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| Phase | Inhalt | Ziele | Material |
|---|---|---|---|
|
1a |
Einführung in den Interaktionskontext und Problematisierung Didaktischer Kommentar |
|
Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. |
|
1a |
Einführung in den Interaktionskontext und Problematisierung Didaktischer Kommentar |
|
Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. |
Session 17 - Part 1: Line Have Musaeus & Michael E. Caspersen (Denmark)
Informatics – a fundamental discipline in the 21st century and a driver for disciplinary renewal
- 15.01.2025
- 16.00-17.00 Uhr (UTC+1)
Session 17 - Part 2: Ina Sander (Germany)
Education about Datafication: Conceptualising Critical Datafication Literacy.
- 15.01.2025
- 17.00-18.00 Uhr (UTC+1)
Session 13 - Part 1: Vince Geiger (Australia)
Evaluating media claims about sustainability through the use of large data sets
- 24.01.2024
- 16.00-17.30 Uhr (UTC+1)
Session 13 - Part 2: Devin W. Silvia (USA)
A learner-centered approach to teaching computational modeling, data analysis, and programming
- 24.01.2024
- 17.30-19.00 Uhr (UTC+1)
Session 14 - Part 1: Ismaila Sanusi (Finland)
The Role of Data in Artificial Intelligence Literacy in school education
- 17.04.2024
- 16.00-17.30 Uhr (UTC+2)
Session 14 - Part 2: Yasmin B. Kafai & Luis Morales-Navarro (USA)
High School Youth Peer Auditing of Machine Learning-Powered Applications to Promote Computational Literacies
- 17.04.2024
- 17.30-19.00 Uhr (UTC+2)
Session 15 - Part 1: Josephine Louie (USA)
Supporting critical data literacy for civic engagement and social justice
- 19.06.2024
- 16.00-17.00 Uhr (UTC+2)
Session 15 - Part 2: Joachim Engel (Germany)
Critical data literacy for democracy education
- 19.06.2024
- 17.10-18.10 Uhr (UTC+2)
Session 16 - Part 1: Anna Fergusson (New Zealand)
Introducing a data science perspective on predictive modelling within a large introductory statistics course: Connecting research with practice
- 11.12.2024
- 13.00-14.00 Uhr (UTC+1)
Session 16 - Part 2: Takashi Kawakami (Japan)
Data-driven modelling approach with mathematical and statistical models at its core in school and teacher education: A focus on a societal perspective
- 11.12.2024
- 14.00-15.00 Uhr (UTC+1)
Session 10 - Part 1: Tor Ole Odden (Norway)
Using Computational Essays to Support Student Creativity and Agency in Science
- 18.01.2023
- 17.00-19.30 Uhr (UTC+1)
Session 09 - Part 1: Nick Horton (USA)
Teaching reproducibility and responsible workflows
- 18.01.2023
- 17.00-18.30 Uhr (UTC+1)
Session 10 - Part 2: Tom Button and Ian Dickerson (UK)
Design decisions in creating short data science courses for pre-university students
- 18.01.2023
- 18.30-19.30 Uhr (UTC+1)
Session 09 - Part 2: Francine Berman (USA)
Teaching Social Responsibility for a Tech-Powered World
- 18.01.2023
- 18.30-20.00 Uhr (UTC+1)
Session 11 - Part 1: Martin Frank and Sarah Schönbrodt (Germany)
How much mathematical modeling is in AI?
- 17.05.2023
- 16.00-17.30 Uhr (UTC+2)
Session 11 - Part 2: Dani Ben-Zvi (Israel)
Reasoning with Data in School-Based Citizen Science
- 17.05.2023
- 17.30-19.00 Uhr (UTC+2)
Session 12 - Part 1: Henning Wachsmuth (Germany)
NLP Research in the Age of Large Language Models
- 29.11.2023
- 16.00-17.30 Uhr (UTC+1)
Session 12 - Part 2: Travis Weiland (USA)
Reading and Writing the World with Data
- 29.11.2023
- 17.30-19.00 Uhr (UTC+1)
Session 03 - Part 1: Graham Dove (USA)
Learning data science through civic engagement with open data
- 02.01.2022
- 16.00-17.30 Uhr (UTC+1)
Session 03 - Part 2: Rob Gould (USA)
Why should students take a data science course?
- 02.01.2022
- 17.30-19.00 Uhr (UTC+1)
Session 04 - Part 1: Arnold Pears (Sweden)
Why Computing Education, and Especially CT, Needs a Broader Perspective!
- 01.04.2022
- 16.00-17.30 Uhr (UTC+2)
Session 04 - Part 2: Jim Ridgway (England)
Education for a fast-changing world: Conceptions of Statistical Literacy and Data Science
- 01.04.2022
- 17.30-19.00 Uhr (UTC+2)
Session 05 - Part 1: Lukas Höper and Carsten Schulte (Germany)
Data Awareness: Be aware of the data!
- 18.05.2022
- 16.00-17.30 Uhr (UTC+2)
Session 05 - Part 2: Orit Hazzan and Koby Mike (Israel)
Teaching Core Principles of Machine Learning with a Simple Machine Learning Algorithm: The Case of the KNN Algorithm
- 18.05.2022
- 17.30-19.00 Uhr (UTC+2)
Session 06 - Part 1: Marc Hauer (Germany)
My AI discriminates? How could this happen and who is to blame?
- 02.06.2022
- 16.00-17.30 Uhr (UTC+2)
Session 06 - Part 2: Michelle Hoda Wilkerson (USA)
A Framework for Exploring the Purposes and Processes of Data Wrangling in Complex Self-Directed Analysis Tasks
- 02.06.2022
- 17.30-19.00 Uhr (UTC+2)
Session 07 - Part 1: Conrad Wolfram (England)
Roadmap to Computational Thinking for the AI age: A challenge for Mathematics and Computer Science Education
- 02.11.2022
- 17.00-18.30 Uhr (UTC+1)
Session 07 - Part 2: Sven Hüsing, Carsten Schulte and Dan Verständig (Germany)
Epistemic Programming and Creative Coding: Programming as an Empowering Means for Self-Expression and Communication
- 02.11.2022
- 18.30-20.00 Uhr (UTC+1)
Session 08 - Part 1: Ute Schmid (Germany)
Learning About and Learning with Artificial Intelligence in School: From Understanding of Basic AI Concepts to Trustworthy and Human-centric AI Tools
- 07.12.2022
- 17.00-18.30 Uhr (UTC+1)
Session 08 - Part 2: Jane Waite (England)
A hands-on workshop to develop a set of potential goals for learning about AI
- 07.12.2022
- 18.30-20.00 Uhr (UTC+1)
Session 01 - Part 1: Jan Mokros and Bill Finzer (USA)
Data Detective Clubs in the Time of COVID-19
- 27.10.2021
- 16.00-17.00 Uhr (UTC+1)
Session 01 - Part 2: Matti Tedre and Henriikka Vartiainen (Finland)
Teaching machine learning in school: Some emerging research trajectories
- 27.10.2021
- 17.30-18.30 Uhr (UTC+1)
Session 02 - Part 1: Tobias Matzner (Germany)
Beyond Bias. Locating questions of injustice in Data Science and Artificial Intelligence
- 24.11.2021
- 16.00-17.30 Uhr (UTC+1)
Session 02 - Part 2: Rolf Biehler and Yannik Fleischer (Germany)
Bringing together statistics and computer science education: Machine learning by decision trees grounded in students’ data exploration experiences
- 24.11.2021
- 17.30-18.30 Uhr (UTC+1)
Jubiläums-Herbsttagung: Arbeitskreis Stochastik 2024
- 10.12.2024
- 09.00 Uhr
- Fuldatal
test
Using Worked Examples for Engaging in Epistemic Programming Projects
Revisiting Fundamental Ideas for Statistics Education From the Perspective of Machine Learning and Its Applications.
Editorial: Research on Data Science Education.
Test
test 2021
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