Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/8651
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Article A Metaverse-Based Fully Immersive Training for Temporomandibular Joint: A Pilot Study(Wiley, 2026) Ozcelik, Erol; Ekici, Saliha Zerdali; Basmaci, Fulya; Cagiltay, Nergiz Ercil; Kilicarslan, Mehmet AliObjective Understanding the temporomandibular joint (TMJ) can be challenging with conventional methods, as its complex anatomy, comprising the articular disc, mandibular condyle, and temporal bone, requires detailed visualisation. Traditional approaches like textbooks and static images often fall short, whereas modern tools such as 3D modelling and virtual reality (VR) offer more effective alternatives. Metaverse technology further enhances this by creating interactive, immersive and collaborative learning environments that simulate real-world experiences. While VR is increasingly used in dental education, research on fully immersive metaverse-based learning remains limited.Methods In this pilot study, a custom metaverse environment was developed to teach TMJ concepts. Then, the effectiveness of conventional and metaverse-based teaching methods in improving dental students' understanding of the TMJ was evaluated experimentally. A randomised trial was conducted with 120 first-year dental students, divided into three groups: classical lecturing, metaverse-based training and a combination of both.Results Findings indicate that students in the metaverse and combined groups outperformed those in the classical lecturing group, with no significant difference between the two metaverse-involved groups.Conclusions This suggests that for highly complex anatomical structures like the TMJ, metaverse-based training alone may be sufficient, eliminating the need for additional traditional instruction. The study highlights the metaverse's potential to enhance dental education by providing a fully 3D, interactive learning experience.Article Citation - WoS: 3Citation - Scopus: 3Expectancy From, and Acceptance of Augmented Reality in Dental Education Programs: a Structural Equation Model(Wiley, 2024) Toker, Sacip; Akay, Canan; Basmaci, Fulya; Kilicarslan, Mehmet Ali; Mumcu, Emre; Cagiltay, Nergiz ErcilObjectiveDental schools need hands-on training and feedback. Augmented reality (AR) and virtual reality (VR) technologies enable remote work and training. Education programs only partially integrated these technologies. For better technology integration, infrastructure readiness, prior-knowledge readiness, expectations, and learner attitudes toward AR and VR technologies must be understood together. Thus, this study creates a structural equation model to understand how these factors affect dental students' technology use.MethodsA correlational survey was done. Four questionnaires were sent to 755 dental students from three schools. These participants were convenience-sampled. Surveys were developed using validity tests like explanatory and confirmatory factor analyses, Cronbach's alpha, and composite reliability. Ten primary research hypotheses are tested with path analysis.ResultsA total of 81.22% responded to the survey (755 out of 930). Positive AR attitude, expectancy, and acceptance were endogenous variables. Positive attitudes toward AR were significantly influenced by two exogenous variables: infrastructure readiness (B = 0.359, beta = 0.386, L = 0.305, U = 0.457, p = 0.002) and prior-knowledge readiness (B = -0.056, beta = 0.306, L = 0.305, U = 0.457, p = 0.002). Expectancy from AR was affected by infrastructure, prior knowledge, and positive and negative AR attitudes. Infrastructure, prior-knowledge readiness, and positive attitude toward AR had positive effects on expectancy from AR (B = 0.201, beta = 0.204, L = 0.140, U = 0.267, p = 0.002). Negative attitude had a negative impact (B = -0.056, beta = -0.054, L = 0.091, U = 0.182, p = 0.002). Another exogenous variable was AR acceptance, which was affected by infrastructure, prior-knowledge preparation, positive attitudes, and expectancy. Significant differences were found in infrastructure, prior-knowledge readiness, positive attitude toward AR, and expectancy from AR (B = 0.041, beta = 0.046, L = 0.026, U = 0.086, p = 0.054).ConclusionInfrastructure and prior-knowledge readiness for AR significantly affect positive AR attitudes. Together, these three criteria boost AR's potential. Infrastructure readiness, prior-knowledge readiness, positive attitudes toward AR, and AR expectations all increase AR adoption. The study provides insights that can help instructional system designers, developers, dental education institutions, and program developers better integrate these technologies into dental education programs. Integration can improve dental students' hands-on experience and program performance by providing training options anywhere and anytime.