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.
BackgroundMedical 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.ObjectivesThis 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.MethodsThirty‐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.ResultsThe 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.ImplicationsThe 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.
This systematic review examines 35 empirical studies featuring the use of think‐aloud interviews in computational thinking (CT) research. Findings show that think‐aloud interviews (1) are typically conducted in Computer Science classrooms and with K‐12 students; (2) are usually combined with other exploratory CT assessment tools; (3) have the potential to benefit learners with special needs and identify the competency gaps through involving diverse participants; (4) are conducted in the absence of cognitive models and standard procedures; and (5) display insufficient definitional and methodological rigor. Theoretically, this review presents a systematic assessment about the application of think‐aloud interviews in CT studies and identifies the limitations in existing CT‐related think‐aloud studies. Practically, this review serves as a reference for studying the cognitive processes during CT problem‐solving and provides suggestions for CT researchers who intend to incorporate think‐aloud interviews in their studies.
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