A longitudinal study of interplay between student engagement and self-regulation

Abstract This study investigates the dynamic interplay between student engagement and self-regulated learning (SRL) in an online project management course. Using learning management system trace data combined with longitudinal self-reported SRL measures, we analysed data from 165 first-year business students through multi-channel sequencing and clustering algorithms. Results revealed three distinct patterns of learning behaviour with significant implications for intervention design: Low-regulating improvers (4.85%) who showed potential for growth with targeted support, Disengaged low regulators (44.85%) requiring comprehensive intervention in both engagement and self-regulation skills, and High-regulating improvers (50.30%) who demonstrated successful adaptation to online learning. Our innovative approach of analysing student progress individually rather than using fixed time points enabled more precise identification of support needs in asynchronous learning settings. The findings demonstrate that both engagement and self-regulation skills can develop over time with appropriate support, suggesting opportunities for adaptive intervention throughout the course. This research provides actionable insights for developing targeted support strategies, including early warning systems for at-risk students and personalized scaffolding approaches based on students' behavioural patterns. These findings advance learning analytics by providing a framework for real-time identification of student needs and evidence-based intervention design in online education. The results particularly emphasize the importance of continuous, pattern-based support for developing self-regulation skills in online environments.