Statistics for Empowerment and Social Engagement: Teaching Civic Statistics to Develop Informed Citizens

Foreword
Democracy Needs Statistical Literacy
References
Contents
Editors and Contributors
Abbreviations
Chapter 1: Why Engage with Civic Statistics?
1.1 Why Do We Need Another Book About Teaching Statistics? Why Now? What? And for Whom?
1.1.1 Why We Need Another Book About Teaching Statistics, and Need It Now
1.1.2 What Is `Civic Statistics´?
1.1.3 So Who Is This Book For?
1.2 Background to the Book: ProCivicStat
1.3 Overview of the Book´s Content
1.4 Educating for Citizenship
References
Part I: Edited by Joachim Engel and James Nicholson
Chapter 2: Back to the Future: Rethinking the Purpose and Nature of Statistics Education
2.1 Introduction
2.2 The Need for Civic Statistics
2.3 What Is Civic Statistics?
2.4 The Unique Features of Civic Statistics
2.5 Exemplifying Features of Civic Statistics: Understanding Evidence Related to Climate Change
2.5.1 Reflecting on the Nature of the Statistics Presented
2.5.2 Unfamiliar Methods and Representations
2.6 What Are Our Ambitions for Civic Statistics?
2.7 Curricular Implications
2.8 A Need for New Teaching Resources
2.9 Concluding Remarks
References
Chapter 3: A Conceptual Framework for Civic Statistics and Its Educational Applications
3.1 Introduction
3.2 Opening Examples: The Facets and Tools in Action
3.3 The Conceptual Framework: Overview of the 11 Facets and Tools Needed for Understanding and Engaging with Civic Statistics
3.4 The Conceptual Framework in Detail
3.5 Task Analysis: Analysing the Demands of Possible Activities via `Radar Plots´
3.6 Summary and Suggestions for Planning Instruction from the Viewpoint of the 11 Facets and Tools
Appendix A
References
Chapter 4: Implementing Civic Statistics: An Agenda for Action
4.1 Introduction
4.2 Analysing the Demands of Instructional Activities Related to Civic Statistics
4.3 Reflecting on Instruction About Civic Statistics
4.3.1 Civic Statistics and General Aspects of Teaching Statistics
4.3.2 Planning Instruction that Can Promote Civic Statistics
4.4 Reflecting on Assessments of Knowledge, Facets and Tools Related to Civic Statistics
4.4.1 General Ideas and Approaches
4.4.2 Analysing the Demands of Assessment Tasks and Systems
4.4.3 Aligning Assessment Schemes with Instructional Goals in Civic Statistics
4.5 A Future Agenda and Recommendations for Action to Promote Understanding of Civic Statistics
4.5.1 Recommendation 1: Develop Activities Which Promote Engagement with Societal Issues and Develop Learners´ Critical Unders...
4.5.2 Recommendation 2: Use Socially-Relevant Data and Texts, and Highlight the Many Features of Civic Statistics (e.g., Multi...
4.5.3 Recommendation 3: Embrace Technologies that Enable Rich Visualizations and Interactions with Data About Relevant Social ...
4.5.4 Recommendation 4: Adopt Teaching Methods to Develop Skills of Critical Interpretation, Applicable to a Wide Variety of D...
4.5.5 Recommendation 5: Implement Diverse Assessments and Evaluation Processes Which Examine and Also Promote Learners´ Abilit...
4.5.6 Recommendation 6: Engage Stakeholders at All Levels in the Process of Systemic Change Designed to Promote the Understand...
4.6 Conclusions and Future Directions
Appendix A
References
Part II: Edited by Rolf Biehler and Peter Kovacs
Chapter 5: Interactive Data Visualizations for Teaching Civic Statistics
5.1 Introduction
5.2 A Brief History of Data Visualisation
5.2.1 Reflecting on Data Visualisation
5.3 Interacting with Civic Statistics
5.3.1 Tools for Displaying Specific Data Sets
5.3.1.1 Arctic Sea Ice Volume
5.3.1.2 Predicting Mortality
5.3.1.3 Predicting Demographic Change
5.3.2 Tools to Support Exploration of Large Data Sets
5.3.2.1 Better Life Index
5.3.2.2 The World Inequality Database
5.3.2.3 United Nations Sustainable Development Goals (SDG) Programme
5.4 Discussion
5.4.1 Criticality Not Nihilism
5.5 Herschel Revisited
References
Chapter 6: Data Sets: Examples and Access for Civic Statistics
6.1 Introduction: The Importance of Real Data
6.2 CivicStatMap
6.3 Micro and Macrodata
6.4 Data Cleaning and Data Management
6.5 Data Sources
6.6 Data Sets
6.6.1 Migration Data Set
6.6.2 Natural Disasters, Ecological Footprint and Biocapacity, and Air Quality Data Sets
6.6.3 Refugees Data Sets
6.7 Connection Between Statistical Concepts, Data Visualization Tools, Type of Graphs and Data Sets
6.8 Conclusion
References
Software, Packages and Platforms Bibliography
Data Sets Bibliography
Chapter 7: Lesson Plan Approaches: Tasks That Motivate Students to Think
7.1 Introduction
7.2 Motivation and Its Properties
7.3 Use of Real Data and Storytelling Approach
7.4 Empowering Teachers
7.5 Teachers´ Version of the Lesson Plan
7.6 Designing Student Activities
7.7 Lesson Plan Demonstration
7.7.1 Setting the Scene
7.7.2 Introducing the Theme
7.7.3 Getting Down to Detail
7.7.4 Student Activities
7.8 Visualization Tools to Promote Data Understanding
7.8.1 Freely Available, Commonly Used Tools
7.8.2 Commercially Available Commonly Used Tools
7.9 Conclusion
Bibliography
Software, Packages and Platforms Bibliography
Data Sets Bibliography
Chapter 8: Seeing Dynamic Data Visualizations in Action: Gapminder Tools
8.1 Introduction
8.2 About Gapminder
8.3 The Gapminder Bubble Chart in Action
8.4 Other Gapminder Tools in Action: Ageing Society
8.5 Conclusion
References
Chapter 9: Data Visualization Packages for Non-inferential Civic Statistics in High School Classrooms
9.1 Introduction
9.2 Requirements for Software for Learning About Civic Statistics
9.3 TinkerPlots, Fathom and CODAP as Software for Learning About Civic Statistics
9.4 The American Community Survey Dataset
9.5 Getting Started with the Exploration of ACS Data
9.5.1 Storage of Data in TinkerPlots, Fathom and CODAP
9.5.2 Distribution of a Single Variable
9.6 Exploring Relationships in ACS Data with TinkerPlots, Fathom and CODAP
9.6.1 Exploring the Relationship Between Two Categorical Variables with TinkerPlots, CODAP and Fathom
9.6.2 Comparing Groups Via TinkerPlots, CODAP and Fathom
9.6.3 Exploring the Relationship Between Two Numerical Variables with TinkerPlots, CODAP and Fathom
9.6.4 Exploring the Relationship of More Than Two Variables with TinkerPlots, CODAP and Fathom
9.7 Summary
References
Chapter 10: Civic Statistics and iNZight: Illustrations of Some Design Principles for Educational Software
10.1 Introduction
10.1.1 Design Principles for Educational Software
10.1.2 What Is iNZight?
10.2 Key Software Functionality for Civic Statistics
10.2.1 Easy Exploration of Multivariate Data
10.2.2 Minimizing Cognitive Load
10.2.3 Provoking Learning
10.2.4 Offering R-Code: Reproducibility and a Bridge from GUIs to Coding
10.2.5 Specific Support for Civic Statistics
10.3 Illustrating iNZight
10.3.1 Making Simple Things Really Simple
10.3.2 Facilitating Exploration
10.3.3 Disaggregation: Exploring Subgroups with Static and Dynamic Graphs
10.4 Essential Analyses for Civic Statistics
10.4.1 Multiple Response Data
10.4.2 Time Series
10.4.3 Maps
10.4.4 Communicating by Customizing: Making Aesthetic Changes and Adding Information to Plots
10.4.5 Give Me the R Code for That
10.5 Future-Proofing Software
10.6 Conclusions
References
Chapter 11: Exploring Climate Change Data with R
11.1 Introduction
11.2 Sources for Climate Data and Visualisations
11.3 Using R in Exploring Climate Change Data
11.3.1 Vostok Ice Core (Antarctica)
11.3.2 Rise of CO2 (``Keeling Curve´´)
11.3.3 Global Average Temperature
11.3.4 Sea Level Rise
11.3.5 Sea Ice Retreat
11.3.6 Using Geographical Maps
11.4 Recommendations for Implementation in a Classroom Environment
11.5 Conclusions
References
Chapter 12: Covid-19 Shows Why We Need Civic Statistics: Illustrations and Classroom Activities
12.1 Introduction
12.2 Features of Civic Statistics
12.2.1 Contexts and Interpretations
12.2.2 The Nature of the Statistics
12.2.3 Unfamiliar Methods and Representations
12.3 Covid-19 and Some Components of Civic Statistics Knowledge
12.3.1 Enabling Processes
12.3.1.1 ICT and Search
12.3.1.2 Quantitative Core
12.3.1.3 Literacy and Communication
12.3.2 Knowledge
12.3.2.1 Statistics and Risk
12.3.2.2 Models, Patterns and Representations
12.3.2.3 Methodology and Enquiry Processes
12.3.2.4 Extensions Into Official Statistics
12.3.2.5 Contextual Civic Knowledge
12.3.3 Engagement and Action
12.3.3.1 Meaning for Society and Policy
12.3.3.2 Critical Evaluation and Reflection
12.3.3.3 Dispositions
12.4 Conclusions
References
Part III: Edited by Iddo Gal and Daniel Frischemeier
Chapter 13: Critical Understanding of Civic Statistics: Engaging with Important Contexts, Texts, and Opinion Questions
13.1 Introduction
13.2 The Centrality of `Context´ for Statistical Literacy and for Understanding Civic Statistics in Particular
13.3 About Meaningful and Important Contexts
13.4 How Do We Bring Meaningful Contexts and Statistics About Them into the Classroom?
13.5 Worthy Tasks and `Opinion Questions´ for Developing Critical Understanding of Civic Statistics
13.6 More About Heuristics for Designing Opinion Questions
13.7 An Example for a Task Involving Opinion Questions
13.8 Beyond Opinion Questions: Towards Critical `Worry Questions´
13.9 Summary and Implications
References
Chapter 14: Implementing Civic Statistics in Business Education: Technology in Small and Large Classrooms
14.1 Introduction
14.2 The Importance and Challenge of Including Civic Statistics in Business Education
14.3 An Implementation of CS: Building Modules into Existing Courses
14.3.1 Exploring Social Problems
14.3.2 Teaching Environments
14.4 A New Course on Civic Statistics
14.4.1 Lesson Structure
14.4.2 Course Design
14.4.3 Options for Adapting and Expanding the Course Design and Activities
14.4.4 Evaluation
14.4.5 Feedback from Students
14.5 General Conclusions: Experiences and Recommendations
14.5.1 Experiences
14.5.2 Recommendations for Introducing Civic Statistics in Classes
References
Chapter 15: Civic Statistics for Prospective Teachers: Developing Content and Pedagogical Content Knowledge Through Project Wo...
15.1 Introduction
15.2 Overview of Goals and Working Style of a Civic Statistics Course
15.3 Structure and Specific Content of the Course
15.4 Implementing Civic Statistic Activities: Mini-projects
15.5 How to Administer a Mini-project
15.5.1 A Lesson Plan for the Mini-project
15.5.1.1 Phase 1: Introduction
15.5.1.2 Phase 2: ``What Is the Topic of the Mini-project?´´ and ``Why Is It Important?´´
15.5.1.3 Phase 3: ``Why Does It Matter?´´ and ``How Is It Measured?´´
15.5.1.4 Phase 4: Data and List of Variables
15.5.1.5 Phase 5: Task and Analysis
15.5.1.6 Phase 6: Discussing Findings in a Plenary
15.6 How to Conduct a Larger Project in Civic Statistics
15.6.1 Introductory Session for a Larger Project
15.6.2 Data Analysis Sessions for a Larger Project
15.6.3 Presentation Session for a Larger Project
15.7 Experiences from Our Course
15.8 Conclusions and Recommendations
References
Chapter 16: Implementing Civic Statistics in Mathematics Teacher Education
16.1 Introduction
16.2 Background: The Autonomous Citizen
16.3 Goals of the Course
16.4 Pedagogical Approach
16.5 Content and Structure of the Course
16.6 The Module: Critical Questioning of Data-Based Statements
16.6.1 Part 1: Comprehending and Dealing with Data-Based Statements
16.6.2 Part 2: Producing and Communicating Data-Based Statements
16.7 Assessment: The Video Project
16.8 Reflections and Lessons Learnt from the Course
16.8.1 Reflections on the CQS Module
16.8.2 Reflections on the Video Project
16.9 General Conclusion and Lessons Learnt
References
Chapter 17: Civic Statistics at School: Reasoning with Real Data in the Classroom
17.1 Fundamental Considerations for Statistical Education at School Level
17.1.1 Reality and Drawbacks of the School Curricula
17.1.2 The Role of Modelling the Real World
17.1.3 The Role of ICT
17.2 Teaching Civic Statistics at the Secondary Level
17.2.1 Competency Conditions
17.2.2 Pedagogical Conditions
17.2.3 Motivational Conditions
17.2.4 Content Design and Sequential Arrangement of Training Units
17.2.5 Content Design and Sequential Arrangement of Project-Based Units
17.2.6 Learning Objectives
17.2.7 Special Learning Opportunities
17.3 Results and Conclusions from Practical Lessons
17.3.1 Competence-Related Findings
17.3.2 Process-Related Findings
17.3.3 Curriculum-Related Findings
17.4 Conclusions
References
Chapter 18: Preparing for a Data-Rich World: Civic Statistics Across the Curriculum
18.1 Background and Introduction
18.2 Interdisciplinary Teaching of Statistics: Promises and Challenges
18.2.1 The Rationale for Teaching Statistics Across the Curriculum
18.2.2 Examples of Cross-Curricular Teaching Efforts
18.2.3 Challenges to Cross-Curricular Statistics Teaching
18.3 Using Existing PCS Resources Across the Curriculum
18.3.1 Example 1: Human Development and World Happiness
18.3.2 Example 2: Some So Rich, Others So Poor
18.3.3 Example 3: Gender Pay Gap, or Do Women Earn as Much as Men?
18.3.4 Example 4: Do More Years of School among Women Impact the Birth Rate?
18.3.5 Example 5: What Is Causing the Earth to Warm Up?
18.3.6 Example 6: What Makes a Vibrant Democracy?
18.4 How to Start Developing Civic Statistics Resources of Your Own
18.4.1 With Your Target Group in Mind, Specify Prerequisites and Learning Goals
18.4.2 Identify a Suitable Topic of Relevance to Society to Be Explored with Data
18.4.3 Sensitize and Motivate Your Students, Relate the Topic to Personal Experience
18.4.4 Use Introductory Material (Cartoons, YouTube Videos, Texts, Media Reports) to Generate Interest and Provide Information
18.4.5 Search for Rich, Relevant Data Sets and Written Texts with Statistical Content
18.4.6 Choose Suitable Digital Tools for Data Visualization and Exploration
18.4.7 Design Activities with Closed and Open Tasks to Promote Critical Thinking
18.4.8 Ask for a Summary and Critical Review
18.4.9 Use Alternate Forms of Assessment to Improve and Evaluate Student Learning
18.5 Summary
References
Chapter 19: Dynamic, Interactive Trees and Icon Arrays for Visualizing Risks in Civic Statistics
19.1 Introduction and Motivation
19.2 Risk Literacy as a Construct
19.3 Modelling Risks and Uncertainty in the Health Domain
19.4 Iconic and En-Active Representations
19.5 Design Ideas for Instruction Based on Icon Arrays, Risks and Uncertainty in a Civic Statistics Context
19.5.1 Dynamic, Interactive Icon Arrays: The Website ``Worth the Risk?´´
19.6 Concluding Remarks
References
Part IV: Edited by Pedro Campos and Achim Schiller
Chapter 20: Reflections on Civic Statistics: A Triangulation of Citizen, State and Statistics: Past, Present and Future
20.1 Introduction
20.2 A Historical Line: The Origin of Statistics, Statistics About People
20.3 A Social Line: Statistics for the People as a Means of Empowering People
20.4 A Critical Line: People´s Reading and Interpretation of Statistics
20.4.1 `Critical Sense´ Related to People´s Knowledge
20.4.2 `Critical Sense´ Related to People´s Social Context
20.4.3 `Critical Sense´ Related to People´s Affective Dimension
20.5 Implications for Education
20.5.1 Air Quality in Flanders
20.5.2 Water Resources in the Xukuru Do Ororub Territory
20.5.3 Coexisting with a Semi-Arid Region
20.6 Discussion
References
Chapter 21: Project-Based Learning with a Social Impact: Connecting Data Science Movements, Civic Statistics, and Service-Lear...
21.1 Introduction
21.2 The Rise of the Communities of Data Science for Social Impact
21.3 DSSG Summer Fellowship: A Project-Based Learning Data Science Program
21.3.1 What Is DSSG Summer Fellowship?
21.3.2 DSSG and Project Based Learning
21.3.3 The (Data Science) Project Pipeline
21.3.4 Fellowship Trends and Outcomes
21.4 The Relationship Between Civic Statistics and Data Movements Through the Case of DSSG Summer Fellowship
21.5 A Master Program with Program-Oriented Project-Based Learning
21.5.1 Bringing Management and Economics Closer to Data Science
21.5.2 Adapting DSSG Summer Fellowship to an Accredited Degree-Granting Program
21.6 Conclusions and Recommendations
Appendix A: The List of Skills Required for Doing Data Science for Social Good, as Listed On DSSG Hitchhickers guide
Appendix B: An Example of the DSSG Summer Fellowship Curriculum (2019); Source DSSG. https://dssg.github.io/hitchhikers-guide/...
References
Chapter 22: Data Science, Statistics, and Civic Statistics: Education for a Fast Changing World
22.1 Introduction
22.2 Disruptive Technologies and Civic Statistics
22.3 Disruptive Thinking: Critical Evaluation and Reflection on Data Science and Ways of Knowing and Acting
22.3.1 On Modelling
22.3.1.1 Challenges for Machine Learning Models
22.3.1.2 Challenges for Analytic Models
22.3.1.3 Revisiting Models, Patterns and Representations
22.4 Education for a Fast Changing World
22.5 Conclusions
References
Chapter 23: Civic Statistics in Context: Mapping the Global Evidence Ecosystem
23.1 Data, Information, Evidence
23.2 On the Evidence Ecosystem
23.3 Elements in the Evidence Ecosystem
23.3.1 Governments
23.3.2 Politicians and Political Leaders
23.3.3 Print and Video Media
23.3.4 Social Media
23.3.5 Cyber Warriors
23.3.6 Survey Data Collectors and Distributors
23.3.7 Sensor Data Collectors and Distributors
23.3.8 `Invisible´ Unaccountable Data Collectors and Distributors
23.3.9 Knowledge Creators and Curators
23.3.10 Advocacy Agencies
23.3.11 Fact Checking Organisations
23.3.12 Investigative Journalists and Leakers
23.3.13 Educators
23.4 The Evidence Ecosystem as a Context for Civic Statistics
23.4.1 Measuring `Democracy´
23.4.2 Assessing Perceptions of Corruption
23.5 Civic Statistics in the Context of the Evidence Ecosystem
23.6 A Brief Conclusion
References
Index