Using Practical‐Based Team Based Learning as a Tool For Providing An Immediate Feedback to the Students During Learning Anatomy
Notice bibliographique
Résumé
Background Although assessment of learning may promote deep learning, it does not provide immediate feedback to drive further learning and training. In addition, with the students being subjected to formative exams during the course of their study, feedback might not be provided appropriately and timely. Students would like to understand and use the reasoning behind judgments and they demand that practical assessment criteria be explained. Team‐based learning (TBL) is a student‐centered learning strategy, which has been confirmed in medical education to enhance learning in small groups. Nevertheless, it has not been implemented during practical anatomy learning that challenges the spatial perception of the learned material in contrast to other disciplines. This study aims to present a novel intervention in using practical‐based TBL in anatomy and its impact as a tool for providing immediate feedback. It also determines students' perceptions of the practical‐based TBL and the effect of the given feedback on anatomy learning. Method An objective structured practical examination (OSPE) formative test setup was used. Students took the test in two successive formats: individually (iRAT) and in teams (tRAT). Individual students rotated around the practical stations in the form of a steeplechase examination during the iRAT. For the subsequent tRAT, photographs of the stations were projected in the classroom to groups of eight students each. The session was concluded by discussing the answers with the tutor who provided an immediate feedback. Students' perception (N=110) was measured using quantitative and qualitative instruments through a self‐administered questionnaire and a focus group discussion. Results The students perceived that the practical‐based setup was a useful tool in providing immediate feedback. They also agreed upon the fact that apart from highlighting areas of their weakness (86%); the practical‐based TBL also provided diverse options for testing knowledge (79%) and further stimulated motivation in them to attend these sessions (80%). Seventy eight of the students indicated that it boosted their self‐confidence to face the examinations, and 80% were able to clarify the reasoning behind judgments. Conclusion Practical‐based TBL is a valuable learning strategy and can be employed as an effective tool for providing immediate feedback during anatomy learning.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,002 | 0,002 |
| 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 | 0,003 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 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 ».