Data, Trees, and Forests -- Decision Tree Learning in K-12 Education
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.
Cite
@article{arxiv.2305.06442,
title = {Data, Trees, and Forests -- Decision Tree Learning in K-12 Education},
author = {Tilman Michaeli and Stefan Seegerer and Lennard Kerber and Ralf Romeike},
journal= {arXiv preprint arXiv:2305.06442},
year = {2023}
}
Comments
To be published in the proceedings of the 3rd Teaching in Machine Learning Workshop, PMLR, 2022