Testing a novel extended educational technology acceptance model using student attitudes towards virtual classrooms

Many technology acceptance models used in education were originally designed for general technologies and later adopted by education researchers. This study extends Davis' technology acceptance model to specifically evaluate educational technologies in higher education, focusing on virtual classrooms. Prior research informed the construction of the model, which contains perceived usefulness, perceived ease of use, behavioural intent, access and convenience, system attributes and self-efficacy. Education-specific constructs include cognitive engagement, feedback, instructor practice and class interaction and communication. Additionally, a new construct called comfort and well-being is introduced. A total of 427 valid responses on a 5-point Likert scale were received from university students. Exploratory factor analysis, confirmatory factor analysis and structural equation modelling were used to analyse the data. The model accounted for 78% of variance of behavioural intent, with comfort and well-being demonstrating the strongest influence. Cognitive engagement, access and convenience influenced perceived usefulness, and system attributes and self-efficacy influenced perceived ease of use. Feedback, instructor practice and class interaction and communication were not significant as educational constructs for this cohort. Based on this analysis, a final extended educational technology acceptance model (EETAM) is proposed for further use and testing.Practitioner notesWhat is already known about this topic Most technology acceptance models used in education were made for general technologies, or do not include factors specific to learning and pedagogy. Most students prefer face-to-face learning experiences and active class engagement. Qualitative research shows that instructional attributes and student comfort and well-being are known to be important for students. What this paper adds A novel extended educational technology acceptance model, informed by prior empirical research, is presented. Confirmation of the importance of including student comfort and well-being in technology acceptance models used in education. The model revealed the heterogeneous nature of the student learning experience. Implications for practice and research Technology acceptance models used in educational settings should include factors specific for education and learning, and student comfort and well-being. We encourage use of the presented model in educational settings to further test the model.