Practical Classes: A Platform for Deep Learning? Overall Context in the First-Year Veterinary Curriculum
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Notice bibliographique
Résumé
The aim of this study is to evaluate the many practical formats that support the first-year veterinary curriculum. These practical classes are diverse in content and style. They include laboratory-based formats, classes involving live animals and cadavers, classes conducted using computer-aided learning tools, study groups, and information technology training. This preliminary study examines ratings for these practical classes, but also relates these ratings to students' approaches to study with the aim of understanding how a deep learning approach manifests itself in the practical setting. The diverse behaviors and attitudes to practical classes are also evaluated in the light of the approaches to study. A questionnaire that evaluated (1) a total of 24 practical classes, (2) the 52-item Approaches to Study Inventory, and (3) 13 behaviors within and attitudes to practical classes was distributed to 69 first-year veterinary students in their final term. Practical classes that involved live animals and cadavers were rated most positively by this group of students. These ratings, however, did not correlate significantly with the deep or surface learning score. The majority of practical classes where the ratings were found to be associated with deep and surface learning were laboratory-based, although overall these practical classes tended to be rated lower than those involving animals. Ratings did not correlate significantly with the strategic approach. A number of behaviors and attitudes to practical classes were also found to be positively and significantly (p=0.0001) associated with the deep learning approach. This preliminary study indicates that this cohort of veterinary students has an overall positive perception of practical classes that permit contact with live animals or cadavers. Although the perception of laboratory-type practical classes was lower overall, the ratings for these practical classes appeared to be influenced by their deep and surface learning scores. We hypothesize that these approaches influence student engagement with and appreciation of laboratory-type classes, but not of classes involving live animals or cadavers. This would suggest that a different "type" of learning is taking place in these different contexts.
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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 | 0,004 | 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 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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