Evaluating the Impact of the Laboratory Learning Environment and Use of a Computer‐assisted Learning Resource on Anatomical Knowledge Recall among Undergraduate University Students
Notice bibliographique
Résumé
Developing lasting knowledge of human anatomy is foundational for students in the health sciences. Anatomy teaching methods are constantly adapting to achieve this goal, despite time and resource limitations. Interventions that target the laboratory environment, such as moving from dissection‐ to prosection‐based teaching or adding computer‐assisted learning resources, are widespread. However, the long‐term effect of such interventions on knowledge retention is not well understood. Accordingly, this study evaluated 1) the influence of the laboratory learning environment (dissection‐ versus prosection‐based) and 2) the impact of a curriculum‐targeted computer‐assisted learning resource on long‐term knowledge recall among non‐medical undergraduate human anatomy students at the University of Guelph. Participants reported their demographic information, approaches to learning in the course, and use of the computer‐assisted learning resource through a combination of online and written surveys. Knowledge recall was assessed through a written test of short‐answer questions administered three months after the course ended. The test included two low‐order questions and two high‐order questions (based on the Blooming Anatomy Tool), which were evaluated using the Structure of the Observed Learning Outcome Taxonomy to yield performance scores. The performance scores were compared between dissection‐ and prosection‐based groups, as well as between low‐ and high‐frequency users of the computer‐assisted learning resource with the Mann‐Whitney U test. Furthermore, multiple linear regression analyses were used to investigate the relationships between 1) the laboratory learning environment and performance and 2) use of the computer‐assisted learning resource and performance, while controlling for students’ approach to learning scores, final grades, and how recently they studied the material. The laboratory environment was not found to influence knowledge recall ( p > 0.05). However, high‐frequency users of the computer‐assisted learning resource demonstrated stronger knowledge recall than low‐frequency users ( p = 0.003) and use of the resource was strongly positively correlated with performance on high‐order questions ( p = 0.014) when controlling for the aforementioned variables. Deep approaches to learning were positively associated with overall knowledge recall ability in both comparisons ( p < 0.05). These findings suggest that the dissection‐ and prosection‐based laboratory teaching approaches at the University of Guelph offered equal opportunities for long‐term knowledge retention; however, using the resource more frequently and pursuing a deeper approach to learning in the course may help students develop stronger long‐term knowledge recall abilities. Therefore, supplemental computer‐assisted learning resources can be used as a low‐risk intervention to support cadaver‐based human anatomy education and promote long‐term knowledge retention.
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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 | 0,001 | 0,000 |
| 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 | 0,001 |
| 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 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 ».