Survey of Resources for Introducing Machine Learning in K-12 Context

Abstract

The benefits of teaching machine learning to K-12 pupils include building foundational skills, useful mental models and inspire the next generation of AI researchers and software developers. However, introducing machine learning in schools has been a challenge even though several initiatives, curriculum design, platforms, projects, and tools exist to demystify the concept. The existing resources are scattered and sometimes overlap. Thereby selecting the appropriate tools to adopt in teaching becomes an arduous task for the teachers and other practitioners. More so, despite the increasing number of papers published in this field, there are still gaps in identifying specific tools and resources for teaching machine learning in K-12 settings. This study presents a literature review on machine learning in K-12 by selecting articles published from 2010 to 2021. Therefore, this paper presents a resource catalog and surveys of tools to help teachers find suitable teaching paths and make the decision to introduce activities that help students understand the basic concepts of machine learning. Based on the research objective, we utilized six databases to extract relevant information, while thirty-nine peer-reviewed articles were collected based on a systematic literature search and were analyzed. This study identified resources, tools, and instructional methods as the main categories of pedagogical items needed to ensure impactful teaching of machine learning in K-12 settings. Besides, the mode of operation, benefits and the challenges of the pedagogical tools for teaching machine learning in K-12 settings were unraveled. The findings also show the increased number of initiatives resulting in tools development to support machine learning teaching. Finally, this study provides recommendations for future research directions to help researchers, policymakers, and practitioners in the education sector identify and apply various resources to aid decision-making in practice and to democratize machine learning practices in schools.