AI Summary & Key Takeaways
AI InsightResults showed that participants developed strategies such as simultaneous review of output and code, use of beacons to narrow down the area to be debugged, and eye fixation on output to form hypotheses.
📌 3 Key Takeaways
Abstract
Learning computational skills such as programming and debugging is very important for K-12 students given the increasing need of workforce proficient in computing technologies. Programming is an intricate cognitive task that entails iteratively creating and revising programs to create an artifact. Central to programming is debugging, which consists of systematically identifying and fixing program errors. Given its central role, debugging should be explicitly taught to early childhood preservice teachers so they can support their future students’ learning to program and debug errors. In this study, we propose using eye-tracking data and cued retrospective reporting to assess preservice teachers’ cognitive strategies while debugging. Several eye-tracking studies have investigated learners’ debugging strategies though the literature lacks studies (a) conducted with early childhood preservice teachers and (b) that focus on blockbased programming languages, such as Scratch. The present study addresses this gap in the literature. This study used mixed methods to triangulate quantitative findings from eye movement analysis and qualitative findings about employed debugging strategies into the creation of descriptive themes. Results showed that participants developed strategies such as simultaneous review of output and code, use of beacons to narrow down the area to be debugged, and eye fixation on output to form hypotheses. But most often, debugging was not informed by a hypothesis, which led to trial and error. Study limitations and directions for future research are discussed.