Using multiple self-regulated learning measures to understand medical students' biomedical science learning
- PMID: 32012330
- DOI: 10.1111/medu.14079
Using multiple self-regulated learning measures to understand medical students' biomedical science learning
Abstract
Context: Understanding self-regulated learning (SRL) is complicated due to the different measures used to identify the key SRL processes. There is a growing trend in applying event measures of SRL (microanalysis and trace) but aptitude measures (questionnaires) continue to be widely used in medical education. A major concern is whether aptitude measures are a valid approach to capture the dimensions of SRL processes. This study examined correlations between SRL microanalysis, SRL trace and the Motivated Strategies for Learning Questionnaire (MSLQ) and how these measures were associated with biomedical science performance.
Methods: An SRL microanalysis assessment interview was administered to 76 first-year medical students individually when performing a biomedical science learning task. All written materials by students were collected for further trace analysis. Students completed an MSLQ 2 weeks before completing their biomedical science course. Correlation analyses were used to determine the correlations between the three SRL assessment measures. Bivariate and multiple analyses were conducted to compare students on different course or task performance using the three SRL assessment measures.
Results: Microanalytic metacognitive monitoring (κ = 0.30, P < .001) and causal attributions (κ = 0.17, P = .009) had statistically significant correlations with use of the SRL trace strategy. MSLQ self-efficacy correlated with microanalytic self-efficacy (r = .39, P = .001). Bivariate tests showed that microanalytic metacognitive monitoring, causal attributions and adaptive inferences, and SRL trace strategy use had significant associations with task performance (P < .05). Microanalytic self-efficacy, metacognitive monitoring and causal attributions, SRL trace strategy use and MSLQ self-efficacy had significant associations with course performance (P < .05). Measures of use of the SRL trace strategy and MSLQ subscales did not show significant associations with task and course outcomes in multiple analyses (P > .05).
Conclusions: Event measures, specifically SRL microanalysis, had greater associations with both task and course outcomes compared with the MSLQ measure. The SRL microanalysis is recommended for the assessment of SRL in biomedical science learning. However, to fully understand medical students' SRL a multidimensional assessment approach that combines event and aptitude measures should be used.
© 2020 Association for the Study of Medical Education and John Wiley & Sons Ltd.
Comment in
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How to conceptualise self-regulated learning: Implications for measurement.Med Educ. 2020 Aug;54(8):684-686. doi: 10.1111/medu.14183. Epub 2020 May 26. Med Educ. 2020. PMID: 32311141 Free PMC article.
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References
REFERENCES
-
- Zimmerman BJ. Attaining self-regulation: a social-cognitive perspective. In: Boekaerts M, Pintrich PR, Zeidner M, eds. Handbook of Self-regulation. San Diego, CA: Academic Press; 2000:13-39.
-
- Sandars J, Cleary TJ. Self-regulation theory: applications to medical education: AMEE Guide No. 58. Med Teach. 2011;33(11):875-886.
-
- Cleary TJ, Zimmerman BJ. Self-regulation empowerment program: a school-based program to enhance self-regulated and self-motivated cycles of student learning. Psychol Schools. 2004;41(5):537-550.
-
- Rovers SF, Clarebout G, Savelberg HH, de Bruin AB, van Merriënboer JJ. Granularity matters: comparing different ways of measuring self-regulated learning. Metacogn Learn. 2019;14(1):1-19.
-
- Ben-Eliyahu A, Bernacki ML. Addressing complexities in self-regulated learning: a focus on contextual factors, contingencies, and dynamic relations. Metacogn Learn. 2015;10(1):1-3.
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