Observed Interactions, Challenges, and Opportunities in Student-Led, Web-Based Near-Peer Teaching for Medical Students: Interview Study Among Peer Learners and Peer Teachers
Bibliographic record
Abstract
BACKGROUND: Near-peer teaching (NPT) is becoming an increasingly popular pedagogical tool in health professions education. Despite the shift in formal medical education from face-to-face teaching toward encompassing web-based learning activities, NPT has not experienced a similar transition. Apart from the few reports on NPT programs hastily converted to web-based learning in light of the COVID-19 pandemic, no studies to date have explored web-based learning in the specific context of NPT. OBJECTIVE: This qualitative study examined the nature of interactions among peer learners (PLs), peer teachers (PTs), and the learning content in a student-led, web-based NPT program for medical students. METHODS: A 5-month-long voluntary NPT program to support first- and second-year medical students' biomedical science learning in the undergraduate medical curriculum was designed by 2 senior-year medical students and delivered by 25 PTs with 84 PLs participating. In total, 9 PLs and 3 PTs underwent individual semistructured interviews at the end of the program to explore general NPT experience, reasons for joining NPT, the effectiveness of NPT, the demand and importance of NPT, and the feasibility of incorporating NPT in the formal curriculum. Interview transcripts were analyzed using a thematic analysis approach. RESULTS: The first general theme focused on the nature of student-student, student-teacher, and student-content interactions. Although PLs were engaged in web-based NPT, there was minimal interaction between students, as most PLs preferred to learn passively and remain anonymous. PLs believed the web-based NPT learning process to be a unidirectional transmission of knowledge from teacher to learner, with the teacher responsible for driving the interactions. This was in sharp contrast to PTs' expectation that both parties shared responsibility for learning in a collaborative effort. The second general theme identified the advantages and disadvantages of delivering NPT on a web platform, which were mainly convenience and teaching skills development and poor interactivity, respectively. CONCLUSIONS: Student-led, web-based NPT offers a flexible and comfortable means of delivering academic and nonacademic guidance to medical students. However, the web-based mode of delivery presents unique challenges in facilitating meaningful interactions among PLs, PTs, and subject content. A blended learning approach may be best suited for this form of student-led NPT program to optimize its efficacy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".