Enhancing Examination Success: the Cumulative Benefits of Self-Assessment Questions and Virtual Patient Cases
Bibliographic record
Abstract
Purpose: Research on the learning benefits of the feedback-rich formative assessment environment of virtual patient cases (VPCs) has largely been limited to single institutions and focused on discrete clinical skills or topical knowledge. To augment current understanding, we designed a multi-institutional study to explore the distinct and cumulative effects of VPC formative assessments and optional self-assessment questions (SAQs) on exam performance. Method: In this correlational study, we examined the records of 1,692 students on their family medicine (FM) clerkship at 20 medical schools during the 2014-2015 academic year. Schools utilized an established online curriculum, which included family medicine VPCs, embedded formative assessments, context-rich SAQs corresponding with each VPC, and an associated comprehensive family medicine exam. We used mixed-effects modeling to relate the student VPC composite formative assessment score, SAQ completion, and SAQ performance to students' scores on the FM final examination. Results: Students scored higher on the final exam when they performed better on the VPC formative assessments, completed associated SAQs, and scored higher on those SAQs. Students' SAQ completion enhanced examination performance above that explained by engagement with the VPC formative assessments alone. Conclusions: This large-scale, multi-institutional study furthers the body of research on the effect of formative assessments associated with VPCs on exam performance and demonstrates the added benefit of optional associated SAQs. Findings highlight opportunities for future work on the broader impact of formative assessments for learning, exploring the benefits of integrating VPCs and SAQs, and documenting effects on clinical performance and summative exam scores.
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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 | 0.014 | 0.115 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".