Statistics and data science education as a vehicle for empowering citizens : a survey report for ICME-15 / Rolf Biehler, Takashi Kawakami, Travis Weiland, Lucía Zapata-Cardona
Inhalt
- Slide 40: Statistics and data science education as a vehicle for empowering citizens (Survey 3, presented at ICME 15, Sydney, July 10, 2024 )
- Slide 41: Team members
- Slide 42: Structure of presentation: our perspectives
- Slide 43: Structure of presentation: our perspectives
- Slide 44: What is data science?
- Slide 45
- Slide 46: What should be taught in data science education?
- Slide 47: Declarations or Guiding Principles
- Slide 48: A fundamental question
- Slide 49: New challenge for the didactic transposition of knowledge into the educational system
- Slide 50: Data in society: New trends
- Slide 51: New Data (interactive) visualizations in the media
- Slide 52: AI and Data Science for Social Good
- Slide 53: AI and Data Science: Risk and Dangers
- Slide 54: Dangers and Risks of (data driven) AI and Data Technology
- Slide 55: (Conflicting) Goals of education
- Slide 56: Distinctions in citizenship education
- Slide 57: Distinctions in citizenship education
- Slide 58: Reviewing the debates on data literacies
- Slide 59: Data Literacy as closely related to statistical literacy
- Slide 60: Data Literacy and mathematical modelling (MM) education
- Slide 61: Data literacy as part of
- Slide 62: Data literacies as shaped by subject-specific epistemologies
- Slide 63: Further concepts of data literacy and data-related competencies and attitudes
- Slide 64: Data science education: Projects and frameworks
- Slide 65: Journal Special Issues on Data (AI) literacy and Data Science Education
- Slide 66: Structure of presentation: our perspectives
- Slide 67: 2. Civic statistics and humanistic perspectives on data literacies education in the U.S. and Europe (Travis Weiland)
- Slide 68: Goals
- Slide 69: Statistics, Data Science, Data Literacies and Citizenship Education
- Slide 70: Conceptual Model for Civic Statistics
- Slide 71: Humanistic Stance Toward K–12 Data Science Education
- Slide 72: Framing: Data Feminism (D’Ignazio & Klein, 2020)
- Slide 73: Survey of Scholarship
- Slide 74: Themes in the Literature
- Slide 75: Reading the World through Data
- Slide 76: Reading the World through Data
- Slide 77: Writing the World through Data
- Slide 78: Writing the World through Data
- Slide 79: Data Structures and Handling
- Slide 80: Data Structures and Handling
- Slide 81: Technology
- Slide 82: Example: Introduction to Data Science Project
- Slide 83: Curriculum IDS
- Slide 84: Example: ProCivicStat Project
- Slide 85: Promoting civic engagement via explorations of evidence
- Slide 86: Example: Writing Data Stories
- Slide 87: Curriculum materials
- Slide 88: Structure of presentation: our perspectives
- Slide 89: Goals
- Slide 90: Contributions to the theoretical debate on Critical data literacy from Latin America
- Slide 91: Contributions to the theoretical debate on Critical data literacy from Latin America
- Slide 92: Contributions to the theoretical debate on Critical data literacy from Latin America
- Slide 93: Contributions to the theoretical debate on Critical data literacy from Latin America
- Slide 94: What is the need for critical data literacy in Latin America ?
- Slide 95: Purposes behind critical data literacy proposals in Latin-American tradition
- Slide 96: Example 1: Data literacy for democracy A classroom experience
- Slide 97: Example 1: Data literacy for democracy A classroom experience
- Slide 98: Example 1: Data literacy for democracy A classroom experience
- Slide 99: Example 2: Data literacy - providing visibility / developing awareness
- Slide 100: Example 2: Data literacy - providing visibility / developing awareness
- Slide 101: Example 3: Data literacy for co-liberation / social justice A workshop with in-service teachers
- Slide 102: Example 3: Data literacy for co-liberation / social justice A workshop with in-service teachers
- Slide 103: Example 3: Data literacy for co-liberation / social justice A workshop with in-service teachers
- Slide 104: Future challenges for the community in Latin America
- Slide 105: Structure of presentation: our perspectives
- Slide 106: Goal
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- Slide 114: An example from teacher education of societal data-rich MM (Kawakami & Saeki, 2024b)
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- Slide 116: An example from teacher education of societal data-rich MM (Kawakami & Saeki, 2024b)
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- Slide 119: An example from teacher education of societal data-rich MM (Kawakami & Saeki, 2024b)
- Slide 120: An example from teacher education of societal data-rich MM (Kawakami & Saeki, 2024b)
- Slide 121: Structure of presentation: our perspectives
- Slide 122: Reviews on AI Literacy (from computer science education perspective)
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- Slide 125: A minimal requirement for data science education’s contribution to AI literacy?
- Slide 126: Decision Trees (DT) as an Exemplary ML method
- Slide 127: Classification task: Predicting the type of habitat lizards have come from (target variable) by several predictor variables
- Slide 128: Decision tree for predicting the type of habitat lizards have come from
- Slide 129: Basic ideas needed I
- Slide 130: Basic ideas needed II
- Slide 131: Two examples on DT from the ProDaBi project
- Slide 132
- Slide 133: Example 1: How can TikTok find out your true age?
- Slide 134: Variables ( ~150) of the YOU-PB data set
- Slide 135: Variables ( ~150) of the YOU-PB data set
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- Slide 139: Construction of trees in the classroom
- Slide 140: Representation of the learning algorithm for building a decision tree: algorithmic thinking
- Slide 141: Example 2 Grade 5/6: Decision Trees unplugged with data cards
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- Slide 145: Structure of presentation: our perspectives
- Slide 146: Future tasks
- Slide 147: Conceptual challenges as imminent task
- Slide 148: STATISTICS IS, ALAS, DIFFERENT. Should statisticians gloat?
- Slide 149: Weak organizational players
- Slide 150: Weak organizational players
- Slide 151: References
- Slide 152: Section 1 References
- Slide 153: Section 1 References
- Slide 154: Section 1 References
- Slide 155: Section 1 References
- Slide 156: Section 2 References
- Slide 157: Section 2 References
- Slide 158: Section 2 References
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- Slide 160: Section 3 References
- Slide 161: Section 3 References
- Slide 162: Section 3 References
- Slide 163: Section 4 References
- Slide 164: Section 4 References
- Slide 165: Section 4 References
- Slide 166: Section 4 References
- Slide 167: Section 4 References
- Slide 168: Section 5 References
- Slide 169: Section 5 References
- Slide 170: Section 6 references