Teaching Medical Microbiology With a Web-Based Course During the COVID-19 Pandemic: Retrospective Before-and-After Study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has imposed unprecedented hurdles on health care systems and medical faculties alike. Lecturers of practical courses at medical schools have been confronted with the challenge of transferring knowledge remotely. OBJECTIVE: We sought to evaluate the effects of a web-based medical microbiology course on learning outcomes and student perceptions. METHODS: During the summer term of 2020, medical students at Saarland University, Germany, participated in a web-based medical microbiology course. Teaching content comprised clinical scenarios, theoretical knowledge, and instructive videos on microbiological techniques. Test performance, failure rate, and student evaluations, which included open-response items, for the web-based course were compared to those of the on-site course from the summer term of 2019. RESULTS: Student performance was comparable between both the online-only group and the on-site comparator for both the written exam (n=100 and n=131, respectively; average grade: mean 7.6, SD 1.7 vs mean 7.3, SD 1.8; P=.20) and the oral exam (n=86 and n=139, respectively; average grade: mean 33.6, SD 4.9 vs mean 33.4, SD 4.8; P=.78). Failure rate did not significantly differ between the online-only group and the comparator group (2/84, 2.4% vs 4/120, 3.3%). While lecturer expertise was rated similarly as high by students in both groups (mean 1.47, SD 0.62 vs mean 1.27, SD 0.55; P=.08), students who took the web-based course provided lower scores for interdisciplinarity (mean 1.7, SD 0.73 vs mean 2.53, SD 1.19; P<.001), opportunities for interaction (mean 1.46, SD 0.67 vs mean 2.91, SD 1.03; P<.001), and the extent to which the educational objectives were defined (mean 1.61, SD 0.76 vs mean 3.41, SD 0.95; P<.001). Main critiques formulated within the open-response items concerned organizational deficits. CONCLUSIONS: Web-based courses in medical microbiology are a feasible teaching option, especially in the setting of a pandemic, leading to similar test performances in comparison to on-site courses. The lack of interaction and the sustainability of acquired manual skills warrant further research.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".