Dynamic Evolution of Self-Regulated Learning Profiles in Blended Learning: A Longitudinal Study of Freshmen and Upper-Level Students

Abstract

Background: Self-Regulated Learning (SRL) plays a crucial role in student success, particularly in blended learning (BL) environments where learners must take greater ownership of their educational journey. Whilst prior research has extensively examined SRL, there remains a gap in understanding how students' SRL profiles evolve over time and how motivation and learning strategies dynamically interact within these profiles. Objectives: This study investigates the dynamic nature of SRL by identifying distinct learner profiles and tracking their evolution throughout a semester in a BL setting. By adopting a person-centred clustering approach, the research provides insights into how students' motivation and strategy use shift over time. Methods: Data were collected from 314 tertiary-level students enrolled in two BL courses, with responses from the Motivated Strategies for Learning Questionnaire (MSLQ) captured at three time points. K-Means clustering was used to classify students into SRL profiles, and longitudinal analysis was conducted to track transitions between profiles over time. Results: The findings revealed three distinct SRL profiles—highly self-regulated, moderately self-regulated, and minimally self-regulated learners—suggesting that students adapt their motivation and strategies in response to course feedback and assessments. The study highlights the fluid and iterative nature of SRL development. Conclusions: This research enhances the theoretical understanding of SRL by empirically illustrating how students' motivation and learning strategies evolve within a semester. Additionally, it offers practical insights for designing interventions to support students with varying levels of SRL, ultimately contributing to more adaptive and effective BL environments. What Are the 1 or 2 Major Takeaways From the Study?: This research significantly advances SRL theory by exploring how students' SRL profiles adapt and evolve over time, shedding light on the cyclical and dynamic nature of self-regulated learning. Additionally, it makes a critical contribution to the field of Learning Analytics (LA) by incorporating motivational constructs–an area often underexplored–offering empirical, theory-driven insights to bridge the gap between research and educational practise.