Is a Dissective Virtual Reality Model an Effective Learning Tool?
Notice bibliographique
Résumé
Three‐dimensional (3D) visualization technology such as virtual reality (VR) has the ability to illustrate and replicate physical dissection, and its novelty has captured the interest of many educational institutions. Unfortunately, the testing of 3D technology lags behind development, and most research is confined to case studies. This study's objective is to (1) analyze the short‐term and long‐term efficacy of VR dissection technology compared to an interactive, physical dissective model, and (2) determine if other factors, such as spatial ability, impacts the effectiveness of learning anatomy from VR models. Based on previous research in our lab, static physical models have been shown to be superior to VR models when learning anatomy. Thus, the physical dissection model is hypothesized to perform better in teaching anatomy. The interactive, physical model consists of a 3D‐printed bony pelvis and fabric perineal structures to effectively display the dissections. The physical model was scanned to produce an identical VR replica which is displayed on an HTC Vive. This crossover study will use undergraduate McMaster University students (n=52) with no formal anatomy education. Participants will be asked to learn anatomical structures from both physical and VR models, and be tested on the knowledge from each model in two separate tests. After 48 hours, they will be tested to determine if either model exhibits better long‐term retention. Tests will include nominal, functional, and spatial questions to assess recognition, critical thinking, and spatial awareness. Preliminary data (n=13) suggests that there is no statistically significant difference between either models when learning anatomy during short‐term testing (p=0.24) and long‐term testing (p=0.054). On short‐term retention tests, participants are receiving an average score of 7(3) and 6(2) out of 15 when learning from the VR and physical models, respectively. During long‐term testing, participants are achieving an average score of 7(3) and 5(2) out of 15 when also learning from the VR and physical models, respectively. Data collection is underway and expected to yield complete results by January 2019. Data from additional participants will further elucidate the impact of VR and physical dissection models on student learning. These results could help guide and improve the development of future anatomy education programs. Support or Funding Information This project was self‐funded. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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 | 0,000 | 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 ».