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Prediction of student‘s academic performance during online learning based on regression in support vector machine

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 met...

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Main Authors: Samsudin, Nor Ain Maisarah, Shaharudin, Shazlyn Milleana, Sulaiman, Nurul Ainina Filza, Ismail, Shuhaida, Mohamed, Nur Syarafina, Md. Husin, Nor Hafizah
Format: Article
Published: International Journal of Information and Education Technology 2022
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QA Mathematics
Online Access:http://eprints.utm.my/id/eprint/101598/
http://dx.doi.org/10.18178/ijiet.2022.12.12.1768
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spelling my.utm.1015982023-06-26T06:55:42Z http://eprints.utm.my/id/eprint/101598/ Prediction of student‘s academic performance during online learning based on regression in support vector machine Samsudin, Nor Ain Maisarah Shaharudin, Shazlyn Milleana Sulaiman, Nurul Ainina Filza Ismail, Shuhaida Mohamed, Nur Syarafina Md. Husin, Nor Hafizah QA Mathematics 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. International Journal of Information and Education Technology 2022-12 Article PeerReviewed Samsudin, Nor Ain Maisarah and Shaharudin, Shazlyn Milleana and Sulaiman, Nurul Ainina Filza and Ismail, Shuhaida and Mohamed, Nur Syarafina and Md. Husin, Nor Hafizah (2022) Prediction of student‘s academic performance during online learning based on regression in support vector machine. International Journal of Information and Education Technology, 12 (12). pp. 1431-1435. ISSN 2010-3689 http://dx.doi.org/10.18178/ijiet.2022.12.12.1768 DOI: 10.18178/ijiet.2022.12.12.1768
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic QA Mathematics
spellingShingle QA Mathematics
Samsudin, Nor Ain Maisarah
Shaharudin, Shazlyn Milleana
Sulaiman, Nurul Ainina Filza
Ismail, Shuhaida
Mohamed, Nur Syarafina
Md. Husin, Nor Hafizah
Prediction of student‘s academic performance during online learning based on regression in support vector machine
description 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.
format Article
author Samsudin, Nor Ain Maisarah
Shaharudin, Shazlyn Milleana
Sulaiman, Nurul Ainina Filza
Ismail, Shuhaida
Mohamed, Nur Syarafina
Md. Husin, Nor Hafizah
author_facet Samsudin, Nor Ain Maisarah
Shaharudin, Shazlyn Milleana
Sulaiman, Nurul Ainina Filza
Ismail, Shuhaida
Mohamed, Nur Syarafina
Md. Husin, Nor Hafizah
author_sort Samsudin, Nor Ain Maisarah
title Prediction of student‘s academic performance during online learning based on regression in support vector machine
title_short Prediction of student‘s academic performance during online learning based on regression in support vector machine
title_full Prediction of student‘s academic performance during online learning based on regression in support vector machine
title_fullStr Prediction of student‘s academic performance during online learning based on regression in support vector machine
title_full_unstemmed Prediction of student‘s academic performance during online learning based on regression in support vector machine
title_sort prediction of student‘s academic performance during online learning based on regression in support vector machine
publisher International Journal of Information and Education Technology
publishDate 2022
url http://eprints.utm.my/id/eprint/101598/
http://dx.doi.org/10.18178/ijiet.2022.12.12.1768
_version_ 1769842078493179904
score 13.211869

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