Size Matters! Evaluating the Effect of Model Size on Anatomy Learning
Notice bibliographique
Résumé
Historically, learning anatomical specimens was limited to studying cadaveric materials, and by extension, specimens which are “life‐sized”. Recent technological advancements in 3D scanning and printing now allow for the production of inexpensive, durable anatomical replicas at virtually any size. This, however, creates a dilemma: what is the most effective model size to learn from? The goal of this project is to discover the appropriate size of an object to learn nominal anatomy and thus provide a critical step in improving anatomic education. We hypothesize that there is a curvilinear relationship between model size and learning, where a model too small or too big would not be the most conducive to learning and an ideal intermediate size can be determined. In this study, undergraduate students (n = 351) without prior anatomical training learned from four bones of varying normal anatomical size and features and were assessed on their ability to identify various landmarks. Thoracic vertebra (VE), hemipelvis (HE), sphenoid (SP), and scapula (SC) was 3D‐printed at four different scalar sizes. The VE and HE models were printed at 50%, 100%, 200%, and 400% scale, while SP and SC models were printed at 50%, 100%, 200%, and 300% scale. Each participant was randomly assigned to a group of two bone models (VE/HE or SP/SC) of a certain size, and randomized across the order in which they learned the models. They were then tested on the respective real bone specimens, followed by a qualitative survey reporting their experience with the 3D‐printed models, a Mental Rotations Test (MRT), and an Operation Span Test (OSPAN). Data collection for the 50% SP/SC group is still ongoing. Multiple regression suggested significant effects of Model Type, Model Size, MRT and OSPAN, (F(9, 596) = 17.96, p = 0.000, R2 = 0.2133). The most significant predictor of test score was MRT, which suggested a 10% increase in MRT score is associated with a ~3% increase in test score. The score variability independently accounted for by MRT and OSPAN was 14.6%, while the variability independently accounted for by model size and type was 7.8%. This means, that while test scores are primarily driven by participants’ mental rotation ability, model size remains an important feature that can be manipulated to improve learning. 3D printing allows for this in a cost‐effective way. Support or Funding Information This study was funded by the Education Program in Anatomy at McMaster University. Many thanks to the University of Buffalo for 3D printing the 400% VE and HE models.
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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 | 0,001 |
| 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 ».