The Influence of Spatial Ability On Anatomy Examination Questions in an Integrated Medical Curriculum
Notice bibliographique
Résumé
Background Students with high spatial visualization ability (Vz) have been found to outperform students with low Vz in anatomy. However, how Vz influences anatomy performance has not been established. Thus, this study aimed to assess the influence of Vz on medical student performance on different levels of anatomy questions categorized by Bloom's taxonomy levels and discrimination index (DI) and to observe the relationship between Vz and anatomy performance. We hypothesized that there would be a positive correlation between Vz and performance on more difficult exam questions categorized by DI and Bloom's taxonomy. We also hypothesized that there would be a positive correlation between Vz and anatomy written exam, anatomy lab exam, and overall anatomy performance. Methods First year medical students in a systems‐based integrated medical curriculum (n=61), completed the Mental Rotations Test (MRT) prior to the start of anatomy to establish Vz. All anatomy exam questions were categorized into four Bloom's taxonomy domains of increasing difficulty level (identification, comprehension, application, and analysis). These questions were also categorized into three tiers via DI. Results No significant relationship (p>0.05) was found between Vz and questions categorized by DI or Bloom's taxonomy. Data also indicated that although entrance Vz plays an insignificant role in medical student anatomy lab exam, anatomy written exam, and overall performance in the anatomy course, there is a correlation between entrance Vz and anatomy performance in the very first systems‐based module (r 2 =0.017, p≤0.05). Discussion These findings suggest that entrance Vz may influence anatomy performance at the beginning of the curriculum; however, students with lower Vz find ways to cope and increase anatomy performance throughout the curriculum. Due to the significant relationship between Vz and the first systems‐based module, further analysis was completed to assess the relationship between Vz and anatomy question difficulty. This analysis indicated that there was no significant interaction between Vz and questions categorized by DI or Bloom's taxonomy within that first systems‐based module (p>0.05), suggesting that Vz's effect on performance in anatomy may not have a relationship with question difficulty categorized by Bloom's taxonomy or DI. Further research is necessary to explore how Vz influences anatomy performance and how students’ ability to train Vz and change study strategies influences the effect of Vz on anatomy performance throughout the medical curriculum.
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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 | 0,001 |
| 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 | 0,000 |
| 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 ».