Learning machine learning with young children: exploring informal settings in an African context

Abstract

ABSTRACT Background and context Researchers have been investigating ways to demystify machine learning for students from kindergarten to twelfth grade (K–12) levels. As little evidence can be found in the literature, there is a need for additional research to understand and facilitate the learning experience of children while also considering the African context. Objective The purpose of this study was to explore how young children teach and develop their understanding of machine learning based technologies in playful and informal settings. Method Using a qualitative methodological approach through fine-grained analysis of video recordings and interviews, we analysed how 18 children aged 3–13 years constructed their interactions with a machine-based technology (Google’s Teachable Machine). Findings This study provides empirical support for the claim that Google’s Teachable Machine contributes to the development of data literacy and conceptual understanding across K–12 irrespective of the learners’ backgrounds. The results also confirmed children’s ability to infer the relationship between their own expressions and the output of the machine learning-based tool, thus, identifying the input-output relationships in machine learning. In addition, this study opens a discussion around differentials in emerging technology use across different contexts through participatory learning. Implications The results provide a baseline for future research on the topic and preliminary evidence to discern how children learn about machine learning in the African K–12 context.