Background Computer-based scaffolding has been intensively used to facilitate students' self-regulated learning (SRL). However, most previous studies investigated how computer-based scaffoldings affected the cognitive aspect of SRL, such as knowledge gains and understanding levels. In contrast, more evidence is needed to examine the effects of scaffolding on the metacognitive dimension and efficiency outcome of SRL. Objectives This study aims to examine the role of computer-based scaffolding in students' metacognitive monitoring and problem-solving efficiency. Methods Seventy-two medical students completed two clinical reasoning tasks in BioWorld, an intelligent tutoring system (ITS) designed for promoting medical students' diagnostic expertise. During solving the tasks, students were asked to report their confidence judgements about proposed diagnoses. Computer trace data were used to identify task completion time (CT) and students' use of three scaffolding types, that is, conceptual, strategic, and metacognitive. Then we calculated students' metacognitive monitoring accuracy (i.e., calibration) and problem-solving efficiency. Results and Conclusions One-sample t-test demonstrated that students inaccurately monitored their learning processes and were overconfident in both tasks. Linear mixed-effects models (LMMs) indicated that the intensive use of metacognitive scaffolding positively predicted students' metacognitive monitoring accuracy. Moreover, strategic scaffolding was negatively related to problem-solving efficiency, whereas metacognitive scaffolding positively influenced problem-solving efficiency. Takeaways This study shows the importance of metacognitive scaffolding in improving the accuracy of metacognitive monitoring and problem-solving efficiency. Findings from this study provide new insights for instructors and ITS developers to optimise the design of scaffoldings. 

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/jcal.12824

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

Background Medical students use a variety of self-regulated learning (SRL) strategies in different medical reasoning (MR) processes to solve patient cases of varying complexity. However, the interplay between SRL and MR processes is still unclear. Objectives This study investigates how self-regulated learning (SRL) and medical reasoning (MR) occurred concurrently in medical students while completing a diagnostic task in an intelligent tutoring system. This study aims to provide new insights into performance differences between high- and low-achieving students in tasks of varying complexity. Methods Thirty-one medical students (67.6% female) from a large North American university were tasked with solving two virtual patient cases in an intelligent tutoring system, BioWorld. BioWorld was designed for medical students to practice clinical reasoning skills deliberately. We collected students' think-aloud protocols, based on which we coded their use of SRL behaviours and medical reasoning activities. We analysed the co-occurrences of SRL behaviours and medical reasoning activities using the epistemic network analysis (ENA) method. Results The SRL behaviour self-reflection and MR activity lines of reasoning co-occurred more frequently in a difficult task than in an easy task. In both tasks, high performers demonstrated more co-occurrences of self-reflection and lines of reasoning than low performers. Moreover, the MR activity conceptual operations co-occurred more frequently with the SRL activities of monitoring and evaluation among high performers compared to low performers in an easy task. Implications The co-occurrences of SRL behaviours and MR processes account for students' performance differences. The design of computer-based learning environments for clinical reasoning should promote the acquisition of both SRL and medical reasoning abilities. Moreover, medical educators should consider task complexity when scaffolding. 

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/jcal.12827

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

Regulating response speed promotes associative learning Regulating response speed promotes learning

Investigating behavior patterns of students during online self-directed learning through process mining

How Does Cognitive Load Interact with Self-Regulated Learning? A Dynamic and Integrative Model

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.

/pdf/examining-the-interplay-between-self-regulated-learning-1hkvpawd.pdf

Zexuan Pan

Papers

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

Examining students’ cognitive load in the context of self-regulated learning with an intelligent tutoring system

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

Insights Into Computational Thinking From Think‐Aloud Interviews: A Systematic Review