Leveraging LMS Logs to Analyze Self-Regulated Learning Behaviors in a Maker-based Course

Existing learning analytics (LA) studies on self-regulated learning (SRL) have rarely focused on maker education that emphasizes student autonomy in their learning process. Towards using LA methods for generating evidence of SRL in maker-based courses, this study leverages logs of a learning management system (LMS) with its activity design aligned with the maker-based pedagogy. We explored frequencies and sequential patterns of students' SRL behaviors as reflected in the LMS logs and their relations with learning performance. Adopting a mixed method approach, we collected and triangulated both quantitative (i.e., system logs, performance scores) and qualitative (i.e., student-written reflections) data sources from 104 students. Based on current LA-based SRL research, we developed an LMS log-based analytic framework to define the SRL phases and behaviors applicable to maker activities. Statistical, data mining, and qualitative analysis methods were conducted on 48,602 logged events and 131 excerpts extracted from student reflections. Results reveal that high-performing students demonstrated some SRL behaviors (e.g., Making Personal Plans, Evaluation) more frequently than their low-performing counterparts, yet the two groups showcased fairly similar sequences of SRL behaviors. Theoretical, methodological and pedagogical implications are drawn for LA-based SRL research and maker education.