PREDICTING ACADEMIC PERFORMANCE OF STUDENTS USING ENSEMBLE TECHNIQUES
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This study explores the application of ensemble techniques in predicting the academic performance of students, aiming to improve the accuracy and reliability of performance predictions. Given the increasing availability of student data and the need for personalized educational strategies, predictive modeling offers valuable insights into factors influencing student success. The research uses a range of ensemble learning algorithms, including Random Forest, Gradient Boosting, and AdaBoost, to analyze academic data from various sources, such as grades, attendance, and demographic information. By combining the strengths of multiple models, ensemble techniques aim to enhance prediction accuracy compared to individual machine learning models. The findings indicate that ensemble methods significantly improve the predictability of student performance, identifying key factors such as engagement, socio-economic background, and prior academic achievement as major predictors. This study demonstrates how data-driven approaches can aid in early identification of at-risk students and inform intervention strategies. The results provide valuable insights for educators and policymakers in tailoring educational approaches to maximize student success and optimize resource allocation.
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