Evaluation of a hybrid peer-teaching method for undergraduate medical microbiology
Bibliographic record
Abstract
Background Microbiology in undergraduate medical education has been traditionally taught through didactic lectures. However, alternative educational strategies such as case-based learning and peer-teaching can be effective teaching methods for medical students. Objectives To evaluate the effectiveness of a newly implemented teaching strategy which integrates both peer-teaching and case-based learning into a second year microbiology course for medical students. Methods The majority of the microbiology course was taught by students under the supervision of an instructor. Students were broken up into groups of 16 and each group member had to teach a specific objective belonging to a microbiology case. Questionnaires were administered before and after the microbiology course (N=42; 71% participation) to assess whether change occurred in students perceptions and opinions of the new teaching format. Course ratings from the Memorial University Medical School Course Evaluation form were analyzed to compare the course ratings of microbiology when it was taught by lectures in 2010/2011 compared to the new hybrid format in 2011/2012. Results Students did not agree that they learn best when taught by a peer (1.49/5) or that the new teaching methods would help them gain a better understanding (1.61/5) and improve their learning (1.51/5) of the course material compared to lectures. These ratings significantly increased by the end of the course (P<0.05), but still remained low. Student comments indicated that they enjoyed the peer interaction involved in the peer-teaching sessions however, the large number of students per group prevented enough time for group discussion. Students were also skeptical that the information being presented by their peers was accurate, decreasing their satisfaction with the peer presentations. The overall course rating of microbiology in 2010/2011 (4.3/5) when taught by lectures was significantly higher than the course rating in 2011/2012 (3.5/5) when taught with the new hybrid format (P<0.05). Conclusions Overall, students were not satisfied with the new hybrid teaching format for microbiology. However, having a smaller number of students per group, allowing more time or group discussion and providing clear expectations and guidelines to students before they teach may increase overall satisfaction with this teaching method.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".