Enriching Anatomy Learning with Virtual Reality Clinical Scenarios
Notice bibliographique
Résumé
Introduction The use of virtual reality (VR) to simply display anatomical specimens typically fails to exploit the potential of VR to enhance the learning environment. For example, anatomy education normally occurs within the confines of a laboratory which is devoid of relevant context, while VR is uniquely capable of providing a clinically‐relevant space that may enhance the anatomy learning experience. However, it is also possible that an enriched VR environment may inhibit learning, perhaps as a result of increased cognitive load. In general, well‐designed educational materials create schemas and organize the acquired knowledge in a way that promotes deeper learning, retention, and easy retrieval in other settings. Efforts must be made, in instructional design, to reduce extraneous and increase germane cognitive load, but how exactly this is done in VR remains an open question. Objective The purpose of this study is to evaluate the educational efficacy of a VR module which includes a clinical scenario presented in an immersive, contextually‐relevant virtual environment. Hypothesis Although learning in a VR enriched environment with a clinical scenario can increase extraneous cognitive load, we hypothesize that it will ultimately facilitate memorization of anatomical structures, when compared with using 3D‐printed physical models or an interactive, 2D environment. Methods Participants with no prior knowledge of pelvic anatomy will be randomly assigned to one of three groups to learn human pelvic anatomy in an identical module presented in VR, an interactive 2D computer‐based module, or physical environment consisting of 3D‐printed models. Prior to the learning phase, participants will complete the Mental Rotation Test (MRT), as well as the Titmus Fly and Titmus Circles tests to assess spatial visualization ability and stereoscopic vision, respectively. They will then complete a pre‐test assessment where they will be asked to identify anatomical structures labeled on a 3D‐printed model. Participants will then be given 10 minutes to learn and memorize pelvic anatomy using their assigned anatomy modality. This will be followed by a post‐test assessment with another set of labeled structures to identify. Finally, participants will complete a survey to share feedback on the learning experience and will complete the Simulation Task Load Index (SIM‐TLX) questionnaire to assess cognitive load. Conclusion The impact of contextually‐relevant enriched virtual environments and clinical scenarios for VR‐based anatomy education have yet to be explored. The findings from this study will provide valuable insight to inform the design of future VR learning tools that not only reduce or limit factors which can impair learning, but also suggest factors within the learning environment which have the potential to improve anatomy learning.
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 | 0,000 |
| 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 | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,003 |
| 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 ».