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.

Keywords

• Classification
• Ensemble Model
• Machine Learning
• Stacking Classifier
• Student Performance