Enhancing Student's Performance Classification Using Ensemble Modeling

Abstract

 A precise prediction of student performance is an important aspect within educational institutions to
improve results and provide personalized support of students. However, the predication accuracy of student
performance considers an open issue within education field. Therefore, this paper proposes a developed approach
to identify performance of students using a group modeling. This approach combines the strengths of multiple
algorithms including random forest (RF), decision tree (DT), AdaBoosts, and support vector machine (SVM).
Afterward, the last ensemble estimates as one of the bets logistic regression methods was utilized to create a robust
and reliable predictive model because it considers The experiments were evaluated using the Open University
Learning Analytics Dataset (OULAD) benchmark dataset. The OULAD dataset considers a comprehensive dataset
containing various characteristics related to the student’s activities thereby five cases based on the utilized dataset
were investigated. The experiment results showed that the proposed ensemble model presented its ability with
accurate results to classify student performance by achieving 95% of accuracy rate. As a result, the proposed model
exceeded the accuracy of individual basic models by using the strengths of various algorithms to improve the
generalization by reducing the potential weaknesses of individual models. Consequently, the education institutes
can easily identify students who may need additional support and interventions to improve their academic
performance.