Replicating Anatomical Teaching Specimens Using 3D Modeling Embedded Within a Multimodal e-Learning Course: Pre-Post Study Exploring the Impact on Medical Education During COVID-19
Notice bibliographique
Résumé
Background The COVID-19 pandemic has had significant effects on anatomy education. During the pandemic, students have had no access to cadavers, which has been the principal method of learning anatomy. We created and tested a customized congenital heart disease e-learning course for medical students that contained interactive 3D models of anonymized pediatric congenital heart defects. Objective The aim of this study is to assess whether a multimodal e-learning course contributed to learning outcomes in a cohort of first-year undergraduate medical students studying congenital heart diseases. The secondary aim is to assess student attitudes and experiences associated with multimodal e-learning. Methods The pre-post study design involved 290 first-year undergraduate medical students. Recruitment was conducted by course instructors. Data were collected before and after using the course. The primary outcome was knowledge acquisition (test scores). The secondary outcomes included attitudes and experiences, time to complete the modules, and browser metadata. Results A total of 141 students were included in the final analysis. Students’ knowledge significantly improved by an average of 44.6% (63/141) when using the course (SD 1.7%; Z=−10.287; P<.001). Most students (108/122, 88.3%) were highly motivated to learn with the course, and most (114/122, 93.5%) reported positive experiences with the course. There was a strong correlation between attitudes and experiences, which was statistically significant (rs=0.687; P<.001; n=122). No relationships were found between the change in test scores and attitudes (P=.70) or experiences (P=.47). Students most frequently completed the e-learning course with Chrome (109/141, 77.3%) and on Apple macOS (86/141, 61%) or Windows 10 (52/141, 36.9%). Most students (117/141, 83%) had devices with high-definition screens. Most students (83/141, 58.9%) completed the course in <3 hours. Conclusions Multimodal e-learning could be a viable solution in improving learning outcomes and experiences for undergraduate medical students who do not have access to cadavers. Future research should focus on validating long-term learning outcomes.
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 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| 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 ».