Publications
The model of learner roles and levels of algorithmic abstraction presented on this website is grounded in the following scientific publications.
Teaching ML Algorithms in K-12: A Classification by Levels of Abstraction
Jan Hendrik Krone, Josua Ginster
Based on a systematic literature review and a qualitative content analysis of 126 K-12 machine learning activities, this paper introduces the classification by levels of algorithmic abstraction that underlies the Coding Table on this website. The level of each activity is determined by the role learners play: user of the algorithm, machine executing its steps, or creator designing it.
In: Proceedings of the 18th International Conference on Computer Supported Education (CSEDU 2026), Volume 2, pages 1302-1312. DOI: 10.5220/0015041700004021 · PDF
Contextual Teaching of Machine Learning in K-12: A Systematic Review and Context Dimensions Framework
Jan Hendrik Krone, Josua Ginster
This paper investigates which learning contexts are used in K-12 ML education and how they position learners in relation to ML. Based on a systematic literature review and qualitative content analysis, it derives CALM (Context Analysis for Learning Machine Learning), a framework for analysing and designing learning contexts in ML education.
In: Proceedings of the 18th International Conference on Computer Supported Education (CSEDU 2026), Volume 2, pages 1093-1104. DOI: 10.5220/0014708300004021 · PDF
ICE-T: A Multi-Faceted Concept for Teaching Machine Learning
Hendrik Krone, Pierre Haritz, Thomas Liebig
This paper reviews didactic principles for teaching computer science, defines criteria and evaluates prominent ML learning platforms, tools and games. It criticises the prevalent black-box portrayal of ML and proposes the ICE-T concept with its facets of intermodal transfer, computational thinking and explanatory thinking.
In: 17th International Conference on Informatics in Schools (ISSEP 2024). arXiv:2411.05424