Institut für vergleichende Studien
Fakultät für Maschinenbau

Titel

Data Science Education for secondary school students
andCivic Statistics Education for preservice teachers
Special lecture at JCOTS 2020
Institute of Statistical Mathematics,
TachikawaDr. Daniel Frischemeier
University of Paderborn
1Institut für vergleichende Studien
Fakultät für Maschinenbau

Introduction
Big Data and Data Science have gained increased importance– e.g., industrial and economic processes, marketing processes,monitoring in politics, etc. (see Gould 2017; Engel 2017)
 Data Science permeates all areas of life and so also for students andschool this field is very important2

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Data Science and its context
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Big Data
Digitalisation
Data protection in social networks
Artificial intelligence, machine learning
Data driven learning algorithms
Digital humanities
....

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Data Literacy
Statistical Literacy
News Literacy
Algorithm Literacy
....

And:
• Social responsibility• Civic statistics like statistics about
migration, economy, crime, health, etc.

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Overview on my lecture: Two projects
ProDaBi (Project Data Science and Big Data in School)

Data Science
Course forsecondaryschool students• Initiated by Deutsche Telekom Foundation• Collaborative Project of Computer Science education (WG Prof. Carsten Schulte)
and statistics education (WG Prof. Rolf Biehler) at the University of Paderborn• Aim: Developing a Data Science curriculum for secondary schools in Germany• Further infos: www.prodabi.de

ProCivicStat (Promoting civic engagement via explorations ofevidence)• Erasmus + Project funded by the European Union• Collaborative project (Durham, Haifa, Ludwigsburg, Paderborn, Porto, Szeged)• ProCivicStat supports statistics teaching that enables
Civic Statistics
students to engage with current social issues
Course for• Aim: Educating teachers and developing material
secondary school(tools, data sets) for teaching civic statistics in secondary school preservice teachers
4• Further infos: http://iase-web.org/islp/pcs/

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Part 1
ProDaBi (Project Data Science and Big Data in School)
Data Science Course for secondary
school students

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Some remarks on Data Science Education and possible
implications for statistics education

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Traditions in statistics education and DS
1.
2.
3.
4.

The PPDAC cycle of Wild and Pfannkuch (1999) & Changes in the
PPDAC cycle
Extending the statistical view of “data”
Extended and new methods for data science
Selecting digital tools for data science: data analysis, data
management, algorithm design

Ridgway, J. (2016). Implications of the Data Revolution for Statistics Education.
International Statistical Review, 84(3), 528–549.
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The PPDAC cycle (Wild & Pfannkuch, 1999)

Wild, C. J., & Pfannkuch, M. (1999). Statistical Thinking in Empirical
Enquiry. International Statistical Review, 67(3), 223-265. (p. 226)8

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Changes in the PPDAC cycle as such
• Data may be „there“ and not collected according to a plan; starting pointof the cycle may be different (Tukey, 1962; Huber, 2010)• Modelling as a step should be added• Classical statistical theory assumes the model to be given• Data science uses new types of algorithmic models (Breiman, 2001)• Validation of model is missing (cross validation; data for training andfor testing)• Prediction as a goal for modelling has to be emphasized• „Conclusions“ as a final process step has to be extended
Statistics aims at „knowledge“; data science and computer science
construct systems  „deployment“ including social responsibility
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CRISP-DM
cycle
Titel

Cross-Industry
Standard Prozess
Modell für Data
MiningSee:
Berthold, M. R., Borgelt,
C., Höppner, F., &Klawonn, F. (2010).
Guide to Intelligent Data
Analysis - How toIntelligently Make Sense
of Real Data. London:
Springer, p. 91010

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Extending the statistical view of “data”• Standard in statistics (not in school):• The rectangular data tables with different variable types• Data of moderate size, multivariate data
• New types of data
• Data collected by sensors• Data collected by personal devices• Transactional data (traffic, supermarket buys)• Images and texts• Data scraped from webpages• Data with geographic information
 Big data; open data (Ridgway, 2016)11

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Selecting digital tools for data science• Standard in schools (in Germany): if at all, limited use of EXCEL and
GeoGebra, graphics calculator, no statistics tool
• In experimental classrooms: TinkerPlots and Fathom (graphicaluser interfaces)• designed for empowering students to do complex data analysis(moderate size), similar to statisticians• No need for „computational knowledge“• Algorithmic details are hidden from the user
• In statistics, many tools such as SPSS, R, (Python,) ...

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TinkerPlots, Fathom, CODAP, etc.

 We will have a closer look at the use of some ofthese tools in this lecture

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Selecting digital tools for data science• Data science requires R or Python– Supporting data management and preparation– Simple and advanced data analytic methods available as libraries– Extensibility and adaptability– Programming environment• R or Python at school level?
Developing a well-designed library for school use possible?
Command interface and extensibility contributes to computational
thinking?
Programming environment supports algorithmic thinking including
coding?
 Complexity of the tool vs. concentrating on the statistical context
 We will use Python, at least taking into account the pre-knowledge ofthe students (computer science courses)

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Introduction

 Implementation of these ideas in secondary schoolclassroom Project ProDaBi

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The project ProDaBi
 Initiated by the Deutsche Telekom Foundation in 2018

 Cooperation of statistics education (Working group Prof. Dr. Rolf
Biehler) and computer science education (Working group Prof. Dr.
Carsten Schulte)
 Goal of the project: Design and Realization of a Data Science
Curriculum for secondary school
Especially: Design, Realization and Evaluation of a projectcourse “Data Science” for secondary school16

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Overview: Development of the project ProDaBi

International
symposium on Data
Science Edu. 2017

First pilot course
in school year
18/19

Evaluation of
first pilot course

ProDaBi
Revising pilot
course, next cycle
19/20

Building a TEA
netowrk
Design of Standalone modules
Goal: Evaluation and further
development of the materials
in team17

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Proceedings of 2017 symposium
See:
Biehler, R., Budde, L., Frischemeier, D., Heinemann, B.,
Podworny, S., Schulte, C., & Wassong, T. (Hrsg.). (2018).
Paderborn Symposium on Data Science Education at
School Level 2017: The Collected Extended Abstracts.
Paderborn: Universitätsbibliothek Paderborn.
Biehler, R., & Schulte, C. (2018). Perspectives for aninterdisciplinary data science curriculum at German
secondary schools In: loc. cit. p. 2-14

See: http://digital.ub.unipaderborn.de/hs/content/pageview/2931582
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Experimental „Project course“
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Collaboration with two secondary schools in Paderborn
Year-long course, 3 school hours per week
Grade 12: students with computer science experience
One major teacher, 2-3 observers
Material was jointly developed by the team
Cycle 1: school year 2018/2019; Cycle 2: school year 2019/2020

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Pilot course in School year

Data
detectives

Big Data
and machinelearning

Data
Scienceproject

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Structure of the project course• Module 1: Data and data detectives• Data exploration with CODAP• Data exploration with Jupyter notebooks• Module 2: Big Data and Machine Learning• Decision trees• Artifical neural networks• Module 3: Data Science projects with partners from industry andadministration• Our Tools• Codap (codap.concord.org)• Python; Jupyter Notebooks
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The project course in the school year 2019/2020

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Module 1:
Data and Data Detectives

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Two units in module 1 “Data and Data Detectives”• 1. Unit: Data analysis with CODAP• 2. Unit: Data analysis with Jupyter Notebooks

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Learning goals for unit 1
Specifically we want the students in the unit 1 “Data analysis with
CODAP” to…
• …explore and analyze a multivariate data set with regard to selected
adequate questions,• …use/apply basic terms of descriptive statistics and statisticalconcepts• …use and evaluate digital tools like CODAP for their data
exploration,• …document and present their data analysis in an adequate form

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Students were first involved in PPDAC cycle

Wild, C. J., & Pfannkuch, M. (1999). Statistical Thinking in Empirical
Enquiry. International Statistical Review, 67(3), 223-265. (p. 226)26

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JIM Study (Youth, Information, Media)
Representative Survey of 12 - 19 year old
students in Germany
Questions:• General information (grade, age, gender)• Use of „classical media“ (books, journals, …)• Use of digital media• Use of social media, messenger, ...
• Use of Youtube• Use of games (Computer, Tablet, console, etc.)
• ...
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Online questionnaire for collecting their own JIM data
• The JIM-study includes plenty of
categorized questions (e.g., "How often do
you use Whatsapp?" "daily", "several times
a week", "once a week", "two times amonth", "once a month", "less often","never") on the media use of German
students.
• To make the JIM study, which is anationwide study, even more interesting, we
decided to modify it and to include
questions which tackle numerical variables(like "How many apps to you have on yoursmartphone?" "How many online accounts
do you have?") and we decided to collectdata with the modified JIM questionnaire at
the schools where our pilot course takes
place.

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JIM project

How to prepare an adequate data
presentation?

Creating PowerPoint
presentation
Presentation in classroom

Generating statistical questions
What are adequate statisticalquestions?

Generating statisticalquestions alongside the JIMdataset

Data analysis with CODAP,
Exploration of statisticalquestion, Generating adequate
diagrams

Diagrams
Percents, relationship between two
categorical variables
Group comparisonsCollecting JIM-PB-data in online survey

Import of JIM-PB-data in
CODAP, data cleaning
Data analysis with CODAP

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Tool for data exploration: CODAP• Easy entrance, quick start (with link)• Exploration of multivariate data (interactive, graphical userinterface, web-based)https://codap.concord.org/releases/latest/static/dg/de/cert/#shared=51283
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CODAP with JIM data

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Some results of students‘ miniprojects

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Instagram use: girls vs. boys

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Different user groups: Facebook vs. Snapchat

14%

29%

42%

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Frequency of Instagram use and number of pictures on one´s
phone

daily
never
number of pictures on one‘s phone

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Fakultät für Maschinenbau

Evaluation after unit 1
Item

Mean

1. The unit “Data detectives with CODAP was motivating”. (n=8)

4.25

2. I want to get more information on the topic JIM study. (n=8)

4.00

3. I was able to cope well with the demands of the unit. (n=8)

4.13

4. What I learned in the unit "Data detectives with CODAP" is important for me
personally in everyday life. (n=8)

3.50

5. What I learned in the unit "Data detectives with CODAP" should become a general
topic in school. (n=8)

4.15

6. The handling of CODAP was easy for me. (n=8)

4.13

7. I would like to work more often with CODAP in the future. (n=8)

3.90

Eight of the 14 students participated in the online survey and rated seven items on a fivepoint scale from "applies" to "rather does not apply"

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Unit 2: Analysis of noise data:
Data analysis with Jupyter Notebooks
 Introducing our students to a first real
“Data Science Problem”

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Data analysis with Jupyter Notebooks (Unit 2)

Jupyter Notebooks ...
• ... were originally developed to support the workflow of data
analysis.
• ... are an open source tool for data science in research,education and industry.
• …consists of cells that can be individually modified andexecuted.
 Differentiation between markdown cells and code cells
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Jupyter Notebooks as coding environment

Markdown cellwith elements
like pictures,videos, etc.

Markdown Zelle:
Textelemente, Grafiken,
Videos, ...

Menu and
ToolbarMenü und Toolbar

Notebook with
Code and
Markdown cell
Notebook mit
Code und Markdown
ZellenCode cell

Code Zelle

Code output
Code Ausgabe

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Where is the location? Exploring noise data (Unit 2)
555.000 sensebox measurement ofnoise at 5 places in the city ofPaderborn?

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Collecting data with Sense Box

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Where is the location: Exploring loudness data

Jupyter Notebooks
are introduced

Environment for
programming with Python

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Prepared Jupyter Notebook

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Data set 5 (4. Aug. - 5. Aug. 2018)

Assignment: Liboriberg?

?

Explanation:• Libori festival• Opening 12:00• Closing time 23:00

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Allocation of further data sets

Liboriberg

Data detectives: The noise on thetrack

Gas station
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Module 2:
Machine learning and artificial
intelligence47

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Two units in module 2

Unit 1: Decision trees– Introduction into machine learning
Unit 2: Artificial neural nets

1. – 3. week

4. – 6. week48

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Unit 1: Decision trees
1. First introduction into decision trees» Understanding the concept data split, Generating a tree on thebasis of a dataset» Comparison of different data splits (misclassification rate),
Greedy algorithmn

2. Decision tree algorithm in form of a pseudo code
3. Programming an algorithmn
4. Applying algorithm to real datasets• Using decision tress to generate insights from the data
• Critical reflection of findings

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Unit 2: Artificial neural nets: Introduction50

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Artificial neural networks: Hand writing recognition with Jupyter
Notebooks

Example from MNIST- data
set

Jupyter Notebook
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Module 3:
Data Science Project52

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Modification of the PPDAC cycle: The CRISP-DM cycle

CRISP-DMasextension of the
PPDACcycle

Cross-Industry
Standard Prozess
Modell für Data
MiningBerthold, M. R.; Borgelt, C.; Höppner, F.; Klawonn, F. (2010).
Guide to intelligent data analysis:
how to intelligently make sense of real data . London: Springer
Science & Business Media.
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Fakultät für Maschinenbau

Module 3: Data Science Project

Modul 3:
• Cooperation company RTB (Paderborn) and city ofPaderborn• Automatic parking data recording• digital traffic control systems, parking spots with gates and car
parks

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Tasks for students• Data group 1: ticket machine data (live) of a parking spot with gates• Data group 2: occupancy data of a car park (live)• Task: prediction of free parking spots

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Raw data from thesoftware for car park
ExplainingData
the variables
Preparation in management.
in the data
set by the
Pythonstudents (JupyterNotebook)

Cleaned csv-Datei ofthe students

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Self organized project work organized in Gitlab

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Deployment of thestudents

http://brainrocket.org/lib
oriberg/
http://www.brainrocket.or
g/königsplatz/

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Outlook ProDaBi II• Revised version of the project course• Creating a network of TEAs (trusted early adopters) who will use thematerial• Stepwise web-publishing of teaching material and accompanying
material for „ordinary“ teachers  dissemination• Creating 6 stand-alone units for introduction into data science, e.g.
Data exploration with Codap

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Part 2
ProCivicStat
Promoting Civic Engagement via Exploration of Evidence:
Challenges for Statistics Education
Civic Statistics Course for secondary
school preservice teachers
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Introduction• Civic statistics are statistics about key phenomena in society such as
employment, health, education, social welfare, or inequality(Ridgway, 2015; Engel, Gal & Ridgway, 2016)• Decisions in politics, society and economy are often based on civic
statistics.
Responsible citizens need statistical skills and knowledge to establish
critical thinking when being confronted with statistical texts and displaysin the media.
Process of enhancing critical thinking should preferably already startin school education, but – since even teaching the traditional contents ofstatistics is challenging for teachers – teachers need a more focused
education on civic statistics and also how to implement civic statistics inschool.

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The project ProCivicStat

Promoting Civic Engagement via Exploration of Evidence:
Challenges for Statistics Education
The project ProCivicStat, funded by the ERASMUS+ program of theEuropean Commission, aims at supporting teachers with specificcourses, materials, tools, and datasets for civic statistics. (seewww.procivicstat.org)

Procivicstat: 6 partner universities

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Demo of materials from the ProCivicStat project

Materials from PCS (see also: http://iaseweb.org/islp/pcs/)• CivicStatMap  Finding materials:– Data sets
– Overview on tools– Lesson plans– worksheets
https://rstudio.up.pt/shiny/users/pcs/civicstatmap/
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Demo of materials from the ProCivicStat project66

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How did we implement these materials in our university
teacher education courses?

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Situation at our university (University of Paderborn)

Situation at the University of Paderborn for preservice teachers:• Bachelor studies: Compulsory course on elementary statistics andprobability and compulsory course on didactics of statistics• Master studies: preservice teachers can choose a seminar to deepen andexpand their knowledge they have gained in the compulsory courses

Goal:Design a course “Statistical literacy in mathematics classroom” embedded in the
ProCivicStat project to develop the statistical content and pedagogical content knowledgeof preservice teachers.
A focus of this course is on promoting critical statistical thinking, so that future teachers
have a sustainable content knowledge, pedagogical content knowledge and a positive
stance towards civic statistics to bring these issues into classroom at school.
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General information on the course

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Situation at our university (University of Paderborn)• First cycle of the course „Statistical literacy in mathematics classroom“
at Paderborn University in winter term 2016/2017 (Biehler,
Frischemeier & Podworny, 2017)• Redesigned course in 2017/2018– Seminar with 11 participants• Preservice teachers for mathematics in lower secondary school• Preservice teachers at the end of their studies, having
successfully attended to a course „Elementary statistics“ and„Didactics of statistics“– 15 sessions (90 minutes each)– Idea to build on their pre-knowledge and develop statistical content
and statistical pedagogical content knowledge of participants especially in regard to critical thinking
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Design of course

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Learning goals
With regard to statistical content knowledge• to deepen students´ knowledge about reading and interpretingsummary statistics and graphical displays (also in the sense ofreading beyond data of Friel, Curcio & Bright, 2001)• to introduce students into statistical concepts and constructs(like correlation and causality, or Simpson’s paradox) relevant
in civic statistics• to introduce into the definition and operationalization ofconcepts (like, e.g., net assets)• to explore multivariate datasets on the base of given and selfgenerated statistical questions.

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Learning goals

With regard to pedagogical content knowledge• to consider contents in civic statistics across subjects• to get to know relevant material (articles, homepages, tools,
datasets, etc.)• to learn to “simplify“ complex situations in civic statistics for use inclassrooms• to develop ideas for implementing civic statistics activities inclassrooms.

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Major idea of the course

Preservice teachers apply their previous gained knowledge
about these statistical constructs and phenomena in more
complex tasks and in project works related to specific issuesof civic statistics (like poverty, unemployment, health,education, etc.).
So we want to…
• confront the participants with social phenomena in Germany,
Europe and the world (e.g., gender pay gap)
• ask them to critically explore a topic taking into accountinvestigations - starting with reports in the media, real andmultivariate data from statistics bureaus• use digital tools for data exploration.
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Types of sessions - overview

Three types of sessions in our seminar:– Refreshing statistical and technological content
knowledge• Introduction to civic statistics• Refreshing different statistical concepts• Analyzing multivariate datasets with Fathom (session 2-5)

– Students working on mini-projects• Several civic statistics topics (session 6-10)– Gender Pay Gap (GPG) project• GPG discussion about adjusted and unadjusted GPG, exploring real
data about GPG and presenting results via PowerPoint (session 1114)
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Design principles of the course: SRLE
Statistical Reasoning Learning Environment (Garfield & Ben-Zvi, 2008, p. 48)

1. Focuses on developing central statistical ideas rather than on presenting set oftools and procedures.
2. Uses real and motivating data sets to engage students in making and testing
conjectures.
3. Uses classroom activities to support the development of students’ reasoning.
4. Integrates the use of appropriate technological tools that allow students totest their conjectures, explore and analyze data, and develop their statisticalreasoning.
5. Promotes classroom discourse that includes statistical arguments and sustained
exchanges that focus on significant statistical ideas.
6. Uses assessment to learn what students know and to monitor the developmentof their statistical learning as well as to evaluate instructional plans andprogress.
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Realization of the course

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Sessions 1-5: Refreshing knowledge

Goal
Refresh the statistical and the technological Fathom knowledge
Content• Def. Statistical Literacy (Gal, 2002), Def. Civic statistics (Engel et al.,2016), Getting to know PPDAC-Cycle (Wild & Pfannkuch, 1999),
Asking adequate statistical questions (Leavy & Frischemeier, inprep.)• Data analysis with Fathom (analyzing student data) (Biehler,
Hofmann, Maxara & Prömmel, 2011)• percentages (row, column, cell)• group comparisons (Frischemeier, 2017)78

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Sessions 1-5: Refreshing knowledge

Content (continued)• critical thinking, how to lie with statistics (Krämer, 2007;
Bauer, Gigerenzer & Krämer, 2015)79

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Sessions 1-5: Refreshing knowledge: Statistical questions

See Frischemeier & Leavy(in preparation)
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Sessions 1-5: Refreshing knowledge: Statistical questions
Initial question

Question after peerfeedback

Question after expertfeedback
See Frischemeier & Leavy(in preparation)
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Sessions 1-5: Refreshing knowledge: Percentages

Analysing Student data with Fathom
Do female students tend to get up earlier than male students?

Row percent
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Sessions 1-5: Group comparisons

Analysing student data with Fathom
In how far differ the female and male students in their
computer use (time in hours per week)
How to comparegroups?
• Center• Spread• Skewness• Shift• P-based• Q-based
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Sessions 6-10: Mini projects

•
•
•
•

Session 6: Statistics about the world (with CODAP)
Session 7: German hospital statistics (with Fathom)Session 8: Inequality in the world (with Gapminder)
Session 9: German road accidents (with a web applet from theGerman Statistical Office)• Session 10: Daily habits of US citizens (with a web applet fromthe New York Times)

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Fakultät für Maschinenbau

Sessions 6-10: Mini projects

Sessions 6-10 (90 mins each) were based on aworksheet• Introduction (What is the topic about & why is it relevant)• Media report (Newspaper article, article from the internet, …)• Background information & Data (How is it measured)• Working phase: Analyzing data (Pregiven and selfgenerated
questions)• Discussing results

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Fakultät für Maschinenbau

Sessions 6-10: Examples

German road accidents (Destatis tool)https://www.destatis.de/DE/Service/Verkehr/Verkehrsunfaelle.html
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Fakultät für Maschinenbau

Sessions 6-10: Examples

Inequality in the world (Gapminder)

Demo: https://www.gapminder.org/tools/#$chart-type=bubbles

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Fakultät für Maschinenbau

Gender Pay Gap project(Session 11-14)

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Fakultät für Maschinenbau

Sessions 11-14: Gender Pay Gap project

Learning goals of Gender Pay Gap projectStudents should…
• get insight into some of the causes of the inequal pay situation
between male and female employees in Germany,• become familiar with the concept of gender pay gap and to beable to distinguish between the adjusted and unadjusted gender
pay gap,• explore the German income structure data set,
• learn to reflect media reports on gender pay gap critically andrelate them to their own analyses,• prepare their results and findings as PowerPoint presentations fortheir classmates
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Fakultät für Maschinenbau

Sessions 11-14: Gender Pay Gap project

Exploring the Gender Pay Gap data in Peers with Fathom

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Fakultät für Maschinenbau

Sessions 11-14: Gender Pay Gap project

Four sessions dedicated to Gender Pay Gap project– Session 11 (GPG 1): Investigation on newspaper articles& background literature– Session 12 (GPG 2): Explore data with tools in pairs– Session 13 (GPG 3): Explore data with tools in pairs– Session 14 (GPG 4): Present findings to colleagues(using PowerPoint presentation)

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Fakultät für Maschinenbau

Sessions 11-14: Gender Pay Gap project, The Dataset
Dataset VSE_2006• exported from the German Bureau of Statistics• 60,552 cases (uncleaned) ; 59,504 cases cleaned• employees from all levels regarding variables such as gender, wage
per month, kind of employment agreement, etc.
• data set contains absolutely anonymised data for research andteaching, generated from the data “Earning Survey” (short: VSE)

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Fakultät für Maschinenbau

Sessions 11-14: Gender Pay Gap project, several topics

Five topics (according to variables of dataset)• Topic 1: Profession• Topic 2: Function (simple to complicated activities, leading position, etc.)• Topic 3: Age• Topic 4: Economy• Topic 5: Region

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Fakultät für Maschinenbau

Sessions 11-14: Gender Pay Gap project, the task

Task (in peers)
Work in teams of two!
You are now to carry out a project work on the "Gender Pay Gap" with your knowledge gained in
the seminar. In doing so, you should independently explore the data set for the 2006 Income
Structure Survey and develop the causes for the gender pay gap on the basis of the available data.
You have learned that the differences in income between male and female workers, which are
published in the media, must to be interpreted with caution because of the different aspects that
determine the difference.
TASK
In this article (see link below), the focus is on the aspect “function”, which has an influenceon the merit difference. Under this perspective, examine the present data set and work out
the extent to which merit differences are caused by the aspect mentioned above.
Source/Link: http://www.bild.de/ratgeber/job-karriere/gehalt-bestimmen-44537794.bild.html
Write a short article and create a PowerPoint presentation that you will resent to your fellow
students.
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Fakultät für Maschinenbau

Sessions 11-14: Gender Pay Gap projectSome insights of the exploration of Gender Pay Gap data:• Women are missing in certain professions, sectors and on
the upper end of the career ladder• Women interrupt their careers and reduce their working
time for family reasons more frequently and for longer
periods than men.
• Individual and collective pay negotiations have not yet
succeeded in effectively overcoming the traditionally low
evaluation of female-dominated professions.
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Fakultät für Maschinenbau

Overall evaluation

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Fakultät für Maschinenbau

Evaluation forms for sessions and tasks

2. … “I liked the contents of today‘s session/task very much.“
3. … “The contents of today’s session/task have a large didacticalpotential for classrooms use.”106

Institut für vergleichende Studien
Fakultät für Maschinenbau

Evaluation forms for sessions and tasks

Regarding the session‘s topic/the session‘s task:
Q1 aims at cognitive aspects (understanding)Q2 aims at dispositions (like)Q3 aims at pedagogical aspects (pedagogical)

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Fakultät für Maschinenbau

Evaluation: Miniprojects and Gender Pay Gap
No

Topic

Understanding
Mean

Like
Mean

Pedagocial
Mean

6

Statistics about the world (Codap)

6

6,3

5,9

7

German hospitals (Fathom)

6,5

4,2

4

8

Inequality in the world (Gapminder)

5,8

5,2

4,4

9

German road accidents (applet)

6,6

5,6

5

10

Daily habits of US cititzens (applet

6,2

5

3,6

11-14

GPG – working

5,9

5,9

4,9

n=11
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Institut für vergleichende Studien
Fakultät für Maschinenbau

Evaluation: Miniprojects and Gender Pay Gap
No

Topic

Understanding
Mean

Like
Mean

Pedagocial
Mean

6

Statistics about the world (Codap)

6

6,3

5,9

7

German hospitals (Fathom)

6,5

4,2

4

8

Inequality in the world (Gapminder)

5,8

5,2

4,4

9

German road accidents (applet)

6,6

5,6

5

10

Daily habits of US cititzens (applet

6,2

5

3,6

11-14

GPG – working

5,9

5,9

4,9

n=11
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Fakultät für Maschinenbau

Evaluation form at the end of the course

2. … “I liked the contents of the course ‘Statistical literacy in mathematics classroom’very much.“
3. … “The course ‘Statistical literacy in mathematics classroom’ gave me very goodideas for the promotion of statistical literacy in mathematics classroom.”
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Fakultät für Maschinenbau

Evaluation of the whole course
No

Topic

Understanding
Mean

Like
Mean

Pedagocial
Mean

6

Statistics about the world (Codap)

6

6,3

5,9

7

German hospitals (Fathom)

6,5

4,2

4

8

Inequality in the world (Gapminder)

5,8

5,2

4,4

9

German road accidents (applet)

6,6

5,6

5

10

Daily habits of US cititzens (applet

6,2

5

3,6

11-14

GPG – working

5,9

5,9

4,9

Whole course

6,1

5,9

5,5

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Fakultät für Maschinenbau

Conclusion &Further plans

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Fakultät für Maschinenbau

Conclusions• Our participants worked statistically on many civic contexts,
which is less common in traditional statistics courses.
• Our participants were very engaged in the mini projects andon the gender pay gap project.
• Our participants developed lesson plans for implementing
some mini-projects in secondary school• The evaluation also shows that our participants liked the
exploration of German Income Structure data and thepresentation of their findings via PowerPoint.
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Fakultät für Maschinenbau

Further plans• Further plans: Revise and repeat the redesigned course for bringing civic
statistics to preservice teachers• Design and develop further projects for secondary preservice teacher
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Fakultät für Maschinenbau

Infos on both projects: ProDaBi & ProCivicStat• ProDaBi: www.prodabi.org• ProCivicStat: http://iase-web.org/islp/pcs/

Questions: dafr@math.upb.de

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