Building student’s performance decision tree classifier using boosting algorithm

Abstract

Student’s performance is the most important value of the educational institutes for their competitiveness. In order to improve the value, they need to predict student’s performance, so they can give special treatment to the student that predicted as low performer. In this paper, we propose 3 boosting algorithms (C5.0, adaBoost.M1, and adaBoost.SAMME) to build the classifier for predicting student’s performance. This research used ¹UCI student performance datasets. There are 3 scenarios of evaluation, the first scenario was employ 10-fold cross-validation to compare performance of boosting algorithms. The result of first scenario showed that adaBoost.SAMME and adaBoost.M1 outperform baseline method in binary classification. The second scenario was used to evaluate boosting algorithms under different number of training data. On the second scenario, adaBoost.M1 was outperformed another boosting algorithms and baseline method on the binary classification. As third scenario, we build models from one subject dataset and test using onother subject dataset. The third scenario results indicate that it can build prediction model using one subject to predict another subject.