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

Tilman Michaeli, Stefan Seegerer, Lennard Kerber, Ralf Romeike
Proceedings of the Third Teaching Machine Learning and Artificial Intelligence Workshop, PMLR 207:37-41, 2023.

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 this Paper

BibTeX
@InProceedings{pmlr-v207-michaeli23a, title = {Data, Trees, and Forests - Decision Tree Learning in K-12 Education}, author = {Michaeli, Tilman and Seegerer, Stefan and Kerber, Lennard and Romeike, Ralf}, booktitle = {Proceedings of the Third Teaching Machine Learning and Artificial Intelligence Workshop}, pages = {37--41}, year = {2023}, editor = {Kinnaird, Katherine M. and Steinbach, Peter and Guhr, Oliver}, volume = {207}, series = {Proceedings of Machine Learning Research}, month = {19--23 Sep}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v207/michaeli23a/michaeli23a.pdf}, url = {https://proceedings.mlr.press/v207/michaeli23a.html}, 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.} }
Endnote
%0 Conference Paper %T Data, Trees, and Forests - Decision Tree Learning in K-12 Education %A Tilman Michaeli %A Stefan Seegerer %A Lennard Kerber %A Ralf Romeike %B Proceedings of the Third Teaching Machine Learning and Artificial Intelligence Workshop %C Proceedings of Machine Learning Research %D 2023 %E Katherine M. Kinnaird %E Peter Steinbach %E Oliver Guhr %F pmlr-v207-michaeli23a %I PMLR %P 37--41 %U https://proceedings.mlr.press/v207/michaeli23a.html %V 207 %X 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.
APA
Michaeli, T., Seegerer, S., Kerber, L. & Romeike, R.. (2023). Data, Trees, and Forests - Decision Tree Learning in K-12 Education. Proceedings of the Third Teaching Machine Learning and Artificial Intelligence Workshop, in Proceedings of Machine Learning Research 207:37-41 Available from https://proceedings.mlr.press/v207/michaeli23a.html.

Related Material