Student performance classification using support vector machine (SVM) with polynomical kernel on online student activities / Muhammad Hareez Mohd Zaki ... [et al.]
The increasing usage of classification algorithms has encouraged researchers to explore many topics, including academic-related topics. In addition, the availability of data from various academic information management systems in recent years has been increasing, causing classification to become a t...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
UiTM Press
2023
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| Subjects: | |
| Online Access: | https://ir.uitm.edu.my/id/eprint/86032/1/86032.pdf https://ir.uitm.edu.my/id/eprint/86032/ |
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| Institution: | Universiti Teknologi Mara |
| Language: | English |
| Summary: | The increasing usage of classification algorithms has encouraged researchers to explore many topics, including academic-related topics. In addition, the availability of data from various academic information management systems in recent years has been increasing, causing classification to become a technique that is in demand by educational institutes. Thereby, having a classification technique is important in researching the data on students’ performance. The purpose of this study is to classify students’ performance by using a polynomial kernel of Support Vector Machine (SVM) on online students’ activities. A new dataset is proposed in this study, which consists of academic and student online behaviours that influence the students’ performance. The proposed dataset also undergoes pre-processing stage to improve the accuracy and identify the significance of the proposed features. The experiment for SVM-POLY classification performance was set with a range of values on the parameters to be optimised by an optimisation algorithm, Grid Search. Classification accuracy, Precision, Recall and f1-score were applied to observe the result and determine the best classifier performance. The experimental results show that SVM – POLY, with a gamma value of 0.005, regularisation value of 0.1 and degree value of 1, come out with the best performance compared to a default value of SVM – POLY. The study is significant towards educational data mining in analysing the students’ performance during online students’ activities. |
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