Abstract—Since the Movement Control Order (MCO) was
adopted, all the universities have implemented and modified the
principle of online learning and teaching in consequence of
Covid-19. This situation has relatively affected the students’
academic performance. Therefore, this paper employs the
regression method in Support Vector Machine (SVM) to
investigate the prediction of students’ academic performance in
online learning during the Covid-19 pandemic. The data was
collected from undergraduate students of the Department of
Mathematics, Faculty of Science and Mathematics, Sultan Idris
Education University (UPSI). Students’ Cumulative Grade
Point Average (CGPA) during online learning indicates their
academic performance. The algorithm of Support Vector
Machine (SVM) as a machine learning was employed to
construct a prediction model of students’ academic
performance. , Two parameters, namely C (cost) and epsilon of
the Support Vector Machine (SVM) algorithm should be
identified first prior to further analysis. The best parameter C
(cost) and epsilon in SVM regression are 4 and 0.8. The
parameters then were used for four kernels, i.e., radial basis
function kernel, linear kernel, polynomial kernel, and sigmoid
kernel. from the findings, the finest type of kernel is the radial
basis function kernel, with the lowest support vector value and
the lowest Root Mean Square Error (RMSE) which are 27 and
0.2557. Based on the research, the results show that the pattern
of prediction of students’ academic performance is similar to
the current CGPA. Therefore, Support Vector Machine
regression can predict students’ academic performance.
Index Terms—Support vector machine, regression, epsilon, cost, linear kernel, polynomial kernel, sigmoid kernel radial basis function kernel.
N. A. M. Samsudin, S. M. Shaharudin, N. A. F. Sulaiman, and N. H. M. Husin is with the Department of Mathematics, Faculty of Science and Mathematics, Universiti Pendidikan Sultan Idris, Tanjong Malim, Perak, Malaysia (corresponding author: Shazlyn Milleana Shaharudin; e-mail: firstname.lastname@example.org, email@example.com, firstname.lastname@example.org, email@example.com).
S. Ismail is with Department of Mathematics and Statistics, Faculty of Applied Sciences and Technology, Universiti Tun Hussein Onn Malaysia, 84600 Panchor, Johor, Malaysia (e-mail: firstname.lastname@example.org).
N. S. Mohamed is with Department of Mathematical Sciences, Universiti Teknologi Malaysia, 81300 Skudai, Johor, Malaysia (e-mail: email@example.com).
Cite: Nor Ain Maisarah Samsudin, Shazlyn Milleana Shaharudin, Nurul Ainina Filza Sulaiman, Shuhaida Ismail, Nur Syarafina Mohamed, and Nor Hafizah Md Husin, "Prediction of Student‘s Academic Performance during Online Learning Based on Regression in Support Vector Machine," International Journal of Information and Education Technology vol. 12, no. 12, pp. 1431-1435, 2022.
Copyright © 2022 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).