Open Access DOI Assigned

Online Learning Fatigue, Academic Engagement, and Dropout Intention Among University Students Under NEP 2020

Volume 3, Issue 3

  • Author(s)Saravanan Muthukumar
  • AffiliationDepartment of Computer Science Education, Government Arts College, Dharmapuri, India
  • Page No.77-80
  • Volume, Issue & YearVolume 3, Issue 3, March 2026
  • Published On2026/02/14
  • JournalInternational Journal of Advanced Multidisciplinary Application (IJAMA)
  • ISSN No.3048-9350
  • DOIhttps://doi.org/10.5281/zenodo.19151024
Article Indexing

Abstract

The rapid institutionalisation of online and hybrid learning modalities in Indian universities following the COVID-19 pandemic, reinforced by the National Education Policy 2020’s directive to expand digital and online education to at least 50 percent of all higher education enrolments by 2035, has generated a largely unexamined crisis of learner fatigue that threatens to undermine the participation and completion gains that digital education promises. This study investigates the antecedents and consequences of Online Learning Fatigue among undergraduate and postgraduate students across eight Indian universities, proposing a structural model in which screen-time overload, lack of social presence, platform usability deficits, and assessment anxiety jointly drive fatigue, which in turn suppresses academic engagement and elevates dropout intention. Data collected from students over an eighteen-week semester using repeated cross-sectional measurement are analysed through Structural Equation Modelling, and the moderating role of institution type is examined. The findings reveal that fatigue peaks sharply during examination approach periods and that social presence deficit is a more potent driver of fatigue than screen-time overload per se — a finding with direct implications for instructional design in university-level online programmes under NEP 2020 implementation.

Keywords: online learning fatigue, NEP 2020, structural equation model, academic engagement, dropout intention, screen overload, social presence, higher education, India, hybrid learning, assessment anxiety, EdTech

References

  1. [1] Bao, W. (2020). COVID-19 and online teaching in higher education: A case study of Peking University. Human Behavior and Emerging Technologies, 2(2), 113-115.
  2. [2] Chen, Z., Feng, Z., & Liu, X. (2021). Online learning fatigue scale: Development and validation. Computers & Education, 173, 104284.
  3. [3] Garrison, D. R., Anderson, T., & Archer, W. (2000). Critical inquiry in a text-based environment: Computer conferencing in higher education. Internet and Higher Education, 2(2-3), 87-105.
  4. [4] Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate Data Analysis (8th ed.). Cengage.
  5. [5] Meijman, T. F., & Mulder, G. (1998). Psychological aspects of workload. In P. J. D. Drenth & H. Thierry (Eds.), Handbook of Work and Organisational Psychology (pp. 5-33). Psychology Press.
  6. [6] Ministry of Education. (2020). National Education Policy 2020. Government of India.
  7. [7] Renes, S. L., & Strange, A. T. (2011). Using technology to enhance higher education. Global Education Journal, 2011(1), 1-9.
  8. [8] Schaufeli, W. B., Martinez, I. M., Pinto, A. M., et al. (2002). Burnout and engagement in university students: A cross-national study. Journal of Cross-Cultural Psychology, 33(5), 464-481.
  9. [9] Tinto, V. (1987). Leaving College: Rethinking the Causes and Cures of Student Attrition. University of Chicago Press.
  10. [10] UGC. (2022). Guidelines on Open and Distance Learning and Online Education. University Grants Commission.
  11. [11] Venugopal, P., & Subbarayalu, A. V. (2021). Impact of COVID-19 pandemic on higher education. Research Journal of Education, 7(3), 100-108.
  12. [12] Wang, C., Pan, R., Wan, X., et al. (2020). Immediate psychological responses and associated factors during the initial stage of the COVID-19 epidemic. International Journal of Environmental Research and Public Health, 17(5), 1729.
  13. [13] Zhao, Y., & Breslow, L. (2013). Literature review on individual learners in MOOCs. Working Paper, MIT Office of Digital Learning.
  14. [14] Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64-70

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