Exploring Manifestations of Learners’ Self-Regulated Tactics and Strategies Across Blended Learning Courses

Abstract

Blended learning (BL) has become increasingly popular in higher education institutions. Despite its popularity and the advances in methodologies for the detection of learning tactics and strategies from trace data, little is known about how they apply to BL settings and, therefore, how students use them to plan, organize, monitor, and regulate their learning in these settings. To address this gap, we analyzed the manifestations of learning tactics and strategies of 267 students across three undergraduate-level BL courses with different course designs, instructional activities, and learning contexts. We employed a data-driven method that incorporates hidden Markov models to determine students’ learning tactics. Then, we employed optimal matching to identify the students’ strategies based on the sequences of tactics they deployed and how they relate to their self-reported self-regulated learning (SRL) skills. Our results indicate that students’ tactics and strategies varied significantly depending on the course design and learning context. Tactics with regard to the use of time management resources were common across courses. In contrast, tactics deployed when revisiting old material and interacting with an SRL support tool were course-specific. We identified strategies related to surface and deep learning and found that surface-level strategies manifested consistently across all courses. These findings contribute to a better understanding of student learning mechanisms in BL environments and have implications for instructional design and SRL support.