Finnish primary school students’ conceptions of machine learning

Abstract

Background and context Language is a powerful tool in shaping thought about abstract domains, including the realm of computing and digital technologies. Review of research on children's machine learning (ML) conceptions suggests that participating children were often provided with conceptual clues about the principles of ML before being asked about their conceptions (i.e., asking them how one could teach a computer). Since the term ML is not explicit about who is learning and from whom, this procedure has arguably steered their answers: with less nudging instruction, ML could also be understood as a process in which a human uses a machine, a computer, for instance, for learning purposes.ObjectiveThis study investigates what kind of conceptions primary school students have about ML if they are not conceptually "primed" with the idea that in ML, humans teach computers.MethodQualitative survey responses from 197 Finnish primary schoolers were analyzed via an abductive method.FindingsWe identified three partly overlapping ML conception categories, starting from the most accurate one: ML is about teaching machines (34%), ML is about coding (7.6%), and ML is about learning via or about machines (37.1%).ImplicationsThe findings suggest that without conceptual clues, children's conceptions of ML are varied and may include misconceptions such as ML is about learning via or about machines. The findings underline the importance of clear and systematic use of key concepts in computer science education. Besides researchers, this study offers insights for teachers, teacher educators, curriculum developers, and policymakers.