Teaching Machine Learning in Basic School: a Systematic Mapping of the State of the Art

• Lívia Silva Marques Federal University of Santa Catarina https://orcid.org/0000-0002-7391-6621
• Christiane Gresse von Wangenheim Federal University of Santa Catarina https://orcid.org/0000-0002-6566-1606
• Jean Carlo Rossa Hauck Federal University of Santa Catarina https://orcid.org/0000-0001-6550-9092

DOI: https://doi.org/10.5753/cbie.sbie.2020.21

Abstract

As Machine Learning (ML) is present in various aspects of our lives, new challenges are presented to education in order to help students to understand its potential and limits. Thus, to obtain an overview of the state of the art on teaching ML concepts in K-12, we carried out a systematic mapping. We identified 39 instructional units mostly focused on basic concepts and neural networks. Several instructional units also cover only partially the ML process, such as data management, or present certain processes only on an abstract level. Results demonstrate that teaching ML in K-12 can increase understanding and interest as well as it contextualizes ML concepts in the lives of the students.

Keywords: Machine Learning, Basic Education, Teaching

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