Observed Interactions, Challenges, and Opportunities in Student-Led, Web-Based Near-Peer Teaching for Medical Students: Interview Study Among Peer Learners and Peer Teachers
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,013 | 0,018 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».