文章基本信息

标题：Teaching Machine Learning in School A Systematic Mapping of the State of the Art
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作者：MARQUES, Lívia S. ; GRESSE VON WANGENHEIM, Christiane ; HAUCK, Jean C. R. 等
期刊名称：Informatics in Education
印刷版ISSN：1648-5831
出版年度：2020
卷号：19
期号：2
页码：283-321
DOI：10.15388/infedu.2020.14
语种：English
出版社：Institute of Mathematics and Informatics
摘要：Although Machine Learning (ML) is integrated today into various aspects of our lives, few understand the technology behind it. This presents new challenges to extend computing education early to ML concepts helping students to understand its potential and limits. Thus, in order to obtain an overview of the state of the art on teaching Machine Learning concepts in elementary to high school, we carried out a systematic mapping study. We identified 30 instructional units mostly focusing on ML basics and neural networks. Considering the complexity of ML concepts, several instructional units cover only the most accessible processes, such as data management or present model learning and testing on an abstract level black-boxing some of the underlying ML processes. Results demonstrate that teaching ML in school can increase understanding and interest in this knowledge area as well as contextualize ML concepts through their societal impact.
关键词：Machine Learning;teaching;K-12