Students Performance Prediction System Using Multi Agent Data Mining Technique

Abstract

A high prediction accuracy of the students’ performance is more helpful to identify the low performance students at the beginning of the learning process. Data mining is used to attain this objective. Data mining techniques are used to discover models or patterns of data, and it is much helpful in the decision-making. Boosting technique is the most popular techniques for constructing ensembles of classifier to improve the classification accuracy. Adaptive Boosting (AdaBoost) is a generation of boosting algorithm. It is used for the binary classification and not applicable to multiclass classification directly. SAMME boosting technique extends AdaBoost to a multiclass classification without reduce it to a set of sub-binary classification. In this paper, students’ performance prediction system using Multi Agent Data Mining is proposed to predict the performance of the students based on their data with high prediction accuracy and provide help to the low students by optimization rules. The proposed system has been implemented and evaluated by investigate the prediction accuracy of Adaboost.M1 and LogitBoost ensemble classifiers methods and with C4.5 single classifier method. The results show that using SAMME Boosting technique improves the prediction accuracy and outperformed C4.5 single classifier and LogitBoost.