Implementation of optimum binning, ensemble learning and re-sampling techniques to predict student's performance

Abstract

Educational data-mining is an emerging area of research that could extract useful information for the students as well as for the instructors. In this research, we explore data mining techniques that predict students' final grade. We validate our method by conducting experiments on data that are related to grade for courses in North South University, the first private university and one of the leading universities in higher education in Bangladesh. We also extend our ideas through discretisation of the continuous attributes by equal width binning and incorporate it on traditional mining algorithms. However, due to imbalanced nature of data, we got lower accuracy for imbalanced classes. We implement two re-sampling techniques, i.e., ROS random over sampling, RUS random under sampling. Experimental results show that re-sampling techniques could overcome the problem of imbalanced dataset in classification significantly and improve the performance of the classification models. Moreover, three ensemble techniques, namely, bagging, boosting AdaBoost and random forests have been applied in this research to predict the students' academic performance.