Predicting Students’ Performance Using a Hybrid Machine Learning Approach

Learning institutions have amassed volumes of educational data from sources such as learning management systems and academic databases. This presents an opportunity for learning institutions to predict students’ performance using Machine Learning (ML) techniques. This paper explores the application of a hybrid ML approach to students’ performance. Previous studies have employed individual ML algorithms for performance prediction; these models often suffer from limitations such as low accuracy and bias towards specific data characteristics. To address these challenges, this study proposes a hybrid ML approach that integrates multiple algorithms to leverage their strengths. The Random Forest classifier was used to reduce variance and handle nonlinear relationships. The Linear Support Vector Classifier (SVC) captured linear patterns within the data, and Logistic Regression was employed as a meta-learner to make the final predictions. By combining these classifiers, the proposed model aims to deliver improved prediction accuracy and robustness.