Data, Trees, and Forests -- Decision Tree Learning in K-12 Education
This addresses the need for K-12 students to gain competencies in understanding and shaping machine learning's societal impacts, though it is incremental in applying existing educational methods to this domain.
The paper tackles the challenge of teaching machine learning to K-12 students by developing a teaching concept that uses decision tree learning through a playful, unplugged approach to foster conceptual understanding and societal reflection.
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.