Ensemble-Based Student Performance Analysis Using Machine Learning Methods

Abstract

The primary objective of educational institutions is to ensure the provision of high-quality education and support to students. Identifying and addressing the needs of students requiring additional assistance is crucial for fostering academic excellence. A student's academic performance serves as a critical determinant of educational success at all levels, significantly impacting their future prospects. Various studies have explored factors linked to individual responses, including family communication, understanding, and anticipating student perspectives on campus, to enhance academic performance. This research aims to analyze and predict student academic performance by leveraging ensemble-based machine learning techniques. Instead of hyperparameter tuning, five diverse ensemble methods, namely Random Forest Regressor (RFR), Extra Trees Regressor (ETR), Gradient Boosting Regressor (GBR), Bagging, and ElasticNet, are utilized on a dataset collected from online repositories. These techniques are employed to forecast student success and academic status based on their behavior and engagement patterns. Through this study, we strive to gain valuable insights into the determinants of student performance and to identify effective approaches for improving educational outcomes. The ensemble-based approach offers a robust and comprehensive analysis of student data, enabling educational institutions to make informed decisions and design targeted interventions to support their students effectively.