Difficult incidents and tutor interventions in problem‐based learning tutorials
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Notice bibliographique
Résumé
CONTEXT: Tutors report difficult incidents and distressing conflicts that adversely affect learning in their problem-based learning (PBL) groups. Faculty development (training) and peer support should help them to manage this. Yet our understanding of these problems and how to deal with them often seems inadequate to help tutors. OBJECTIVES: The aim of this study was to categorise difficult incidents and the interventions that skilled tutors used in response, and to determine the effectiveness of those responses. METHODS: Thirty experienced and highly rated tutors in our Year 1 and 2 medical curriculum took part in semi-structured interviews to: identify and describe difficult incidents; describe how they responded, and assess the success of each response. Recorded and transcribed data were analysed thematically to develop typologies of difficult incidents and interventions and compare reported success or failure. RESULTS: The 94 reported difficult incidents belonged to the broad categories 'individual student' or 'group dynamics'. Tutors described 142 interventions in response to these difficult incidents, categorised as: (i) tutor intervenes during tutorial; (ii) tutor gives feedback outside tutorial, or (iii) student or group intervenes. Incidents in the 'individual student' category were addressed relatively unsuccessfully (effective < 50% of the time) by response (i), but with moderate success by response (ii) and successfully (> 75% of the time) by response (iii). None of the interventions worked well when used in response to problems related to 'group dynamics'. Overall, 59% of the difficult incidents were dealt with successfully. CONCLUSIONS: Dysfunctional PBL groups can be highly challenging, even for experienced and skilled tutors. Within-tutorial feedback, the treatment that tutors are most frequently advised to apply, was often not effective. Our study suggests that the collective responsibility of the group, rather than of the tutor, to deal with these difficulties should be emphasised.
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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 | 0,002 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 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 | 0,000 |
| 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 découle