Student Performance Classification using Support Vector Machine (SVM) with Polynomical Kernel on Online Student Activities

Abstract

—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.