Volume 1 , Issue 1 , PP: 08-22, 2022 | Cite this article as | XML | Html | PDF | Full Length Article
Rose Aljanada 1 * , Ghadeer W. Abukhalil 2 , Aseel M. Alfaisal 3 , Raghad M. Alfaisal 4
Doi: https://doi.org/10.54216/IJAACI.010101
This inclination is caused by the fact that the topic of technology incorporation has not received enough attention. The use of information and communication technology (ICT) like Google Glass has allowed instructors and students to engage in a technology-based educational setting because of the subsequent dramatic transformation. Yet, just a small number of schools and universities have started using Google Glass in their classrooms. This research aims to look at Google Glass adoption in the UAE. We reasoned those educating instructors and students about Google Glass's effective capabilities would help them make up their minds about adopting the device in classrooms. The layout of a framework that connects TAM with other influential factors is discussed in this study. To improve the interaction between instructors and learners in the classroom, this research explored the incorporation of the technology acceptance model (TAM) with the widely acknowledged potent features of the gadget, such as the teaching and learning mediator, "Motivation," and trust and information privacy. 750 questionnaires from various universities were acquired in total. According to the student's survey data gathered, the research model was studied using partial least squares-structural equation modeling (PLS-SEM) and machine learning models. The findings showed a significant association between motivation, trust, and privacy, as well as perceived usefulness and perceived ease of use of Google Glass. Moreover, the adoption of Google Glass was substantially correlated with perceived usefulness and perceived ease of use. The perceived ease of use, trust, and privacy are all important factors in the adoption of Google Glass. These results' practical implications for subsequent research were also discussed.
Google Glass , Technology Acceptance Model , PLS-SEM , and Machine Learning Models.
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