Abstract-This paper introduces and compares some techniques used to predict the student performance at the university. Recently, researchers have focused on applying machine learning in higher education to support both the students and the instructors getting better in their performances. Some previous papers have introduced this problem but the prediction results were unsatisfactory because of the class imbalance problem, which causes the degradation of the classifiers. The purpose of this paper is to tackle the class imbalance for improving the prediction/classification results by over-sampling techniques as well as using cost-sensitive learning (CSL). The paper shows that the results have been improved when comparing with only using baseline classifiers such as Decision Tree (DT), Bayesian Networks (BN), and Support Vector Machines (SVM) to the original data sets.
Thai-Nghe et al. REF improved predictive results by using over-sampling technique as well as cost-sensitive learning to tackle class unbalance problem.
Improving Academic Performance Prediction by Dealing with Class Imbalance
2009 Ninth International Conference on Intelligent Systems Design and Applications
2009 Ninth International Conference on Intelligent Systems Design and Applications
Computer Science