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    A Conceptual Framework for Moderating Effect of COVID-19 on e-Learning Predictors: An Empirical Study on Student's Perspective in Sindh, Pakistan

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    Authors
    Khan, I.
    Ali Shah, M.H.
    Ibrahim, N.M.
    Koondhar, Y.
    Thartori, Voltisa
    Shah, A.
    Mohadis, H.M.
    Date
    2023
    Type
    Conference Paper
    
    Metadata
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    Citation
    Khan, I. and Ali Shah, M.H. and Ibrahim, N.M. and Koondhar, Y. and Thartori, V. and Shah, A. and Mohadis, H.M. 2023. A Conceptual Framework for Moderating Effect of COVID-19 on e-Learning Predictors: An Empirical Study on Student's Perspective in Sindh, Pakistan. In Proceedings of 2023 IEEE 8th International Conference on Engineering Technologies and Applied Sciences (ICETAS), 25 - 27 October 2023, Bahrain.
    Source Conference
    2023 IEEE 8th International Conference on Engineering Technologies and Applied Sciences (ICETAS)
    DOI
    10.1109/ICETAS59148.2023.10346439
    Faculty
    Global Curtin
    School
    Global Curtin
    URI
    http://hdl.handle.net/20.500.11937/96308
    Collection
    • Curtin Research Publications
    Abstract

    The COVID-19 epidemic affected each aspect of life in the world; among them, the education sector was the most affected. The sudden shift from physical class teaching to online mode faced by students brought several challenges, and these challenges need to be investigated and researched to find out the effects on student performances, especially students in underprivileged countries like Pakistan. This study aims to investigate the problems faced by students during COVID-19, emphasized by scholarly literature on the key issues and variables, such as System Characteristics, Computer-anxiety, computer Self-Efficacy, facilitating-conditions, social-influence, and COVID-19. Moreover, this study also aims to formulate a conceptual framework using the UTAUT model for finding the moderating impact of the COVID-19 epidemic on e-Learning Factors. The initial findings indicate that the questionnaire tool selected for this study is appropriate for further study since coefficient alpha () values are below the specified level. The analysis indicates that System Characteristics, Computer Anxiety, Computer Self-Efficacy, facilitating conditions, social influence, and COVID-19 variables under investigation carry insightful (Quantitative data) BI (0.902), SI (0.941) FC (0.780), SC (0.950), CA (0.876), CSE (0.852), and CVD (0.904) investigation and needs further extensions to study deeper in the domain.

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