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Prediction of Student’s Academic Performance during Online Learning based on Regression in Support Vector Machine

Nor Ain Maisarah Samsudin, Shazlyn Milleana Shaharudin, Nurul Ainina Filza Sulaiman, Shuhaida Ismail, Nur Syarafina Mohamed, and Nor Hafizah Md Husin

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: norainmaisarah28@gmail.com, shazlyn@fsmt.upsi.edu.my, aininafilza@gmail.com, hafizah.husin@fsmt.upsi.edu.my).
N. S. Mohamed is with Department of Mathematical Sciences, Universiti Teknologi Malaysia, 81300 Skudai, Johor, Malaysia (e-mail: nursyarafina@utm.my). 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: shuhaida@uthm.edu.my).
N. S. Mohamed is with Department of Mathematical Sciences, Universiti Teknologi Malaysia, 81300 Skudai, Johor, Malaysia (e-mail: nursyarafina@utm.my).

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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).

General Information

  • ISSN: 2010-3689 (Online)
  • Abbreviated Title: Int. J. Inf. Educ. Technol.
  • Frequency: Monthly
  • DOI: 10.18178/IJIET
  • Editor-in-Chief: Prof. Dr. Steve Thatcher
  • Executive Editor: Ms. Nancy Y. Liu
  • Abstracting/ Indexing: Scopus (CiteScore 2021: 1.3), INSPEC (IET), UGC-CARE List (India), CNKI, EBSCO, Electronic Journals Library, Google Scholar, Crossref, etc.
  • E-mail: ijiet@ejournal.net

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