Data Mining Analysis on Student’s Academic Performance through Exploration of Student’s Background and Social Activities

Educational data mining techniques are widely used in academic prediction on student performance in classroom education. However most of the existing researches were studied and evaluated student coursework performance against the passing grade of the exam. In this paper, we performed analysis to identify the significant and impact of student background, student social activities and student coursework achievement in predicting student academic performance. Supervised educational data mining techniques, namely Naïve Bayesian, Multilayer Perceptron, Decision Tree J48 and Random Forest were used in predicting mathematic performance in secondary school. The prediction was performed on 2-level classification and 5-level classification on final grade. The experimental results have shown that student background and student social activities were significant in predicting student performance on 2-level classification. The model can be used for early predicting student performance to help in improving student performance on the subject.