[2305.06442] Data, Trees, and Forests -- Decision Tree Learning in K-12 Education
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Computer Science > Computers and Society

arXiv:2305.06442 (cs)
[Submitted on 10 May 2023]

Title:Data, Trees, and Forests -- Decision Tree Learning in K-12 Education

Authors:Tilman Michaeli, Stefan Seegerer, Lennard Kerber, Ralf Romeike
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Abstract:As a consequence of the increasing influence of machine learning on our lives, everyone needs competencies to understand corresponding phenomena, but also to get involved in shaping our world and making informed decisions regarding the influences on our society. Therefore, in K-12 education, students need to learn about core ideas and principles of machine learning. However, for this target group, achieving all of the aforementioned goals presents an enormous challenge. To this end, we present a teaching concept that combines a playful and accessible unplugged approach focusing on conceptual understanding with empowering students to actively apply machine learning methods and reflect their influence on society, building upon decision tree learning.
Comments: To be published in the proceedings of the 3rd Teaching in Machine Learning Workshop, PMLR, 2022
Subjects: Computers and Society (cs.CY); Machine Learning (cs.LG)
ACM classes: K.3.2; I.2.6
Cite as: arXiv:2305.06442 [cs.CY]
  (or arXiv:2305.06442v1 [cs.CY] for this version)
  https://doi.org/10.48550/arXiv.2305.06442

Submission history

From: Tilman Michaeli [view email]
[v1] Wed, 10 May 2023 20:24:38 UTC (1,983 KB)
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