Developing an educational framework for using mobile learning during the era of COVID-19

Author:

Alhumaid Khadija

Abstract

This paper focuses on the impact of fear emotion upon technology adoption by educators and students during Covid-19 pandemic. Mobile learning (m-learning) has been applied as the educational social platform within higher education institutes, public as well as private. The research hypotheses were associated with the Covid-19 influence on m-learning adoption with the rise of the coronavirus increasing types of fear. Such fears include fear caused by the education failure, family lockdown, and loss of social relationships. Teachers and students are mostly fearful of these aspects of the situation. An integrated model was established within the research, using theoretical models; the Planned Behavior theory, the Technology Acceptance Model, and the Expectation-Confirmation Model. The proposed integrated model (using PLS-SEM software) was analyzed using an online survey data, with 420 respondents from Zayed University, UAE. The findings indicated that attitude was the best predictor for using the m-learning system, followed by continuous intention, expectation confirmation, perceived usefulness, ease-of-use, perceived fear, behavioral control, and satisfaction. According to the research, during the coronavirus pandemic, if the m-learning system is adopted for educational reasons, the learning and teaching outcome proves quite promising. Yet there is a fear of the family being stressed, or of loss of friends, and also a fear of the results of future schooling. It is therefore necessary to assess the students efficiently during this pandemic so that the situation can be managed emotionally.

Publisher

Growing Science

Subject

Artificial Intelligence,Computer Networks and Communications,Computer Science Applications,Communication,Information Systems,Software

Reference1 articles.

1. Developing an educational framework for using mobile learning during the era of COVID-19;Alhumaid;International Journal of Data and Network Science,2021

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