Methodological Analysis of Machine Learning Courses for Elementary to High School Students

Abstract

The number of studies exploring different aspects of Machine Learning (ML) in K-12 contexts has increased, making it imperative to synthesize existing research. This study presented a comprehensive review of the current state of research on ML education from K-12, drawing attention to both current research hotspots and gaps in the literature that should be addressed by future studies. We looked at 45 articles published at conferences and in journals that focused on certain aspects of K-12 ML education via these four lenses: curriculum development, technical development, pedagogical development, and teacher training/professional development. We found that (a) there is a lack of ML materials for K-8 and informal settings, (b) more research is needed on how ML can be integrated into subject domains other than computing, (c) most studies focus on pedagogical development, (d) there is a lack of teacher professional development programs, and (e) more evidence of the societal and ethical implications of ML should be considered in future research. Although the study's authors note several caveats and suggestions for further study, the findings are nonetheless applicable for improving the quality of research in the rapidly expanding field of K-12 ML by educating teachers, researchers, and instructional designers.