Examining the Interplay between Self-regulated Learning Activities and Types of Knowledge within a Computer-simulated Environment


  • Shan Li McGill University
  • Xiaoshan Huang McGill University
  • Tingting Wang McGill University
  • Zexuan Pan University of Alberta
  • Susanne P. Lajoie McGill University




epistemic network analysis, self regulated learning, types of knowledge, temporal co-occurrence, computer-simulated environment, research paper


This study examined the temporal co-occurrences of self-regulated learning (SRL) activities and three types of knowledge (i.e., task information, domain knowledge, and metacognitive knowledge) of 34 medical students who solved two tasks of varying complexity in a computer-simulated environment. Specifically, we explored the effects of task complexity on SRL activities, types of knowledge, and their interplay using epistemic network analysis (ENA). We also compared the differences between high and low performers. The results showed that the use of SRL activities, especially the planning and monitoring activities, was more intensive in a difficult task compared to an easy task. Students also used more domain knowledge to solve the difficult task. For both tasks, domain knowledge and metacognitive knowledge co-occurred most frequently, followed by the co-occurrence of domain knowledge and planning. Nevertheless, the interplay of SRL activities and types of knowledge is generally different between the two tasks. Moreover, we found that high performers used significantly more metacognitive knowledge than low performers in the easy task. However, no significant differences were found in the use of SRL activities between high and low performers in both tasks. This study makes theoretical, methodological, and practical contributions to the area of SRL in clinical reasoning.


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How to Cite

Li, S., Huang, X., Wang, T., Pan, Z., & Lajoie, S. P. (2022). Examining the Interplay between Self-regulated Learning Activities and Types of Knowledge within a Computer-simulated Environment. Journal of Learning Analytics, 9(3), 152-168. https://doi.org/10.18608/jla.2022.7571