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

Abstract

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.

Exploring the co‐occurrence of students' learning behaviours and reasoning processes in an intelligent tutoring system: An epistemic network analysis

“…Metacognitive strategy is an important component of selfregulated learning (SRL) (Corno, 1986). SRL's central claim is that metacognitive monitoring processes generate internal feedback about temporary learning outcomes, prompting learners to regulate and adapt subsequent actions (Li et al, 2022;Winnie & Hadwin, 1998).…”

“…Metacognitive strategy is an important component of self‐regulated learning (SRL) (Corno, 1986). SRL's central claim is that metacognitive monitoring processes generate internal feedback about temporary learning outcomes, prompting learners to regulate and adapt subsequent actions (Li et al, 2022; Winnie & Hadwin, 1998). Appropriate planning, effective self‐monitoring, and adequate self‐reflection are all consistently associated with accurate clinical reasoning diagnoses (Artino et al, 2014; Brydges & Butler, 2012).…”

Computer‐based scaffoldings influence students' metacognitive monitoring and problem‐solving efficiency in an intelligent tutoring system

“…Previous studies have provided empirical evidence on the positive effects of SRL behaviours, such as goal‐setting, strategic planning, and self‐reflection, on learning outcomes within CBLEs (Öztürk & Çakıroğlu, 2021). Several scholars have also employed learning analytics and data mining techniques to extract the temporal and sequential characteristics of SRL from trace data generated by CBLEs and differentiated high‐ and low‐performing students regarding SRL dynamics (Bannert et al, 2014; Li et al, 2022; Wang et al, 2023).…”

How does online employee training enhance organizational growth? The benefits of integrating educational technology into workplace learning

“…In terms of the function of technology to facilitate learners’ learning outcome, emerging research have examined the effectiveness of self-adaptive prompts and hints in boosting users self-regulated learning capacity (e.g. learners: Azevedo et al , 2004; Greene et al , 2010; Li et al , 2022; school teachers: Huang et al , 2020, 2022). In addition, education researchers have also explored the trajectory of human behaviours in technology rich learning environments to promote sound user learning experience.…”