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

• Published in: Informatics in education Vol. 19; no. 2; pp. 283 - 321
• Main Authors: Marques, Lívia S, Gresse von Wangenheim, Christiane, Hauck, Jean Carlo Rossa
• Format: Journal Article
• Language: English
• Published: Vilnius Vilniaus Universiteto Leidykla 2020; Vilnius University Press; Institute of Mathematics and Informatics; Vilnius University Institute of Mathematics and Informatics, Lithuanian Academy of Sciences; Vilnius University
• Subjects: Algorithms; Artificial Intelligence; Computer science; Computers; Curricula; Education; Educational Technology; Elementary Secondary Education; Extracurricular Activities; Faculty Development; Guidelines; High School Students; Higher education; ICT Information and Communications Technologies; Imagery; Informatics; Information Science; Initiatives; Instructional Materials; k-12; Literacy; Machine learning; Man Machine Systems; Mapping; Mathematical Models; Neural networks; Oral Language; Secondary schools; Skill Development; Society; Software; State-of-the-art reviews; Statistical Inference; Students; Teaching; Teaching machines; Teaching Methods; Technology Uses in Education; Units of Study; Machine Learning; K12; teaching
• ISSN: 1648-5831; 2335-8971
• DOI: 10.15388/infedu.2020.14

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.