Using Metacognitive Tools to Scaffold Medical Students Developing Clinical Reasoning Skills
Bibliographic record
Abstract
BioWorld is a technology-rich learning environment that incorporates metacognitive tools to scaffold learners as they diagnose virtual patients. To argue for a diagnosis, users add patients’ symptoms and test results to a virtual notepad known as the evidence palette, and then prioritize and summarize this evidence. This study investigated differences in medical students’ and expert physicians’ use of evidence while making diagnoses in BioWorld. Experts and students were compared on the quantity and content of evidence items selected for 3 cases. To evaluate the use of the evidence palette as a metacognitive tool, the proportion of prioritized evidence, confidence and accuracy of the two groups were compared. To evaluate the use of the evidence palette as a cognitive tool, the proportion of summarized evidence was compared. The problem–solving patterns of the experts in this study were consistent with principles of expertise that have been identified in the literature. The evidence palette was an effective cognitive tool for both students and experts, but less effective as a metacognitive tool for students. Further research is necessary to develop appropriate metacognitive scaffolds to address confirmation bias and accuracy awareness for difficult cases.
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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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".