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Author: Toker, SacipPublication Index: PubMed

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    Citation - WoS: 1
    Citation - Scopus: 2
    Predictors of E-Democracy Applicability in Turkish K-12 Schools
    (Springer, 2022) Sendag, Serkan; Toker, Sacip; Uredi, Lutfi; Islim, Omer Faruk
    Today, the COVID-19 pandemic has paved the way for a more democratic climate in K-12 schools. Administrators and teachers have had to seek out new ways through which to interact. This raises two questions; "What about the quality of interaction and participation in decision-making?" and "Which factors affect the level of participation in decision-making?" The aim of the current research is to determine the factors that predict the applicability level of e-democracy (i.e., "reporting and declaring opinions" and "decision-making") in K-12 schools. An associational research design was used in order to attain the main goal of the study, with Discriminant Function Analysis (DFA) technique used to analyze the factors predicting the applicability level of e-democracy. Data were collected from a total of 765 inservice K-12 teachers through a questionnaire developed by the researchers. DFA results showed "motivation to participate," "the level of participatory democracy in the country," and higher levels of the "use of Twitter" as the significant determinants of different levels of e-democracy application. Moreover, the results also indicated that those participants with the belief of e-democracy's applicability at the decision-making level found the "motivation level of stakeholders" to be the most critical. Their level of Twitter use was higher. They also believed that the level of participatory democracy in the country was at a higher level. Another result of the DFA pointed to "security and ethical issues," and lower levels of the "use of Twitter" as factors differentiating the group believing that e-democracy can be applicable with reporting and the declaration of opinions to administrators from the other groups. The discussions highlighted the critical role of participation level in e-democracy within K-12 schools.
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    Citation - WoS: 3
    Citation - Scopus: 3
    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
    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.