Hands-on tasks make learning visible: a learning analytics lens on the development of mechanistic problem-solving expertise in makerspaces

Abstract

This study investigated the impact of participating in a year-long digital-fabrication course on high-school seniors’ problem-solving skills, with a focus on problems involving mechanistic systems. The research questions centered on whether working in a makerspace impacted students’ abilities to solve such problems and whether the process data generated during problem-solving activities could be used to identify the different problem-solving approaches taken by the participants. A novel set of hands-on, mechanistic problems were created to answer these questions, and the results showed that after taking part in the course students performed significantly better on these problems, with the post-course students making more progress towards the solutions than the pre-course students. The process data revealed two distinct problem-solving approaches for each problem, one adopted primarily by experts (the expert approach) and one by pre-course students (the novice approach). The post-course students were more likely to adopt the expert approaches, which were strongly associated with better performance on each problem. The study found that participation in the course made the high-school students better able to “see” the various components and their ways of interacting, making them more like expert engineers.