Evaluating Autostereoscopy (Alioscopy <sup>TM</sup> ) Use for Anatomy Education
Notice bibliographique
Résumé
Introduction Traditional anatomy learning relies on models and cadaveric specimens that are time and resource intensive to produce, which compromises their accessibility. To mitigate this, the use of three‐dimensional visualization technology (3DVT) to learn anatomy has substantially increased. Still, learning in an immersive virtual reality (VR) environment may pose new challenges, including increased self‐reported levels of cognitive load and cybersickness likely due to its immersive nature that isolates the learner from their surroundings. Autostereoscopy is a novel and potential solution, as it provides a headset‐free stereoscopic view of a three‐dimensional (3D) model. There is, however, a paucity of information about the use and educational efficacy of autostereoscopic 3DVT. The purpose of this study is to examine the strengths and limitations of implementing a non‐immersive autostereoscopic (Alioscopy TM ) screen for learning anatomy. Methods A large‐scale study on the efficacy of VR, autostereoscopy, and 3D printed physical models for learning anatomy is currently underway. We suspect that cognitive load and cybersickness may compete with learning capacity. Based on the literature, we hypothesize the Alioscopy TM screen to represent a sort of middle‐of‐the‐road option, with moderate cognitive load and cybersickness levels (VR>Alioscopy TM >physical), that supports large‐group learning (as opposed to single‐user immersive VR) yet retains the accessibility associated with digital assets (as compared to physical specimens). As such, it is prudent to consider the feasibility of implementing an Alioscopy TM screen for anatomical teaching and summarize the strengths and limitations of the technology. Results According to the company’s website, 3D images on the 42” Alioscopy TM display we are using can be viewed from 2.5m to 9.0m away, with optimal results at 4.0m, by a theoretical maximum of “20 to 50 people spread over an area of 90°”. We suspect a practical limit of 14 people per display (2 people for each of the 7 viewing zones) and a comfortable limit of 7 (1 per zone). This allows viewers to comfortably situate themselves in a “sweet spot” where each of their eyes receives a clear, distinct image, which is required for the stereoscopic effect. The stereo‐3D effect is prominent, with objects allowed to both protrude from and recess into the display considerably. Display content was created using a plugin (provided by Alioscopy TM ) for the videogame engine Unity, allowing existing Unity content to function on the modality easily. Conclusion The Alioscopy TM screen represents a novel approach to promoting material accessibility and viewing 3D stereoscopic images in a group setting. The nature of this set up may both decrease the inherent issues most immersive 3DVT impose and offer an opportunity for collaborative learning not available with the use of single‐user headsets.
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 | 0,001 | 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 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».