Temporal Trajectories of Self-Regulation Learning: A Markov-Chain Approach to Modeling and Grouping Student Behaviors

• In Event: Modeling and Supporting Self-Regulated and Metacognitive Learning

Abstract

Despite decades of research on self-regulated learning (SRL), little work has been done to explore learners’ SRL sequential profiles and their connection to cognitive capacities and learning performance. Our study employs the first-order Markov model to identify different SRL sequence profiles as medical students diagnosed virtual patients in a computer-simulated learning environment. Two clusters emerged. Cluster 1 was characterized by complex cyclical SRL patterns, with frequent help-seeking and monitoring. In contrast, cluster 2 exhibited a strong linear monitor-to-evaluation process. Additionally, Cluster 1 reported higher metacognitive self-regulation and effort regulation, but lower learning outcomes than Cluster 2. Our results suggest that metacognitive complexity could overwhelm cognitive resources, demonstrating a crucial role of metacognitive regulatory efficiency in academic
success.

Authors

• Shasha Li, McGill University

• Tingting Wang, Renmin University of China

• Jie Gao, McGill University

• Nikki G. Lobczowski, McGill University

• Krista R. Muis, McGill University

• Adam Kenneth Dubé, McGill University

• Susanne P. Lajoie, McGill University