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IEEE Access

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The way in which the emotion of fear affects the technology adoption of students and teachers amid the COVID-19 pandemic is examined in this study. Mobile Learning (ML) has been used in the study as an educational social platform at both public and private higher-education institutes. The key hypotheses of this study are based on how COVID-19 has influenced the incorporation of mobile learning (ML) as the pandemic brings about an increase in different kinds of fear. The major kinds of fear that students and teachers/instructors are facing at this time include: fear because of complete lockdown, fear of experiencing education collapse and fear of having to give up social relationships. The proposed model was evaluated by developing a questionnaire survey which was distributed among 280 students at Zayed University, on the Abu Dhabi Campus, in the United Arab Emirates (UAE) with the purpose of collecting data from them. This study uses a new hybrid analysis approach that combines SEM and deep learning-based artificial neural networks (ANN). The importance-performance map analysis is also used in this study to determine the significance and performance of every factor. Both ANN and IPMA research showed that Attitude (ATD) are the most important predictor of intention to use mobile learning. According to the empirical findings, perceived ease of use, perceived usefulness, satisfaction, attitude, perceived behavioral control, and subjective norm played a strongly significant role justified the continuous Mobile Learning usage. It was found that perceived fear and expectation confirmation were significant factors in predicting intention to use mobile learning. Our study showed that the use of mobile learning (ML) in the field of education, amid the coronavirus pandemic, offered a potential outcome for teaching and learning; however, this impact may be reduced by the fear of losing friends, a stressful family environment and fear of future results in school. Therefore, during the pandemic, it is important to examine students appropriately so as to enable them to handle the situation emotionally. The proposed model has theoretically given enough details as to what influences the intention to use ML from the viewpoint of internet service variables on an individual basis. In practice, the findings would allow higher education decision formers and experts to decide which factors should be prioritized over others and plan their policies appropriately. This study examines the competence of the deep ANN model in deciding non-linear relationships among the variables in the theoretical model, methodologically.







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Mathematical model, Artificial neural networks, Technology acceptance model, Education, COVID-19, Uncertainty, Pandemics

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Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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Open Access


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Gold: This publication is openly available in an open access journal/series

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