The Significant Factors Affecting Students’ Academic Performance in Online Class: Multiple Linear Regression Approach

  • Norwaziah Mahmud Universiti Teknologi MARA Cawangan Perlis
  • Nur Syuhada Muhammat Pazil Universiti Teknologi MARA Cawangan Melaka
  • Nur Afifah Nazurah Azman Universiti Teknologi MARA, Cawangan Perlis


The COVID-19 pandemic, which began in Wuhan City, China in 2020, has thrown Malaysia's academic sector into disarray. Students' academic performance changes dramatically when they move from face-to-face classes to full implementation of online distance learning (ODL). The purpose of this study is to investigate the factors that affect students' academic performance during the COVID-19 pandemic using Multiple Linear Regression (MLR). The research was carried out at UiTM Perlis Branch, and 54 bachelor's degree students from four faculties were invited to take part. During the analysis, gender, hours students spent in online learning, hours students spent on preparation before class, number of subjects taken, credit hours, hometown areas and internet connection, act as independent variables whereas CGPA as the dependent variable, were examined. This study was carried out using SPSS software and Excel. The result shows that the hometown areas and hours students spent preparing before class contributed significantly to the model while others did not. It is shown that students who live in rural areas did much better in academic performance than students who live in cities, and the more students spend on preparing themselves before class, the lower is their CGPA. Other factors tend to be insignificant and it might be because of the limited time in collecting data, small sample size and unequally-sized groups. For future research, it is recommended to have more time in collecting data and add more sample sizes by extending it to diploma students to gain more accurate results.


Balkhair, A. A. (2020). Covid-19 pandemic: A new chapter in the history of infectious diseases. In Oman Medical Journal (Vol. 35, Issue 2).
Daoud, J. I. (2018). Multicollinearity and Regression Analysis. Journal of Physics: Conference Series, 949(1).
Dhakal, C. P. (2018). Multiple Linear Regression in SPSS. Article in International Journal of Science and Research.
El Said, G. R. (2021). How Did the COVID-19 Pandemic Affect Higher Education Learning Experience? An Empirical Investigation of Learners’ Academic Performance at a University in a Developing Country. Advances in Human-Computer Interaction, 2021.
Forson, I. K., & Vuopala, E. (2019). Online learning readiness: perspective of students enrolled in distance education in Ghana. The Online Journal of Distance Education and E-Learning, 7(4), 277–294.
Gopal, R., Singh, V., & Aggarwal, A. (2021). Impact of online classes on the satisfaction and performance of students during the pandemic period of COVID 19. Education and Information Technologies, 26(6), 6923–6947.
Gossenheimer, A. N., Bem, T., Carneiro, M. L. F., & De Castro, M. S. (2017). Impact of distance education on academic performance in a pharmaceutical care course. PLoS ONE, 12(4).
Hdii, S., & Fagroud, M. (2018). The effect of gender on university students’ school performance: the case of the National School of Agriculture in Meknes, Morocco. Culture & Society, 9(1), 67–78.
Hsu Wang, F. (2019). On prediction of online behaviors and achievement using self-regulated learning awareness in flipped classrooms. International Journal of Information and Education Technology, 9(12), 874–879.
Huntington-Klein, N., & Gill, A. (2021). Semester course load and student performance. Research in Higher Education, 62(5), 623–650.
Klees, S. J. (2016). Inferences from regression analysis: Are they valid? Real-World Economics Review, 74, 85–97.
Lim, I. (2020, May 30). Reality for Malaysia’s university students: Online learning challenges, stress, workload; possible solutions for fully digital future until Dec. Malay Mail.
Mahdy, M. A. A. (2020). The impact of COVID-19 pandemic on the academic performance of veterinary medical students. Frontiers in Veterinary Science, 7.
Mahmud, N., Ali, N. A., Pazil, N. S. M., & Jamaluddin, S. H. (2022). Predicting dengue outbreak in Selangor using holt-winters models. International Journal of Academic Research in Business and Social Sciences, 12(1), 1240–1249.
Ng, S. F., Zakaria, R., Lai, S. M., & Confessore, G. J. (2016). A study of time use and academic achievement among secondary-school students in the state of Kelantan, Malaysia. International Journal of Adolescence and Youth, 21(4), 433–448.
Nicola, M., Alsafi, Z., Sohrabi, C., Kerwan, A., Al-Jabir, A., Iosifidis, C., Agha, M., & Agha, R. (2020). The socio-economic implications of the coronavirus pandemic (COVID-19): A review. In International Journal of Surgery (Vol. 78, pp. 185–193).
Rachmawati, S., Mutrofin, & Sumardi. (2021). The effect of online learning and parental guidance towards the result of XI social students’ learning on Geography course at SMAN 5 Jember. IOP Conference Series: Earth and Environmental Science, 747(1).
Sankaran, S., & Sankaran, K. (2016). Improving online course performance through customization: An empirical study using business analytics. International Journal of Business Analytics, 3(4), 1–20.
Weidlich, J., & Bastiaens, T. J. (2018). Technology matters - The impact of transactional distance on satisfaction in online distance learning. International Review of Research in Open and Distance Learning, 19(3), 222–242.
How to Cite
MAHMUD, Norwaziah; MUHAMMAT PAZIL, Nur Syuhada; AZMAN, Nur Afifah Nazurah. The Significant Factors Affecting Students’ Academic Performance in Online Class: Multiple Linear Regression Approach. Jurnal Intelek, [S.l.], v. 17, n. 2, p. 1-11, july 2022. ISSN 2682-9223. Available at: <>. Date accessed: 05 feb. 2023. doi: