Examining the interplay of affect and self regulation in the context of clinical reasoning
There is an active strand of research on how affect and self-regulatory activities influence performance and learning outcomes, but the mechanisms through which they interact during learning remain poorly understood. Additionally, these constructs have been under-researched in medical education. Usi...
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Veröffentlicht in: | Learning and instruction 2021-04, Vol.72, p.101219, Article 101219 |
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Sprache: | eng |
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Zusammenfassung: | There is an active strand of research on how affect and self-regulatory activities influence performance and learning outcomes, but the mechanisms through which they interact during learning remain poorly understood. Additionally, these constructs have been under-researched in medical education. Using multimodal data in the context of a clinical reasoning task for medical students learning case diagnosis, we explored the temporal nature of cognition, affect, motivation, and self-regulation. With a sample of n = 10 medical students, we collected data on self-regulated learning (SRL) processes through think-aloud analyses; emotion data through facial expressions; and achievement goal orientations and habitual emotion regulation strategies through self-report questionnaires. Results from our sequential data mining techniques and quantitative analyses suggested that high-performing medical students (who arrived at an accurate diagnosis) differed from low-performing students (who did not arrive at correct diagnosis): Low performers reported higher performance goal orientation, and expressed more emotions overall, than high performers. High performers exhibited marginally more monitoring SRL behaviours, while low performers tended to orient and reorient throughout the task. High and low performers also differed on the co-occurrences of, and sequential transitions between, emotions and SRL behaviours. The implications of these findings with respect to research and medical education are discussed.
•SRL is described as dynamically unfolding over time in a cyclical manner.•We examined relationships between basic emotions and SRL, ER and AGOs.•Multimodal data channels for better understanding the nature of SRL.•Think-aloud and facial expressions investigating co-occurrences of SRL and emotions.•Sequential data mining techniques to examine patterns of SRL processes and emotions. |
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ISSN: | 0959-4752 1873-3263 |
DOI: | 10.1016/j.learninstruc.2019.101219 |