An Interactive, Student-Centered Approach to Teaching Large-Group Sessions in Veterinary Clinical Pathology
Notice bibliographique
Résumé
INTRODUCTION: The purpose of this study was to describe and evaluate an interactive, student-centered approach to teaching large-group sessions in Veterinary Clinical Pathology. The strategy was designed to operate in the place of expository lectures and to encourage a deep approach to learning though discussion and problem solving. METHODOLOGY: The teaching strategy ran over two hours and required students to answer a series of questions on the topic to be discussed before attending the session. In the first part of the session, limited information and laboratory data related to a series of cases were presented to the students for discussion and analysis. These cases were selected on the basis of their usefulness for discussion in relation to the answers to the previously set questions and to reinforce an approach to the analysis of laboratory data. After a break, students were given a series of multiple-choice questions, related to the topic previously discussed, to answer. Students were given the opportunity to discuss the reasons for their answers. Finally, the students were given information and laboratory data from an unknown case and asked to analyze them, through a mechanism previously practiced in small-group tutorials, in order to reach conclusions and to consider the need for further investigation and implications for case management. A consensus diagnosis and plan for the case was reached after reflective observation and discussion. The teaching strategy was evaluated, utilizing teacher reflection and a student questionnaire, on the basis of its success in encouraging active and simulated experiential learning. CONCLUSION: The evaluation of one session indicated that students strongly valued the strategy in relation to actively engaging them in discussion, providing feedback on how they were learning, and enhancing their understanding of how theoretical knowledge can be applied to actual clinical cases. These pedagogical principles appeared to give students greater confidence in analyzing laboratory data through a mechanism of diagnostic reasoning. More sessions of this kind, tied to specific content or skills areas, will allow better evaluation of the perceived student outcomes, which can then be correlated with actual student outcomes through formal assessment.
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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 | 0,006 | 0,004 |
| 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 | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».