Abstract

The talk will raise the question of whether and how AI and ML should be taught differently from other themes in the CS curriculum at school. The tentative answer is that these topics require a paradigm shift for some teachers and that this shift has to do with the changed role of algorithms, of data, and of the societal context. The talk will present three small teaching examples from middle schools to illuminate the possible differences in teaching. The first example draws upon the Matchbox computer and successors like the sweet learning computer to teach the machine learning process, the second is about enactive teaching of Decision Trees, and the third about analysing location data. (Note: please have a fruit, ideally an apple, at hand during the presentation for some interactive elements!)

Speakers:

Dr. Carsten Schulte is a professor of computing education research at Paderborn University, Germany. His work and research interests are the philosophy of computing education and empirical research into teaching-learning processes (including eye movement research). Since 2017, he has been working together with Didactics of Mathematics (Paderborn University) in the ProDaBi project, in which Data Science and Artificial Intelligence are prepared as teaching topics. He is also PI in the collaborative research centre ‘Constructing Explainability’ on explainable AI.

Yannik Fleischer is a PhD student in mathematics education research at Paderborn University, Germany. His main research interest is to develop a concept to teach machine learning methods in school with a focus on decision trees, and to evaluate this by developing and examining teaching materials in practice. Since 2019, he has been supervising year-long project courses on data science in upper secondary and developing, implementing, and evaluating teaching modules for different levels in secondary school, mainly about machine learning with decision trees.

Lukas Höper is a PhD student in computing education research at Paderborn University, Germany. His main research interest is to develop the concept of data awareness for computing education and evaluate this by developing and examining teaching materials in practice. Since 2020, he has been working on data awareness in the ProDaBi project, among other topics on AI and Data Science in schools.