Self‐regulated learning in simulation‐based training: a systematic review and meta‐analysis
Bibliographic record
Abstract
CONTEXT: Self-regulated learning (SRL) requires an active learner who has developed a set of processes for managing the achievement of learning goals. Simulation-based training is one context in which trainees can safely practise learning how to learn. OBJECTIVES: The purpose of the present study was to evaluate, in the simulation-based training context, the effectiveness of interventions designed to support trainees in SRL activities. We used the social-cognitive model of SRL to guide a systematic review and meta-analysis exploring the links between instructor supervision, supports or scaffolds for SRL, and educational outcomes. METHODS: We searched databases including MEDLINE and Scopus, and previous reviews, for material published until December 2011. Studies comparing simulation-based SRL interventions with another intervention for teaching health professionals were included. Reviewers worked independently and in duplicate to extract information on learners, study quality and educational outcomes. We used random-effects meta-analysis to compare the effects of supervision (instructor present or absent) and SRL educational supports (e.g. goal-setting study guides present or absent). RESULTS: From 11,064 articles, we included 32 studies enrolling 2482 trainees. Only eight of the 32 studies included educational supports for SRL. Compared with instructor-supervised interventions, unsupervised interventions were associated with poorer immediate post-test outcomes (pooled effect size: -0.34, p = 0.09; n = 19 studies) and negligible effects on delayed (i.e. > 1 week) retention tests (pooled effect size: 0.11, p = 0.63; n = 8 studies). Interventions including SRL supports were associated with small benefits compared with interventions without supports on both immediate post-tests (pooled effect size: 0.23, p = 0.22; n = 5 studies) and delayed retention tests (pooled effect size: 0.44, p = 0.067; n = 3 studies). CONCLUSIONS: Few studies in the simulation literature have designed SRL training to explicitly support trainees' capacity to self-regulate their learning. We recommend that educators and researchers shift from thinking about SRL as learning alone to thinking of SRL as comprising a shared responsibility between the trainee and the instructional designer (i.e. learning using designed supports that help prepare individuals for future learning).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.027 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".