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

被引:0
|
作者
Michaeli, Tilman [1 ]
Seegerer, Stefan [2 ]
Kerber, Lennard [3 ]
Romeike, Ralf [2 ]
机构
[1] Tech Univ Munich, Computing Educ Res Grp, TUM Sch Social Sci & Technol, Munich, Germany
[2] Free Univ Berlin, Comp Educ Res Grp, Berlin, Germany
[3] Otto Nagel Gymnasium, Schulstr 11, D-12683 Berlin, Germany
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中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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 K12 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.
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页数:5
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