ANTECEDENTS TO THE UNDERPRIVILEGED UNDERGRADUATE STUDENTS' INTENTION TO PARTICIPATE IN ONLINE CLASSES

Year 2024, Volume: 25 Issue: 1, 118 - 135, 01.01.2024

Zarin Khan Moon Md. Al Amin Md. Hossain Alı Md. Mahedi Hasan

https://doi.org/10.17718/tojde.1224195

Abstract

COVID-19 pandemic has forced educational institutions to use e-learning systems. Bangladesh is no exception; many students come from underprivileged families who are not well-off. This study aimed to explore the antecedents to the underprivileged undergraduate students' intention to participate in online classes in Bangladesh through the integration of the Technology Acceptance Model, Information Systems Success Model, and Theory of Planned Behaviour. We used confirmatory factor analysis (CFA) to test the hypotheses. The non-probability sampling method was used to select 394 respondents by dint of the subjective judgment of the researchers. Using smart PLS software, the data were analyzed with Structural Equation Modeling (SEM). It was divulged that e-Learning usage intention (BI) is influenced by attitude (ATT), perceived usefulness (PU), students' online learning satisfaction (SOS) and subjective norms (SN). But perceived ease of use (PEU) and system quality (SQ), internet service quality (ISQ) and perceived behavioral control (PBC) do not influence BI. Even ISQ does not influence SOS. It was also revealed that PEU mediated attitude and PU, and PEU and SQ also influenced SOS. The study contributes to e-Learning literature by incorporating three models which may guide policymakers in understanding how to integrate students from all social classes into e-learning systems to eliminate academic digital discrimination.

Keywords

Bangladesh, underprivileged undergraduate students, intention for e-learning

References

• Adam T, Kaye T & Haßler B (2020) The Maldives and Sri Lanka: Question & Answer Session. EdTech Hub Helpdesk Response No.18). EdTech Hub. https://doi.org/10.53832/edtechhub.0018
• Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50(2), 179-211. https://doi.org/10.1016/0749-5978(91)90020-T
• Akour, I., Alshurideh, M., Al Kurdi, B., Al Ali, A., & Salloum, S. (2021). Using machine learning algorithms to predict people's intention to use mobile learning platforms during the COVID-19 pandemic: Machine learning approach. JMIR Medical Education, 7(1), 1-17. https://doi.org/10.2196/24032
• Al-Amin, M., Al Zubayer, A., Deb, B., & Hasan, M. (2021). Status of Tertiary Level Online Class in Bangladesh: Students' Response on Preparedness, Participation and Classroom Activities. Heliyon 7 (1), e05943. https://doi.org/10.1016/j.heliyon.2021.e05943
• Al Amin, M., Arefin, M. S., Hossain, I., Islam, M. R., Sultana, N., & Hossain, M. N. (2021). Evaluating the determinants of customers' mobile grocery shopping application (MGSA) Adoption during COVID-19 pandemic. Journal of Global Marketing, 1-20. https://doi.org/10.1080/08911762.2021.1980640