Dynamics of self-regulated learning: The effectiveness of students’ strategies across course periods

Proper self-regulating skills are essential in the new reality of digital learning in higher education. Research has shown that the trace data of students’ learning management system activity can identify various online learning tactics and strategies, but also their transitional dynamics, which are linked to academic performance. This study builds on this work by examining how learning tactics and strategies change within individual courses and how this relates to academic performance. A substantial dataset of 41 courses over two academic years at one university is analyzed. Employing Markov models on trace data, we identify prevalent tactics and strategies students use throughout courses. Our study examines shifts in strategy usage, comparing patterns between the initial and latter stages of the courses. The results reveal distinct clusters of learning strategies and their impact on academic achievement. Notably, deep learning strategies show significantly superior performance to surface approaches, especially when maintained over time. Moreover, students who consistently apply the same strategy score higher than those who are inconsistent. However, consistent surface learners score significantly lower than inconsistent learners. Underscoring such longitudinal trends could help interventions, aiding educators in targeting students with weaker strategies at specific times to boost their effectiveness and efficiency. This research contributes to a nuanced understanding of self-regulated learning behaviors in online educational contexts by showing the importance of dynamic transition of learning strategies for educators, instructional designers, and policymakers to enhance student learning experiences and outcomes.