Dissecting the Contemporary Clerkship: Theory‐based Educational Trial of Videos Versus Lectures in Medical Student Education
Bibliographic record
Abstract
BACKGROUND: Despite increasing use of the flipped classroom (FC) technique in undergraduate medical education, the benefit in learning outcomes over lectures is inconsistent. Best practices in preclass video design principles are rarely used, and it is unclear if videos can replace lectures in contemporary medical education. METHODS: We conducted a prospective quasi-experimental controlled educational study comparing theory-based videos to traditional lectures in a medical student curriculum. Medical students enrolled in an emergency medicine clerkship were randomly assigned to either a lecture group (LG) or a video group (VG). The slide content was identical, and the videos aligned with cognitive load theory-based multimedia design principles. Students underwent baseline (pretest), week 1 (posttest), and end-of-rotation (retention) written knowledge tests and an observed structured clinical examination (OSCE) assessment. We compared scores between both groups and surveyed student attitudes and satisfaction with respect to the two learning methods. RESULTS: There were 104 students who participated in OSCE assessments (49 LG, 55 VG) and 101 students who participated in knowledge tests (48 LG, 53 VG). The difference in OSCE scores was statistically significant 1.29 (95% confidence interval = 0.23 to 2.35, t(102) = 2.43, p = 0.017), but the actual score difference was small from an educational standpoint (12.61 for LG, 11.32 for VG). All three knowledge test scores for both groups were not significantly different. CONCLUSIONS: Videos based on cognitive load theory produced similar results and could replace traditional lectures for medical students. Educators contemplating a FC approach should devote their valuable classroom time to active learning methods.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".