Citation

Ng, Hu and Mohd Azha, Azmin Alias and Yap, Timothy Tzen Vun and Goh, Vik Tor (2022) A Machine Learning Approach to Predictive Modelling of Student Performance. F1000Research, 10. p. 1144. ISSN 2046-1402

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Abstract

Many factors affect student performance such as the individual’s background, habits, absenteeism and social activities. Using these factors, corrective actions can be determined to improve their performance. This study looks into the effects of these factors in predicting student performance from a data mining approach. This study presents a data mining approach in identify significant factors and predict student performance, based on two datasets collected from two secondary schools in Portugal. Methods – In this study, two datasets are augmented to increase the sample size by merging them. Following that, data pre-processing is performed and the features are normalized with linear scaling to avoid bias on heavy weighted attributes. The selected features are then assigned into four groups comprising of student background, lifestyle, history of grades and all features. Next, Boruta feature selection is performed to remove irrelevant features. Finally, the classification models of Support Vector Machine (SVM) , Naïve Bayes (NB) , and Multilayer Perceptron (MLP) origins are designed and their performances evaluated.

Item Type:	Article
Uncontrolled Keywords:	Student performance, data mining, support vector machine, naïve bayes, multilayer perceptron
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Computing and Informatics (FCI) Faculty of Engineering (FOE)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	29 Jul 2022 01:23
Last Modified:	29 Jul 2022 01:23
URII:	http://shdl.mmu.edu.my/id/eprint/10240

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