Predicting dropout in professional online learning: a pilot study on self-regulated learning strategies from surveys and trace data - Eldemellawy, Hala; Fouarge, Didier; Segers, Mien (Frontline Learning Research, 2026)
This pilot study investigates the relationship between self-regulated learning (SRL) strategies and early dropout in a largely underexplored professional online learning context. Forty-two professionals (N = 42) enrolled in a four-week course delivered via a workplace Learning Experience Platform (LXP) supporting self-directed, adaptive learning pathways. SRL was measured using self-reports and behavioural trace data collected during the first week. A dual-method analytic approach was employed: top-down models (logistic regression and decision trees) tested predefined SRL predictors, while bottom-up methods (hierarchical clustering and process mining) uncovered emergent learner profiles without prior assumptions. Trace-based SRL indicators, particularly the number of unique actions on the platform, time spent during week one, participation in the forum, and completion of an action plan, were significantly stronger predictors of dropout (p < .05) than self-reports, where only the goal-setting subscale was significant. Despite the small sample size limiting generalisability, the study demonstrates that early behavioural engagement patterns can reliably predict learner persistence, often outperforming self-reported SRL measures. These findings advance our understanding of SRL in professional online learning environments and provide a replicable analytic framework for integrating trace data and surveys. By identifying actionable SRL behaviours linked to course completion, this study contributes toward the development of adaptive workplace learning systems capable of detecting and mitigating early disengagement in real time.