Browsing by Author "Basmaci, Fulya"

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Evaluation of the Effects of Avatar on Learning Temporomandibular Joint in a Metaverse-Based Training
(Wiley, 2024) Basmaci, Fulya; Bulut, Ali Can; Ozcelik, Erol; Ekici, Saliha Zerdali; Kilicarslan, Mehmet Ali; Cagiltay, Nergiz Ercil; Computer Engineering; Software Engineering
PurposeAvatars, representing users in the digital world, can influence users' behavior and attitudes. This study evaluates the impact of representing dental students receiving temporomandibular joint (TMJ) education in the metaverse via an anonymous or identified avatar.MethodsParticipants included 80 dental students in their fourth and fifth years of study. They were randomly assigned to either the avatar group (identified avatar) or the control group (anonymous avatar). Prior to training, participants completed a demographic questionnaire and a pretraining knowledge assessment. TMJ training was conducted in the metaverse for both groups. Pre- and post-training assessments included the Spielberger State-Trait Anxiety Inventory and a shyness scale to ensure group comparability. A post-test consisting of five questions was administered to both groups after 2 weeks of training.ResultsThere were no significant differences in pretraining scores for prior knowledge (p = 0.67), trait anxiety (p = 0.28), state anxiety (p = 0.92), or shyness (p = 0.42) between the avatar and control groups, indicating comparability at baseline. Post-training analysis revealed significantly higher post-test scores in the avatar group (median = 80) compared to the control group (median = 60) (p = 0.03).ConclusionsMetaverse environments offer various benefits for students, educators, and educational institutions in health education programs. Representing learners and their identities in training environments can enhance learning outcomes.
Citation - WoS: 2
Citation - Scopus: 2
Expectancy 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 Ercil; Information Systems Engineering; Software Engineering
ObjectiveDental 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.