Virtual Reality to Teach Human Anatomy – An Interactive and Accessible Educational Tool
Notice bibliographique
Résumé
Virtual reality (VR) is a revolutionizing technology. Prior to 2016 VR systems were cost prohibitive, and user‐unfriendly. The Oculus Rift, first introduced in 2013, revolutionized the VR field by bringing forth the first commercialized VR system. In 2017, many affordable VR systems have been introduced into the commercial market, some costing less than a typical cellphone. The VR systems consist of a wearable headset, two controllers that allow the user to naturally interact with objects in the VR space, and lastly sensors connected to a computer allow for precise tracking of all head and hand movements. VR is a tool with endless possibilities, it can be implemented in any course as a teaching or supplementary resource. Within the VR space, users may play a PowerPoint presentation or a video, open a document, draw in 3D VR space any object. In VR, users can also host a live class; students may watch on their mobile devices, or enter the interactive VR space with their own system. Additionally, in VR, a user can change their environment to anything they desire, including access to any space, anywhere in the world with internet access. Therefore, bringing the laboratory to the student and the instructor. VR is the tool that can revolutionize the delivery of distance education material. The University of Guelph offers a comprehensive dissection‐based human anatomy course to approximately 400 third‐ and fourth‐year undergraduate students yearly. Additionally, in collaboration with Guelph Humber, a first‐year anatomy course is offered off‐site to 120 kinesiology students, with a laboratory component hosted weekly at the University of Guelph. In the fall of 2017, a strike affecting all colleges in Ontario limited the ability to deliver off‐site lectures for 5 weeks. During the strike, innovate ways of lecture delivery were successfully used to meet course objectives, including narrated PowerPoint presentations and virtual reality lessons. Various anatomical lessons were delivered online to students in various formats, addressing the thorax and abdominal regions. In week 3, students were sent a video (Abd1) demonstrating the blood supply of the abdomen (Celiac Trunk, SMA, IMA and all associated branches, relationships, and developmental concepts) (Fig. 1). A week later, students were sent another video, demonstrating the same lesson, however in VR format (AbdVR) (Fig. 2). Abd1 is 22.5 minutes in length, attained an average view duration of 4.05 min, a total view time of 280 min, and average view percentage of 18%. Whereas AbdVR is 20 minutes in length, attained an average view duration of 9.1 min, a total view time of 542 min, and an average view percentage of 45%. Abd1 audience retention decreased gradually throughout the video, whereas AbdVR performed with an above‐average retention rate, and had several large peaks of retention throughout, all corresponding to important concepts being shown, such as the visualization of anterior and posterior branches of the superior pancreaticoduodenal artery – a concept that would be difficult to show two‐dimensionally (Fig 3). Through an online survey, students indicated that VR videos allowed them a better understanding of the complex 3D nature of the human body. Concepts such as the blood anastomosis of the elbow became easier to understand and visualize. Students also appreciated having a “person” speak to them as opposed to a narrated video. Overall, VR educational videos performed exceptionally well in showcasing anatomical concepts with depth. This abstract is from the Experimental Biology 2018 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 | 0,000 |
| 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 ».