & Bull, 2017), among others. Likewise, self-efficacy level is mediated by
the nature of relationship between the parent and the early childhood educator (Chung, Marvin,& Churchill, 2005). In the Philippines, where CDW’s also act as learning facilitators (United
Nations Educational, Scientific and Cultural Organization[ UNESCO], 2007), contextualizing
teacher self-efficacy as applied in the day care situation is fitting.
Resilience is another variable gleaned as essential among professionals working with very
young children. This term has been diversely defined and used in various disciplines, but themost basic of its definitions, which was adapted in the context of this study, is that it pertains
to people’s “ability to bounce back and recover from stress…and return to previous level ofpsychologically healthy functioning” (Boyle, et al. 2014, p. 301, citing Carver, 1998, and Smithet al., 2008 and). Considering the multifold function of the CDW and the arduous nature of thechild care profession (Levy & Poertner, 2014), exploring DCWs’ resilience is substantial in
discovering their needs, concerns and professional satisfaction amidst a stressful work
environment (Hegney, Rees, Elay, Osseiran-Moisson, & Francis , 2015). In the context of childcare profession, a study by Bouillet, Ivanec and Miljević-Riđički (2014), revealed that, at the
average, child care professionals, that is, child care educators, rated themselves as having highlevels of resilience. The same authors further found that those who perceived being highly
resilient also evaluated themselves as capable of fostering resilience among children.
Resilience was found to be quantitatively associated with life satisfaction (Özbey, Büyüktanir,& Türkoglu, 2014), hope and positive behaviors (Hsing-Ming & Mi-Tao, 2008); and,qualitatively, with teachers’ agency, freedom, hope and trust (Sumsion, 2004). In turn,supportive working environment (Bouillet et al., 2014) and capacity building activities such as
training (Hraha, 2012) were found to be promoters of resilience.
By and large, it can be gathered from the brief review of empirical findings that there areinteractions between self-efficacy and resilience, and other work-related factors. The present

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study, with the goal of augmenting gaps in understanding the experiences of CDWs in the
country, explored the profile of selected Filipino CDWs in terms of work-related factors
deemed as reciprocal determinants of efficacy and resilience among childcare professionals
and illustrated the nature of associations between and among self-efficacy, resilience, and thesework-related factors. Specifically, it attempted to answer the following research questions:
1. What is the profile of the selected CDWs in terms of perceived barangay support,
perceived parental cooperation; and satisfaction about the physical workplace, thechildren they cater to, their job, and their life in general?
2. What are the psychosocial needs and resources of the CDWs?
3. What are the levels of self-efficacy, its subscales, and resilience among the selected
CDWs?
4. How do work-related variables associate with self-efficacy and resilience?
5. What is the nature of relationship between self-efficacy and its subscales, andresilience?
By answering these research questions, the study hopes to contribute to the scant literature on
day care in the Philippines and to provide insights towards the development andimplementation of initiatives to empower and build capacities of CDWs.
Methods
Research Design
The study employed descriptive associative method to depict the profile of work-related
factors, levels of perceived self-efficacy and resilience; and, the nature of associations among
the study variables.
Study Participants
Fifty-eight (58) CDWs who were participants to a capacity building program for DCWs were
purposively selected as respondents in this study.
Scope and limitations
This study was only limited to CDWs from selected areas in Southern Tagalog. It only focusedon the respondents’ perceived self-efficacy and resilience in relation to their work experiences.
Since the sample was not randomly selected, correlation coefficients were used as descriptive
statistics.
Measures
The study used a questionnaire consisting of: agreement/disagreement questions on barangay
support, parent cooperation, and satisfaction with physical environment, children, job, and life;a modified version of Bandura’s (2006) Teacher Self-Efficacy Scale (alpha of .93 in thissample); and Smith, et al.’s (2008) Brief Resiliency Scale (alpha of .84 to .90, in Smith et al.,2008; alpha of .59 in this sample).
Data Gathering and Analysis Procedure
The questionnaires were group administered among the respondents in one of the capacity
building sessions they attended. Ethical implementation of the study was ensured by making
sure that informed consent was sought and the significance and use of the survey werediscussed. Participants were informed about the nature and purpose of the study and theiridentities were not revealed in any part of this report to ensure anonymity and confidentiality.

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After data collection, data were analyzed using descriptive and correlation statistics.
Associations between work-related factors and the levels of self-efficacy and resilience were
described using cross-tabulations. Relationship among the summative scores on the selfefficacy and resilience scales was on the other hand explored using Spearman rank-ordercorrelation.
Results, Interpretation, and Discussion
Profile of the Study Participants across Work-Related Factors
The first research question inquired about the profile of the study participants in each of thework-related factors explored in this study, especially on areas of support, cooperation andsatisfaction. To address this research problem, a set of agreement/disagreement questions wereasked so that respondents can express assent or dissent based on their experiences. Table 1
presents the frequency of respondents’ distribution according to their agreement/disagreementon the various work-related factors.
Table 1 : Agreement or disagreement on attitude questions on work-related variables
Work-Related Variables
Supportive barangay officials
Cooperative parents
Satisfying physical working environment
Satisfying day care pupils
Satisfying job
Satisfying life, in general

Agree/Yesf (%)
39 (67.2)
37 (63.8)
31 (53.4)
46 (79.3)
55 (94.8)
57 (98.3)

Disagree/Nof (%)
19 (32.8)
21 (36.2)
27 (46.6)
12 (20.7)
3 (5.2)
1 (1.7)

As reflected in Table 1, almost all of the respondents perceived themselves as having asatisfying job and a satisfying life. A large number also shared being satisfied with the children
they cater to in the day care. These results aligned with the findings of Abulon (2013), which
revealed that despite the lack of monetary rewards, Filipino DCWs manifest high satisfaction
with their work and life as child development service providers.
Interestingly, while the majority of the respondents were on the affirmative side, they were
somewhat divided in terms of perceived support from barangay, of perceived cooperation of
day care children’s parents, and of their satisfaction with the physical workplace (i.e., physicalstructure of the DCC). This implies that the CDWs’ experiences on the supportiveness andcooperativeness of stakeholders in their community tend to be variable. Although there weremore who perceived support and cooperation, a nearly equal number of respondents perceived
otherwise. This entails that support and cooperation have to be further explored on a case-tocase basis such that conducting local researches at the barangay-level might provide additional
knowledge about the dynamics between and among ECCD stakeholders. It must be noted that
Philippine ECCD policy urges parents and local government officials to prioritize the provisionof needs among the very young both through home-based and center-based programs(Philippine Congress, 2012). Gaining insights on how stakeholders work towards achieving
ECCD goals is favorable.
Another salient point that can be gleaned from the results is the noticeable divide among the
respondents regarding their satisfaction about the physical structure of the DCCs. Studies haveshown how significant the physical environment is in learning and in the holistic development

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Volume 6 – Issue 3 – Winter 2018of the child (World Health Organization, 2004; Higgins, Hall, Wall, Woolner & McCaughey,2005). Interestingly, improving the built environment also demands communication andcollaboration among stakeholders that include parents and community officials (Higgins, et al.,2005; World Health Organization, 2004). Hence, it might be an interesting direction to see in
future research how levels of support and cooperation relate to, or even predict, satisfying
physical environments in center-based programs such as day care, and, probably, how asatisfying physical environment relates to DCWs’ and day care children’s educational
outcomes. Appropriate attention and planning of the classroom are needed for the learning tomeet its goals and needs (Puteh, et.al, 2015).
Perceived Psychosocial Needs and Resources
The second research question explored the perceived needs and resources of day care workers.
In general, the study found that needs and resources pertain to any of the following dimensions:financial, material/physical, knowledge, people, and/or personal traits.
Perceived Needs. Respondents were asked to identify needs in order of priority by answering
the question: What are your needs as a Day Care Worker? Responses were content analyzed
and there were four major categories identified: physical/material, financial, knowledge andpeople. Strings of statements by the respondents were tallied per category, the frequency
distribution of which is presented in Table 2.
Table 2: Frequency distribution of respondents according to perceived topmost needs
Topmost need
f
%
28
48.28
16
27.59
9
15.52
3
5.17
2
3.45

Category of
Needs
Physical/Material
Financial
Knowledge
PeopleNo response

Physical/Material. Statements pertaining to physical/material needs appeared with the highest
frequency in all levels of priority. More than 48% of the respondents indicatedphysical/material needs as their topmost need. Needs which pertained to physical/material
needs that were identified by the respondents were learning materials, school supplies, toys,books, paintings/murals, DCC classrooms, kitchen, playground, sound system, blackboard,
comfort room, and renovation of classroom.
Financial. Some 27.59% of the respondents noted as their first priority statements, whichpertain to financial/fiscal needs. Examples of actual statements falling under this category as
written by participants are as follows: money, budget, financial assistance, and honorarium.
Knowledge. Almost 16% of the respondents identified as their first priority needs that fall under
the Knowledge category. Examples of actual statements written by participants and which fallunder this category are as follows: seminars, more knowledge, learning, how to do first aid,
and techniques how to handle children. The present study did not ask in detail what kind of
training the respondents wanted to have although behavior management and first aid
administration were specifically identified. Managing children’s learning and behavior wasamong the problems in day care development programs suggested by Elarco (2011). On theother hand, the study of Cadosales (2011), identified content delivery, specifically “teaching

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Volume 6 – Issue 3 – Winter 2018the letter sound” (p. 252), as among the activities for enrichment needed by child development
workers. Child development workers seem to recognize the need for higher education, training
and expertise (Boyd, 2013) to be able to perform their roles better. Early childhood educators
are concerned in many different complex skills needed in teaching as well as looking atdifferent issues in their students’ speech, language, behavior and others.
People. A category that was least mentioned by respondents as a first priority but which cameas a second frequently mentioned need as a second and third priority, was People. Interestingly,
while only a little more than 5% of the respondents identified needs related to people as theirfirst priority, approximately 12% of them noted it as a second priority while almost 7% of themnoted it as a third priority. People-related needs mentioned by respondents were cooperationof parents and barangay officials.
Perceived Resources. Likewise, respondents were asked to identify their topmost resourcesby answering the question: What resources do you have? Content analysis of the responses
revealed four major categories: physical/material, people, financial and personal traits, aspresented in Table 3.
Table 3: Frequency distribution of respondents according to perceived topmost resources
Topmost need
f
%
15
25.86
13
22.41
8
13.79
6
10.34
16
27.59

Category of
Resources
Physical/Material
PeopleFinancial
Personal Traits
No response

Physical/Material. Statements pertaining to physical/material resources appeared with thehighest frequency as the topmost resource (26% of the respondents). Responses that pertained
to physical/material resource as identified by the respondents were recycled materials, oldmaterials, day care center, surroundings, visual arts, TV and comfort room.
People. Garnering the second highest frequency as a topmost resource identified by 22.41% of
respondents, people appeared to be a quintessential resource among CDWs. Mother, child,parents, barangay officials, barangay captain, CDW, and DSWD officials were among thepeople-related resources identified by the respondents.
Financial. With the third highest frequency as a topmost resource noted by almost 14% of the
respondents, financial resource was also mentioned. Among the responses which fell under thiscategory were personal income, parents’ contribution, and registration/entrance fee.
Personal Traits. While only noted by 10.34% respondents as a topmost resource, personal traits
were also among the notable resource worth mentioning. Among the personal traits identified
as a resource were helpful, talent in being a mother, inner strength, political will, willingness
to learn, experiences, generosity and cooperation.
Levels of Self-Efficacy, Self-Efficacy Subscales, and Resilience
The third research question in this study focused on the levels of self-efficacy and resilienceamong the respondents. Table 4 shows respondents’ frequency distribution as well as the cross-32

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tabulation between self-efficacy and resilience levels. Low level in a variable was defined ashaving a score on or below the mean (<M) while high level was defined as having a scoreabove the mean (>M), as determined throught the samples’ distribution. Table 5, on the other
hand, presents the mean scores of the study participants in self-efficacy, self-efficacy subscales,
and resilience.
Table 4: Cross-tabulating self-efficacy levels with resiliency levels
Resilience
Level (R)
Low
High
Total

Self-Efficacy Level (SE)
Low
High
f (%)
f (%)
TOTAL
14 (24.1) 13 (22.4)
27 (46.6)
13 (22.4) 18 (31.0)
31 (53.4)
27 (46.6) 31 (53.4)
58 (100)

Frequency distribution shows that respondents were divided almost equally between the Low
and High Scoring groups in both self-efficacy and resilience. Similarly, cross-tabulation showsthat categorizing respondents to self-efficacy levels while considering resilience levels, also
divided the respondents nearly equally on four groups: High Efficacy-High Resilience (31%),
Low Efficacy-Low Resilience (24.1%), High Efficacy-Low Resilience (22.4%) and, Low
Efficacy-High Resilience (22.4%).
Table 5: Descriptive statistics on participants’ self-efficacy and resilience scores
Study variables
Self-efficacy (SE)
Efficacy to influence decision-making
Instructional efficacy
Disciplinary self efficacy
Efficacy to enlist parental involvement
Efficacy to enlist community involvement
Efficacy to create a positive school climate
Resilience

Range ofexpected scores30-150
2-10
9-45
3-15
3-15
4-20
8-40
6-30

M

SD

121.65
8.45
36.66
12.79
12.33
13.36
34.17
20.88

13.30
1.01
4.51
1.48
1.85
2.96
4.52
3.36

Viewing levels of self-efficacy and resilience using measures of central tendency, however,revealed that, at the average, respondents had a mean Self-Efficacy score of 121.65 (SD=13.30)
and a mean Resilience score of 20.88 (SD=3.36). These were relatively higher than the
expected average score in the scales, which is 90 for self-efficacy and 18 for resilience. The
same was true for all the self-efficacy subscales where respondents gained mean scores higher
than the expected average scores in the subscales. Perception of high resilience among childcare providers has already been mentioned by Bouillet, Ivanec and Miljević-Riđički (2014).
What the current finding offers as a novel insight is how the CDWs perceived their selfefficacy. Apparently, the respondents believed that they are highly self-efficacious, in general,and even in aspects of decision-making, instruction, discipline, encouraging community andparental support, and creating a positive climate in the day care. These results may gain lightwhen viewed against earlier findings associating self-efficacy and life and job satisfaction(Chen & Scannapieco, 2010). Although there was no attempt in the present study to33

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Volume 6 – Issue 3 – Winter 2018sophisticatedly associate satisfaction and efficacy, it must be noted that the respondents
reported high satisfaction about their job and life, and this might just link with their high levelof self-efficacy and vice versa. Henceforth, an interesting direction to pursue is to further
establish the interaction between work-life satisfaction and efficacy among Filipino DCWs
using elaborate psychometric measures to establish how strong and generalizable thisassociation is.
Work-Related Factors, Self-Efficacy, and Resilience
The fourth research question problematized how the various work-related variables exploredin this study relate with self-efficacy and resilience. Since the respondents were purposively
selected, more sophisticated statistical measures of association could not be employed. Hence,descriptive cross-tabulation was used to glean interactions that may reveal points of interest in
future studies. Emphasis in the discussion was given on respondents who fell under highefficacy (31 of 58) and high resilience (31 of 58) groups.
Table 6: Cross-tabulating self-efficacy and resiliency levels with perceived support
from barangay
Perceived
Supportfrom Barangay
Unsupportive
Supportive
Total

Self-Efficacy
Level (SE)
Low
High
Total
f (%)
f (%)
9 (15.5) 10 (17.2) 19 (32.8)
18 (31.0) 21 (36.2) 39 (67.2)
27 (46.6) 31 (53.4) 58 (100)

Low
f (%)
9 (15.5)
18 (31.0)
27 (46.6)

Resilience
Level (R)
High
f (%)
10 (17.2)
21 (36.2)
31 (53.4)

Total
19 (32.8)
39 (67.2)
58 (100)

Notably, 21 of 31 or some 68% of those with high level of self-efficacy, and 21 of 31 or some
68% of those with high level of resilience perceived officials in their barangay as supportive.
Policy-wise, the Philippine government, through the Republic Act No. 6972, otherwise known
as Barangay-Level Total Development and Protection of Children Act, (Philippine Congress,
1990), stresses the involvement of the barangay, in the promotion of child care anddevelopment goals, through the establishment of the DCCs, now referred to as the childdevelopment centers. The immediate community is a source of support to the caregivers incommunity-based childcare center, which, in turn, serves as a doorway for the provision ofother social services that relate to health, nutrition, water and sanitation and the like (Munthali,
Mvula, & Silo, 2014). Hence, when the child development worker deems barangay officials as
supportive to the projects and activities of the DCC, the former becomes more confident thats/he will be able to achieve the desired outcomes for the day care, and will also be spiritedenough even when faced with challenges.
Notwithstanding the role the barangay plays in teacher self-efficacy and resilience, Reyes(1996), in an evaluation of the implementation of day care policy in the Philippines, however,suggested that looking at smaller units such as “purok” or zones, rather than the barangay, as
target areas for the creation of DCCs might make the day care program more manageable. From
this, it can be speculated that, perhaps, barangay governments’ cooperation with the day care
worker might also be challenged by confounding priorities, which can possibly be addressedby engaging a smaller group of families to own the day care program and be deeply involved
in it. This leads to the issue of parental cooperation.

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Table 7 : Cross-tabulating self-Efficacy and resiliency levels with perceived parental
cooperation
Perceived
Parental
Cooperation
Uncooperative
Cooperative
Total

Self-Efficacy
Level (SE)
Low
High
Total
f (%)
f (%)
11 (19.0) 10 (17.2) 21 (36.2)
16 (27.6) 21 (36.2) 37 (63.8)
27 (46.6) 31 (53.4) 58 (100)Resilience
Level (R)
Low
High
Total
f (%)
f (%)
10 (17.2) 11 (19.0) 21 (36.2)
17 (29.3) 20 (34.5) 37 (63.8)
27 (46.6) 31 (53.4) 58 (100)In this study, the majority of the respondents with high efficacy and with high resilience also
perceived that parents in the day care were cooperative (21 of 31 or appx. 67%, and 20 of 31
or appx. 65%, respectively). There was no available literature explaining this result in thecontext of CDWs’ work. However, studies on elementary school teachers noted that there ispositive relationship between teacher self-efficacy and parent support (Stipek, 2012). On theother way around, a study among preschool teachers noted that the teacher also has to strategize
to enhance parental involvement, thereby also making parents more self-efficacious (Pelletier& Brent, 2002). Most importantly, the day care worker and the day care parents have to
dialogue and level off in terms of what parental involvement means, as a study also showedthat parental involvement is perceived differently by various stakeholders (Herrell, 2011).
Table 8. Cross-tabulating self-efficacy and resiliency levels with perceived satisfaction

Satisfaction
Physical structureof the DCCs
Children being
catered to
Job as a day care
worker
Life, in general

Perceived
Satisfaction
Unsatisfied
SatisfiedUnsatisfied
SatisfiedUnsatisfied
SatisfiedUnsatisfied
SatisfiedSelf-Efficacy
Level (SE)
Low
High
f (%)
f (%)
12 (20.7)
15 (25.5)
15 (25.9)
16 (27.6)
7 (12.1)
5 (8.6)20 (34.5)
26 (44.8)
0 (0)3 (5.2)27 (46.6)
28 (48.3)
0 (0)1(1.7)
27 (46.6)30(51.7)

Resilience
Level (R)
Low
High
f (%)
f (%)
12 (20.7) 15 (25.5)
15 (25.9) 16 (27.6)4(6.9)
8(13.8)23 (39.7) 23 (39.7)
1(1.7)
2 (3.4)26(44.8)29(50.0)0 (0)1(1.7)
27 (46.6) 30(51.7)

Most importantly, many of those who fell into the group with high efficacy and high resilience
also perceived being satisfied in various aspects of their work life such as with the pupils theycater to (26 of 31, and 23 of 31, respectively), with their job (28 of 31, and 26 of 31,
respectively), and with their life in general (30 of 31, and 30 of 31, respectively). Skaalvik,
E.M. and Skaalvik, S. (2010) gleaned that job satisfaction links well with self-efficacy,
especially among teachers. Resilience has also been deemed as associated with job satisfaction
among nurses (Matos, Neushotz, Griffin, & Fitzpatrick, 2010), and happiness among preschool
teachers (Mojdegan, Moghidi, & Ahghar, 2013). In the study among preschool teachers,
Yousofi, Rezaei, and Yonesi (2014) found that self-efficacy is a correlate and predictor of job
satisfaction. Remarkably, they also found job motivation as related to efficacy. In the case of
the CDW, it may be interesting to further explore what are their motivations at work, whichmake them highly satisfied. In the present study, being with children was gleaned as satisfying.

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It might be explored more comprehensively in succeeding research as a potential job motivator
associated with efficacy and resilience, especially that caring for others, i.e., the children one
caters to, is deemed as a source of well-being for teachers (Nilsson, Ejlertsson, Andersson, &Blomqvist, 2015).
Having gleaned the profile of work-related factors vis-à-vis levels of self-efficacy andresilience, it can be said that, in this particular study, highly efficacious and resilient CDWs arecharacterized as having: supportive barangay officials; cooperative day care children’s parents;
satisfaction with pupils; job satisfaction; and life satisfaction in general.
Intercorrelations among Self-Efficacy, Self-Efficacy Subscales, and Resilience
The fifth and last research question in this study desired to describe the nature and magnitudeof association between and among self-efficacy, its subscales, and resilience. After testing forassumptions, correlation was calculated using the Spearman rank-order technique. Table 9
presents the correlation coeffecients derived from this analysis.
Table 9: Correlations among self-efficacy, self-efficacy subscales, and resilience
Study Variables
DM IE
DE
PI
CI
SC Resilience
Self-efficacy (SE).592 .896 .660 .729 .678 .902
.333
Efficacy to influence decision-making.496 .219 .318 .383 .545
.119
(DM)
Instructional efficacy (IE)
.579 .624 .425 .789
.274
Disciplinary self-efficacy (DE).547 .399 .544
.201
Efficacy to enlist parental involvement.441 .580.308(PI)
Efficacy to enlist community
.474
.252
involvement (CI)
Efficacy to create a positive school.358
climate (SC)Correlation analysis revealed that self-efficacy has a positive moderate correlation withresilience (r = 0.333), such that along with the increase in the respondents’ belief that s/he isable to perform tasks as a child development worker was also an observable increase in her/his
perceived ability to bounce back from adversities, vice versa. This result supports the earlier
findings of Mojdegan, Moghidi, and Ahghar (2013), which established significant relationshipbetween self-efficacy and resilience among preschool teachers in Iran, and the results of thestudy of Kusma, Groneberg, Nienhaus, and Mache (2012), which revealed positive correlation
between self-efficacy and resilience among early childhood educators.
According to Bandura (1997, 1977), self-efficacy predicts behaviors. When understood fromthe context of CDWs who are faced with various responsibilities, having a belief that one iscapable of influencing various dimensions of work and profession (self-efficacy) may benecessary in reassuring oneself that one is capable of thinking, planning and executing
activities that would unlock difficulties and challenges (resilience). Similarly, a childdevelopment worker who perceives oneself as capable of transcending adversities (resilience)
may tend to feel more abled in influencing one’s working environment (self-efficacy).
Exploring specifically on the self-efficacy subscales provides additional information as to how
this link between resilience and self-efficacy might ensue. Among the subscales, efficacy to36

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involve parents (r = 0.308) and efficacy to foster a positive learning climate (r = 0.358) were
the ones which resilience had positive moderate correlation with. This is consistent with theearlier result of the cross-tabulations on self-efficacy and parental involvement. As parents areperceived as more cooperative, the DCW might feel more adequate in influencing parental
involvement, hence, gain confidence in one’s ability to face adversities side by side with the
day care children’s parents. The day care children’s parents might act as social support uponwhich the day care worker can rely in challenging times, especially in concerns related to childcare and development. In conjunction with this parent-CDW relationship is also the ability of
the DCW to establish an environment nurturing enough for collaboration to take place, as wellas, conducive enough for the delivery of effective services for the day care children.
Furthermore, looking at the inter-correlations among the self-efficacy subscales, it could be
gleaned that efficacy to influence decision making had a high positive relationship with
fostering positive school climate (r = 0.545), while low to moderate positive relationship withthe rest. Hughes and Pickeral (2013) noted that positive school climate is a shared responsibility
among the different stakeholders. Hence, a DCW who finds oneself efficacious in making
decisions,that is, having sufficient share of power and responsibility within the working
environment,may also tend to feel efficacious in promoting a positive and conducive climate
in the day care environment.
Instructional self-efficacy, on the other hand, had high positive relationship with most of thesubscales, with correlation coefficients ranging from 0.579 to 0.789, except with community
involvement with which the relationship was moderate positive. Disciplinary self-efficacy also
had moderate to high positive relationship with several subscales, except with influencing
decision-making (r = 0.219) and encouraging community involvement (r = 0.399), with which
it had low to moderate correlation.
In the Philippines, the day care does not only act as platform for health and psychosocial
services but also serve as a learning platform (UNESCO, 2007). This reality, perhaps, makes
instructional and disciplinary efficacy a quintessential dimension of DCWs’ general selfefficacy. The DCCs mends the gap that families’ inability to afford pre-school education
creates, which motivates the CDW to focus on teaching, among other tasks. The high degreeof association between instructional and disciplinary efficacy with the rest of self-efficacy
subscales might insinuate that CDWs’ efficacy in other dimensions strongly goes along with
their efficacy in facilitating learning and managing children’s behaviors. This insight fits wellwith the findings of Cadosales (2011), which underscores CDW’s expressed need for activities
that will train them become better in teaching, specifically in “teaching strategies, productionof instructional materials, and pedagogy” (p.247).
Finally, efficacy in encouraging parental involvement, encouraging community involvement,
and fostering positive climate all had moderate to high positive relationship with othersubscales, with coefficients ranging from 0.318 to 0.789. As constantly manifested inaforementioned discussions, the DCWs self-efficacy links with their ability to mobilize
parents, families, and the community to take part in promoting care and development of thevery young.
Summary, Conclusion, and Recommendations
In conclusion, this descriptive study established that, in this particular group of selected CDWs:

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1. The majority reported satisfaction with the children they cater to, their work, and theirlife in general.
2. There was no distinct consensus regarding their perceptions on barangay support,
parental involvement, and satisfaction with the physical structure of the day care.
3. The majority of highly efficacious and highly resilient CDWs also perceived having
support, cooperation, and satisfaction.
4. Self-efficacy and resilience are moderately and positively correlated.
The sparse literature about CDWs in the Philippines made it challenging for this study to situate
the analysis of its findings against the backdrop of real experiences from Filipino DCWs.
Insights from allied areas such as early childhood education, taking heed from what is known
regarding self-efficacy and resilience of preschool teachers and other childcare providers (e.g.,nurses), however, made it somewhat possible to clarify the nature of association among thestudy variables and its implication on the circumstances of CDWs. Insights from the limited
local literature on day care programs were also deemed meaningful. Hence, this study highly
recommends that considerable research attention be given to child development programs andits stakeholders in the Philippine setting. Both qualitative and quantitative empirical
explorations would be meaningful in bridging the knowledge gap. Validating the outcomes ofthis study by having a larger and randomly selected sample, alongside conducting in-depth
qualitative investigations, might also provide a more comprehensive and holistic overview on
CDWs’ efficacy and resilience and on CDWs’ lived experiences, in general.
Taking into consideration the outcomes of this study, several opportunities emerge in bothresearch and extension activities with and for CDWs. Exploring signature strengths of CDWs
might be meaningful. While there was a mention of a few personal traits as resource, there
remains to be more focus on external resources, i.e., physical/material, financial, people, which,although are equally important, might come in arbitrary forms and amount. There is a need tosolidify internal resource capabilities such as values, character and ego strength so as to dispose
the CDWs for the grueling duties demanded from them.
Capacity building, particularly in resource generation and inter-agency collaboration, may help
CDWs exhaust all potential fiscal support within the community. Integrating exercises to
explore inner strengths might also be a direction in extending support among CDWs. At theforefront of the government’s thrust to strengthen ECCD at the local level is the CDW patiently
steering the program’s rudders on a daily basis. Providing the CDWs with opportunities toenhance their efficacy and resilience by providing them with more opportunity to take part indecision making in the day care, encouraging active parental and community involvement anddesigning and implementing capacity building programs that would enhance their skills inpedagogy and child care, might just promote their confidence in their field as professionals,
and might just inspire them to continuously become better as caregivers to the Filipino child.

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Corresponding author: Katherine Del Rosario
Contact email: kedelrosario@up.edu.ph42

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Role of Educational Technologies Utilizing the TPACK Framework and 21st Century
Pedagogies: Academics’ Perspectives
Tirtha Goradia
Endeavour College of Natural Health, Australia

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Abstract
With the rapid development in information technology and the need to acquire 21st century
skills, global trends in higher education are shifting towards using digital pedagogies. In lightof this, Koehler and Mishra (2009) developed the Technological Pedagogical Content
Knowledge (TPACK) framework to integrate technology with teaching. The framework has
now been explored and implemented in various educational institutions. This study aims tocollect academics’ perspectives on various technologies and pedagogies used at the institutethrough the lens of the TPACK framework. A mixed-methods study, using a survey-basedquestionnaire, was undertaken to collect academics’ perspectives. The study revealed a wide
range of technologies and pedagogies being used to enhance 21st century competencies andskills. The TPACK framework provides a useful tool to gauge the learning environment anddisplays a complex interaction between technology, pedagogy and content knowledge specificto the learning environment. The results highlight the need to use technology for innovationand to renovate contemporary teaching practices for 21st century learning.
Keywords: TPACK, pedagogies, technologies, higher education, 21st century

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Introduction
The last decade has seen an exponential increase in the use of information technology within
the Higher Education (HE) sector. Most higher education institutions have incorporated
teaching with technology to enhance the learning experience for students. New learningtechnologies are being implemented with the aim of enhancing student engagement andacademic outcomes. With the development in information technology, current trends in HE are
now embracing a wide range of technologies which include, but are not limited to, discussion
forums, simulation, virtual reality, webinars, wiki space, Kahoot, as well as social media
technologies.
Further, as a result of globalization in HE and the demands of current knowledge age, the needsof the 21st century learners have been changing. 21st century pedagogies have therefore
stemmed from this need to provide learners with the opportunity to develop necessarycompetencies and skills to meet the current demands. Global trends in HE are therefore shifting
towards digital pedagogies. According to Lai (2011) digital technologies can enhance learning
experiences when used as a medium to support collaboration and construction of knowledge.
This paper reviews the TPACK framework as developed by Koehler and Mishra (2009). The
TPACK framework was introduced with the aim of integrating technology into teaching. The
framework involves a complex interaction among three major components: content, pedagogy,
and technology. TPACK studies have been explored in various educational institutions to study
the relationship between technology and pedagogy with opportunities, as well as challenges,
having been identified in the process. The TPACK model has allowed for increase in bothstudent engagement and collaboration, as well as flexibility in learning (Lye, 2013). Academics
considered the TPACK framework as a heuristic for exploring the dynamic elements for
effective teaching with technology (Glowatz & O'Brien, 2017). On the other hand, academics
perceive limitations with specific tools in terms of design and usage and have raised concernswith use of TPACK framework in the context of specific tools such as social networking sites(Glowatz & O'Brien, 2015, 2017; Lye, 2013).
This study evaluates academics’ perspectives on various technologies and pedagogies that are
being used at the institute to determine whether they contribute to 21st century learning. The
data would allow academics to rethink about digital technologies and how this can improvelearning experiences.
Literature Review
Shulman (1986, 1987) described the categories of knowledge that a teacher requires to promotecomprehension among students. In particular was the knowledge of content and pedagogy thatblended together to create a flexible learning environment for diverse groups of students. The
TPACK framework builds on Shulman’s work to include technology for effective teaching andemerges from interactions among content, pedagogy, and technology Koehler and Mishra(2009).
Components of TPACK framework
The three main components of the framework are: Content knowledge, Pedagogicalknowledge and Technological knowledge. See Figure 1.

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Figure 1. The TPACK framework and its knowledge components (Koehler & Mishra, 2009)
Content knowledge involves the lecturers’ grasp on the subject content. This would include
scientific facts, theories, evidence-based reasoning as well as discipline specific practices.
Pedagogical knowledge involves lecturers’ knowledge about teaching and learning. This
includes ways of representing and formulating the subject content that make it comprehensible
to others (Shulman, 1986). Technology knowledge involves understanding technology for
information processing, communication, and problem solving (Koehler, Mishra, & Cain,2013).
A complex interaction between the three domains gives rise to an additional three components:
pedagogical content knowledge, technological content knowledge and technologicalpedagogical knowledge (see figure 1.) Technological cntent knowledge refers to lecturer’s
knowledge on use of appropriate technology in order to communicate the content material
within specific discipline. Pedagogical content knowledge includes appropriate methods ofteaching to convey a specific content. Here the teacher knows the subject matter and uses
different ways of representing it. Technological pedagogical knowledge demonstrates how aparticular technology enhances teaching and learning. Technology can be used differently tosuit the context and purpose.

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The framework has been implemented in various educational institutions with most of them
reporting average levels of technology integration in their teaching and learning process. The
studies have identified further need for improvement in technological, pedagogical and content
aspects of teaching and learning skills (Benson & Ward, 2013; Lye, 2013). While someeducators emphasize technology over pedagogy, others prefer pedagogical knowledge overtechnology for an effective TPACK implementation. In contrast, other studies have foundimprovement in students’ knowledge and skills especially within the science domain(Sheffield, Eva, Gibson, Mullaney, & Campbell, 2015). In general most educators believe
competency with TPACK as a core attribute essential for professional development in theteaching and learning environment.
21st Century
The need for 21st century learning and skills. Today’s world faces challenges such as
climate change, socio-economic inequality, unemployment, globalization, and cultural
diversity. “The 21st century is volatile, uncertain, complex and ambiguous” (Acedo & Hughes,
2014, p. 504). Additionally, with the development in information technology there is a growing
need to keep abreast with technology. Educators, therefore, highlight the importance ofrestructuring education system such as to prepare 21st century learners to face these complex
challenges. Educators as well as the public support the notion that higher-order thinking skills
are essential to face these complex issues and involve creativity, critical thinking, collaborationand lifelong learning (Acedo & Hughes, 2014; Sacconaghi, 2006; Scott, 2015)
21st century skills. The Framework for 21st Century Learning (Partnership for 21st Century
Learning, 2007) identifies the skills, knowledge and expertise required by students in order tobe successful in the current digital economy. The 21st century skills are categorized as: learningand innovation skills (creativity, critical thinking, problem solving, communication andcollaboration); information, media and technology skills; and life and career skills. These arebriefly described below (Applied Educational Systems, 2018):
Creativity: Allows students to review concepts from a different perspective
which ultimately leads to innovation.
Critical thinking: A skill that allows students to analyze evidence and form
judgement to solve problems.
Problem-solving: Refers to the ability to solve problems in an effective andtimely manner.
Communication: A skill that allows students to effectively convey ideas amongst
peers.
Collaboration: Collaboration means getting students to work together in order tofind solution to a problem.
Information literacy: Understanding facts, figures, statistics, and data.
Media literacy: Understanding the methods in which information isdisseminated.
Technology literacy: Understanding of the tools used to disperse theinformation.
Life and Career skills: Allows for personal and professional growth thereby
leading to lifelong learning.
21st century pedagogies. Several educators have highlighted the significance of 21st century
pedagogies (Acedo & Hughes, 2014; Breslow, 2015; Kivunja, 2014, 2015; Scott, 2015). These47

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are the pedagogies that develop higher-order thinking skills, metacognitive skills, and support
deeper learning through collaboration.
Unver and Arabacioglu (2014) have reflected on inquiry-based learning (IBL) and problembased learning (PBL) as pedagogies that can solve current age challenges through theacquisition of problem solving skills. The authors identify IBL as a learning activity whereby
learners acquire knowledge from direct observations by using deductive questions. They
further differentiate it from PBL as a learning activity whereby learners learn throughinvestigation, explanation and resolution of meaningful problems. Similarly, Snow and Torney(2015) through their mixed-methods study suggest that inquiry-based learning has the potential
to develop students’ cognitive skills at a higher level thereby promoting problem-solving,
critical thinking and leadership skills. Furthermore, empirical research on inquiry-basedlearning reveals enhanced academic performance and student engagement in students using
this pedagogy (Summerlee & Murray, 2010).
Several researchers have reviewed collaborative learning as a pedagogy to facilitate learning(Beccaria, Kek, Huijser, Rose, & Kimmins, 2014; Fakomogbon & Bolaji, 2017; Scott, 2015).
In particular, the study by Fakomogbon and Bolaji (2017) revealed that collaborative mobile
learning through portable devices or smartphone could enhance motivation, academic
outcomes, and engagement through sharing knowledge, group discussions, and groupassessments with group members. According to Scott (2015), learners through collaborative
learning participate in higher-order thinking such as managing, organizing, critical analysis,
problem resolution, and creating new knowledge.
The availability of digital technologies has also generated informal ways of learning to support
formal studies (Kwok-Wing & Smith, 2017). Digital technologies such as laptops, mobile
phones, Google, iPads, tablets, as well as social networking sites such as Facebook and Twitterare quite popular among students and are being used to complement formal learning methods.
Several researchers, therefore, are now investigating the role of informal methods within thelearning space. Kwok-Wing and Smith (2017) suggest incorporating mobile and digitaltechnologies in the formal courses in conjunction with more traditional methods of learning inorder to cater for the diverse learning style of students. Kivunja (2015) recommends higher
education institutions to utilize social media technologies as effective pedagogies to support
effective learning, teaching and assessment in the 21st century. These technologies create
opportunities for experiential learning. The author believes that experiential learning can be avery effective way for cognitive processing as it involves the reciprocal exchange of discourse
amongst students so as to develop a deeper understanding of pedagogical content knowledge.
Yet another pedagogy that has been explored in higher education is constructivist learningwhich emerges from the concepts developed by pioneers, Piaget and Vygotsky. Constructivist
learning allows for students to actively construct their own knowledge in order for learning to
be meaningful and effective (Afify, 2018; Alt, 2017; Asiksoy & Ozdamli, 2017; Noel, 2015;
Scott, 2015). Noel (2015) suggested use of blogs in education to create a constructivist learning
environment that supports knowledge development through student engagement, reflection andcollaboration. Asiksoy and Ozdamli (2017) in their study on education technologies forconstructivist learning found that the most frequently used tool was the computer and the mostcommon platform was learning management system. Additionally, Afify (2018) found digitalconcept mapping an effective tool in support of constructivist learning theory.

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Scott (2015) believes the current educational system with fixed curriculum and delivery
method obscures personalised learning and suggests the need to adopt personalised learningfor 21st century education. This form of learning caters to individual needs of students and canbe achieved by using flexible curricula and learning opportunities (Deed et al., 2014; Scott,2015; Waldrip, Yu, & Prain, 2016). For students, this allows development of autonomy,
motivation, and self-regulation (Deed et al., 2014; Scott, 2015). Emerging technologies suchas mobile applications, e-portfolios, blogs, audios, videos allow implementation of
personalized learning. The shift from traditional educational system to adopting more
personalized learning would require a major cultural change in higher education institutions
and involvement of various stakeholders.
Among recent trends in higher education, flipped learning is gaining much attention as a newpedagogy. In a flipped classroom, lectures are viewed beforehand whilst in the class students
are engaged in more student-centered activities in collaboration with other students, andapplying knowledge (Kyung Hye, Kwi Hwa, & Su Jin, 2018; Sletten, 2017; Zipp, Maher, &Olson, 2017). A flipped environment has the potential to improve student’ motivation to learn,enhance self-directed learning skills, and promote reflection and critical thinking, thereby
enhancing learning (Zipp et al., 2017). However, the flipped learning method has gained mixed
review with some academics reporting success while others facing challenges. Further researchin this area is being conducted to understand the applicability of flipped learning within higher
education institutions.
Rationale
21st century pedagogies should rely on research based pedagogies and learning technologies
through real world contexts. The rationale for this research was therefore to collect academics’
perspectives on various technologies and pedagogies being used at our institute through thelens of the TPACK framework. The data collected through this study will provide a detailed
snapshot of whether the technologies and pedagogies contribute to 21st century competencies
and skills. As graduates from Endeavour College of Natural Health are natural healthpractitioners who would have to engage with the demands of the current knowledge age, theywould need the competencies and skills to face such demands. Therefore this study will also
help in providing data to the college for the development of appropriate skills.
Methodology
The study was conducted at Endeavour College of Natural Health which is a national provider
of complementary medicine in Australia and the departments include Biosciences,
Naturopathy, Nutrition, Acupuncture, and Myotherapy. This involved a mixed methods study
using a survey based questionnaire. The mixed methods research design was considered forthis study as the objective of the study was to gather qualitative and quantitative responses. The
quantitative responses would gather information on the use of technologies and pedagogies
with respect to frequency while the qualitative responses would gather information onacademics’ perspectives. The survey instrument covered technologies that were being used at
the college and covered pedagogies that were discussed in the literature review. As the study
aimed to gather academics’ perspectives, all teaching staff were approached for this research.
Participants involved permanent staff and casual academics across all departments as in figures
2 and 3. An estimate of 100 participants was made based on the current teaching capacity at
the college. Ethics approval was gained from the Human Research Ethics Committee andEndeavour Research Committee. Once ethical approval was obtained, academics were49

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approached and provided with the link to the survey. An information sheet and consent form
was included on the front page of the survey instrument. The survey outlined the aims andpurpose of the study, including a description of what is required of the participant once econsent is obtained. The information sheet also explained that the participation in the survey isvoluntary and if they do not agree to participate they would remain anonymous. Those agreeing
to participate would also remain anonymous. The survey comprised of five sections. Section
A gathered information on participants’ profiles. Section B gathered information on reasonsfor using technology, content taught using this technology as well as any perceived constraints.
Section C explored the Pedagogical Content Knowledge component by framing the researchquestion: How did this pedagogy help in teaching specific content? Section D explored
Technological Content Knowledge by framing the research question: How did this educational
technology/tool best suit to address subject specific content? Lastly section E explored
Technological Pedagogical Knowledge by framing the research question: How did thistechnology enhance student learning?
Participants were provided with a list of pedagogies with a brief description for each. Following
are the 21st century pedagogies identified based on the literature review:
Problem-Based learning - An approach where students acquire the knowledge
through solving the problem. The aim here is to solve the problem. For
example: Case studies.
Inquiry-Based learning - An approach where a problem/scenario is used toincite students to question context, to find information that supports underlying
principles and to reflect upon the wider implications. The aim here is to raisequestions.
Constructivist learning - An approach where students actively construct theirown understanding/knowledge through student-student and teacher-student
collaboration in order for learning to be meaningful and effective.
Personalized learning - Learning is personalized when learners are motivatedto learn as they view the learning task as being engaging and meaningful.
Pedagogies that cater for individuals.
Collaborative learning - A form of social interaction that allows students toshare their ideas and learning experiences, thereby promoting learning
performance of the group as well as of individuals. For example: groupassessment.
Informal learning - As opposed to formal learning, informal learning is interest
driven and occurring incidentally, done in one’s own time or throughparticipation in various social groups using digital and mobile technologies.
Flipped learning - As opposed to a conventional class, flipped learning is anapproach where information is introduced to students before class using
technology (such as mobile devices) thereby allowing more engaging learning
activities during in-class time.
Data Analysis
Pedagogies and technologies were analysed with respect to frequency and qualitative
responses. The data gathered by both research methods was brought together to reveal acomplete picture. The integration occurred during the interpretation of qualitative andquantitative results. Subgroup analysis of qualitative responses was done. The responses toreasons provided for using particular technologies were grouped into following categories:

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Volume 6 – Issue 3 – Winter 2018student engagement, improving retention, collaborative work, notification, flexibility, user
friendly, assessment purposes, learning purposes, national consistency, and teaching purposes.
Results and Discussion
The findings from the survey analysis are presented in this section. The profile of theparticipants in the survey captures representation across both casual and permanent staff andthe time period they have been teaching at the institute. 20 participants responded to the survey.

Figure 2. Position in college

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Figure 3. Duration of time teaching at college
Reasons for using particular technologies
Figures 4 and 5 review reasons for using particular technologies. The qualitative responses on
reasons for using various technologies received from the participants were grouped into
categories such as: student engagement; improving retention; collaborative work; notification;
flexibility to students; user friendly; assessment purposes; learning purposes; national
consistency; and teaching purposes. The results show that a discussion forum was highly rated
by our participants and student engagement was a common reason for using various
technologies. This finding is similar to that found in a research conducted by Glowatz andO'Brien (2017) where discussion forums in the form of the learning management system was
widely used and student engagement appeared as the driving influence for using various
technologies. Online quizzes were considered user friendly as they allowed for automatic
grading thereby saving marking time. An online quiz, discussion forum and wiki forum weretools rated for assessment purposes. Additionally, YouTube, concept maps, virtual patient,screencast recordings, webinar, e Portfolio and Kahoot were the tools considered for teaching
and learning purposes. These are the tools that allow for visual, kinesthetic, and auditory
activities and therefore are important technologies to cater to different types of learners.
Participants were also asked to identify any constraints in using technologies. The reasons werecategorized as tools not being applicable to the discipline, logistic issues, and tools not suitable
to the context. Participants also reported that students often preferred to contact the lecturer
through email as opposed to making any posts on the discussion forum.

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Figure 4. Reasons for using particular technologies

Figure 5. Reasons for using particular technologies
Pedagogical Content Knowledge

Figure 6. Pedagogical Content Knowledge

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Figure 6 represents information on how the pedagogy helped in teaching specific content
including whether it built on prior knowledge; related to real world scenarios; helped inunderstanding abstract phenomena; connect different concepts; and/or allowed problem
solving. Participants were asked to rate the 21st century pedagogies to show how they helpedin teaching specific content.
Our findings reveal that problem based learning, enquiry-based learning and constructivist
learning were highly rated. These are the pedagogies that enhance deeper learning as opposed
to superficial learning and promote higher order thinking skills. In contrast, collaborative
learning had minimal rating. Other research (Dureta, Christley, Denny, & Senior, 2018;
Fakomogbon & Bolaji, 2017; Kivunja, 2015) has shown positive correlation between
collaborative learning and academic outcomes and also emphasized use of collaborative
assessments. Scott (2015) believes that unless knowledge is created, communicated and sharedthrough collaboration, this can stifle creativity and creativity is an important element of 21st
century competencies. Our finding on collaborative learning therefore seems a concern. Ourfindings also show minimal rating for personalized learning and flipped learning. Besides, there
has been significant research on use of social media platforms such as Facebook and mobile
technologies as educational tools. These tools are also popular with students. However, ourresearch has shown contradictory results with no rating for informal learning.
Technological Content Knowledge
The research question framed to address this component of the TPACK framework was: How
did this technology/tool best suit to address subject specific content? Participants were asked
to indicate how technology helped in understanding the content. Figures 7, 8, and 9 revealed
tools used for understanding abstract phenomena, 3D concepts and accessing additional
resources respectively.

Figure 7. Helped in understanding abstract phenomena

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Figure 8. Helped in understanding 3D concepts

Figure 9. Accessing additional resources

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Technological Pedagogical Knowledge
Figures 10 and 11 show responses to how the technology enhanced student learning.

Figure 10. Technological Pedagogical Knowledge

Figure 11. Technological Pedagogical Knowledge
Pedagogical knowledge encompasses knowledge of various teaching strategies; student
learning processes, class management; evaluating student outcomes; and above allunderstanding cognitive, social, and behavioral aspects of student learning. Therefore, ourparticipants were asked to rate various technologies against wide range of pedagogical criteria.
While every technology had a specific role in enhancing learning environment, discussion
forum was the highly rated tool and fulfilled a wide range of pedagogical criteria. This
information could also be discipline specific as the technology tools are related to discipline
specific pedagogies. As the majority of our participants belonged to the Biosciences
department, there could have been an element of bias although similar findings are found inother TPACK related research. TPACK studies conducted over the period from 2002-2011 hadscience and mathematics as major subject domains where TPACK studies were explored (Wu,2013) and this could mean that these are the areas just like Biosciences where technologies arewidely embraced.

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To summarise, the author identified several key observations relevant to each component of
the TPACK framework:
1) Reasons for using particular technologies: A discussion forum on the learning
management system was the most widely used tool and student engagement was thedriving influence behind using these tools. Lecturers also used tools that cater todifferent types of learners and their learning styles. These perceptions relate to studentcentricity and reflect on the innovative use of digital technologies to cater to different
learners.
2) Pedagogical Content Knowledge: Lecturers embrace problem-based learning, inquirybased learning and constructivist learning as pedagogies for 21st century learning.
These pedagogies allow for engaging with the content at a deeper level and promoting
higher-order thinking skills. Survey results reveal limited interest among lecturers inusing collaborative learning, informal learning methods, personalized learning, andflipped learning. These findings generate the need to look at different ways of
introducing personalised learning through informal methods. With the use of emerging
technologies, cultural shift and flexible curriculum there is a potential to enhance 21st
century learning. Providing learning opportunities to collaborate with others withinproblem-based learning and enquiry-based learning can eliminate the resistancetowards collaborative learning to some extent.
3) Technological content knowledge: A wide range of technologies were used especiallyin understanding abstract phenomena which is central to Biosciences learning. A better
understanding of how technologies can be used innovatively to suit other disciplines isessential.
4) Technological pedagogical knowledge: Participants used technologies to understandcognitive, social, and behavioral aspects of student learning. To that end, a discussion
forum was commonly preferred. The knowledge of innovative technologies andpedagogies can help academics to improve student learning. Although technology has
been related to functional fixedness, lecturers need to rethink innovative ways of using
technologies to customize pedagogical purposes (Koehler et al., 2013).
Recommendations
This study captures academics’ perspectives on various technologies and pedagogies used toenhance 21st century learning in higher education. It is worthwhile understanding the rationale
behind this perspective. How do lecturers know that these technologies and pedagogies best
suit to address teaching and learning purposes? Probably, this information can be derived frompersonal experience, student feedback, grades/course outcomes, and monitoring engagement
analytics. Further research is recommended to evaluate how participants know technologies
and pedagogies best suit the purpose. Additional perspective from students’ point of view ontechnologies is suggested to complement the findings of this study.
Study Limitations
With a small number of participants completing the survey, the likelihood of generalizing thefindings of this study is limited. Besides, the majority of the participants belonged tobiosciences department thereby limiting interpretations to other disciplines. Time constraint
could probably also have limited the participation of respondents as the survey was open only
for one month duration of time. Despite these limitations, the results identify various

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Volume 6 – Issue 3 – Winter 2018technologies and pedagogies for 21st century learning and consider the TPACK framework as
an effective tool to develop teaching and learning skills.
Conclusion
This research investigated the role of technologies and pedagogies for 21st century learningfrom academics’ perspectives. Our findings reveal a wide range of technologies and pedagogies
are being embraced at Endeavour College of Natural Health. The TPACK framework provides
a useful tool to gauge the learning environment. The framework highlights a complex
interaction between technology, pedagogy and content knowledge and allows educators to use
innovative technologies and renovation to contemporary teaching practices. The shift towards
digital pedagogies in higher education is looking to contribute to the development of 21st
century competencies and skills thereby preparing learners to face global challenges in thecurrent knowledge age. The literature review showed some academics prefer technologies overpedagogies while others place pedagogies above technologies. This study supported the
dynamic influence between the three components of the TPACK framework: content,
technology and pedagogy. To educators, the TPACK framework serves as a lens through whichteaching practices can be viewed and reflected upon thereby making the learning environment
more conducive to student learning. This study provides the rationale for providing teacherswith further training in the TPACK area.

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Corresponding author: Tirtha Goradia
Contact email: tirtha.goradia@endeavour.edu.au

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Artificial Intelligence and the Student Experience: An Institutional Perspective
Kriti Khare
University of Alberta, Canada
Brian Stewart
University of Eastern Finland, Finland
Anshuman Khare
Athabasca University, Canada

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Abstract
The paper outlines the potential for Artificial Intelligence (AI) to positively impact student
success. This will be approached from a student life-cycle perspective, taking an integrated
view of the student experience and identifying where AI can be most beneficial. Current usagesof AI in education will be considered, in addition to those being experimented with and thosestill being considered. The paper will view the adoption of AI in education from acomprehensive perspective, considering technological, social, political, economic, cultural andethical factors, providing a frame for understanding of the benefits and constraints of the mostintelligent of information technology in the educational realm.
AI has started to emerge in educational institutions in the form of chat bots that are being used
to provide student services as well as providing learning supports. Automated paper grading
has started to be used, while academic advising and assessment are being trialed.
Keywords: higher education, artificial intelligence, Student Experience Practitioner Transitions
frameworks

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Artificial Intelligence (AI) has been increasing in its use in our everyday lives spanning a broad
swath of uses ranging from personal assistants, purchase reference and prediction, smart
homes and cars, fraud detection, online customer support, and even assisting personal
relationships. This increasing use is fueled by the use of machine learning, computer modelling,
and algorithmic creation enabled by ever bigger data sets combined with ever more capable
technological capabilities driven by Moore's Law (Schaller, 1995) and Metcalfe's Law(Hendler & Golbeck, 2008).
The upward and accelerating trajectory of AI, encapsulated in the concept of thesingularity, has drawn both excitement and concern from scientists, economists, and politicaland business leaders. The largest fear is that AI will outsmart its creators allowing the machines
to turn the tables and become the masters, using our psychology to program our behavior.
Further disquiet exists with respect to ethical considerations (Moore, 2006), governance ofappropriate usage (Khatri & Brown, 2010) and to instances where programming bias have beenshown to exist in early deployments of the technology (Devlin, 2017). These concerns are valid
and remain to be addressed, however it is not our intention to pursue these here. We are viewing
real applications of AI to education that are practical and achievable in the near term. More
broad-based sociopolitical and economic issues are not discussed in this paper. Nor are
implications for the curriculum and the almost certain requirement for the incorporation of AI
literacy and information accuracy into all disciplines, lest intellectual laziness yield anunverified trust to systems that were based on their creators’ assumptions.
The student higher education experience can be considered as a series of interdependent,
overlapping, but not necessarily sequential, phases. This life-cycle approach is often used byadministrators to manage student life as it distinguishes the critical elements of experience
allowing the design and delivery of focused administrative services. The student lifecycle inhigher education is defined as the journey of the student from first contact with an institution
through to becoming an alumnus. The ultimate goal of a student is academic achievement
accompanied by self-development through the academic experience. The academic success ofstudents, however, relies on a composite of all aspects of the student’s life. These other aspects
include mental welfare and support, social interactions, sports and physical health, effective
life balance, all of which contribute to the experience the student has in their higher education
career (Morgan, 2013).
Applying a technology into a complex environment, particularly one as traditional as higher
education, is a very challenging endeavour. As with many technologies, the key question iswhere to start, what use case would provide a fair test of the technologies capabilities? The
purpose of this paper is to address this by providing an approach for the coherent adoption of
AI into higher education institutions to lessen both the cost and time for its benefits to beavailable. The use of the student lifecycle and the grouping of activity sets creates target groups
for experimentation and piloting within definable and accepted domains, allowing for effective
hypothesis testing, collaboration and comparison with other institutions. While not wishing tounderestimate the degree of difficulty such a shift may incur, it is reasonable to suggest thatsuch an approach will improve the rates of early adoption and the speed to production.
One model developed to use this framework to understand the student journey, outlining thedifferent stages that a student transitions through during their academic career, is the Student
Experience Practitioner Transitions (SEPT). The model was developed to educate and guidepractitioners about the various kinds of supports students need at each stage (Morgan, 2013).

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In this article, using the Student Experience Practitioner Transitions (SEPT) model developedby Morgan (2013) as a basis, five potential areas in the process where artificially intelligent
systems can be incorporated are analyzed (Figure 1). The functionality that these systems will
perform, the tasks they would take over from the professors, teaching assistants and support
staff, as well as related research, are discussed.

Figure 1. The Student Experience Practitioner Transitions (SEPT) framework (Source:
Morgan, 2018)
The Student Experience Practitioner Transitions model has six stages of student life, each stage
examining the unique position of a student at a certain point of time in his or her higher
education (Morgan, 2013). The stages can be summarized as follows:
1. First Contact and Admissions – At this stage, the student makes an application to an
institution based on his/her interests.
2. Pre-arrival – The student has been accepted to a program and any other requirements
must be completed before the student arrives to embark on the course of study, such asreceipt of final transcripts.
3. Arrival and orientation – Introduction to how the degree will be completed and gettingused to campus life.
4. Induction to study – The first year of education when the student is introduced tocoursework.
5. Reorientation and induction – gaining new skills as one progresses through their degree
by taking advanced courses.
6. Outduction – entering the job market.
By viewing the student experience as a journey consisting of integrated steps one can use AI
to analyse the steps and determine interdependencies between them to develop an integrated
model of student behaviour. This model can greatly assist the understanding of behavioural
determinants that impact students throughout their lifecycle. It further provides a classification
for data sets that are available for model and algorithmic development.

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The Artificial Intelligence Process
AI is a broad field that is comprised of many disciplines including computer science, statistics,
linguistics, psychology, and decision science. It is essentially concerned with getting acomputer to replace human intelligence in assigned tasks. Given the breadth of the field it is
not surprising that there are quite a few definitions of AI. In addition these are non-constant as
the capabilities develop. What was once considered AI begins to be seen as algorithmic
development or big data analytics. A commonly accepted breakdown is to view AI as theoverarching rubric which encompasses Machine Learning, which further encompasses Deep
Learning. Rich and Knight (1991) state that “Artificial Intelligence (AI) is the study of how tomake computers do things which, at the moment, people do better” (p. 3).
The definition of Artificial Intelligence, as stated in the first Volume of the Handbook of
Artificial Intelligence is that “Artificial Intelligence (AI) is the part of computer science
concerned with designing intelligent computer systems, that is, systems that exhibit thecharacteristics we associate with intelligence in human behavior - understanding language,
learning, reasoning, solving problems, and so on” (Barr & Feigenbaum, 1981, p. 3). Thisdefinition is appealing, as in this paper we are imagining a system that can correlate data fromdifferent sources and present options and pathways to students based on their interests andeligibility, similar to a human counsellor. For the propose of this paper we take AI’s meaning
in the broadest sense, any use of a computer to replicate or substitute human intelligence toprovide insights through the application of various machine enabled analytical processes tolarge data sets. Insights from Artificial Intelligence are only possible when data is available
related to the sought for insight. This data may be collected by surveying people, gathered frompeople completing tasks, automatically generated and stored by a system in log files, enteredin by an analyst, etc. Data may be structured, always in a particular format e.g. form entereddata; semi structured, complying to a structure e.g. emails; or unstructured, where it does notcomply to a given structure e.g. photographs. The latter two require reprocessing to be usable.
After looking through all the data that is available and identifying the sources that would be
helpful, this data has to be transferred and stored in a database or on a server, making it
available in a format that the AI algorithm can process. Once the algorithm processes the data,
insights can be obtained. Figure 2 describes a very simple and generic model: Data Generation– Data Storage – Data Processing – Actionable Insights.

Figure 2. A simple AI process
Based on this Input-Process-Output model, data has to be collected and made available in aform for each stage in the Student Lifecycle, that the AI program can process and then yield67

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insights that the student and organization can act upon. In the next sections, possible places
where AI insights would be helpful are reviewed and a reference grid shows a high-level data
to lifecycle stage mapping, articulating the data needed and the potential constraints andbenefits of applying AI at each stage.
Incorporation of Artificial Intelligence at the Classroom Level
The mechanics have to be explained here and then the experience. The student would firstinteract with the system and input their interests, performance in studies to date, work
experience, amongst other information. The intelligent system would then be able to provide alisting of the programs that the student is eligible for at the institute. If this system is utilizedby more than one institution, programs across institutions might be suggested to the student as
well. Possible pieces of information that could be utilized to train such a system could be theinformation from current students who are pursuing a program at the institution, their interests,
the programs that they might have considered before pursuing the one they are in as well as
career prospects. Similar data may be collected from alumni, noting the career that they are
pursuing.
Once the student decides on a program of study and is accepted, the system would be able toshow the student possible scholarships, volunteer opportunities, as well as present program
specific information about preparing for the first day, book a tour of the campus, residence
services, library services, potential student clubs (based on the interests that the system isalready aware of), time scheduling, and so on. This would potentially cover the second andthird stages of SEPT.
After classes have been selected and a term has been successfully completed, the system would
be able to offer more refined job and volunteer opportunities based on the skills that the student
has learned as a result of this education. Sweeney, Lester and Rangwala (2015) and Sweeney,
Rangwala, Lester and Johri (2016) cited in Khare, Lam and Khare (2018) “predict whether the
combination of courses that a student is taking in the current term would overwhelm thestudent. Thus, their research gives insight to students about courses they are taking, tocounselors who advise the students about the course load and to instructors on considering
differing course combinations” (p.43).
This would be an intelligent system that knows what the student is studying in all courses, thedeadlines coming up as well the next set of courses that would become open to the student ifs/he does well in his/her current set. This would integrate data from the various courses and the
database of course dependencies to show the student possible what-if schedules for next term
and year, one that can be changed according to interests and constraints facing the student. The
system would also be able to analyze integrated data from the numerous sources to present thestudent his/her best options (Woolf, Lane, Chaudhri, & Kolodner, 2013). This also relates tothe field of learning analytics and student competences and skills. Zhang and King (2016)
analyse the order in which questions should be presented based on the knowledge level of thestudents. This can be further applied to the skills that courses teach and other courses require.
By keeping track of the time commitments of the student based on selected courses, volunteer
work and part-time job, the student would have a more comprehensive idea of how their time
is being spent and if they can take on more work. Woolf et al (2013) elaborate on the 21st
century skills that artificial intelligence should work to address. These include self-direction
and self-assessment. With information organized in a coherent way and presented such that68

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only eligible options are shown, a student would be in better control of making choices thatlead to the success he desires.
Intelligent systems make it possible for students to build a schedule for the term, while at thesame time presenting financial aid and volunteer opportunities tailored to them for which they
are eligible. This would reduce the number of human hours spent in counselling students
regarding the courses they are eligible for as well as reviewing scholarship applications because
the students would only be able to apply for the ones they are eligible for.
The Outduction of a student at the end of their course of study requires students to make achoice. Some students will seek to continue studying by taking another degree or pursuing an
advanced degree. AI systems can provide guidance from their academic record as to a preferredcourse of study. Most students will want to move on to the workforce to start their careers. AI
can assist students by providing career tools to align their aspirations with the pathways to getthere. Further AI career coaches can provide personalized advice based on the student’s history,
experience, locational choice, skills combined with career requirements to supply students
needed further study tracks and possible staging and development paths. IBM’s AI powered
Blue Matching system is an example of a job matching service that demonstrates the realpotential here (Clegg, 2017).
All of the above are dependent on available and useable data. This is not a trivial requirement
as most of the data will not be in a directly useable form and will need either significant
reworking or will require to be newly gathered in the desired format. Machines, like humans,need information to learn from, they also need, at least with the present state of technology,
humans to shape that data for them. While quicker when they have the appropriate data,
available AI systems have yet to be able to effectively forage on their own behalf.
Forum Monitoring by Intelligence Systems
Forum monitoring offers another opportunity for AITAs to improve the efficacy ofcollaborative forums. Currently human Teaching Assistants (TAs) are asked to check the forum
at least once a day such that all questions could be answered within 24 hours. The same rules
are applied to emails and TAs are encouraged to ask the students to post a question on theforum if its answer would be helpful to other students.
Questions are often related to the concepts being studied in the class and the assignment. Thus,
the scope of the questions is often defined. Using data from forum posts from previous
iterations of the course, machine learning can be applied and trained to associate questions withanswers. This reduces both response time (or eliminates it) and the effort of TA’s to researchand create an answer. If a set of resources are associated with each topic, it is possible to pointout these resources to students, just like the TA would have done (Khare, Lam, & Khare, 2018).
Algorithms can be used to time themselves for answering the student and if no answer is foundor it is taking too long, a mechanism can be put in to notify a teaching assistant. Once the TA
resolves the question, the algorithm may be trained further to answer it next time or to flag
certain questions that must go to TA directly. Going beyond answering questions, reviewing
the forum posts for the understanding of the students, using content analysis and text mining
techniques can determine if the discussion coverage is as expected (Khare, Lam, & Khare,2018).

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Tutoring and Advising
The use of intelligent agents and chatbots are growing rapidly in use in consumer electronics
and customer service. Commonly referred to as chatbots these devices use AI to provide
context aware information to the user, usually in a relatively narrow range. The application ofthis technology to support students enables the potential for expanded one-on-one tutor
engagement, not economically achievable in existing teaching models. These are being trialledin education, varying from providing administrative and service information to supporting
academic study. A number of studies suggest that chatbots, also known as intelligent tutors or
intelligent teaching assistants, are significantly beneficial to students and positively add to thestudent experience and probability of success. IBM Watson supported chatbots are being usedin Deakin University (Deakin University, 2015) in Australia to provide student guidance to lifeon the campus and in the cloud, and at GeorgiaTech’s online master’s in computer science toprovide teaching assistance. Both have proven very successful after initial training, due in largepart to the question set remaining relatively consistent allowing AI to prove effective (Maderer,2016). Georgia State University noticed that not all students who accept admission offers enroll
in Fall. They called this phenomenon “summer melt” (Ravipati, 2017). They employed an AI
chatbot called AdmitHub to significantly increase the number of students that enroll after
admission by improving communication with students using text messages (Ravipati, 2017).
Studies by Steenbergen-Hu and Cooper (2013; 2014) and VanLehn, (2011) have indicated that
AI tutors are as good or better than human tutors, however the evidence is unclear, and theexperimental designs used to date do not provide an unambiguous answer. Moreover, humanto human interaction for teaching is unlikely to be replaced in the near term. As humans, we
still need personal connections for inspiration, compassion, self-reflection, imagination and life
context. Learning is every bit as much emotional and social as it is teaching technique andtechnology. The replication of intelligence by machines may not be matched by their abilities
to emote or socialize, indeed the concept of artificial emotion seems to be a contradiction interms. Thus, the ability of humans to express empathy and to provide emotional as well as
intellectual understanding to form connections and form social bonds will ensure, at least for
the near future, human advising and tutoring are superior supports for human learning.
Grading and Assessments
Many aspects of grading automation are currently in practice and are well accepted within thehigher education community. In many instances logical rules and rubrics are used for grading
and these can be taught to a program, such would result in a reduction in the number of human
hours spent on grading. These are, in the main, straightforward tabulations of choice typequestions, where there is a predetermined correct answer. Assessments and grading of lessdiscrete answer sets, in particular long essay questions, are not likely to receive as warm awelcome by academics. Nonetheless AI is being applied to the grading of short and long essay
types and is showing considerable success. The work done in this field includes automatic
feedback. Zhang, Shah and Chi (2016) have worked on automatically grading short answer
questions. Automatic Essay Grading is a growing field of research that aims to grade long essay
questions (Page, 1994; Rudner & Liang, 2002; Chen, Liu, Lee, & Chang, 2010; Dong & Zhang,2016). Recent work by Dong and Zhang (2016) elaborates the potential of deep learningalgorithms to grade essays.
Christian (2018) formulated a rule-based system to evaluate C++ programs in the early stages
when a student is learning a new concept by grading and informing the student on where he iswith respect to the solution and learning objective.

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It is quite likely to see AI aided formative assessment technology being employed as this aids
academic productivity and assists students in their development of understanding and masteryof the material. Chen, Breslow and DeBoer (2018) analyzed a blended learning environment
and the effects of immediate corrective feedback on student behavior. They found that the
feedback led to reflective studying, and higher performance was predicted for students who
used the corrective feedback feature.
Delivering content based on the performance or understanding of the student
Adaptive learning has been much trumpeted over the years and was seen as one of the early
benefits of online or computerised enabled learning. The use of games in education follows asimilar pedagogical approach of uncovering within a dependent interrelated environment ofknowledge objects or situations. Squire and Jenkins (2003) discuss the pedagogical potentialof games. Polin (2018) elaborates upon the features of games that make them spaces that
support learning. Lamb, Annetta, Firestone and Etopio (2018) provide insights into the kind ofgames – Serious Games, Educational Serious Games and Educational Simulations that havethe most impact on students’ cognition and behavior. However, the recent troubles of Knewton,
one of the flagship companies in this area (Young, 2017), indicates that this is still an area thathas yet to fulfil its potential.
Self-paced learning is where the program can judge when a new topic has to be introduced oran older topic has to be reviewed by the student. The models used by Intelligent tutoring
systems would be helpful here to determine when a student has learned a concept and is readyto move on to the next one (Lin & Chi, 2016; David, Segal, & Gal, 2016). The data fromassignments and practice questions as well as response time is often used to find the state of‘learned’ and build a student model which represents the knowledge of students (Lin, Shen, &Chi, 2016). These systems provide feedback, timely guidance and explanations when students
make mistakes (Shute, 2008). They keep track of the learning outcomes and are able todetermine the content appropriate to the student’s difficulty level (VanLehn, 2006). In this way,
students’ learning experience has a bigger focus than the lessons themselves.
Intelligent Tutoring Systems such as Carnegie Learning and Front Row have been tailored toschool students. To the best of our knowledge, there are no cognitive tutors at university level.
These could be used to supplement the understanding of students.
Augmenting this information about the student with ‘smart content’, Cram101, built by Content
Technologies Inc., uses artificial intelligence to breakdown the textbook into smaller sections,
including chapter summaries, practice questions and flashcards to form a ‘digestible “smart”
study guide’ (Faggella, 2017).
AI applied to the classroom level can provide significant benefits which are even more tellingin the online environment where the delivery of courses can be enhanced economically throughAI enabled automation. The ability to provide responsive support to students on a consistentbasis, although inferior to human interaction, is nonetheless superior to current online delivery
models that normally have time limited support due to the cost of human TAs. Even moderatequality support that is consistently available is preferable to high quality support that is rarely
available. Moreover, a human TA or professor can review and provide additional support to
the AITA whether due to AI restrictions or by providing additional learning moments. The
potential impacts on the online business model are substantial, by reducing the total cost ofproviding online courses, a significant lowering of tuition to students is possible. Such would
allow greater access to secondary and tertiary education particularly in less economically71

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Volume 6 – Issue 3 – Winter 2018advanced countries. Slightly ironic that artificial intelligence may be the key to growing global
human intelligence.
Combining Artificial Intelligence with the SEPT Model
There is an iterative loop here as data generates insight creating the need for additional datathat generates enhanced insight. It also can demonstrate the gaps in data for a given desired
insight as well as potential insights from given data sources. By doing this we can show thepotential for AI to provide benefit to learning and student success. Morgan (2013) identified
five themes in the SEPT model – curriculum and assessment, pedagogy, support, finance, andemployment. Table 1 is based on the data available from these themes and presents a further
analysis of the data sources, possible insights, and barriers to using the data.
Table 1. Artificial Intelligence (AI) Lifecycle Matrix using SEPT framework
Question\StageWhat data
is available
for each
stage

What arethe sourcesof this dataWhatsystems
does theinformation
reside inFirst
Contact andAdmissions
Student
interests
Subjects
taken by
student
Grades in
subjects
Degreeapplied for
Courses tobe taken in
the degreeFeeestimates
Possiblescholarships
and financial
aid
Part-time
workopportunities
Student
application
Referenceletters for the
student
Previous
examination
results
Careerdevelopment
system at theinstitution

Pre-arrival

Arrival andorientation

Induction to
study

Reorientationand induction

Outduction

Subjects
selected by
student tostudy –
coursework
Moneymanagement
adviceFinancialsupportavailable
Placement
options
Internships
Volunteering
opportunities

Subjects thatmake up the
degreeAssessments
Moneymanagement
adviceFinancialsupportavailable
Placement
options
Internships
Volunteering
opportunities

Subjects
being
studiedAssessments
Moneymanagement
adviceFinancialsupportavailable
Placement
options
Internships
Volunteering
opportunities

Skills gained after
a term/year of
study
Subject choices
available
Moneymanagement
adviceFinancial support
available
Placement options
Internships
Volunteering
opportunities
Future job
opportunities
Professional
development
opportunities

Job
opportunities
Further study
prospects
Debt control
Professional
development
opportunities
Transitioning
into aworkspace

Student
profileFinancialprofile ofstudent
Scholarships
applied forand rewards
Careerdevelopment
system at theinstitution
Student
Information
SystemCustomer
RelationshipStudent
profileFinancialprofile ofstudent
Scholarships
applied forand rewards
Careerdevelopment
system at theinstitution
Student
Information
SystemCustomer
RelationshipStudent
profileFinancialprofile ofstudent
Scholarships
applied forand rewards
Careerdevelopment
system at theinstitution
Student
Information
SystemLearning
outcomes andcompetencies ofprevious courses
Job profiles ofgraduates fromselected degree
Careerdevelopment
system at theinstitution

Requirementsof graduate
degrees
student iseligible for
Job profilesof graduatesfrom selected
degreeCareer plans

Student
Information
SystemFaculty databases

Student
Information
System72

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Question\StageWhat
Insights
can wedevelop foreach stage

First
Contact andAdmissions

Degrees andCourses thatmatch andbest fit
student’s
interest andlikelysuccess
What-if
Financialscenarios
Optimal path
to complete
degreeVolume 6 – Issue 3 – Winter 2018

Pre-arrival

Arrival andorientation

Induction to
study

Reorientationand induction

Outduction

Management
SystemFaculty
databases

Management
SystemFacultysystems
Residential
database
Student
union
systems

Residential
database
Learning
management
systemStudent union
databases
Library systems
Physical activity
systems
Volunteer systems

Faculty
databases
Residential
database
Learning
management
systemStudent union
databases
Careerdevelopment
systemVolunteering
choiceOptimalschedule tomanagecoursework,
work andvolunteering
Steps to taketo besuccessful inclasses
Careeropportunities

Time
management
Subjects to
takeCampusOrientation

Faculty
databases
Residential
systems
Learning
management
systemStudent
union
databases
Librarysystems
Volunteersystems
Optimalstudy plan
Resource
suggestions
andprovision
Study aides
and course
strategies
Practice
assignments
Dailyschedulemanagement

Optimal study plan
Resource
provision
Study aides andcourse strategies
Practice
assignments
Schedule tomanagecoursework, workand volunteering
Time management
Careeropportunities
Job shadowing
opportunities
Projects/Workshop
suggestions thatwould strengthen
job prospects later
Skills that areunderdeveloped orneed improvement
Learning progressand successsupports

Best fit Jobs
to apply for
Further job
specific
training
opportunities
Application
deadlines forgraduate
programs
Best fit
International
study
opportunities

Residential
choiceTimemanagement

Whatbarriers tousage andvalue exist

Privacy of student information
Maturity of IT systems
Data accuracy, availability and accessInstitutional culture and policies
Lack of systems integration
Lack of data sharing agreements with external organisations
Lack of appropriate data
Lack of understanding of AI

What
enablers tousage andvalue exist

Data governance to develop and ensure consistent data architecture in combination withstandardized data definitions
Identification of additional key data sources that bear on student experience
Policy review of data sharing to facilitate and frictionless accessIntegration of data and applications to provide real time data across the enterprise73

IAFOR Journal of Education

Question\StageWhere canit go next

First
Contact andAdmissions
Enhancedengagementof potential
students
through
intelligent
selection andinformation
provision to
selectinstitutions,programmes
and career
paths

Volume 6 – Issue 3 – Winter 2018

Pre-arrival

Arrival andorientation

Induction to
study

Reorientationand induction

Outduction

Augmented
reality
campusguide
Campus life
planner
Residential
plannerSocial life
assistants

Data sharing
across theinstitution toprovideinsights into
success
patterns
Learning
materials
agent toreduce costs

Real time
data
availability
for improved
decisionmaking on
programmes
and course
selection

Study buddy
agents
Virtual andaugment reality
learning resources

Integration
with work
placement
dbases toprovide bestfit
opportunities
for job
placement
and careeradvisement

Conclusion
The two main bodies of systems analyzed in this paper were those of the student lifecycle andthe learning management system. While the lifecycle management systems keep track ofoverall student progress, the courses they have taken, venues of financial support, volunteer
opportunities and schedule management, the learning management systems are focused on theacademic progress of the student. From this it is seen that student lifecycle management relies
more on the administrative staff while learning management systems are supervised by theacademic staff at a higher education institute.
As with all innovations their adoption is not an either-or, but a blending and integration of thestrengths of both the existing and the new to provide a superior capability. If we, therefore,
assume that we can combine activities currently managed by these systems with an intelligent
system, we can begin to redesign a new system of education, one breaking out of the traditionalroles of professors, teaching assistants and support staff, making them facilitators of knowledge
and managers of the new systems. At the classroom level, in many cases, it will free up time
to use the in-class time in a different way. The concept of flipped classrooms is where students
study the assigned material at home and come to the class to discuss it. This might be
supplemented with learning activities in class such as discussing complex concepts or
examining real-world examples. On the other hand, at an administrative level, work of support
staff may be reduced with regards to providing counselling to the student about potential next
steps as the student would already have access to the courses they are eligible for, credits they
can take from other universities, the career venues and graduate programs open to them basedon his/her current performance and volunteer opportunities to augment his/her resume, to namea few areas. The advantages of such an integration of the student lifecycle and learning
management systems is to give students the choices and options, right for them, at the righttime in their higher education journey.
Another system using artificial intelligence which we did not explore here is at the academic
department and faculty level for curriculum design. Such a system would analyze the needs ofthe discipline, trends in the current job market and research as well as the new knowledge skills
that graduates require for being successful. This information would feed into design anddevelopment of new courses and programs, enhancing student experience by increasing theiremployability.

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This paper has attempted to show that AI is and can be a significant aid to all aspects of thestudent experience and to the organisations, structures, processes and people that make up
educational systems. It further provides an architectural approach that is a coherent
representation of real experience, which provides a context for experimentation and thedevelopment of a referenceable literature. It does not try to make the case that AI is superior
or equal to human equivalents, rather it attempts to demonstrate the benefit of synergistic
integration of both forms of support for student success, allowing each to support the other toprovide what they are best at. By so doing all stakeholders benefit and the student experienceis improved with the expectation that student success increases pari passu.
Finally, we need to be aware of techno-solutionism to address wickedly complex problems,
but we should also explore its capabilities to find the best-fit for its application and benefit to
an increasingly expensive and technology resistant system. The pace and depth of adoption
will depend not only on the continued growth of AI capabilities, but also on the opening andsharing of data and the acceptance of a highly conservative system unused to collectively
integrating technology into its corpus. By providing an adoption framework this paper desires
to enable a more structured and efficient introduction of AI technology into Higher Education.
One that can benefit students, faculty and administrators through an enhanced user experiencethat improves the ability of educational institutions to deliver on their core mission of teaching,learning and research.

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Corresponding author: Anshuman Khare
Contact email: Anshuman.khare@fb.athabascau.ca

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Teaching Them before We Teach: The Effectiveness of Conducting Classroom
Experiments before Teaching the Underlying Theory
Raymond Li
Hong Kong Polytechnic University, Hong Kong
Tina Wong
Hong Kong Polytechnic University, Hong Kong

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Abstract
This study examines the effectiveness of classroom experiments conducted before therelevant theories were taught. The experiments were used to provide students with first-handexperience of decision-making under various rivalry settings and to demonstrate several key
predictions of oligopoly models. Statistical methods were used to analyze the effectiveness ofthese experiments in helping students master the concepts covered by the experiments. In
general, students had a positive experience in the process and they found the experiments
useful in stimulating their interest and helping improve their understanding of the relevant
theories. Statistically, students who took part in the experiments performed significantly
better in an exam question on oligopoly markets.
Keywords: economics education, classroom experiments, prior knowledge, decision making,oligopoly model, treatment-effect model80

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Introduction
Economics is a discipline that can be characterized by rigorous theories developed to explain
the behavior of economic agents or businesses. These theories can be evaluated byobservation of actual human behavior or empirical analyses involving the use of relevant data.
However, since economic decisions are usually made in a non-laboratory environment, it isdifficult to evaluate these theories directly. To address this problem, economists havedesigned experiments that can be used to collect economic data under a controlledenvironment. These experiments can be useful in testing the validity of economic theories, aswell as in teaching and learning. Learning is more effective when students can relate to what
they are being taught. Oftentimes, students fail to understand economic models because theylack the first-hand experience and/or prior exposure to the context addressed by the models.
The problem gets worse when the models are mathematical and students’ attention getsdiverted away from the economic reasoning behind the models. To help students understandthese models, classroom experiments have been increasingly adopted by instructors. As notedby Neral and Ray (1995), classroom experiments not only provide students with concrete
examples of the phenomena that the economic theories attempt to describe, but also theexperience which can be related to particular aspects of the theories.
The effects of classroom experiments on student learning have been examined by various
studies in the economics education literature. To our knowledge, in all but one of the existingstudies (namely, Cartwright and Stepanova, 2012), classroom experiments were introduced
after a relevant concept was taught. This approach helps reinforce concepts by allowing
students to apply what they have learned in a (semi-)controlled environment. The aim of ourresearch was to examine the efficacy of classroom experiments in improving student learning
outcomes when the experiments are conducted before the relevant concepts are covered. We
postulate that by giving students first-hand prior experience on the subject matter, it becomeseasier for them to learn as a first-person rather than a third-party, and this enables students tomaster the models more effectively. We believe the results of this study will enable thedevelopment of a better understanding of the experiential learning pedagogy.
Our study began with an optional session of a computerized classroom experiment, wherestudents were required to compete against the others in the classroom by making a series ofbusiness decisions in order to maximize their profits. The market conditions were known to
the students, but relevant theories of this market setting were not yet covered in the lecture.
Students’ feedback on various aspects of the experiment were collected through aquestionnaire and their course results were used to examine the effectiveness of ourclassroom experiment on improving students’ learning outcomes. An endogenous treatmenteffect regression model was used to mitigate the potential bias that voluntary experimentparticipation may exert on our regression results.
In the next section, we review the literature on experiential learning and the application ofexperimental learning in economics. Section 3 describes our experimental set up and somepreliminary data on student learning outcomes. Section 4 contains the details of ourregression analysis and Section 5 concludes.

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Literature Review
Overview of Experiential Learning
The development of experiential learning began in the 20th century. Dewey (1916) first
defines experiential learning as “learning by doing”. Hoover and Whitehead (1975) laterelaborate that this learning approach is self-directed which includes a high level of active
involvement and participation. According to Kolb and Kolb (2005), the primary focus ofexperiential learning is transformation of experience to knowledge. Knowledge is constructed
through learners’ experience, reflection and thinking. Experiences serve as the basis forparticipants to reflect and think, and these reflections offer new implications to them. Kolb
and Kolb (2005) point out that experiential learning is a process that draws out students' ideas
about a topic and allows them to integrate new experiences with existing concepts.
Wolfe and Byrne (1975) suggest four major tasks of experiential learning, namely, design,
conduct, evaluation, and feedback. Design consists of specifying learning objectives,
producing activities for students and identifying factors affecting student learning. In order tocreate a favorable learning environment with structured learning experience, conduct is thefollowing task which involves controlling the design. While evaluation is important byoffering opportunities for participants to express what they have benefited, feedback is acontinuous monitoring process aiming to improve the learning approach further.
Classroom experiments offer students with opportunities to discover economic concepts bythemselves (Emerson, 2014). By engaging with the learning materials more fully, students
are able to think deeper about the subject matter. This learning approach enhances students’learning motivation because students will engage with the subject matter more as they must
apply theoretical knowledge to conduct experiments for solving real-life problems (Hawtrey,2007). In addition, experiments can significantly raise the degree to which students found thecourse stimulating (Ball, Eckel, & Rojas, 2006). Personal skill development is alsoemphasized under experiential learning (Egbert & Mertins, 2010). Students are encouraged to
explain subject matters to each other or work effectively and support each other in teams. As
a result, they can develop self-organization skills and team spirit through experiments. The
benefits obtained from this learning experience will last beyond the lesson.
According to Egbert and Mertins (2010), instructors can also gain from experiential learning
by enjoying teaching more. Classroom experiments serve as a good starting point for problem
discussion, enhance instructor-student interactions, and help inspire students to understandtheoretical concepts through discussions about the experiments. It is rewarding for instructors
to see students being intrinsically motivated with their course design. By arousing students’intellectual curiosity and having enjoyable interaction with them, instructors’ satisfaction
from teaching will likely be raised.
Despite the merits mentioned above, the challenges of this active learning approach should
not be ignored. Unlike traditional instructional approach, it is time intensive for the instructor
to plan and prepare courses with experimental learning. Besides, uncertainties always exist
when conducting experiments and the results of the experiments may not be predicted andcontrolled easily by the instructor. Emerson (2014) recommends running trials ofan experiment so that the sample results will be useful for understanding the pitfalls that may
arise. In light of this, instructors are advised to state the solutions to deal with those pitfalls intheir manuscript in advance. Moreover, students may complain that learning with82

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experiments is time consuming as they are required to do more work. As noted by Egbert andMertins (2010), this is true as students often focus only on examinations. Thus, it is essential
for students to understand the importance of learning informally through this active learningtechnique. In order to motivate students to be passionate in classroom experiments, Emerson(2014) encourages instructors to develop classroom experiments on those topics wherestudents would enormously benefit from seeing the concept in action.
Previous Studies on Experimental Learning in Economics
According to Emerson and English (2016), the existing literature on the efficacy ofexperimental learning can be classified into two categories. Studies in the first category focus
on particular experiments and examine the effects of these experiments on student learning in
related topics. Frank (1997) and Gremmen and Potters (1997) are two major previous studies
under this category. Frank (1997) examines the impact of a simple tragedy of the commons
experiment while Gremmen and Potters (1997) study the effects of an international economic
relations simulation game on student learning. By comparing the assessment marks, bothstudies found that students who engaged in or observed the experiment performed better andlearned more about the economic model than those who are not involved in the experiment.
The experiments in both studies were conducted after the delivery of a relevant lecture.
Studies in the second category examine whether exposure to the experimental learningpedagogy improves students’ overall course achievements. In general, these studies arebroader in nature and conduct experiments on an extensive range of economics topics. The
empirical findings under this category of research are mixed. On one hand, Emerson andTaylor (2004), Ball et al. (2006) and Dickie (2006) found that experimental learning
improves students’ examination performance in general. In all three studies, students weredivided into control groups and experiment groups. Traditional lectures or seminars wereconducted in the control groups while the experimental learning approach was adopted inanother group. Emerson and Taylor (2004) administer experiments covering topics such as
supply and demand, sales taxes, externalities, monopolies and adverse selection while Ball etal. (2006) employ experiments relating to taxes, public goods, tragedy of the commons andother economics concepts. Dickie (2006) conducts experiments relating to the topics ofcomparative advantage, demand and supply, sales tax and effects of minimum wage.
Regression models were used in all these studies. Emerson and Taylor (2004) identify student
learning as a function of student specific characteristics, such as aptitude, education
background and teaching methodology. Ball et al. (2006) focus on analyzing final
examination performance and include year of study, gender and whether students have taken
economics in high school as the independent variables in their study. Dickie (2006) measures
how classroom experiments with and without grade incentives affect learning of
microeconomics and supplements comparisons of treatment means by collecting additional
data such as student's cumulative grade point average (GPA), composite score on theAmerican College Test (ACT), number of semester credit hours passed, race, and gender. All
three studies documented that students participating in the experiments perform significantly
better in the examinations than those in the non-participating group. More recently, Rousu etal. (2015) examine whether or not providing monetary incentives will enhance students’
examination performance. They find strong evidence that students who played a classroom
experiment game with real monetary consequences eventually performed better in theexamination than those who played a hypothetical game and those who did not play at all.

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On the other hand, Cardell et al. (1996) were not able to find evidence on improved student
achievement through experimental learning. They employed computerized experiments
relating to demand and supply, public goods and income redistribution. Including students’gender, GPA, prior experience in taking economics course, attendance rate, Scholastic
Aptitude Test (SAT) scores, age and ethnicity as the independent variables in the regression
model, there is no statistically significant difference between students from the experimentand control groups in the performance of the Test of Understanding College Economics(TUCE). However, Cardell et al. (1996) noted that their results are preliminary because there
is no direct control for sample selection and variations in the behavioral impact of instructors
cannot be fully controlled until the experiment is completed.
Durham et al. (2007) found mixed results on the influence of experimental pedagogy on
students’ examination performance. They include class size, age, gender, ethnicity, ACT
exam score, GPA, attendance rate and students’ major in the regression model. Their researchshows that students participating in experiments perform better than the non-participants inquestions illustrating the concepts of demand and supply, cartels, resource allocation andpublic goods. However, participants were outperformed by non-participants in areas ofmonopoly and diminishing marginal utility.
While the majority of research studies on classroom experiments are focused on university
education, Eisenkopf and Sulser (2016) focus on high school students in the Germanspeaking area of Switzerland. They randomly assigned students into an experiment and aconventional teaching group. Data suggests that their classroom experiments did not offer asignificant benefit to students in terms of average test scores. The authors point out that thislack of significant benefit can potentially be due to the fact that economic theories taught at
the high school level are generally less abstract, hence limiting the effectiveness of classroom
experiments.
Instead of comparing student achievements with and without the use of experiments, Yandell(2004) and Cartwright and Stepanova (2012) studied other aspects of experimental learning.
Yandell (2004) examines the influence of the number of experiments on student
achievements. Experiments adopted in this study cover topics such as double oral auction,
production function, public goods and prisoners’ dilemma. A comparison of the examination
performance between students who are exposed to only two sets of experiments and thosewith six sets reveals no statistically significant difference. It is concluded that additional
experiments do not pose positive impacts on student achievement. Cartwright and Stepanova(2012) compare the performance of students who attended and/or wrote a report on an
experiment versus those who did not. They observed a 40-60% improvement in students’score in a classroom experiment-based test question if students had written a report on thatexperiment. Their findings illustrate the benefit of integrating classroom experiments withsome form of assessment.
Our study falls into the first category as classified by Emerson and English (2016). We
focused on one set of oligopoly market experiment and study the effectiveness of theexperiment on improving students’ learning outcome. But unlike Frank (1997) and Gremmen
and Potters (1997) in which the experiments are conducted after the relevant lectures, we
conduct the experiments before the relevant lecture is delivered.

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Experimental Design and Data Description
Background and Experimental Design
This study was implemented on a calculus-based intermediate microeconomics course
offered in the Spring semester of the 2015-16 academic year at the Hong Kong Polytechnic
University. The enrolment size was 59, with students either majoring in Investment Scienceor minoring in Business Economics. As this course is at the intermediate level, all students
have prior knowledge in economics. Similar to the market structure curriculum of mostmicroeconomics courses, perfect competition and monopoly models are covered before
oligopoly models, such that students have a firm grasp on the concept of profit maximization
before the introduction of interdependency between firms. An experiment session wasinserted in the teaching schedule between the monopoly and oligopoly models. The session
was pre-announced and held during a normal class time. Attendance at the session was
voluntary, just as class attendance was voluntary.
Two experiments were adopted from Economics-games.com, an online platform offering freeeducational games and experiments for teaching economics. One was based on the Stackelberg
model and the other on the Cournot model. Each student used an individual electronic device(mobile phone, tablet or laptop computer) to access the experiment system. In order to keep
track of students’ decisions and participation, they were required to log into the system with
their student ID. They were then paired up with an unknown counterpart in class, whom they
had to compete against. Instructions were shown to students before each experiment,
outlining the setting of the market, the objective (i.e. profit-maximization), and the choicevariable. In both experiments, the market demand function and marginal cost of production
were known to every participant. Students were required to submit a production quantity
decision to the system, either in turn (Stackelberg game) or simultaneously (Cournot game).
After receiving all the decisions, the system calculates the profit earned by each participant
and a leaderboard was projected on the screen so that everyone knows the outcomes. Eachgame was repeated five times. To encourage serious participation, a special bonus mark wasgiven to the top three students who achieved the highest cumulated profit in each set ofexperiment.
Students were asked to share and review their experience after each series of experiment.
Unsurprisingly, all top-performers had factored into their decisions the expected behavior of
their rivals. This outcome allowed the instructor to stress the importance of strategic behavior
in oligopoly markets. As the experiment session was held before the discussion of therelevant models, it gave students a first encounter with the context of the oligopoly models.
This helps pave the way for the actual discussion of the actual models because students canrelate to the models with their personal experience. It also helps arouse students’ interest andincrease their motivation in learning the models, as they would like to know how they could
have achieved better results.
Questionnaire Results
At the end of the semester, students were required to complete a questionnaire evaluating thecharacteristics of the experiments and the perceived effectiveness of the experiments inhelping them meet the learning objectives. The scope of this research and the use of students’
data (including questionnaire feedback and course results) were explained clearly to students
in the introduction of the questionnaire. Students were given the option to leave thequestionnaire blank if they do not wish to have their data used for this research. All

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Volume 6 – Issue 3 – Winter 2018questionnaires were collected by a student representative and the questionnaires were kept in
the School’s administration office until final examination results were finalized and released.
Students were required to indicate on a five-point scale their opinion towards various
statements about the experiment (1 = strongly disagree; 2 = disagree; 3 = neutral; 4 = agree; 5= strongly agree). Students were generally positive to both the experiment setup and theassociated learning experience. The arithmetic means of the responses to the statements range
from 3.97 to 4.17. The primary objectives of the experiments are to stimulate students’interest in the subject matter and help students understand the relevant course content. As
reported in Table 1 and Figure 1, 80% or more of the students agreed (i.e. giving a rating of 4or above) to the questionnaire statements addressing these two objectives (S3 and S4). Also,
most students can see the relevance of the experiments to their study (S5). Overall, students
were positive towards the learning experience provided by the experiments, with over 70% of
students agreeing to statement S6.
Table 1. Questionnaire statements and mean score
Statement
S1. The experiment requirements are easy to understand.
S2. The competitive nature of the experiments increased the level of excitement.
S3. The experiments help stimulate my interest in the subject contents.
S4. The experiments enhanced my understanding on how competitors interact in various market
structures.
S5. I can see the relevance of the experiments to my studies.
S6. In general, the experiments have provided me with valuable learning experience and knowledge
in the topic concerned.

Mean
4.09
4.17
4.03
4.06
4.09
3.97

Figure 1. Percentage distribution of questionnaire responses
Impact on Learning Outcomes
We postulate that participation in the experiment session helps improve students’
understanding of the oligopoly theories and subsequently improving their learning outcome.

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In this study, we quantify student learning outcome through a final examination question
devoted to oligopoly models. All questions in the final exam were compulsory and theoligopoly question accounted for 20 out of 100 points of the examination. Figure 2 compares
the major assessment performance of students who participated in the experiment (EXP=1)
and those who did not (EXP=0). We can see that the experiment group performed noticeably
better in the mid-term test (MT), overall final examination (EXAM_TOT), questions in the
final examination addressing other topics (EXAM_OTH), as well as the oligopoly question(EXAM_OLI). Table 2 summarizes the results of four t-tests of equal means on theassessment scores of the two groups. According to the p-values for the two-sided tests, thenull hypothesis of equal mean is strongly rejected at 1% significance for each assessment
outcome. The same results hold irrespective of the assumption on equality of variance.

Figure 2. Student assessment outcomes
Table 2. Two-sample t-tests of equal means assuming unequal variances
Mean

VarianceObs

t-statp-value

EXP=1
EXP=0

70.62954.417283.711275.123

35
24

3.665

0.001

EXAM_TOT
EXP=1
EXP=0

62.60048.458194.071325.042

35
24

3.237

0.002

EXAM_OTH
EXP=1
EXP=0

50.85741.333116.773186.841

35
24

2.856

0.007

EXAM_OLI
EXP=1
EXP=0

11.7437.125

29.55040.375

35
24

2.905

0.006

MT

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The results in both Figure 2 and Table 2 apparently suggest that students who participated in
the experiment session were the “better” ones in terms of their intellectual ability and/or
motivation, because they performed significantly better in all assessment tasks. As a result,better performance in the oligopoly question, at least on its own, does not necessarily imply
that the experiment session was beneficial to students’ learning. However, on closer
examination of Figure 2, we can observe a more pronounced difference between the meanscores in EXAM_OLI relative to that of the other assessments. The mean scores of theexperiment group were 30%, 30%, 23% and 65% higher than the control group in MT,
EXAM_TOT, EXAM_OTH and EXAM_OLI, respectively.
Econometric Model and Results
In order to isolate the impact of our experimental session on students’ learning outcome, we
further analyze students’ score in the oligopoly question under a reduced-form education
production function framework:
EXAM_OLI = f (student ability, study effort, learning attitude, experimentparticipation)
Since we do not have detailed and reliable data on students’ ability, study effort and learning
attitude during the semester, we use students’ net continuous assessment score (= continuous
assessment score minus participation score), CA, as a proxy variable to control for the effectof these factors on students’ performance in the oligopoly question. Specifically, we
anticipate a positive relationship between CA and EXAM_OLI. This yields the following
basic regression model:
EXAM_OLIi = b0 + b1CAi + b2EXPi + ei

(1)where subscript i identifies the individual students and e is an error term. Since participationin the experiment session was voluntary, students who participated were likely to be more
motivated (and/or with better learning attitude). Therefore, experiment participation is likely
to be endogenous. We accommodate this sample selection problem by estimating equation (1)as an endogenous treatment effects model (Maddala, 1983, ch.9), in which EXPi is assumed
to stem from an unobservable latent variable, EXPi*, that depends on students’ classparticipation and overall performance during the semester. Experiment participation ismodelled as follows:
EXPi* = g0 + g1PARTi + g2CAi+ ui(2)

EXPi = 1 if EXPi* > 0, = 0 otherwise(3)

where PART is a class participation score ranging between 0 and 10 to reflect a student’s
level of involvement in class and u is an error term for the treatment-assignment model. It is
expected that both g1 and g2 are positive.
We estimate the endogenous treatment effects model with a one-step control-function
estimator (Wooldridge, 2010, sec.14.2). The results are presented in Table 3. A Wald test on
the null hypothesis of no correlation between e and u has a c2 statistics of 1.340 and a p-valueof 0.247, meaning that the outcome and treatment equations are statistically independent. In
the treatment equation, the estimated coefficient for PART is statistically significant (p <88

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0.000) but that for CA is not (p = 0.201). It is unlikely that the statistical insignificance is due
to multi-collinearity because the pairwise correlation coefficient between CA and PART isonly 0.313. This means students who participate more actively in class are more likely toattend the experiment session, but a student’s overall performance during the semester does
not affect experiment participation. Turning to the outcome equation, we can see that thecoefficients for both CA and EXP are statistically significant. As expected, a student’s overall
performance during the semester has a positive influence on the score of the oligopoly
question. This implies that students who are more capable and/or with better learning attitude
perform better in the oligopoly question. More importantly, experiment participation has a
discernible positive effect on students’ performance in the oligopoly question. On average,
students who participated in the experiment scored 5.417 points higher than the other groupin this 20-point question. This serves as a strong support to our hypothesis that classroom
experiments conducted prior to relevant lectures can also help improve student learning
outcomes.
Table 3. Endogenous treatment-effects regression results
Estimated
CoefficientStandard
Error

p-value-1.097
0.276
5.417

3.024
0.133
2.187

0.717
0.038
0.013

-6.020
0.735
0.069

1.870
0.144
0.054

0.000
0.000
0.201

Outcome equation

b0
b1
b2
Treatment equation

g0
g1
g2

Discussions and Conclusions
The goal of this study is to examine whether classroom experiments can improve student
learning outcomes when the experiment is conducted before the relevant concepts arecovered. We found that the group of students who voluntarily participated in an oligopoly
experiment session performed noticeably better than the control group in all assessment tasks
throughout the semester, but there is a much more pronounced difference between the twogroups in their performance in a final exam question on oligopoly (65% difference) relative
to that of the other assessments (23-30% difference). This potentially implies that experimentparticipation has a positive influence on students’ performance in the exam question related
to the classroom experiment. Our endogenous treatment effects regression results further
reveal that experiment participation raised a student’s score by 5.417 points on average in a
20-point question that was related to the experiment. Findings from the end-of-semester
student evaluation questionnaires also indicate that students have gained valuable learning
experience and knowledge in the topic concerned through the experiments. Their interest in
the subject contents were stimulated and their understanding on how competitors interact invarious market structures were enhanced. As discussed in the literature review section above,
experiential learning is about the transformation of experience to knowledge. Our experiment89

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Volume 6 – Issue 3 – Winter 2018served the role of providing students with a relevant experience in the context of the theories
to be taught. It also helped students grasp the key components of the theories. The lectureteaching then guided the students through a reflection and thinking process to help themcreate knowledge. The effectiveness of this approach is demonstrated in the student learning
outcomes.
Our results supplement existing findings in the literature by showing that experiments
conducted before a relevant theory is taught can also improve student learning outcomes.
Rather than helping students retain knowledge by allowing them to apply the theories theylearned, our experimental design put students in the context of which a theory addresses. Our
approach can provide students with prior knowledge and experience that is important for theunderstanding of a theory and improve students’ motivation in learning the theory. Although
our study focuses on the use of classroom experiments, the practical implication of our results
is more far-reaching. In order to help associate students with the relevant subject matters to
improve learning, lecturers may explore other effective and appropriate means that can beused to provide students with prior knowledge and experience. These means are course or
topic-specific, and they may include case study, role play, gamification, etc., just to name afew examples. It should also be noted that the adoption of our approach is not limited to theeconomics and business disciplines. Given the educational value of first-hand priorknowledge, our approach may as well be suitable for other academic disciplines (e.g.
humanities, social sciences, applied sciences, etc.) with a focus on real-world applicability.
Of course, our current research is not without limitations. Firstly, the sample size of thisstudy is not big (around 60 students). This may have limited the capability of the endogenous
treatment-effect model in correcting for the potential bias in our sample selection. Secondly,
it will be more ideal if a comparison can be made between a group doing experiments before,
and another group after relevant theories are taught. However, this calls for a very different
experimental design and is out of the scope of this research.
To our knowledge, this study is the first that documents the efficacy of the use of classroom
experiments in an Asian university. We encourage more research in this area with a broader
geographical or demographical coverage, so that economic education researchers can better
understand the application of experiential learning in different cultural settings. Further
research in this area can also try to randomize the assignment of students into the experimentand control groups. This may minimize the influence of self-selection as encountered in thisstudy.

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References
Ball S.B., Eckel C. & Rojas C. (2006). Technology improves learning in large principles of
economics classes: Using our WITS. American Economic Review, 96 (2), 442–446.
Cardell N.S., Fort R, Joerding W, Inaba F., Lamoreaux D., Rosenman R. & Bartlett R. (1996).
Laboratory-based experimental and demonstration initiatives in teachingundergraduate economics. American Economic Review Papers and Proceedings, 86(2), 454–459.
Cartwright E. & Stepanova A. (2012). What do students learn from a classroom experiment:
not much, unless they write a report on it. Journal of Economic Education, 43 (1), 48–
57. https://doi.org/10.1080/00220485.2012.636710
Dewey J. (1916). Democracy and education: An introduction to the philosophy ofeducation. New York, NY: Macmillan Company.
Durham Y., McKinnon T. & Schulman C. (2007). Classroom experiments: Not just fun andgames. Economic Inquiry, 45 (1), 162–178.
https://doi.org/10.1111/j.1465-7295.2006.00003.x
Egbert H. & Mertins V. (2010). Experiential learning with experiments. International Review
of Economics Education, 9 (2), 59–66. https://doi.org/10.1016/S1477-3880(15)300505
Eisenkopf G. & Sulser P.A. (2016). Randomized controlled trial of teaching methods: Doclassroom experiments improve economic education in high schools? Journal of
Economic Education, 47 (3), 211–225.
https://doi.org/10.1080/00220485.2016.1179143
Emerson T.L.N. (2014). Anyone? Anyone? A guide to submissions on classroom
experiments. Journal of Economic Education, 45 (2), 174–179.
https://doi.org/10.1080/00220485.2014.889970
Emerson T.L.N. & English L.K. (2016). Classroom experiments: Teaching specific topics orpromoting the economic way of thinking? Journal of Economic Education, 47 (4),288–299. https://doi.org/10.1080/00220485.2016.1213684
Emerson T.L.N. & Taylor B.A. (2004). Comparing student achievement across experimental
and lecture-oriented sections of a principles of microeconomics course. Southern
Economic Journal, 70 (3), 672–693. https://doi.org/10.2307/4135338
Frank B. (1997). The impact of classroom experiments on the learning of economics: An
empirical investigation. Economic Inquiry, 35 (4), 763–769.
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Gremmen H. & Potters J. (1997). Assessing the efficacy of gaming in economic
education. Journal of Economic Education, 28 (4), 291–303.
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Hawtrey K. (2007). Using experiential learning techniques. Journal of Economic
Education, 38 (2), 143–152. https://doi.org/10.3200/JECE.38.2.143-152
Hoover J.D. & Whitehead C.J. (1975). An experiential-cognitive methodology in the firstcourse in management: Some preliminary results. Developments in Business
Simulation and Experiential Learning, 2, 25–30.
Kolb A.Y. & Kolb D.A. (2005). Learning styles and learning spaces: Enhancing experiential
learning in higher education. Academy of Management Learning & Education, 4 (2),
193–212. https://doi.org/10.5465/amle.2005.17268566
Neral J. & Ray M. (1995). Teaching tools experiential learning in the undergraduate
classroom: two exercises. Economic Inquiry, 33 (1), 170–173.
https://doi.org/10.1111/j.1465-7295.1995.tb01854.x

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Rousu M.C., Corrigan J.R., Harris D., Hayter J.K., Houser S., Lafrancois B.A., Onafowora
O., Colson G. & Hoffer A. (2015). Do monetary incentives matter in classroom
experiments? Effects on course performance. Journal of Economic Education, 46 (4),341–349. https://doi.org/10.1080/00220485.2015.1071214
Wolfe D.E. & Byrne E.T. (1975). Research on experiential learning: Enhancing theprocess. Developments in Business Simulation and Experiential Learning, 2, 325–336.
Yandell D. (2004). Effects of integration and classroom experiments on student learning andsatisfaction. Journal of Economics and Economic Education Research, 5, 17–28.
Corresponding author: Raymond Li
Contact email: ray.li@polyu.edu.hk

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Learner-Generated Digital Media (LGDM) as an Assessment Tool in Tertiary Science
Education: A Review of Literature
Jorge Reyna
University of Technology, Sydney, Australia
Peter Meier
University of Technology, Sydney, Australia

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Abstract
Learner-Generated Digital Media (LGDM) in tertiary science education focuses on researchskills, inquiry, active learning, teamwork, and collaboration. LGDM across disciplines isunder-theorised, under-researched, and only in its early development. This paper evaluates theresearch in the field of LGDM in tertiary science education. The literature review had fourstages – identification, screening, filtering, and selection of relevant scholarly research. Results
showed that research in the field of LGDM assignments had been done without a systematic
approach to designing, implementing, and evaluating the assessment task. Most studies
neglected student digital media training and are characterised by a lack of compelling marking
rubrics or strategies to ensure efficient groupwork. Studies also lack rigorous methodologies
for data capture to evaluate the intervention and they use small sample size cohorts anddifferent digital media types that require different sets of production skills. With the empirical
data available, validation of the benefits of LGDM assignments in science education is notpossible, and studies have limited scalability. These gaps in the literature create a need todevelop theoretical models for the design, implementation, and evaluation of LGDM in theclassroom. This paper discusses future research needs in this field and the implications forassessment design.
Keywords: learner-generated digital media, digital media literacies, science education,
student-created content, authentic assessments

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Learner-Generated Digital Media (LGDM) can be defined as digital artefacts developed by
students to showcase their learning (Reyna, Hanham, & Meier, 2018). To date, there is noconsensus in regards to naming and LGDM can be called, for example, student-generated
digital media, student-created content, user-generated content, multimedia projects, andstudents as co-creators of content. LGDM include different media types such as podcast, digitalstory, screencast, animation and video, which adds an extra layer of complexity to a highly
atomised field of research. For instance, to conduct a literature review in the field, it wasnecessary to use a multi-search strategy. The rationale is to use digital media as a vehicle forlearning the subject content and developing graduate attributes such as technological skills(Buckingham, 2007), time management (Frawley, Dyson, Tyler, & Wakefield, 2015; Pearce& Vanderlelie, 2016), teamwork and collaboration (Coulson & Frawley, 2017), conflict
resolution (Reyna, Horgan, Ramp, & Meier, 2017), and for fostering student engagement andcreativity (Coulson & Frawley, 2017; Hoban, Nielsen, & Shepherd, 2015; Pearce, 2014).
Research conducted in the last decade in the field of education has described the use of digital
media assignments with the main focus on reflective practices for pre-service teachers(Kearney, 2013; Rich & Hannafin, 2009). In contrast, in science disciplines, it can beconsidered a novel approach focused on the development of research skills, inquiry, and active
learning (Hoban et al., 2015). Documented examples include use in biology (Pirhonen & Rasi,2016), health sciences (Pearce & Vanderlelie, 2016), and pharmacology (Henriksen,
Henriksen, & Thurston, 2016; Nielsen, Hoban, & Hyland, 2017; Reyna, Meier, Geronimo, &Rodgers, 2016). Other disciplines where it has been used include computer programming(Powell & Robson, 2014; Vasilchenko et al., 2017), geology (Reyna et al., 2017), mathematics(Calder, 2012; McLoughlin & Loch, 2012), and engineering (Anuradha & Rengaraj, 2017).
LGDM across disciplines in higher education is considered under-researched (Hakkarainen,2009), under-theorised (Potter & McDougall, 2017), and lacking in practical frameworks to
implement it outside the Education discipline (Reyna et al., 2018). There is a lack of rigorousstudies evaluating its effectiveness in different disciplines (Duffy & Jonassen, 2013; Hoban etal., 2015; Kearney & Schuck, 2005). This literature review will cover the different approaches
trialled to embed LGDM into tertiary science education, and it will critically evaluate theassumptions, theoretical models (if any), and the methodology for evaluating the intervention
and its outcomes. Media range from audio podcast (Bartle, 2015), which is considered anelementary form of digital media, to digital story (Rieger et al., 2018), screencast (Yang & Lau,
2018), animation (Wishart, 2017), and video (Hoban et al., 2015; Wishart, 2017). This
literature review will also identify research gaps that have an impact on the implementation ofdigital media assignments in science curricula.
Literature review
Methodology
Research in the field of LGDM is segmented, due to the different names used to describe theintervention such as: (1) digital media for learning (DML); (2) learner-generated content(LGC); (3) student-generated content (SGC); (4) student-generated multimedia (SGM); and(5) learner-generated digital media (LGDM). An additional layer of complexity is the different
digital media types, for example, podcast, digital story, screencast, animations, digital video,
and so on. This literature research excluded blogs and wikis because, although they are formsof digital media, they do not promote multimodal representation of content like the other digital
media types. These types of digital media do not need a storyboard for their production. New
forms of digital media such as 360-degree video, Virtual Reality (VR), Augmented Reality(AR), and games also fell outside the scope of the review. A multi-research strategy captured

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Volume 6 – Issue 3 – Winter 2018available research in the field of LGDM. The literature review followed a protocol ofidentification, screening, filtering, and selection, the crucial steps for information-gathering(Figure 1).

Figure 1. Literature review workflow on LGDM in science education
The keywords presented were used in the process of data gathering across reputableeducational databases such as A+ (Informit), ERIC (EBSCO), Education Database (ProQuest),
Education Research Complete (EBSCO), and LearnTechLib (AACE). The screening provided
all papers that came up with the search. The filtering of papers left only peer-reviewed journals,
conference papers, books, and student-created content. Suitable papers were downloaded andimported on EndNote X8, and duplicate papers were removed. Generic keywords such as
DML, LGC, SGC, and LGDM in conjunction with ‘science education’ generated the highest
number of results (n=412 papers). In most of the cases, the keywords found papers on using
digital media to deliver subject content, for example, DML (n=322 papers), LGC (n=52papers), SGC (n=36 papers), and LGDM (n=2 papers). In the case of podcast (n=87 papers),
only four were on science students creating podcasts. Digital story (n=12 papers) was reduced
to six papers, while screencast (n=5) was reduced to four papers. Digital video (n=43 papers)
was reduced to eleven papers, and blended media (n=2) to one paper. Blended media papers
were added to digital video as in essence; this is a video in the digital media industry.
Animation (n=106 papers) was reduced to nine papers (Figure 1). The following sections
present research on learner-generated podcast, digital story, screencast, animation, and video.
Learner-generated podcasts
A podcast is an audio file, usually recorded and compressed for online delivery (MP3 format),which can be delivered via web platform and downloaded directly to mobile devices for users
to listen to (Geoghegan & Klass, 2008; Reyna et al., 2018). Educators have previously
identified the benefits for students of the learner-generated podcast. Students can learn subject

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content by researching topics and preparing storyboards before recording audio podcasts(Hobbs, 2017). This task helps them to gain a deep understanding of the topic by narrating thecontent of the podcast (Digiovanni, Schwartz, & Greer, 2009). In the process of designing a
podcast, students also develop new skills such as critical thinking (Frydenberg, 2006),
teamwork, and collaboration (Lazzari, 2009; McGarr, 2009). It also improves technical skills,
gets students motivated (Cane & Cashmore, 2008), encourages the development of reflective
learning skills (Forbes, 2015; Lazzari, 2009), transforms the learner from a passive consumerof information into a producer, and enhances student creativity (Struck et al., 2011).
In science education, the available research on podcasting is limited and difficult to compare
because of inconsistent approaches to evaluation. Furthermore, it has methodologicalproblems, for example, some studies collected data equal to or less than a semester, usedcomparatively small undergraduate cohorts of less than 400 (first-year students), or used aqualitative approach via surveys and individual interviews (Fernandez et al., 2015). A
qualitative study implemented learner-generated podcasts for a first-year chemistry class(n=350-400), with students allocated to groups of three. The study evaluated students’
perceptions, task completion, motivation, and engagement and concluded that the learnergenerated podcast led to deep learning of the subject content (Bartle, Longnecker, & Pegrum,2011). The study used two Likert scale questions: (1) The podcast activity helped me to get abetter understanding of chemistry; and (2) The podcast activity was an enjoyable activity. Two
open-ended questions related to the advantages and disadvantages of podcasts for learning werealso asked, and analysed using thematic analysis. The survey response rate was 35%.
Limitations of the study included insufficient survey items to measure what was claimed(student perceptions, task completion, motivation, and engagement). A continuation of thestudy with a first-year chemistry class (n=352) (Pegrum et al., 2015) used a quantitative
approach and supported the previous study. This study found a significant improvement inmarks attained by students who engaged in podcast creation for learning when compared to aprevious year cohort. The main limitation of this study was that comparison data was from theprevious year when podcasting was not used. Neither study used a theoretical model to design
the podcast assessment task, nor was media training offered to students.
In one case, a geography subject used a three-step model that included pre-production,
production, and post-production (Kemp, Kotter, Mellor, & Oosthoek, 2009). Pre-production
included brainstorming, logical structuring of the topic, and storyboarding. The production
stage covered special effects like music and sound, designing the introduction, and recording,
editing, and mixing the podcast. The post-production stage required producing a writtensummary or outline of the podcast and submitting the audio file to iTunes. This model isvaluable but did not incorporate relevant aspects of podcast content discussed previously, suchas the type of content, length, style, purpose, or the pace and intonation. The model is probably
the most complete so far, but it does not have the educator and student role embedded in it. For
example, how will this model inform the educator about designing a learner-generated podcast
task? How will it inform the student about the assessment task? A continuation of the study a
few years later concluded that the task enhanced student learning, competence with technology,
creativity, and science communication skills (Kemp, Mellor, Kotter, & Oosthoek, 2012). The
evaluation had a qualitative approach using informal discussion with students and the teaching
team, and a questionnaire comprising closed and open-ended items. The study used data from2008 (n=40) and 2010 (n=61) and noted low response rates to the questionnaire. This study
used a theoretical model (Kemp et al., 2009) that informed the design of the task and also thecreation of a marking rubric. Limitations included the methodology for gathering the data andthe small size of the cohorts. Another qualitative study in postgraduate engineering students

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(in a mathematics subject) adopted an ‘action research’ approach (planning, action,observation, and reflection) to gauge students’ views on learner-generated podcasting. Students
appreciated the intervention but found podcast creation to be time-consuming and difficult(Adams & Blair, 2014).
In learner-generated podcast in science education, there is no comprehensive model for its
implementation in the classroom which considers content and technical aspects and highlights
educator and student roles. Studies are limited because it is a new approach. Most of theexisting studies used a qualitative approach to data-gathering and analysis and did not use aframework to design the task. Studies are difficult to compare as they were undertaken indifferent disciplines and different settings. Most studies did not provide student training for thetask. The research on podcasting and student learning in tertiary science education is thusinconclusive.
Learner-generated digital stories
Digital storytelling involves making a 3-5 minute video composed of images and voice-over(Martinelli & Zinicola, 2009). It is an arts-based research method that has the potential toexplain complex narratives (Rieger et al., 2018) and to engage the audience and trigger theiremotions by creating an unforgettable experience (Reyna et al., 2017). This digital media typecan be created using PowerPoint, Movie Maker, iMovie, or similar software (Frazel, 2010;
Hussain & Shiratuddin, 2016). Outside science disciplines, digital storytelling has been used
to close the gap between facts and understanding, prompt reflection on experiences, embody
agency, and assist meaning construction and formation of identity (Chan, Churchill, & Chiu,
2017; Martinelli & Zinicola, 2009; Niemi & Multisilta, 2016; Özüdoğru & Cakir, 2017;
Shelton, Warren, & Archambault, 2016). In public health campaigns, digital stories are used toeffectively convey complex messages to the general public (Rieger et al., 2018), but digitalstorytelling in higher education is still under-studied and under-used (Dewi, Savitri, Taufiq, &Khusniati, 2018).
In science disciplines, digital stories have been used to deploy content in blended learning(Molnar, 2018), but rarely used for students to engage in the creation of content. Learnergenerated digital stories have the potential to help students in the classroom to learn bytranslating complex scientific concepts into personal narration. For example, in the process ofdrafting a storyboard, students have the opportunity to transform information into a simple
visual representation (Martinelli & Zinicola, 2009). Regrettably, use of digital stories in scienceeducation as an assessment task is infrequent. The reason behind this is that the scientificcommunity does not see digital stories as a rigorous methodology for presenting information(Martinelli & Zinicola, 2009; Schrum, Dalbec, Boyce, & Collini, 2017).
In undergraduate biology (Year 1) and environmental science (Year 2), the digital story has
been used in assessment tasks (Ross, 2015). However, the methodology for this research was
not straightforward and did not include the analysis of data. Students reported the task of
producing the assignment to be time-consuming (+30 hrs). The study used a marking rubric
that assessed content, creativity, and language. ‘Creativity’ seems mismatched with this rubric,
which was more concerned with the technical aspects of the task. Outside creative disciplines,
how can creativity be measured objectively? The research did not include qualitative or
quantitative data. As a result, the study is inconclusive.
A study has reported on the use of digital story in postgraduate science education to improvestudent communication skills. The framework used was based on the process developed by the98

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Story Center in 2014 and included seven steps: (1) students see examples; (2) concept check;(3) brainstorming; (4) script and critique; (5) storyboard; (6) production; and (7) exhibition andevaluation. The methodology for the study was unclear, and it used three examples of digitalstories developed by students, surveys, and interviews, but the data was not included in thepaper. The study concluded that digital stories provided an opportunity for ESL science
students to explore digital media and multimodal communication, learn about the subject topic,
and improve communication skills (Purser, 2015). Another study with undergraduate (n=8)
and postgraduate students (n=4) used the framework described and asked six open-ended
questions to evaluate the intervention. Students said that they enjoyed the freedom to creatematerial using their ideas and skills and reported minor technical issues (Martinelli & Zinicola,2009).
The uses of learner-generated digital stories in science education are in its early stages.
Although theoretical frameworks for storytelling have been applied, a methodology for
gauging student learning or perception is not comprehensively explained in the literature. The
research in this field seems to be more anecdotal than rigorous.
Learner-generated screencasts
Screencasts are recordings of the computer screen, with or without narration, using software
such as Camtasia Studio, CamStudio, Macintosh QuickTime, or online applications like
Screencast-o-Matic. They have become popular in higher education to develop training
materials for students in flipped classroom interventions (Carney, Ormes, & Swanson, 2015;
Talbert, 2014). Student-generated screencasts are only a recently emerging trend in higher
education, and the literature is scarce. A literature search on student-generated screencasts
identified only four papers in science education, exclusively in computer programming
subjects. In one study, students were asked to create screencasts as a form of note-taking intutorials. The trial included two groups, the group of students creating screencasts and theothers who did not. The research presented data from four semesters (n=225) and reported only
on test scores. Findings suggested that students who created screencasts as note-taking during
tutorials achieved better scores than students who used traditional note-taking (Powell, 2015).
The limitations of this study included the lack of survey data and interviews and the possibility
that students shared their screencasts with their peers.
A second study reported the use of learner-generated screencasts as tests of complement code
writing. Previously, students had been asked to provide screenshots for the task (Woods, 2015).
This paper’s research methodology is unclear, and it is not known how many students
participated in the trial. The author concluded that the screencasts generated by the students
helped the instructor to evaluate the assessment task. The intervention was teacher-centred
rather than student-centred. The author also mentioned that the screencasts helped students toreflect on code writing. A similar approach was reported as a useful teaching approach ingeometry, where students used screencasts for self- and peer-review (Shafer, 2010).
It is questionable whether creating a screencast that may, for example, not require a script canbe considered learner-generated content. In contrast, if the task is to create a training video on
how to use software or an application, students will need to be familiarised with the tool andlearn a storyboard approach. They will also need working knowledge about editing thescreencast and about digital media principles like colour schemes and typography, as they arelikely to use on-screen text and prompts.

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Studies of learner-generated screencasts in tertiary science education are currently rare in theliterature, and future studies should consider the pedagogy behind the task. For instance,
students learning about an application or software by preparing screencast training material
would be an ideal use of screencast. That task would require a storyboard to help students tolearn the software. There is a need to undertake studies on learner-generated screencasts thatuse defined methodologies and large samples to test their effectiveness for student learning.
Learner-generated animations
Animation is a sequence of frames put together to create a sense of motion. Producing
animations was a time-consuming task until a decade ago. Designers could spend weeks
creating an animated story. The affordability of technology helped to overcome this problem.
For example, services such as Pow-toon (Graham, 2015) and GoAnimate (Stratton, Julien, &Schaffer, 2014) allow students to create animations in a short timeframe. This type of animation
is called whiteboard animation, and it has been highlighted to communicate concepts online(Türkay, 2016). Online companies are using this approach to showcase their products on social
media. In the past, animations were created using Flash Professional and required knowledgeof ActionScript coding (Moock & Epstein, 2001). Educators of pre-service teachers coined theterm ‘slowmation’ (slow animation) to refer to a type of student-created animation (Hoban,
2007; Hoban et al., 2015; Jablonski, Hoban, Ransom, & Ward, 2015). New names for existing
categories of digital media will only create more atomisation of the LGDM literature andshould preferably be avoided.
Constructionist theory, instructional design frameworks, and semiotic theory have all beenused to explain learning with animations. For instance, when students prepare an animation to
explain a science concept, they clarify, check, and refine their understanding (Hoban, Nielsen,& Carceller, 2010). Although there is no existing framework for implementing learnergenerated animations in the classroom, the literature does discuss possible features of such amodel, like purpose, timing, orientation, materials, and technology (Hoban & Nielsen, 2013).
With pre-service science teachers, case study design and discourse analysis (n=3) have beenused to understand learning through creating a science animation. Research with pre-service
teachers found that the process of meaning-making involved in such exercises fostered learningand reinforced the scientific concepts being conveyed. Multimodal representation of content,such as writing, still images, and voice-over, helped them to learn (Hoban & Nielsen, 2013).
This research is comprehensive but cannot be generalised to a large cohort of students outside
the discipline of education due to its qualitative nature. A study where undergraduate
pharmacology students created animations during a two-hour tutorial found that students were
anxious (39%), apprehensive (27%), intimidated (26%), lacking time to complete the project(67%), and lacking technical skills (54%) (Pearce, 2014). However, the study reported thatstudents agreed that they had developed problem-solving, critical thinking, oral
communication, teamwork, and management skills from the exercise. This research did not use
a theoretical model for assignment design, and students did not receive media training. The
data presented was gathered from a qualitative survey alone.
There is a lack of extensive studies to reinforce previous findings on the impact of animationin science education. The current affordability and ease of production of whiteboard animations
created entirely online opens the possibility of a large-scale study to gauge their effect onlearning further.

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Learner-generated video
Learner-generated digital video for tertiary science education is the most common form of
LGDM represented in the literature. Advantages of student-created digital video in education
include the affordability of experiential learning (Coulson & Frawley, 2017), development ofgraduate attributes (Frawley et al., 2015; Pearce & Vanderlelie, 2016), new ways to represent
knowledge (Hobbs, 2017), student engagement (Graybill, 2016), group collaboration (Coulson& Frawley, 2017; Pearce, 2014), project management (Cox, Vasconcelos, & Holdridge, 2010),
and the development of technical skills (Morel & Keahey, 2016). Empirical data to validate
these advantages are not available in existing research. Studies in the field have a flavour ofguesswork, small samples, a qualitative nature, and lack of theoretical models to guide
implementation of the assessment task. Moreover, most studies did not provide student training
in video production. These drawbacks make it challenging to compare studies.
In a third-year undergraduate course in physiotherapy (n=75), no framework was used to
implement the LGDM assessment task, and no training in video production was delivered to
students. The results reported were mixed (Coulson & Frawley, 2017). Students reported stressand anxiety from problems related to the time given to complete the assignment, the group
work involved, and assignment design issues. The study used a qualitative survey alone togauge student perceptions and evaluate the intervention. A study in a geography subject
followed the same pattern and lacked a framework to implement the assignment. However, it
used a six-phase approach for the assessment task: (1) topic selection; (2) thesis statement andinformation/image gathering; (3) first narrative draft; (4) storyboarding; (5) videography
workshop; and (6) viewing of videos on YouTube. Evaluative data was collected from routine
institutional student surveys at the end of the semester (Graybill, 2016). The study reported
student satisfaction with the assessment task, but issues with groupwork contributions and alack of technical skills for creating a video.
Another study conducted with postgraduate students in health information management (n=8),
using a qualitative survey, claimed that the assessment task developed critical thinking by
creating a video that reinforced learning (Morel & Keahey, 2016). The study also suggested
the development of project planning, management, and collaboration skills. It highlighted theneed for clear assignment instructions and expectations, student training support, and strategies
to improve groupwork such as assigning roles and responsibilities. The limitations of the study
were its small sample size and the qualitative nature of the data.
Research with fourth-year pharmacy students (n=92) and second-year health sciences students(n=83) across two different institutions, using a qualitative survey, reported that students
enjoyed working in teams and the creative nature of the task. They also felt that they developed
graduate attributes such as problem-solving, critical thinking, communication skills, and time
management (Pearce & Vanderlelie, 2016). However, the study reported that students were
anxious (59%) and apprehensive (87%) about the task. Students did not receive video training
or any technical assistance, and the assessment task did not use a theoretical framework.
Limitations of the study included students undertaking different assessment tasks and being
evaluated at different times.
Other studies on learner-generated digital video in science education (biology and geography)
have the same limitations described above. These studies have in common small sample sizes,
qualitative surveys, and lack of student training in digital media production (Anderson, 2013;
Fuller & France, 2016; Pirhonen & Rasi, 2016). They concluded that both students andeducators required coaching in video production (Fuller & France, 2016) and that storyboards

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were essential to master subject content before moving to video production (Pirhonen & Rasi,2016). As previously suggested in the field of educational technology, it is likely that there are
more cases of LGDM implementation in science education that have not been formallyevaluated and published (Liu, 2016).
Conclusion
Learner-Generated Digital Media in tertiary science education is currently in its embryonicstages. There is no practical model for implementing LGDM assignments in the classroom
which can be applied regardless of the digital media type. The lack of a model means that
LGDM as an assessment tool is under-theorised and the lack of coherent methodologies toevaluate the student learning experience means the field is under-researched. However, a
deficit in educator knowledge of digital media production workflow and digital media
principles adds an extra layer of complexity to using LGDM assignments. The lack ofcompelling marking rubrics and neglect of student training provides evidence for this claim.
These gaps in knowledge could explain the current status of research in the field. Learnergenerated digital content, regardless of the type, has been acknowledged to have various
advantages for science learning.
There is a great need to develop a practical framework for the design, implementation, andevaluation of LGDM assignments in tertiary science education. Ideally, the framework would
be applicable across disciplines and different digital media types such as podcast, animation,
digital story, or video. Its purpose would be to guide educators in designing, implementing,and evaluating digital media assignments and to get students to understand the rationale of theassessment task. The framework should be student-centred and should consider digital media
training, groupwork contributions, student feedback, reflection, and so on.
Looking at the gaps in the literature, it will be necessary to develop a second model to informstudent digital media training which considers conceptual, functional, and audiovisual skills.
Conceptual skills developed here are searching for information and producing a storyboard,essential steps for students to understand the content before moving to the digital media
production stage. Learning functional skills will ensure that students are capable of using
digital media applications and will reduce the anxiety and apprehension reported with LGDM
assignments. The digital media principles that apply to the creation of compelling digital media
will develop audiovisual skills. Currently, most research on LGDM assignments perpetuatesthe ‘digital natives’ myth which leads to neglect of student training in digital media. Lack ofstudent training could be due to the limited working knowledge of educators outside thedisciplines of visual design, multimedia, film, or digital media about digital media production
workflow.
A third model could use a taxonomy of digital media types, based on the skills required todevelop the different types of digital media. This framework would inform educators indesigning the LGDM task, mark weighting, group size, and comprehensive marking rubrics.
From the student perspective, this taxonomy would inform them about the skills and training
they need to produce LGDM assignments and to succeed in the assessment task.
Finally, a model is needed to define the minimum audiovisual skills required to produce digital
media, for example, the digital media principles for production of compelling digital media
artefacts, such as layout design, colour theory, typography, use of images, and basic video
techniques. The US literature has highlighted that problems are not related to technology

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hand, research papers on LGDM there have reported that students successfully produced
quality digital media presentations (Coulson & Frawley, 2017; Pearce & Vanderlelie, 2016).
Without an understanding of digital media principles and a good marking rubric, how can weevaluate the quality of LGDM content objectively? Moreover, how can educators fairly mark
LGDM assignments if students do not receive formal training in digital media principles? It istherefore essential for educators implementing LGDM assignments to have a soundunderstanding of digital media production and its principles. LGDM should not be used
exclusively as a vehicle for learning content, but also for learning to communicate effectively
using digital media. Effective communication in the digital space is a required attribute for21st-century graduates.
With the creation of the models discussed, it will be possible to apply a systematic approach todesigning LGDM assignments for science education. Finally, a methodology for evaluating
learning with digital media creation will be required to fill the gap in the literature and validate
current assumptions about the benefits of LGDM. This approach should include a validated
survey to gauge student attitudes to technology for learning and career development, theirunderstanding of the assignment, their knowledge construction, and open-ended questions.
Methodological triangulation of surveys against group dynamics and student marks should
provide a sharper picture of the effectiveness of LGDM assignments.
This paper has highlighted the potential of LGDM assignments for science education, but
rigorous studies taking systematic approaches to assignment design, implementation, andevaluation are required to validate assumptions.

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Corresponding author: Jorge Reyna
Contact email: jorge.reyna@uts.edu.au

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Towards a Cross-Cultural Conceptual Framework for Researching Social andEmotional Education
Edurne Scott Loinaz
HAHA Academy, London

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Abstract
The central aim of this study was to investigate how different countries practice social andemotional education (SEE) using a comparative research design to create a cross-culturalconceptual framework. The study used a sequential quantitative-qualitative analysis with acomparative design that included 750 teachers. Cross-cultural differences were found in the
research sample regarding teachers’ self-perceived role in socialising emotion: specifically, theteachers’ openness to emotional expression in the classroom, and what social and emotional
aptitudes were more likely to be included as part of SEE provision. More variation was foundin these variables internationally compared to intranationally. A conceptual framework using
two dimensions was created in order to aid future cross-cultural research regarding SEE
provision and the study of emotional rules in the teaching profession: the Ideal Affect(likelihood of suppressing rather than expressing emotion) and the Ideal Self (likelihood ofdeveloping skills for independence versus interdependence).

Keywords: social and emotional education, comparative education, emotional wellbeing,social and emotional learning, emotional intelligence

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Social and emotional education (SEE) is the educational process that aims to develop socialand emotional competencies, both intrapersonal (e.g., developing feelings of self-worth, selfdiscipline and managing stress), and interpersonal (e.g., safeguarding and promoting thewellbeing of others, negotiating and resolving conflict and appreciating diverse perspectives).
Given past findings that culture influences the way adults socialise children’s emotions(Friedlmeier, Corapci, & Cole, 2011), it is unfortunate how scant the research dedicated tocross-cultural differences in SEE provision currently is. The necessity for research in this area
is made all the more obvious the more emotional wellbeing is researched: take, for instance,
the longitudinal study by Layard, Clark, Cornaglia, Powdthavee, and Vernoit (2014) whichfound that a person’s wellbeing as an adult is more dependent on their emotional health when
they were a child compared to their academic attainment in school and their level of wealth asan adult. How schools develop social and emotional competencies and promote emotional
wellbeing in children and young people is thus of great importance. This paper aims to fill the
gap in the research literature by conducting the first multiple-country study regarding teachers’
beliefs and practice of SEE in order to create a conceptual framework to compare SEE
provision from culture to culture for future research. This will hopefully aid in the crosscultural study of ‘emotional rules’ in the teaching profession, and how these impact otheraspects of learning and school life (Zembylas & Schutz, 2009).
Literature review
The available literature regarding SEE includes the evaluation of social and emotional learning
programmes in schools cross-culturally (Sklad, Diestra, De Ritter & Gravesteign, 2012;
Wigelsworth et al., 2016); a comparison of educational policy relating to social and emotional
skills (Domitrovich, Durlak, & Gullotta, 2015; OECD 2015; Emery, 2016); and a summary of
relevant SEE policy and best practice in various countries (Fundacion Botin, 2008, 2011, 2013,2015). This research, however, does not focus on teachers’ opinions and beliefs regarding SEE,
and tends to treat teachers as faceless variables in the testing of outcomes (i.e., whether
students’ social and emotional aptitudes improved after a SEE intervention using psychometrictesting). Research that does exist involving teachers’ opinions and practice of SEE have so far
been done as single-country studies: in Greece (Triliva and Poulou, 2006; Poulou, 2017), in
Australia (Djambazova-Popordanoska, 2016), and in Turkey (Esen-Aygun & Sahin-Taskin,2017).
In order to research multiple countries for the present study, the variables of culture were
explicitly defined as information (ideas, beliefs, values, skills, attitudes, and knowledge)
acquired from other individuals via social transmission mechanisms (e.g. teaching, imitation)(Mesoudi, 2011). The most common method to compare different cultures in past research has
been the use of cultural dimensions, and the first systematic review of studies of culturaldifference was completed by Inkeles and Levinson (1969). They proposed three ‘culturalissues’ that commonly differentiated groups: relation to authority; self-concept and thedefinition of gender roles; and conflict resolution, which primarily relied on the expression
versus inhibition of emotion. Inkeles and Levinson’s work greatly influenced the Dutch
comparative psychologist Geert Hofstede (1980) who used 100,000 standardised
questionnaires given to IBM workers in over 53 countries to identify the variables that would
predict the cultural differences in his dataset. Hofstede identified four cultural variables in total
and scored each country’s cumulative answers as a position from 0-100 on each dimension.
Taras, Kirkman and Steel’s (2010) ‘A Three-Decade, Multilevel, Meta-Analytic Review of
Hofstede's Cultural Value Dimensions’ found 598 studies that used Hofstede’s framework

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representing over 200,000 participants and concluded that the dimensions remain theoretically
relevant to the study of cultural differences.
Hofstede’s 1986 paper, ‘Cultural Differences in Teaching and Learning,’ was used in thepresent research to create a series of hypotheses using two specific dimensions - theUncertainty Avoidance (UA) Index (the degree to which members of a society feel
uncomfortable with uncertainty and ambiguity), and the Masculinity Index (MI) (the degree ofdifferentiation of gender and the division of emotional roles). Hofstede used these dimensions
to predict cultural differences in the teacher-student relationship - ‘the device par excellenceby which that culture itself is transferred from one generation to the next’ (Hofstede, 1986, p.
302) - and these predictions helped form the hypotheses for cultural difference in SEE
provision that were used in the present research (summarised in Table 1).
Table 1. Cultural differences in teacher/student relationships and predictions for social andemotional education provision.
Low UA
Students feel
comfortable in
unstructured
learning
situations.

Low MI
System rewards
students’ social
adaptation.

Minimum
emotional andsocial roledifferentiation
between thegenders.
Interdependence
ideal.

SEE hypotheses
SEE has vague
objectives, and is nottimetabled. Low
training in SEE.
Preference for implicit
SEE skills and relianceon modelling. Low
expression of emotion.
SEE hypotheses
SEE is believed to be
as important as
academic subjects.
Teachers feel
responsible forsocialising students.

High UAStudents feel
comfortable instructured learning
situations.

SEE hypotheses
SEE has precise objectives,and is timetabled. Hightraining in SEE. Preference
for explicit SEE skills andreliance on didactic teaching.
High expression of emotion.

High MI
System rewards
students’ academic
performance.

SEE hypotheses
SEE is believed to be lessimportant than academic
subjects. Teachers do not feel
responsible for socialising
students.

Similar replies to theimportance of SEE
from both male andfemale teachers.

Maximum
emotional andsocial roledifferentiation
between thegenders.
Independence
ideal.

Different replies to theimportance of SEE between
male and female teachers.

Interpersonal skills areprioritised(safeguarding andpromoting thewellbeing of others;social skills,
negotiating andresolving conflict;appreciating diverse
perspectives).

114

Intrapersonal skills areprioritised (self-discipline;
setting goals; developing
feelings of self-worth;recognising triggers of anger;
understanding, and labelling
emotion; relaxation
techniques).

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Methodology
The study used a sequential quantitative-qualitative analysis with a comparative design, with
750 teachers in an initial quantitative phase participating in a questionnaire, and 22 teachers in
the following qualitative phase participating in semi-structured interviews. The comparative
design used a contrast of contexts method which works best when the cases that it juxtaposes
are maximally different (Skocpol & Somers, 1980). Thus, the four case studies for this current
research project were chosen from Hofstede’s (1986) cultural groupings that were most likely
to socialise emotion differently, as well as other variables to differentiate the cases (morespecifically, whether the country had SEE policy, and whether the education system wascentralised or decentralised). The case studies chosen were:
1. United Kingdom: a highly decentralised education system with varying levels of
SEE provisions (High MI, Low UA)
2. Spain: a regionally-centralised education system with varying levels of SEE
provisions due to region-specific initiatives (Low MI, High UA)
3. Sweden: a highly decentralised education system, with no SEE provision (LowMI, Low UA)
4. Greece: a highly centralised education system, with no government-funded SEE
provision (High MI, High UA)
The questionnaire was also devised to compare both international variation, and intranationalvariation in ten Likert scale questions. Frequency distributions by item were examined for bothsignificance value (p) and magnitude of Cohen’s effect size (d). This was due to themethodological and theoretical significance of within-culture variation, discussed in more
depth by Au (1999), who warned that average levels of conformity in each culture cannot reveal
cross-cultural difference in variance, and what is needed is the standard deviations of measures
between each of the case studies.
For the quantitative data collection, surveys were used to collect original data using the Opinio
web-based survey software. In order to have as many teachers participate as possible, and to
be able to have a random sample, virtually every school in each of the four countries was sent
an invitation email to participate using the SwiftMailer software and University College
London (UCL)'s simple mail transfer protocol (SMTP) email server. A copy of thequestionnaire invitation was sent to every school or teacher email address collected fromeducation department websites and publicly available ‘freedom of information’ documents.
The self-selection bias is thus one of the biggest limitations of the study.
For the qualitative data collection, semi-structured interviews were conducted with 22 teachers
who were a sub-sample of the original quantitative sample (and included every teacher who
self-selected to take part in a 50-minute interview as part of the prior questionnaire). The
demographics for both the participants in the quantitative and qualitative section of the researchwere similar: 73% female and 26% male; 52% held an undergraduate degree, 46% a
postgraduate degree, and 2% a high school degree; 13% were preschool teachers, 52% primaryteachers, and 35% secondary teachers; 11.7% were 20-30 years old, 22.3% 31-40 years old,
32.7% 41-50 years old, 30% 51-60 years old, and 3.2% 60+ years old. Questionnaire responses
to open-ended questions and interview responses were analysed using Braun and Clarke’s(2006) six-phase model of thematic analysis, as well as quantified to ascertain their frequency.
Full information on the purposes of the research were provided to all participants in the initial115

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email sent to teachers, plus an invitation to be included in the dissemination of the findings.
All participants had the right to withdraw from the research at any time.
Findings
Ideal affect
From the hypotheses created from Hofstede’s (1986) uncertainty avoidance index, it waspredicted that the Swedish and UK teachers (who rated low on the uncertainty avoidance index)
would model the suppression of emotion (the inhibition of affect) and favour the control andmanagement of emotion in their classrooms. The curriculum would have vague objectives - if
any at all - and SEE would be more likely to be infused into the curriculum as implicit skills
learnt via modelling, rather than taught as a separate subject. Furthermore, most teachers would
not receive specific SEE training. These predictions and the current study’s findings for thesetwo countries are summarised in Table 2.
Table 2: Results in cultural differences in teacher/student relationships and social andemotional education provision in Low Uncertainty Avoidance cultures.
Prediction

Were the predictions confirmed by the findings?
UK

SwedenSEE has vague
objectives, andis nottimetabled.

No. Out of the four countries, the UK was
the most likely to have SEE timetabled(61% of preschool and primary schools,
and 56% of secondary schools). However,this was partly due to the Labour
government’s SEAL* framework which
was discarded by the Coalition
government in 2010.

Yes. SEE did have vague objectives andno SEE framework had been created nor
implemented in Sweden. Only 26% of
Swedish primary teachers and 34% of
secondary teachers said they timetabled
SEE.

Teachers model
the suppressionof emotion.

Yes. Only 63% of UK teachers in thesample agreed that teachers should be
comfortable expressing their emotions inclass - the lowest percentage in thesample.

Low training in
SEE.

No. UK had the highest training in SEE
out of the four case studies (40% of UK
teachers said they had received training in
SEE).
No. 38% of primary school teachers, and
34% of secondary school teachers said
they taught SEE implicitly - the lowest
percentage in the sample. Developing
students’ social and emotional aptitudes
were more likely to be mentioned by UKteachers also.

Yes. Although 73% of Swedish teachers
agreed that teachers should be
comfortable expressing their emotions inclass, only 51% of teachers agreed thatnegative-evaluating emotion should be
displayed in the classroom.
Partly. Although 38% of Swedish teachers
said they had received training for SEE,
Sweden had the highest drop in SEE
training over the past two decades.
Yes. Teachers were much more likely toteach SEE implicitly (67% in primaryschool, and 56% in secondary school).
Swedish teachers discussed the quality of
their interactions with their students andmodelling more frequently, rather thandeveloping and assessing students’ socialand emotional skills explicitly.

Preference for
implicit SEE
skills andreliance onmodelling.

*The Social and Emotional Aspects of Learning’ (SEAL) programme (Department for Education and Skills,
2005) was the Labour government’s universal, whole-school social and emotional education programme createdas an ‘objective list model’: a series of skills as defined by a steering group (42 competencies in total in fiveskill groups: self-awareness, self-regulation, motivation, empathy and social skills), that could be measured andassessed by teachers. By 2010 SEAL was operational in 90% of primary schools and 70% of secondary schools(Humphrey, 2012).

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On the other hand, Spanish and Greek teachers who rated high on the uncertainty avoidancescale would allow for more emotion to be displayed in the classroom (the expression of affect)
and prioritise how emotions should be communicated. The SEE provision in Greece and Spain
would have explicit objectives, would be more likely to be timetabled in the school day andtaught didactically as well as by modelling, and most teachers would receive training. These
predictions and the corresponding findings are summarised in Table 3.
Table 3. Results in cultural differences in teacher/student relationships and social andemotional education provision in High Uncertainty Avoidance cultures.
Prediction
SEE has precise
objectives, and istimetabled.

Teachers encourage
the expression ofemotion.

High training in SEE.

Preference for explicit
SEE skills and relianceon didactic teaching.

Were the predictions confirmed by the findings?
Spain
GreeceNo. Spanish primary school
No. Greece does not currently have ateachers were more likely to
SEE framework, and was the least
teach SEE using relational
likely to have the subject timetabled out
approaches and modellingof all the four case study countries: 32%(66%) rather than timetablingof Greek primary school teachers and
SEE with precise objectives
20% of secondary school teachers in the(29%). In comparison, 44% of sample timetabled SEE provision.
Spanish secondary school
teachers timetabled SEE
showing a significant
difference between SEE
provision in primary andsecondary school.
Yes. 83% of Spanish teachers
Partly. 67% of Greek teachers said theyin the sample agreed thatshould feel comfortable expressing theirteachers should be comfortable emotions in class - the second lowest in
expressing their emotions inthe sample. However, Greek maleclass - the highest in theteachers were found to be significantly
sample. 72% of teachers also
more comfortable expressing theiragreed that negative-evaluating emotions in class, including negative
emotion should be displayed in emotion, compared to female Greek
the classroom - again, theteachers (p < 0.05, d = .44 suggested ahighest in the sample.
small to moderate practicalsignificance).
Partly. The Spanish teachers
Yes. 34% of teachers trained in SEE,
were the least likely to havewith a large emphasis on teacher
received SEE training (23%),
training for SEE in the university - a
however, Spain had the largest percentage that was relatively highincrease in new teachers
compared to other countries.
training for SEE relative to theother countries.
Partly. A higher percentage of Yes. When SEE programmes were
Spanish teachers were found to implemented, these were more likely toteach SEE explicitly in
be taught explicitly focusing on
secondary rather than indeveloping and assessing students’
primary.
social and emotional skills.

Ideal self
The second Hofstede dimension that was used in the present research was the Masculinity
Index. For this dimension it is the UK and Greece that are rated high on the index, predictingthat the Greek and UK teachers would emphasise skills that help students be independent, for117

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example: self-discipline, setting goals and developing feelings of self-worth. Other hypotheses
included that teachers of different genders would also hold different views in terms of their
responsibility to socialise students, and that there would be a greater tendency for teachers tobelieve that the role of education is solely academic achievement and not the socialisation of
pupils (which they would believe to be the responsibility of parents/guardians). The findings
for these two countries are summarised in Table 4.
Table 4. Results in cultural differences in teacher/student relationships and social andemotional education provision in High Masculinity Index countries.
Prediction
SEE emphasises
intrapersonal skills more

SEE believed to be lessimportant than academic
subjects.

Maximum differentiationin gender responses
regarding SEE.

Female teachers more
likely to feel responsible
for socialisation

Were the predictions confirmed by the findings?
UK
GreecePartly. 56% of UK teachers
Partly. 45% of Greek teacherstaught intrapersonal skills
taught intrapersonal skills (which
regularly (e.g., developing
was relatively higher compared tofeelings of self-worth, selfSwedish responses), but 52% of
discipline, managing stress) Greek teachers in the sample said
the highest in the sample.
they were more likely to regularly
However, UK teachers were just teach interpersonal skills (e.g.,
as likely to teach interpersonal
safeguarding and promoting theskills (65%).
wellbeing of others, negotiating
and resolving conflict, appreciating
diverse perspectives).
Partly. This was a subject that
Yes. SEE was largely defined byhugely divided the UK
Greek teachers as a means to an
participants with one groupend to improve academic
believing SEE was beyond their attainment.
remit, and the other believing
SEE was the keystone tolearning.
No. UK had the leastYes. Greece had a significant
differentiation between male and difference between male andfemale teachers in the present
female teachers in multiple
study.
responses compared to the othercountries: male teachers
felt more comfortable expressing
emotion, believed they had better
teacher-student relationships, andthat they had better relationships tostudents’parents compared to their female
colleagues.
No. Both male and female
No. Both male and female teachers
teachers believed themselvesbelieved themselves responsible
responsible for socialisation- no for socialisation - no significant
significant difference was found. difference was found.

In turn, Spain and Sweden which are rated low on the masculinity index by Hofstede, werehypothesised to be more likely to help students learn skills that let them live in harmony withothers, such as: safeguarding and promoting the wellbeing of others, social skills, negotiating
and resolving conflict and appreciating diverse perspectives (empathy). Both male and female
teachers would feel responsible in socialising students, and think this responsibility to be as

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important as the academic achievement of their students. The predictions are summarised inTable 5.
Table 5. Results in cultural differences in teacher/student relationships and social andemotional education provision in Low Masculinity Index countries.
Prediction
SEE emphasised
intrapersonal skills
more.
SEE believed to be asimportant as academic
subjects.

Minimum
differentiation ingender responses
regarding SEE.

Both genders feel as
responsible forsocialising students.

Were the predictions confirmed by the findings?
Spain
SwedenYes. More interpersonal skills
Yes. More intrapersonal skills were
were regularly taught by Spanish
regularly taught by Swedish teachers
teachers (63%) compared to(53%) compared to intrapersonal
intrapersonal skills (49%).
skills (41%).
Yes. There was a large
Yes. Although SEE is treated ascommitment to SEE and theoutside of the teacher’s remit, it was
importance of emotion todefinitely within the school’s remit,
learning. Those teachers whoand school counsellors are available
believed school was simply about to all students in Sweden.
academic attainment made up asmall minority of the sample.
No. A significant difference was
No. Female teachers believed
found in multiple answers.
themselves more responsible forFemale Spanish teachers weresocialisation than male teachers (seefound to be more likely to believe below).
that emotion is fundamental tolearning, that children can betaught SEE skills, that they should
be responsible for socialising
students, and that their students
were offered enough opportunities
to verbalise their emotions.
No. Female teachers believed
No. Female teachers believed
themselves more responsible tothemselves more responsible forsocialise students than malesocialisation than male teachers inteachers in the sample
the sample (p < 0.05, d = .48(p < 0.05, d = .29 suggested asuggested a small to moderatesmall practical significance).
practical significance).

Intraregional versus international comparisons
A common objection in the literature regarding the comparative field is that international
comparisons tend not to take into account the differences within each country, what is referred
to in the literature as the intranational differences (Au, 1999). To address this issue, Likert
scales in the current study were also assessed at the regional level to analyse what intranational
differences did exist. Two items were chosen for this exercise, one with the most cross-culturaldifferentiation: ‘Not enough attention is devoted to social and emotional education in myschool’ representing a divergence in two groups (Greece and Spain versus Sweden and theUK), and one with the least cross-cultural differentiation: ‘My students have consistentbehaviour goals between home and school’ representing the least divergence (where Greece,
Sweden and the UK had similar responses compared to Spain). Four regions with the highest
number of respondents were chosen for each of the case study countries: Attica, Macedonia,
Peloponnese and Thessaloniki for Greece (n=83); Balearic islands, Canary islands, Castile
Leon and Navarra for Spain (n=166); North Middle, South Sweden, Stockholm and West

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Sweden for Sweden (n=75); and East Anglia, Midlands, Scotland and South East England for
the United Kingdom (n=152).
When looking at the Likert scale with the most cross-cultural divergence (‘Not enoughattention is devoted to social and emotional education in my school’), only one statisticallysignificant difference at p < 0.05 was found intranationally: this was in Spain between Navarra
and the Canary Islands (d = 0.45, which suggested a moderate practical significance), which,fittingly, are regions found almost 2,500 kilometres away from each other. Whereasinternationally the variance in effect sizes varied in effect from d = 0.18 to d = 0.92,
intranationally the variance in effect sizes varied from d = 0.007 to d = 0.47. International
differences were thus more statistically significant and of a larger practical significance thaninterregional differences within the same country for this Likert scale (see Table 6). As can beseen with each of the four regions in each country, Spain and Greece were far more likely tohave higher means than Sweden and the UK, suggesting that teachers from the former countries
were more likely to be dissatisfied with their school’s SEE provision compared to Sweden andthe UK- this corroborates the international differences of the entire sample.
Table 6. Average mean answer from four individual regions in each case study country ‘Not
enough attention is devoted to social and emotional education in my school’.
UK
Mean
SD
Number
ScotlandEast Anglia
Midlands
GreeceMeanSD
NumberAthens
Peloponnese
Thessaloniki
Spain
Mean
SD
NumberBalearic
islands
Castile Leon
Canary islands
SwedenMean

South East
2.63
1.2
62
0.08
0.15
0.19
Macedonia
3.3
1.1
23
0.17
0.21
0.33
Navarra

3.43
0.9
37
0.17 -

East Anglia
2.43
1.4
40
0.03
Peloponnese
3.06
1.2
16
0.1
Castile
Leon
3.27
2.96
1
1.1
60
25
-

0.47
0.45*

0.29 0.29

South

Scotland
2.53
1.2
19
0.08
0.11
Attica
3.11
1.2
27
0.04
0.15
Balearic Isl.

Stockholm
2.88

2.79

120

Midlands
2.39
1.3
31
Thessaloniki
2.94
1.1
17
Canary Isl.
2.95
1.2
44
-

0.01 -

North
middle

West
2.5

2.46

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Number
Stockholm
North middle
West

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1.1
17
0.08 0.4
0.4

1.2
29

0.8
16

1
13

0.28 0.3

0.04 -

Note: The means of responses are presented in descending order. Higher means represent agreement that notenough attention is devoted to social and emotional education in the teacher’s school. The grid is organised topresent all pairwise comparisons and indicates the magnitude of Cohen’s effect size (d) where .2 is small, .5 ismoderate and .8 is large. * Significant at the (p) < 0.05 level ** Significant at the (p) < 0.01 level ***Significant at the (p) < 0.001 level

The questionnaire item with the least divergence (at least between Spain compared to Greece,
Sweden and the UK) was ‘My students have consistent behaviour goals between home andschool’. This item had no statistically significant differences intranationally, although theinternational variations were found to be very similar in terms of effect sizes, with the largest
effect size internationally being d = 0.59, and intranationally being d = 0.51. The regional
differences, nevertheless, mimicked the overall groupings of the international findings, with
Spanish regions being differentiated from the regions in the three other countries; that is to say,Spanish teachers were more likely to agree in every region that their students had consistentbehaviour goals between home and school, whereas teachers from UK, Greece and Sweden
were more likely to disagree (see Table 7).
Table 7. Average mean answer from four individual regions in each case study country ‘Mystudents have consistent behaviour goals between home and school’.
UK

East Anglia

South East

Midlands

Scotland

Mean

3.2

3

2.9

2.8

SD
NumberSouth East
Midlands
Scotland

1.2
39
0.17
0.24
0.38

1.1
50
- 0.08 0.2

1.3
33

0.9
19

GreeceMeanSD
Number
Thessaloniki
AthensMacedonia
Spain
Mean
SD
Number

0.09 -

Peloponnese Thessaloniki Attica
3.1

3.1

Macedonia
3

2.7

0.88
0.8
0.87
15
16
26
0 0.11
0.11 0.44
0.45
0.33
Balearic Isl. Castile Leon Canary Isl.
3.5
3.5
3.4
0.89
0.85
0.97
53
31
41

0.93
19

121

Navarra
3.2
0.91
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Castile Leon
Canaryislands
NavarraSwedenMean
SD
NumberNorth
middleSouth
Stockholm

Volume 6 – Issue 3 – Winter 2018

0 0.11

0.11 -

0.33

0.34

West

North
middleSouth0.21 Stockholm

3.1
1.1
12
0 -

3.1
0.73
14

2.9
0.74
15

2.6
0.86
29

-

-

0.21
0.51

0.27 0.62

0.37 -

Note: The means of responses are presented in descending order. Higher means represent agreement thatteacher’s students have consistent behaviour goals between home and school. The grid is organised to present all
pairwise comparisons and indicates the magnitude of Cohen’s effect size (d) where .2 is small, .5 is moderateand .8 is large. * Significant at the (p) < 0.05 level ** Significant at the (p) < 0.01 level *** Significant at the (p)< 0.001 level

Discussion
Uncertainty Avoidance Index
The only reliable prediction based on Hofstede’s (1986) cultural dimensions across the fourcase studies regarding the Low Uncertainty index was the expression rather than inhibition ofaffect. This dimension originally described by Inkeles and Levinson (1969) was created to
explain differences in conflict resolution by inhibiting or expressing emotion. Taras, Kirkman
and Steel (2010) also found that the predictive power of culture was higher than that of otherdemographic variables regarding emotion (or the ‘ideal affect’ of any given culture), and thepresent research corroborates this finding. Whereas Hofstede’s Uncertainty Avoidance
dimension was able to correctly identify the differences in treatment of emotion in theclassroom, it did not do so in the case of Greece on account of gender – Greek female teachers
felt more inclined to inhibit emotion rather than express it. The UK education system was foundto act more in line with high uncertainty avoidance countries like Spain and Greece (using
specific objectives regarding SEE and emphasis on teacher training), contrary to Hofstede’s
predictions as well. One correct prediction was Sweden’s SEE provision which was more inline with lower uncertainty avoidance countries with its vague objectives and reliance on
implicit approaches.
As they relate to SEE provision, the findings highlight that the inhibition as well as expressionof emotion are currently being socialised both implicitly via modelling and a focus on theteacher-student relationship (relational approach), as well as by explicitly developing andassessing students’ social skills, especially self-regulation and the management of emotion(competence-based approach). Hofstede’s dimensions were found to not predict culturaldifferences due to two reasons: (1) the socio-political context (for example, in the UK the Low
Uncertainty cultural dimension seemed more likely to predict the SEE policy of more
Conservative governments, rather than more Liberal policy, such as the SEAL framework);and (2) the age of the students the participants were teaching (dimensions were more likely to
predict teacher-student relationships in secondary school rather that primary school which

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could imply that Hofstede’s dimensions are more applicable to teacher-student relationships ofolder rather than younger students).
Masculinity Index
The Masculinity Index was partly found to be a reliable predictor for what skills were more
likely to be taught in each culture: intrapersonal skills (e.g., developing feelings of self-worth,self-discipline, managing stress) versus interpersonal skills (e.g., safeguarding and promoting
the wellbeing of others, negotiating and resolving conflict, appreciating diverse perspectives).
This was especially true for the United Kingdom, although these results might just indicate thatthe emotional and social skills that were part of the questionnaire were more relevant to UK
teachers than to teachers in the other case study countries - especially since the framework ofskills used in the study was similar to the UK’s SEAL framework. As for the culturaldifferences regarding gender, this highlights a fundamental flaw in Hofstede’s Masculinity
Index: the cultural dimensions depend on cultural differences remaining the same, and culture
is treated as a relatively stable concept with ‘centuries-old roots’. The inability of thedimensions to predict gender differentiation in three out of the four cases begs to differ. Thechanges in gender relations over the 30 years since Hofstede wrote his dimensions (and almost
50 years since Inkeles and Levinson’s meta review) highlight how culture is not that stable a
concept.
The Masculinity Index, however, was found to be able to predict the difference in ‘selfconcept’. Barrett and McIntosh (1982) identified this as the differences between Right and Leftpolitical ideology. The Left represents the self as one dependent on other people, and theschooling environment is portrayed as a locus of affection that improves students’ social andemotional skills for these interdependent relationships. The Right represents the need for selfhelp, self-support, self-sufficiency and self-respect, and sees the family (and concomitantly,
the school system) as a means of instilling authority and a code of behaviour. Another correct
prediction was the similarities between Greece and the UK, both high on the masculinity index,
where teachers were found to not be as confident about teaching social and emotional skills tostudents as they were more traditional subjects, and where teachers were more likely to be
divided about the importance of academic attainment versus social and emotional education.
Intraregional versus international comparisons
The results show that in the case of teachers’ opinions regarding SEE, the more intranationalvariation there is, the less international variation, and vice versa. In other words, culturaldifferences regarding the socialisation of emotion do exist, along with differences in SEE
provision. This is an important finding for future comparative research. A limitation to thisanalysis was the size of the samples of the individual regions - especially in Sweden and Greece- and this intranational comparison would need to be recreated with a larger sample to assessthe differences more thoroughly.
Recommendations
Despite the weaknesses in Hofstede’s dimensions to predict cultural differences in SEE
provision, it was still an incredibly helpful starting point to begin to research a topic that hasreceived little attention in the past. As Feyerabend (1975) advised, ‘Theories become clear andreasonable only after incoherent parts of them have been used for a long time. Suchunreasonable, nonsensical, unmethodical foreplay thus turns out to be an unavoidable
precondition of clarity and of empirical success’ (p. 18). Precisely for this reason, more detailed
variables of cultural differences in SEE need to be identified, and for this reason the current

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study recommends using an updated conceptual framework to study social and emotional
education in the future (see Figure 1). This conceptual framework combines all of the correct
predictions of Hofstede’s dimensions in the current study: the dimension of ideal affect(whether the teacher is more likely to feel comfortable expressing emotion in the classroom ornot), and ideal self (whether the teacher is more likely to focus on skills for interdependence or
independence). However, this conceptual framework could not incorporate the incorrect
predictions of Hofstede’s dimensions – such as the cross-cultural difference in emotional
expression according to teachers’ gender – and other conceptual frameworks are needed to
study these particular cross-cultural differences.
It is important for future cross-cultural SEE research to highlight the differences between SEE
provision in cultures that are more likely to suppress emotion compared to those that do not, as
well as research the outcomes of differing ‘ideal affect’ on mental wellbeing in general. That
teachers in some cultures are more likely to suppress emotion in the classroom is an important
finding considering that adults socialise children’s emotions by modelling (and thus students
are being taught to suppress their emotions). In this respect, it does not matter how extensive
and timetabled social and emotional education is if the aim of the provision is to more easily
suppress emotion; negative consequences to regularly suppressing emotion have been
extensively studied, particularly to the teacher’s mental health and the concomitant
desensitisation to other people’s emotions (Cameron & Payne, 2011; Taxer & Frenzel, 2015;
Lee et al., 2016). Without discounting the effects of income inequality and socio-politicalfactors on mental health (World Health Organisation, 2009), emotional wellbeing can be
influenced by many other variables that have not received as much attention in the research
literature, and it should be a subject of further study just how much culture and the socialisationof emotion in schools are factors in overall emotional wellbeing.

Figure 1. Plot graph of Ideal Affect (suppression versus expression of emotion) and Ideal Self
(interdependence versus independence skills).

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Conclusion
Cultural differences in teachers’ opinions regarding social and emotional education were found
to exist in the present study, along with differences in SEE provision from culture to culture.
Hofstede's dimensions were able to predict cross-cultural differences in two aspects: thesuppression versus expression of emotion, and the emphasis on intrapersonal skills versus
interpersonal skills. These correct predictions have been used to create a conceptual frameworkfor future research to identify how SEE seeks to develop ideal affect and the ideal self.
However, the correct predictions of Hofstede’s dimensions were vastly outnumbered by what
they failed to take into account, including: differences in teachers’ opinions according togender, the differentiation of the teacher-student relationship between primary and secondary
school, and what countries were more likely to teach SEE more implicitly (relational) rather
than explicitly (competence-based approach). The author’s hope is that the present study canserve to highlight the basic differences in SEE from culture to culture and serve as a foundation
on which future research can be built.

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References
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Corresponding author: Edurne Scott Loinaz
Contact email: edurne@haha.academy

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The Effects of Using Representations in Elementary Mathematics: Meta-Analysis of
Research
Andrzej Sokolowski
Lone Star College, Houston, Texas, USA

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Abstract
The current study provides a meta-analysis of global research on using representations to
support the learning of mathematics in Pre-K through Grade 5. A total of 13 primary studies
encompassing 1,941 subjects was analyzed. The weighted mean effect size for the 13 studies
was reported to be ES = 0.53 (SE = 0.05). A 95% confidence interval around the overall mean
– Clower = 0.42 and Cupper = 0.63 – proved its statistical significance and its relative precision.
The calculated effect size signifies strong, robust support for the use of representations in PreK through Grade 5 mathematics classes and highlights the importance of providing students
with opportunities to construct and explore transitioning between various forms ofrepresentations. Moderator analysis revealed differences among the effects due to a different
type of representation, grade levels, and concepts taught. A synthesis of moderator effects
allowed for a formulation of a general way of applying representations that produces maximum
learning effects and that the teachers can adopt in their school practice. While the effect sizes
provided a means of determining the most effective ways of applying representations,questions about how to develop students’ transitioning from one representation to anotherremain unsolved. A further discussion of the impact of the study findings beyond theboundaries of elementary mathematics classrooms follows.
Keywords: meta-analysis, mathematical representations, internal and external representations,mathematics teaching and learning, elementary school130

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Introduction
It is well documented that reading and understanding a mathematical context requires
embodying various abstract entities such as symbols, graphs, and tasks encoded symbolicallyin a language that students can comprehend. Given that elementary students are at the stage ofdeveloping their abstract thinking, designing effective teaching strategies that would allow
such communication is not an easy task for mathematics educators and curriculum designers
thus attempts are made to make the process more accessible for the learners. Research findings(e.g., Clark & Mayer, 2011) show that learning mathematical objects and the development ofcorresponding mental images are linked. On the other hand, imagination and the ability toconstruct, retrieve and explore internal representations form foundations for learning ofmathematics (Lingefjärd & Ghosh, 2016). The ultimate question that was posited in this study
was what representations are the most accessible to an elementary math student.
Researchers (e.g., Hoffler & Leutner, 2007) have determined that people learn more deeply
from words supported by graphics than from words alone. This finding corresponds with themodern view on mathematical learning, which claims that utilizing multiple representations
and making connections between graphical, symbolic, and verbal descriptions of mathematical
relationships will empower and simultaneously help students develop a deeper understandingof mathematical relationships and concepts (National Council of Teachers of Mathematics[NCTM], 2000; Porzio, 1999). Following this notion, a general agreement exists that using
different mathematical forms of representations and translating between these forms, are key
skills in mathematics (e.g., Ainsworth, Bibby, & Wood, 2002). In order to respond effectively
to learners’ perception, further research is necessary. Nistal, Van Dooren, & Verschaffel (2012)noted that there was a need for research that would focus especially on the contextual factorsthat promote flexible representation choice for students in mathematics. It was hoped that thisstudy would shed more light into this area.
Representations, especially their graphical forms, can also be perceived as learning experiencesthat are transmitted to the learner by pictorial media (Clark & Mayer, 2011). As such, they help
the learner identify meaningful pieces of information and link the information with thelearner’s prior experiences. Although the constructs of using diverse forms of representations
to enhance the development of mathematical concepts and problem-solving techniques havebeen widely researched (e.g., Jitendra, Star, Rodriguez, Lindell, & Someki, 2011; WeberRussell & LeBlanc, 2004), a formal meta-analysis in this domain was not found using standard
library search engines. Students’ early experiences with the content of mathematics might havea profound impact on their further engagement and success in the subject. Therefore this study
emerged to fill the gap and to provide a broader view of using representations to support thelearning of mathematics in elementary school.
Theoretical Background
Representations and Constructivist Learning Theory
The effect of using representations is not new to the mathematics education community.
However, it has recently attracted more attention by being supported by the constructivist
learning theory that leads contemporary research in education (Cuoco, 2001). By treating
mathematical concepts as objects, and thus embodying them with representations that can beobserved and manipulated, a construction of mental pictures in the students’ minds can beevoked (Dubinski, 1991). Such constructed mental pictures are stored in students’ long-term

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memory and are available for retrieval. Research (Zazkis & Liljedahl, 2004) suggests that oneof the ways to induce the process of converting concepts to objects is to create representations
and act on them. Clark and Mayer (2011) suggested that knowledge acquisition is based on thefollowing principles of learning: dual channel – people have separate channels for processing
visual/pictorial material and auditory/verbal material; limited capacity – people can actively
process only a few pieces of information in each channel at one time; and active processing –learning occurs when people engage in appropriate cognitive processing such as attending torelevant material and organizing the material into a coherent structure. Active learning appears
as a method that supports the linkage of external representations with internal images.
Human cognitive architecture (Paas, Renkl, & Sweller, 2003), states that the most crucial
structures affecting the rate of information processing are working memory and long-term
memory. Human working memory has limited capacity as opposed to long-term memory,where capacity is unlimited (Kintch, 1998). For the information to be stored in a learner’s longterm memory, it needs to be processed initially through its working stage. Being presented withcomplex information, the learner might feel overwhelmed, which might result in theinformation not being fully processed. This state will consequently block the information fromreaching the learner’s long-term memory and from being learned and accumulated. The
primary goals of using representations are to reduce the contextual load by converting theinformation to a visual form and to transmit such information to the learner’s visual channel.
This process in return will reduce the need for high capacity of working memory. The virtue
of using representations is rooted in their capacity to present the knowledge of conveyablegraphical embodiments supported by verbal elaborations rather than vice versa. Suchknowledge presentation creates appealing conditions for being longer retained and accessible
for future usage.
Representations in Mathematics
Representations are broadly defined as passive entities. By learner’s active engagement, they
are transformed into active semiotic resources (Thomas, Yoon, & Dreyfuss, 2009) and can be
stored in a learner’s long-term memory. Knowledge externalized by graphics is easily
retrievable for analysis and can be readily exhibited and communicated (Ozgun-Koca, 1998).
Representations as a means by which individuals make sense of situations (Kaput & Roschelle,1997) can be expressed in forms of combinations of written information on paper, physicalobjects, or a carefully constructed arrangement of thoughts. Schnotz (2002) emphasizes thedistinction between descriptive (symbolic) and depictive (iconic) representations. While
depictive representations are most useful to provide concrete information and are often
effective as specific information, descriptive representations usually express abstract
information. Duval (2006) claimed that using various representations in mathematics classes
is a necessity because only multiple external representations allow learners to utilize thedifferent advantages each representation offers. Falcade, Laborde, & Mariotti, (2007) claimed
that the link between external representations and internal representations goes beyond pureanalogy in their functioning and rests on the real tie that can be recognized between particular
tools (external representations) and particular signs (internal representations).
Each representation of a mathematical object brings some aspects to the fore, simultaneously
hiding other aspects of the object and thus affecting the way the object is seen (Laborde, 2007).
Representations can also be used to explore aspects of a context that might otherwise not beapparent to a learner; they amplify properties of mathematical structures not easily imaginable(Monk, 2003). In the process of knowledge accumulation, representations are converted into132

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internal images. Mediated by the level of entry into learners’ memory systems, representations
are categorized as external or internal. Both types of representations are interrelated in the sensethat the meaning of an internal representation stored in a learner’s long-term memory strongly
depends on the learner’s perception of its external counterpart.
External representations (see Figure 1) encompass physically embodied, observableconfigurations – such as pictures, concrete materials, tables, equations, diagrams, and drawingsof one-, two-, or three-dimensional figures or various forms of schemata (Jitendra et al., 1998).

A. Pictorial

B. Schemata

C. Concrete

Figure 1. Examples of representations using in elementary school.
(Source: https://www.google.com)
All these embodiments can be provided in the form of drawings or can be digitalized bycomputer programs. They can also be generated by the instructor as he/she introduces therepresentations to the learners. External representations also encompass dynamic graphics,
which are generated with the help of technology, for example, graphing calculators orcomputer-based simulations (Goldin & Shteingold, 2001). Ainsworth and Van Labeke (2004)
categorize external representations as time-persistent representations, time-implicit
representations, and static representations. In mathematical terms, time-persistent
representations are embodied by algebraic functions, time- implicit by relations and static
representations would encompass any drawings that students produce, not necessarily placing
their products in a coordinate system.
Eisenberg and Dreyfus (1991) noted that students might end up with an incorrect solution if
their algebraic skills are not strong; however, if they possess the skills to graphically solve theproblem or support its solution process, the graphed representation might serve as a backup or
a way of solution verification. Being exposed to mathematical representations, learners“acquire a set of tools that significantly expand their capacity to model and interpret physical,
social, and mathematical phenomena” (NCTM, 2000, p. 4). In this regard, external
representations can also serve as a means to overcome students’ misconceptions in scienceclasses (Thompson & Logue, 2006).
Internal representations encompass mental images corresponding to internal formulations ofwhat human beings perceive through their senses and as such they cannot be directly observed.
Internal representations are defined as the knowledge stored in a learner’s long-term memory.
Internal representations are formulated based on one’s interaction with the environment(external representations) and are altered throughout a lifespan. In the process of learning,
external representations prompt the emergence of internal representations. Being able toformulate concepts’ internal representations through the process of understanding theirexternal embodiments and retrieving the mental pictures plays an essential role in133

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communicating messages in mathematics. Hiebert and Carpenter (1992) maintained thatlearners establish a strong relationship between created external and internal representations
and that the strength of linking these representations determines students ‘understanding.
Furthermore, internal representations of the knowledge accumulated through experiencing
visual representations produce stronger impulses in learners’ long-term memory. Enablingthese experiences by engaging and intellectually stimulating learners through carefully
designed learning environments supported by representations deems to be a significant factor
in nurturing effective learning and developing students’ mathematical knowledge. Nitsch et al.
(2015) found that that to understand the concept of function, that is central in mathematics
curriculum, it important not only to know the different mathematical representations offunctional dependency, but also the translations between these forms of representations. For
students to develop a holistic understanding of the concept of mathematical functions, they
have to be able to identify the connecting elements and to combine these representations.
Synthesis of Prior Research
As the constructivist theory strongly supports the use of representations in the learning process,several research studies have explored the effects of using representations on students’ mathconcept understanding. These results converge with contemporary theories of cognitive load
and multimedia learning principles developed by Clark and Mayer (2011) and have practical
implications for mathematical instructional designs. A meta-analysis of 35 independent
experimental studies conducted by Haas (2005) shed light on using representations as a meansof supporting teaching methods at the secondary school level. Haas concluded that math
instruction, supported by multiple representations, manipulatives, and models, produced a high(ES = 0.75) effect size. Schemas, which are defined as generalized representations that link
two or more concepts are frequently researched at the Pre-K through fifth-grade level. For
example, Jitendra and colleagues (1998) found that having students of Grades 2-6 categorize
problems and then having them solve the problems by using schemas produced a positive
medium-size learning effect (ES = 0.45). The virtue of using representations embodied byschemas is that they are easily converted by learners into internal representations, and, as such,they can be stored in long-term memory and allow for treating diverse elements of information
regarding larger, more general units (Kalyuga, 2006). According to Pape and Tchoshanov(2001), schematic representations also lead to enhanced student problem-solving performance.
Another group of researchers investigated whether representations should be provided to
students or if the students should be the producers of representations (e.g., De Bock,
Verschaffel, Janssens, Van Dooren, & Claes, 2003; Rosenshine, Meister, & Chapman, 1996).
These scholars concluded that if representations are provided, their forms must be sufficiently
informative and detailed to be transferrable by students into mathematical algorithms. They
also emphasized that having students construct their representations benefits the learners themost. The importance of possessing the ability to transfer a given context (e.g., a story problem)
into a representation was highlighted by Jonassen (2003), who claimed that successful problem
solving requires the comprehension of relevant textual information along with the capacity tovisualize that data and transfer it into a conceptual model. Following Riley, Greeno and Heller(1984), developing students’ abilities to identify the matching representation that helps withproblem conceptual understanding should emerge as a priority of elementary mathematics
teaching.

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Representations are also used to support the introduction of new mathematical concepts. For
example, several studies (Tzur, 1999) were conducted on the development of students’
notations of fractional parts of areas, called fair sharing, which provided a meaningful
representation of dividing a whole into parts that were then easily comprehended by elementary
students. Hiebert (1988) noted that students’ understanding of new ideas strongly depends onthe degree to which the learners are engaged in investigating the relations between new
representations and the representations whose understanding is already mastered. A study
conducted by Ross and Willson (2012) not only supported the claim that mathematics students
learn more effectively when instruction focuses on using representations but, moreover, provedthat the most effective strategies for building representations are those rooted in constructivist
learning theory. The range of using representations in Pre-K through fifth grade is wide, thus
synthesizing the experimental research findings and identifying the most effective strategies
manifests as a worthy undertaking.
Challenges of Inducing Representations in Pre-K through Grade 5
Investigating the effect of using representations has recently attracted more attention due tobeing supported by the constructivist learning theory that leads contemporary research ineducation (Cuoco, 2001). Such constructed mental pictures are stored in students’ long-term
memory and are available for retrieval. Research (Zazkis & Liljedahl, 2004) suggests that oneof the ways to induce the process of converting concepts to objects is to create representations
and act on them. Sfard (1991) concluded that the process of transferring abstract mathematicalconcepts into their mental images is challenging for both the learner and the instructor, who is
to guide the learner through the transferring processes. What are the challenges faced byelementary school children as they attempt to embody mathematical structures into visual
representations?
Equations and their conceptualization are frequently investigated in K-5 mathematics research.
Swafford and Langrall (2000) noted that students generally can make use of various
representations and can identify patterns between isolated variables, but they cannot findconsistency among a larger set of variables or generalize the patterns and convert them into
mathematical forms. Dreyfus (1991) suggested four learning phases with representations: using
one representation; (using more than one representation; (making links between parallel
representations; and integrating the representations. Representations at the elementary schoollevel encompass general structures used in mathematics such as ratio, rate, percent or newly
developed schemata for problem-solving, thus pinpointing and understanding how to uncoverthese principles acts as a catalyst for selecting correct representation. According to Swafford
and Langrall (2000), the emphasis in the curriculum at the pre-algebra level should be on
developing and linking multiple representations to generalize problem situations. They
concluded that the lack of generalization skills is rooted in instruction focused on reaching only
the initial stages of problem analysis and leaving the process of generalization for the students
to formulate. A similar conclusion was reached by Deliyianni, Monoyiou, Elia, Georgiou, andZannettou (2009), who observed that first-graders restricted themselves to providing uniquesolutions even though the questions required a general pattern formulation. Other researchers(e.g., Lesh & Harel, 2003) have shown that elementary school children bring potent intuitions
and sense-making tools, yet how to mediate these intuitions with abstract math concepts toembody these concepts into representations is a challenge still facing the mathematics researchcommunity.

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Research Methods
Meta-analysis, with its quantitative methods, was used to compute the research findings. Metaanalytic techniques provide tools to assess the learning effect size of treatments, considering
a gathered pool of studies as a set of data collected within prescribed criteria. By allowing
the measurement of the effect sizes according to the population of participants in primarystudies, such undertaking allows for analyzing a larger number of studies that can vary by
population sizes and also by the conduct (Gijbels, 2005). Furthermore, meta-analysis allows
also for employing subgroup moderator analysis and extracting factors that contribute to themagnitude and direction of the mean effect size.
Research Problems
Based on the prior research, a hypothesis suggesting that using representations in mathematics
helps students comprehend abstract mathematical concepts and enhances the skills of theconcepts’ applications emerged for this study. Understanding the degree to which
representations help learners comprehend the different mathematics entities, compared to
traditional methods of instruction, constituted the main objective of this study and guided theresearch questions:
1. What are the magnitude and direction of the learning effect sizes of using
representations in Pre-K through fifth-grade mathematics when compared to
traditional teaching methods?
2. What are the possible moderators that affect students’ achievement and whatclassroom settings produce the most optimal learning effect sizes whenrepresentations are used?
3. What are the features of the most effective representations in mathematics suitablefor Pre-K through Grade 5 levels?
It is hoped that the answers to these questions will advance the knowledge of using
representations and assist math curriculum policymakers to design effective learning materials.
Data Collection Procedure
This meta-analysis sought to encompass 12 years of global research on using representationsin Pre-K through fifth-grade mathematics, with student groups ranging in age from 3 to 12, inboth public and private schools, with a minimum sample size of 15 participants. In the processof collecting the applicable research studies, ERIC (Ebsco), Educational Full Text (Wilson),
Professional Development Collection, and ProQuest Educational Journals, as well as Science
Direct, Google Scholar, and other resources available through a university library, were used
to identify relevant studies published between January 1, 2000, and December 31, 2012. While
extracting the relevant literature, the researchers used the following key terms: graphicalrepresentations, mathematics education, primary, students, and experimental research. Inorder to broaden the search, the terms graphics, visualization, and problem-solving were also
used. Such defined criteria returned 131 papers, out of which 13 satisfied the conditions formeta-analysis (13 effect sizes). Several studies, although providing valuable findings, were
rejected due their qualitative type (e.g., Castle & Needham, 2007) or due to their focus oncomparing the effects of using representations that did not contain control groups (e.g., CoquinViennot & Moreau, 2003).

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Coding Study Features
The main construct under investigation was the learning effect of using representations in PreK through fifth-grade mathematics classes. While some of the characteristics, for example,
year of study conduct, locale, or type of research design, were extracted to support the study
reliability, others, like grade level or intervention type, were extracted to seek possible
moderators. Following are the descriptions of these features that were further aggregated toapply a subgroup moderator analysis.
Grade. This variable described the grade level of the group under investigation and referred togroups ranging from kindergarten to Grade 5.
Descriptive parameters. Descriptive parameters encompassed the locale where the studies
were conducted, the date of publication, and the sample size representing the total number ofsubjects under investigation in experimental and control groups.
Publication bias. All studies included in this synthesis were peer-reviewed and published as
journal articles; thus, no additional category for publication was created.
Group assignment. This categorization refers to the mode that was used to select and assign
research participants to treatment and controlled groups. Two main groups were identified: (a)randomized, where the participants were randomly selected and assigned to the treatment andcontrol group; and (b) quasi-experimental, where the participants were assigned byadministrator selection. This categorization is aligned with Shadish, Cook and Campbell’s(2002) definitions of group assignment.
Type of research designs used in the meta-analysis. Only pretest-posttest experimental
studies with control groups were synthesized.
Intervention. The intervention (treatment approach) was classified into four categories
reflecting the type of representations used in Pre-K through fifth-grade mathematics as definedby Swing, Stoiber, and Peterson (1988) and Xin and Jitendra (1999): pictorial (e.g.,
diagramming); concrete (e.g., manipulatives); mapping instruction (e.g., schemata based); andother (e.g., storytelling, keywords).
Output assessment. This variable described the assessment instrument and indicated whether
the assessment was developed by the researcher or was standardized.
Data Analysis
The following analysis is organized deductively. It begins by describing the general study
characteristics, moves to discuss the mean effect size, and concludes by presenting subgroupmoderator computations. Such established sequence follows the order of the study researchquestions.
General Study Characteristics
The summary of the study characteristics extracted from the pool of experimental pretestposttest studies is presented in Table 1.

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Table 1. Tabularization of experimental pretest-posttest study features
Authors

Date

Locale

RD

SS

GL

IRT

Alibali, Phillips, & Fischer
Van Oers
Poland, Van Oers, &Terwel
Xin, Zhang, Park, Tom,Whipple, & Si
Booth & Siegler
Csikos, Szitányi, &KelemenGamo, Sander, &Richard
Terwel, Van Oers, Van Dijk,&Van den Eeden
Casey, Erkut,Ceder, & Young
Jitendra, Griffin, Haria,
Leh, Adams, & Kaduvettoor
Fuchs, Fuchs, Finelli, Courey,& HamlettSaxe, Taylor, McIntosh, &Gearhart
Fujimura

2009
2010
2009
2011

USA
Netherlands
Netherlands
USA

QE
QE
QE
QE

91
239
54
27

3rd & 4th
4th
2nd
4th

Pictorial
Pictorial
Schemata based
Schemata based

2008
2012
2010
2009

USA
Hungary
FranceNetherlands

R
QE
QE
R

52
244
261
238

1st
3rd
4th & 5th
5th

Pictorial
Pictorial
Schemata based
Pictorial

2008
2007

USA
USA

QE
QE

76
88

Pre-K
3rd

Other (storytelling)
Schemata based

2004

USA

R

436

3rd

Schemata based

2005

USA

QE

84

4th & 5th

Pictorial

2001

Japan

R

51

4th

Concrete

Note. SS = sample size, GL = grade level, RD = research design, QE = quasi-experimental, R = randomized,
IRT = intervention representation type.

The data revealed that there is substantial diversity in the representations used in elementary
mathematics classes, ranging from schemas supporting problem-solving to storytelling
supporting operations on fractions. The majority of the studies (nine, or 69%) were quasiexperimental, while four (31%) were randomized. Regarding grade, a dominating group of six
studies was represented by fourth grade. Because problem-solving dominates the math learning
objectives in K-12 math education, how representations help students improve their problemsolving techniques emerged as a possible moderator of the study. Considering the type ofintervention, pictorial (six studies, or 46%) and schemata based (five studies, or 38%)
dominated the pool.
The Mean Effect Size and its SignificanceThe quantitative inferential analysis in the form of a meta-analysis was performed on pretestposttest experimental studies. The outcome variable, defined as the overall effect size of using
representations in mathematics teaching was sought in this meta-analysis. Student achievementscores were further expressed as effect size computed using mean posttest scores ofexperimental and control groups and coupled standard deviation using Hedge’s (1992) formula.
For the meta-analytic methods to be applied, the responses for the experimental studies werestandardized, and the accuracy of the effect sizes was then improved by applying Hedge’s(1992) formula:x -x
g= 1 2s*
In this formula; x1 represents the posttest mean score of the treatment group, x2 represents
the posttest mean score of the control group, and s * represents pooled standard deviation.
This process allowed the elimination of the sampling bias (Lipsey & Wilson, 2001).

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The overall weighted mean effect size for the 13 primary studies (13 effect sizes) was reported
to have a magnitude of 0.53 (SE = 0.05) and a positive direction. A 95% confidence intervalaround the overall mean – Clower = 0.42 and Cupper = 0.63 – indicated a nonzero population
effect and its relative precision (Hunter & Schmidt, 1990). According to Lipsey and Wilson(2001), an effect of 0.53 is of medium size. Herein, the overall effect’s magnitude along with
its positive direction indicated that the score of an average student in the experimental groups
was 0.53 of a standard deviation above the score of an average student in the control groups.
By incorporating the U3 Effect Size Matrix (Cooper, 2010), the average pupil who was taught
mathematics structures using representations scored higher on unit tests than 70% of students
who were taught by traditional methods. Thus, it can be deduced that using representations in
the teaching of mathematics, as a medium supporting instruction, has a profound impact on
students’ math concept understanding when compared to conventional methods of teaching.
Therefore, contextualizing math ideas and letting students embed math operations in contexts
meaningful to them has a positive effect on storing the ideas in their long-term memory. Table
2 provides a summary of the individual effect sizes of the meta-analyzed studies along with
their confidence intervals and qualitative findings.
Table 2. Effect sizes of using representations in Pre-K through Grade 5
Study(FirstAuthor)ES

SE

95% CI
Lower Upper

Alibali(2009)

0.92

0.22

0.19

1.05

Van Oers(2010)

0.23

0.13

0.36

0.89

Poland(2009)

1.22

0.29

0.04

1.23

Xin
(2011)

0.60

0.39

-0.19

1.44

Booth
(2008)

0.20

0.28

0.05

1.19

Csikos
(2012)

0.62

0.13

0.36

0.88

Gamo
(2010)

0.61

0.14

0.34

0.91

Research Findings
The strategy of representing theprocess of equalizing equations
improved problem representation
techniques.
Children improved fraction
understanding when they wereallowed to construct own
representations guided by theteacher.
Introducing dynamic
schematizing improved
understanding of the concept of
the process during problemsolving.
Conceptual representations
helped students learn the processof problem solving.
Providing accurate visual
representations of the magnitudesof addends and sums increased
children’s computational skills.
Presenting word problems withdifferent types of visualization(e.g., arrows) improved
techniques of problem solving.
Mapping data into graphicalrepresentations helped students
with problems involving
fractions.

139

Source of Assessment

Researcher designed.

Researcher designed.

Researcher-createdschematizing test.

Used textbook items
adopted by the districts;
Cronbach ’s alpha = 0.70.
Wide Range Achievement
Test–Expanded (WRAT–
Expanded).
Test items adopted from
National Core Curriculum;
Cronbach’s alpha = 0.83.
Researcher designed.

IAFOR Journal of Education

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Terwel(2009)

0.41

0.13

0.36

0.88

Casey
(2008)

2.00

0.31

0.38

2.63

Jitendra(2007)

1.36

0.22

-0.12

1.07

Fuchs
(2004)

0.22

0.19

0.26

0.99

Saxe
(2005)

0.33

0.22

0.18

1.07

Fujimura(2001)

0.71

0.29

0.05

1.20

Having students learn to design
representations helped them bringmore model-based knowledge tothe structure of mathematics
problems.
Representing geometry concepts
in a story context improved mathknowledge retention.
Addition and subtraction: usedgraphics to support multiple
representations.
The applied schema for problemsolving improved students’
algorithmic outcomes.
Percent: represented fractions
with standard part-to-whole
representations.
Highlighting the idea of physicalunits in setting the proportions
improved students’ conceptual
understanding.

Researcher developed
criteria; Cronbach’s alpha= 0.76.
Used Kaufman-Assessment
Battery for Children (KABC; Kaufman &Kaufman, 1983).
Used Pennsylvania System
of School Assessment mathtest.
Researcher-developed.
Researcher-developed.

Researcher developed;
interrater agreement 97%(N = 76).

Note. ES = effect size, SE = standard error.

Calculated confidence intervals (CIs) for each effect size revealed that 11 of the effect sizes
fell within 95% confidence intervals. The researcher used Statistical Package for the Social
Sciences (SPSS) software to visualize the position of the effect sizes as well as the confidence
intervals for each study around the computed overall mean of the pool of studies. Some means
were revealed to be outside of the area of the funnel graph (see Figure 2).

Figure 2. Funnel graph for the pretest-posttest experimental studies.

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The individual effect sizes of some of the studies showed to be outside of the confidence
intervals indicating a lack of homogeneity of distributions within the study pool. This was also
depicted by the significant p-value (p < 0.001). As the purpose of a meta-analytic study is tocompute effect size (Willson, 1983), the lack of homogeneity does not undermine the validityof the calculated mean effect; rather, it explicates the characteristics of the studies, indicatingthat some of them originated from different distributions.
The highest learning effect size (ES = 2.00) was generated in a study conducted withkindergarten pupils who were exploring the creation of verbal representations of geometry
concepts (Casey, Erkut, Ceder, & Young, 2008). This study revealed that immersing mathconcepts in an environment that students can relate to their experiences and fantasies and letting
students explore the links makes the math concepts tangible and results in them being easily
stored in students’ long-term memories. Another study with a high effect size (ES = 1.22),
conducted by Poland, Van Oers and Terwel (2009), investigated the impact of dynamic
representations on kindergarten students’ math achievement. Dynamic representations
provided more opportunities for having the learners explore their structures, thus generating ahigher engagement factor and consequently higher learning effects. A positive learning effectof students’ explorations was also advocated by Lesh and Harel (2003), who concluded thatsuch situated learning enhances the processes of mathematical modeling that can play a vitalrole in developing students’ scientific curiosity and they are problem-solving beyond theprimary school level.
Analysis of Moderator Effects
The process of a further synthesis of the studies’ features allowed for identifying the following
moderators: treatment length, mode of introducing the representations, grade level, and content
standards. The moderators were further disaggregated by their levels. Where applicable, thelevels within the moderators were contrasted, and inferences on differences were made. The
following criteria were applied to disaggregate the moderators.
Treatment length. The treatment length followed a partition established by Xin and Jitendra(1999): short – less than one week; intermediate – between 1 week and one month; and long more than one month.
Mode of representation induction in the lesson cycle. This moderator followed operationalroles of representations and contained two levels: concept introduction and problem-solving.
Grade level. The large range of grades was distributed into two levels according to standard
classification (NCTM, 2000). The lower group level encompassed all students from Pre-K to
Grade 3, and the upper level included Grades 4 and 5.
Content standards. This moderator reflected general standards examined in the studies thatwere clustered into the following: number and operations, proportions, and geometry. A
summary of the weighted effect sizes is presented in Table 3.

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Table 3. Summary of subgroups’ moderator effect sizes
Moderator and Levels
Grade Level
Lower: Pre-K through 3
Upper: 4-5
Representation Type
Pictorial
Schemata based
Concrete
Other
Treatment Length
Short
Intermediate
Long
Content Standard
Numbers and operations
Geometry
Ratio and proportions
Mode of Induction
Concept Introduction
Problem-solving

N

ES

SE

95 % CI
Lower

Upper

6
7

0.60
0.47

0.08
0.07

0.45
0.33

0.76
0.60

6
5
1
1

0.45
0.49
0.71
2.00

0.06
0.09
0.29
0.31

0.32
0.31
0.05
1.38

0.57
0.67
1.20
2.63

5
4
4

0.46
0.53
0.60

0.07
0.10
0.10

0.31
0.33
0.40

0.61
0.72
0.80

10
2
1

0.45
1.61
0.71

0.06
0.22
0.29

0.34
0.17
0.05

0.56
0.24
0.20

7
6

0.68
0.49

0.07
0.08

0.54
0.34

0.82
0.64

Note. N = number of participants, ES = effect size, SE = standard error.

The computing of the mean subgroup effect sizes provided a basis for answering more detailed
research questions. When compared by grade level, the effect of using representations washigher in Pre-K through Grade 3 than in Grades 4-5. This conclusion might be supported by
the fact that as students’ progress with learning math concepts, they learn more abstract
semantics that might be difficult to embody in representations, for instance, the idea of fraction
division. Students can follow the initial and final stage of the process. However, the diversityof the methods of dividing that is embodied by the syntax of division along with the various
representations of rational expressions might not be entirely comprehended and thus it needs
more clarity.
When mediated by the type of representation, concrete and others produced the highest effectsizes; yet, their significance could not be fully apprehended because each subgroup wasrepresented by a single primary study. When pictorial representations (ES = 0.45) andschemata-based representations (ES = 0.49) were contrasted, schemata representations showeda higher impact on student learning, which supports the findings of other scholastic research(e.g., Jitendra et al., 2007; Terwel, Van Oers, Van Dijk, &Van den Eeden, 2009; Xin et al.,2011). Overall, schemata-based representations and their applications emerged as the maintype of representations supporting problem-solving. According to Owen and Sweller (1985), a
schema is a general cognitive structure that allows the learner to categorize the problem andthen apply specific tools to solve it. A moderate effect size (ES = 0.49) indicates that thislearning strategy helps students understand underlying mathematical ideas in given word
problems and solve them. Hiebert and Carpenter (1992) posited that while developing theschemas, students activate a complex network of concepts stored in their long-term memory.
Furthermore, the networks constitute the model that will be called an internal representation of
the domain embodied by an external representation. As Xin et al. (2011) suggested, instead of
telling students, for instance, the numerical magnitude of the unit rate, e.g., ten apples per142

IAFOR Journal of Education

Volume 6 – Issue 3 – Winter 2018basket, demonstrating students a real concrete representation along with its symbolic
mathematical model will better support the conceptual understanding and its mathematicalembodiment.
How should representations be furnished to effectively develop the conceptual networks?
Learners can be provided with the representations, or the representations can be derived by thelearners under the teacher’s guidance. It is inferred from this study that providing students with
opportunities to derive the representations deems to be a more effective teaching strategy
because it allows the learners to retain the concepts longer and apply them in new situations
more frequently.
An interesting direct variation was observed when the effect sizes were contrasted withtreatment lengths. It became apparent, from examining this relation, that the longer thetreatment, the higher the effect size (ES = 0.46 for short treatments, ES = 0.53 for intermediate,and ES = 0.60 for long). This result provides support for applying representations in classes
daily.
Concerning content standards, geometry representations yielded a higher effect size (ES =1.61). This result reflects the visual nature of content in this branch of mathematics, which by
virtue is rooted in representations. The analysis of the concluding subgroup—mode of inducing
in the lesson cycle—allowed contrasting the effect sizes of using representations to support
conceptual understanding and problem-solving. It is apparent that representations are more
effective with concept introduction (ES = 0.69) than problem solving (ES = 0.49). Thus, one
could conclude that supporting concept introduction with representations builds a stronger
network of impulses in students’ long-term memory.
Summing all these findings led to the formulation of a classroom setting that would generate
the highest learning effect sizes. It seems that using concrete representations to introduce
geometry concepts in Pre-K through Grade 3 would yield the highest learning effects.
The research findings also allow for the formulation of recommendations for effective
representations. Fujimura (2001) concluded that representations should share similar features
as the target domain and must be manipulative so that children can explore and uncoverembedded math structures by themselves. He further suggested that representations should bedesigned in a way that they develop children’s creativity in constructing mathematical models.
Casey et al. (2008) found that students retain mathematical knowledge if the knowledge isembedded in a story context. Developing mathematical knowledge through sequenced
mathematics problems related to the storyline is also suggested by the researcher of the metaanalysis. Booth and Siegler (2008) highlighted accuracy and transparency of representations
as a significant factor affecting students’ mathematical learning in early grades, whereas
Poland et al. (2009) brought forth the idea of using dynamic representations to support theprocesses of arithmetic operations.
As mathematics seeks to develop students’ concise, abstract thinking, the results of this
synthesis show that it also needs to reflect on representations that students use in daily life andwhose contents are adequate to their experiences. Presenting artificially created representations
that do not adhere to students’ experiences might disconnect mathematics concepts from therealm and rather support the notion that mathematics is an abstract subject.

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General Conclusions
The findings of this study support the following hypothesis: representations help Pre-K throughfifth-grade students learn and apply abstract math concepts, especially when such
representations are applied to support new concept understanding and students’ problemsolving skills. Certain limitations and recommendations for further research emerged from thisstudy, as discussed below.
Threats to Research Validity
The primary parameter limiting the study findings was a lower-than-expected pool of primarystudies that satisfied the conditions to be meta-analyzed. The validity of the study computations
was supported by double research data coding at the initial and concluding stages of the study
process. Any potential discrepancies were resolved. Although strictly specified, the literaturesearch was undertaken with broader conceptual definitions in mind that allowed for, assuggested by Cooper (2010), adjustment of the definitions and strengthening of the literature
relevance. Thus, as the initial literature search revealed that representations in Pre-K throughGrade 5 are often used to support problem-solving, the term problem solving was then used tolocate more studies.
Schemata as a Major Type of External Representations
Among different representations (see Table 1) schemata-based representation and theirapplications emerged as the most commonly used to support problem-solving. According toOwen and Sweller (1985), a schema is a general cognitive structure that allows the problem
solver to categorize the problem and then apply certain tools to solve it. A moderate effect size
(ES = 0.49) indicates that this learning strategy helps students understand underlying mathideas in given word problems. Hiebert and Carpenter (1992) posited that in the process ofdeveloping the schemata, students’ thinking blends a complex network of concepts in one
coherent picture. Furthermore, the networks constitute a mental model that will be called aninternal representation of the domain imaged by an external representation. The conceptual
networks can be developed either by representations provided by the teacher or byrepresentations derived by the learners under a teachers’ guidance.
Is having students use schemata sufficient to have them learn the holistic picture of the meaningof this mathematical representation? Several researchers concluded that once children are
exposed to certain representations – for instance, schematic representations to solve problems– they retain those methods and apply the schemas regardless of age (Coquin-Viennot &Moreau, 2003). Some scholars noted (e.g., Castle & Needham, 2007), this idea cannot be
overemphasized; children also need some working space to analyze problems and devise theirways to solve the problems with the support of provided schemata. Thus schemata should beperceived as suggestions for mathematization of certain patterns not as fixed formulas to use.
It seems that more research should focus on having students recognize the type of scientificunderpinning of the problem that students should apply to determine the principles embedded
in a given word problem.
Hiebert and Carpenter (1992) proposed four semantic categories (schemata) for arithmeticaloperations that are: change, combine, compare, and equalize. Using these schemata to model
story problems allows certain flexibility, for example, in some cases can be perceived ascompare, or compare can suffice to combine. Emphasizing the schemata to reach the final
solution reduces learners’ opportunities to explore and be immersed in the process of analyzing

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the problem structure. There can also be cases when two or more schemata can be used insuccessions. For example, in order to compare, students might need to combine similarelements first. Thus students should be allowed to exhibit flexibility in applying the schemata
and interpret them. However, that the primary meaning of each should be consistently
executed. To illustrate that consider the following problem discussed by Jitendra et al., (2007):
Music Mania sold 56 CDs last week. It sold 29 fewer CDs last week than this week. How manyCDs did it sell this week? This problem was intended to support the idea of compare. There is
merit to use the schema of compare in this problem, but is the schema compare the mostrepresentative to mathematize the process of selling the CDs? The problem mentioned two
events happening at two different time instants referring to similar objects, can then the learner
be directed to considering rather finding the difference? Thus would the schemata of change
better describe the process and elicit its solution? It seems that referring students to compare
gears their thinking toward the output of the problem thus finding the final product, not toward
the principal process, the change that supported the process of reaching the output. By directing
students’ attention to the problem output, the phase of problem analysis is reduced. Referringto the problem context and considering the definition of change as Change = Final value –Initial value, and solving for Change, one will receive Change = This week sells – Last week
sells. Substituting the given values results in 29 = This week sells – 56, that leads further toThis week sells = 56 + 29 which leads to the conclusion that Music Mania sold 85 CDs. With
the implementation of change, the representation involved negative numbers that perhaps were
not intended in Jitendra’s study. Thus to further discuss the applicability of this problem to
Grade 3 math curriculum, the problem would have to be redesigned, however providing
students with the flexibility of exercising the underlying process that is missed is worth further
research. Zooming further change in quantity values is concluded by subtracting the initialvalue from the final value: Change = Ending – Beginning. This standard definition of change
is applied not only in mathematics to calculate, for example, instantaneous or average rate ofchange (e.g. Stewart, 2006) but also in sciences, especially in physics where the concept ofchange is often used to calculate change of temperature, or object’s displacement (e.g.,
Giancoli, 2005).
One might be interested in learning how the schemata of change are induced in the literature.
The idea of using change was introduced by Marshall (1995) see Figure 3 and was modeled by
the following problem; Jane had 4 video games. Then her mother gave her 3 video games forher birthday. Jane now has 7 video games.
+3
Video GamesViCHANGE

4
Video Games

7
Video GamesBEGINNING

ENDING

Figure 3. Representation of change inspired by Marshall (1985).
Change in quantities values is concluded by subtracting the initial value from the final value:
Change = Ending – Beginning. This standard definition of change is applied not only inmathematics to calculate, for example, instantaneous or average rate of change (e.g. Stewart,

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Volume 6 – Issue 3 – Winter 20182006) but also in sciences, especially in physics where the concept of change is often used to
calculate change of temperature, or object’s displacement (e.g., Giancoli, 2005). This equation
can be rearranged to reflect Marshal’s idea Beginning + Change = Ending. However, therearranged form is not aligned with the fundamental principle of the process of change that
seems to be the core idea of the problem. If the schema of change were to be used, then perhaps
the diagram could have been redesigned to reflect on the difference in the quantity amount that
represents the change.
These two examples were brought up to signify a need for verifying interdisciplinary
consistency of the interpretations of the fundamental concepts that are meant to support
problem-solving in K-5 mathematics. It is understood that the equations symbolizing theschemata can be rearranged and executed with a dose of flexibility. What stages were being
used would depend on individual perception, yet general foundations for problem analysis
would perhaps require more consistency. Perhaps establishing fewer schemata and letting
students manipulate on them to reflect the process of a given problem could be an alternative
avenue to pursue? The mathematical operations behind calculations of change, combine,compare, and equalize are very fundamental in sciences, and it seems that understanding theircore meanings might have a profound impact on students success on problem-solving not onlyat an elementary but also at a high school level and beyond.
Looking Ahead: Linking the Representations
Cheng (1999) proposed four learning stages that can help students in developing conceptual
understanding through using representations: domain, external representation, concept, and theinternal network of concepts. While moving from one stage to another to reach the internal
network, the learner is immersed in four processes: observation, modeling, acquisition, andintegration. Except the study conducted by Rittle-Johnson, Siegler, & Alibali (2001) andTerwel et al. (2009), the majority of the gathered research did not explicate on these processes,
focusing instead on applying fixed models without discussing their possible modifications.
It seems that possessing the right representation does not suffice for an understanding. To
confirm an understanding, one needs to be able to put this representation through its paces,
explaining and predicting novel cases. Thus, to have an understanding of a representation is to
be in a state of readiness, taking the representation as a point of departure in the solution
process, not as an unquestionable formula a representation. Terwel et al. (2009) proved thathaving students explore and modify given representations produced the highest effect size. Thiscan be supported by the effects of induced math modeling phases that allowed the students tolink representations with the constraints of real scenarios (Sokolowski, 2018).
Applying representations often creates exploratory learning environments (English & Watters,2005) that consequently can be guided by inductive or deductive inquiry processes. Thus, otherthemes worthy of a further investigation emerged; should the use of representations to beorganized inductively, as suggested by Nunokawa (2005)? How do elementary school students
perceive these two major scientific inquiries? Are these inquiries rooted in virtues ofmathematical representations, or content-domain? Having students develop principles ofrepresentations by identifying commonalities due to applications and use such representations
to model other contexts beyond the boundary of a math classroom would be an interesting
pursuit for future studies.

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Bridging Representations Used at Elementary and High School Levels
There are other questions can be generated from this study, for example; how does the use ofrepresentations evolve as students’ progress with their mathematics classes, especiallyschemata-based that dominate problem-solving in Pre-K through Grade 5. Fuchs, L., Fuchs,
D., Finelli, Courey, & Hamlet (2004) suggested using schemata more extensively for problemsolving also at the high school level, especially targeting students with learning disabilities.
Having high school students derive processes of transitioning from, for example, proportion toa linear or rational function or from percent rate to an exponential function seem like valuable
topics to explore.
Another conclusion calls for extending the idea of using schemata to sciences and othersubjects in a consistent manner that will carry out their general principles. This transition would
help broaden the meanings and consequently built a stronger image of these representations instudents’ long-term memories. Do students experience applications of similar representationsin their science classes? Should these main avenues of knowledge acquisition depend on thenature of the representation (schemata or pictorial) or their general purpose? Further researchin these areas is needed, and it is believed that this paper provides some prompts for suchactions.

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Corresponding author: Andrzej Sokolowski
Contact email: Andrzej.Sokolowski@lonestar.edu

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Reviewers | IAFOR Journal of Education Volume 6 – Issue 3
The editorial team would like to thank the following reviewers for their contributions topeer review for this issue of the journal. Their dedication and assistance are greatly
appreciated.
Najwa Saba ’Ayon
Jose Barlis
Christina Belcher
Angelina Borican
Bachir Bouhania
Ivy Chia
Shiao-wei Chu
Joanie Crandall
Caroline Dacwag
Dandy George Dampson
Luisa Daniele
Raymond Q Datuon
Denise de Souza
Alaster Scott Douglas
Shaher Elayyan
Alice Schmidt Hanbidge
Habsah Hussin
Leda Kamenopoulou

Intakhab Khan
Lynda Leavitt
Jin LeeJessica Loh
Thelma Manansala
Massoud Moslehpour
Nomazulu Ngozwana
Anuli Njoku
Vasileios Paliktzoglou
Agnes Papadopoulou
Sonal Mobar Roy
Jahan Shams
Pearl Subban
Anna Toom
Kyle Whitfield
Tony Yeigh
Huanshu Yuan

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ISSN: 2187-0594