Data, Trees, and Forests -- Decision Tree Learning in K-12 Education
Tilman Michaeli Stefan Seegerer Lennard Kerber Ralf Romeike
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.