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Self-Regulated Learning and Learning Outcomes in Undergraduate and Graduate Medical Education: A Meta-Analysis
1Uniformed Services University of the Health Sciences, USA.
Self-regulated learning (SRL) strategies positively impact medical education outcomes. These strategies showed a stronger effect on affective learning and during the clerkship phase for medical trainees.
Area of Science:
- Medical Education Research
- Educational Psychology
Background:
- Self-regulated learning (SRL) is crucial for medical trainees' academic success.
- Existing research indicates SRL's importance, but a comprehensive meta-analysis is needed to quantify its impact across different outcomes and training levels.
Purpose of the Study:
- To conduct a meta-analytical review evaluating the effect sizes of SRL strategies on diverse learning outcomes in medical education.
- To explore moderators influencing the SRL-learning outcome relationship, including outcome types and training phases.
Main Methods:
- A systematic search of five databases identified 61 relevant studies.
- A three-level meta-analysis was employed to assess the association between SRL strategies and affective, cognitive, and behavioral learning outcomes.
- Meta-regression was used for moderator analyses, examining factors like outcome type, training level, SRL subscales, and study quality.
Main Results:
- A significant positive average effect size (r=.212) was found between SRL strategies and overall learning outcomes.
- SRL strategies demonstrated a greater impact on affective outcomes compared to test scores, behavioral outcomes, and mental health.
- The association was stronger in the clerkship phase than the pre-clerkship phase of undergraduate medical education.
Conclusions:
- SRL strategies are effective in enhancing medical education learning outcomes, particularly affective ones.
- The effectiveness of SRL strategies varies across different training stages, with greater impact observed during clinical clerkships.
- Further research could explore specific SRL components and their differential effects on various learning domains.
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