Enhancing Student's Performance Classification Using Ensemble Modeling

A precise prediction of student performance is an important aspect within educational institutions toimprove results and provide personalized support of students. However, the predication accuracy of studentperformance considers an open issue within education field. Therefore, this paper proposes a developed approachto identify performance of students using a group modeling. This approach combines the strengths of multiplealgorithms 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 robustand reliable predictive model because it considers The experiments were evaluated using the Open UniversityLearning Analytics Dataset (OULAD) benchmark dataset. The OULAD dataset considers a comprehensive datasetcontaining various characteristics related to the student’s activities thereby five cases based on the utilized datasetwere investigated. The experiment results showed that the proposed ensemble model presented its ability withaccurate results to classify student performance by achieving 95% of accuracy rate. As a result, the proposed modelexceeded the accuracy of individual basic models by using the strengths of various algorithms to improve thegeneralization by reducing the potential weaknesses of individual models. Consequently, the education institutescan easily identify students who may need additional support and interventions to improve their academicperformance.