Single Classifiers and Ensemble Approach for Predicting Student’s Academic Performance

In recent time, educational data mining (EDM) has received substantial considerations. Many techniques of data mining have been proposed to dig out out-of-sight knowledge in educational data. The Knowledge obtained assists the academic institutions to further enhance their process of learning and methods of passing knowledge to students. Consequently, the performance of students soar and the educational products are by no doubt enhanced. In this study, a novel student’s performance prediction model premised on techniques of data mining with Students’ Essential Features (SEF). Students’ Essential Features (SEF) are linked to the learner’s interactivity with the e-learning management system. The performance of student’s predictive model is assessed by set of classifiers, viz. Bayes Network, Logistic Regression and REP Tree. Consequently, ensemble methods of Bagging Boosting and Random Forest are applied to improve the performance of these single classifiers. The results obtained reveal that there is a robust affinity between learner’s behaviors and their academic attainment. Results from the study shows that REP Tree and its ensemble record the highest accuracy of 83.33% using SEF. Hence, in terms of Receiver Operating Curve (ROC), boosting method of REP Tree records 0.903, which is the best. This result further demonstrates the dependability of the proposed model.