Abstract
This study proposed a statistical investigate the pattern of students’ academic performance before and after online learning due to the Movement Control Order (MCO) during pandemic outbreak and a modelling students’ academic performance based on classification in Support Vector Machine (SVM). Data sample were taken from undergraduate students of Faculty of Science and Mathematics, Universiti Pendidikan Sultan Idris (UPSI). Student’s Grade Point Average (GPA) were obtained to developed model of academic performances during Covid-19 outbreak. The prediction model was used to predict the academic performances of university students when online classes was conducted. The algorithm of Support Vector Machine (SVM) was used to develop a model of students’ academic performance in university. For the Support Vector Machine (SVM) algorithm, there are two important parameters which are C (misclassification tolerance parameter) and epsilon need to identify before proceed the further analysis. The parameters was applied to four different types of kernel which is linear kernel, radial basis function kernel, polynomial kernel and sigmoid kernel and the result was found that the best accuracy achieved by SVM are 73.68% by using linear kernel and the worst accuracy obtained from a sigmoid kernel which is 67.99% with parameter of misclassification tolerance C is 128 and epsilon is 0.6.
Highlights
This study proposed a statistical investigate the pattern of students’ academic performance before and after online learning due to the Movement Control Order (MCO) during pandemic outbreak and a modelling students’ academic performance based on classification in Support Vector Machine (SVM)
This study mainly focused on finding the pattern of students’ academic performance before during online learning due to the COVID-19 pandemic outbreak by referring on their Grade Point Average (GPA)
The best accuracy of model achieved when type of kernel be a linear kernel with 73.68% and the worst accuracy get from a sigmoid kernel which is 67.99%
Summary
This study proposed a statistical investigate the pattern of students’ academic performance before and after online learning due to the Movement Control Order (MCO) during pandemic outbreak and a modelling students’ academic performance based on classification in Support Vector Machine (SVM). The algorithm of Support Vector Machine (SVM) was used to develop a model of students’ academic performance in university. This study mainly focused on finding the pattern of students’ academic performance before during online learning due to the COVID-19 pandemic outbreak by referring on their Grade Point Average (GPA). This study to analyse the prediction of academic performance of Universiti Pendidikan Sultan Idris (UPSI) undergraduates’ students after they completely attend whole one semester by studying online based on classification in Support Vector Machine (SVM)
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More From: Turkish Journal of Computer and Mathematics Education (TURCOMAT)
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