Effectiveness of learning through online modules compared to lecture format in medical undergraduate curriculums
Bibliographic record
Abstract
Background Interactive modules have been shown to be effective avenues for medical education. With recent changes to medical undergraduate curriculums, particularly at UBC, modules can serve an important role for both students and educators as a resource for creating well‐rounded content. As part of week 63 in the UBC undergraduate curriculum (Abnormal Uterine Bleeding), we created an educational module on gestational trophoblastic disease (GTD) to serve as an independent curricular activity. The purpose of this study was to evaluate the effectiveness of developing the curricular content of GTD into an online educational module. Methods Second year UBC medical students were evaluated in two sequential academic years with one class receiving a lecture on GTD (lecture‐based group) and one class completing a module on GTD during allocated curricular time (module‐based group). The educational module on GTD was developed with a focus on an interactive, case‐based approach. Surveys evaluating subjective knowledge acquisition were administered. Results Of the module‐based group, 59% felt their knowledge of GTD was strong or very strong compared to 21% of the lecture‐based group after the educational intervention. Fewer students in the module‐based group identified their knowledge of basic science concepts (placental development, genetics) as poor or very poor after completing the module. Of those who completed the module, 86% would like to see more educational modules for other curricular topics. Conclusions Educational modules can help support evolving medical undergraduate curriculums and provide subjectively similar, if not improved educational outcomes. Overall, students would like to see more educational modules on curricular topics. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".