Applying Augmented Reality to Convey Medical Knowledge on Osteoclasts to Users of a Serious Game: Vignette Experiment
Notice bibliographique
Résumé
BACKGROUND: Visualization technology is enhancing interactive learning by merging digital content with real-world environments, offering immersive experiences through Augmented Reality (AR) in fields like medical education. AR is being increasingly used in medicine and dental education to improve student learning, particularly in understanding complex concepts such as bone remodeling. Active learning strategies, supported by AR, boost student autonomy, reduce cognitive load, and improve learning outcomes across various disciplines. AR is gaining popularity in higher education as it enhances active learning, reduces cognitive load, and improves cognitive, meta-cognitive, and affective outcomes, particularly in medical and nursing education. The effectiveness of immersive AR in enhancing understanding of complex physiological processes is still unclear, with a lack of rigorous studies on its impact and how to effectively convert academic content into AR. OBJECTIVE: We assess the capacity of AR-enhanced content for learning medical knowledge with a state-of-the-art AR game, published along with a modern cell atlas of the oral cavity. To assess AR-enhanced content for learning, we formulated hypotheses on the general impact on learning (H1), specific improvements in learning (H2) and the long-term retention (H3). METHODS: An AR serious game was developed to represent current knowledge on osteoclasts for classroom use. The game was evaluated in an unblinded face-to-face vignette experiment (39 participants): Learning outcomes on "Osteoclasts" were compared between the AR game (17 participants) and a textbook-only option (20 participants) conveying the same content. Participants were randomly assigned and learning success was measured at three time-points, immediately after the experiment session, one week later, and one month later, via web-based surveys. RESULTS: The AR serious game elicited strong interest in the topic (perceived relevance in ARCS, W= 10417; P < .001) and motivated students by increasing self-efficacy (confidence in ARCS, W = 11882.5; P = .023) and satisfaction (in ARCS, W = 4561; P < .001). The learning outcomes were comparable to text-based self-learning (T = 2.0103; PBonferroni = .095). Furthermore, curious students benefited more from interactive learning methods compared to text-only methods and had higher learning success (T = -2.518; P = .019). CONCLUSIONS: Introducing new technology such as AR into teaching requires technological investment, updated curricula, and careful application of learning paradigms. We found support for improved motivation (H1) and some evidence of AR's baseline effectiveness (H2a). While we couldn't confirm AR's impact on visual tasks overall (H2b), we noted an interesting interaction between curiosity and visual task outcomes (H2c), as well as how game design influences student perception of the material (H2d). Due to attrition, long-term learning outcomes (H3) could not be assessed. AR-based learning may particularly benefit curious students, who often struggle with text-heavy methods. As students are increasingly accustomed to brief, engaging content, teaching approaches must adapt.
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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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| 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) | 0,001 | 0,001 |
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 ».