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Predicting Student Outcomes for Enhanced Learning Using Hybrid Machine Learning Approach
Abstract
Student outcomes in enhanced learning environments can be effectively predicted using advanced machine learning techniques. This study proposes an integrated approach combining Random Forest and XGBoost models to predict student performance accurately. Random Forest provides stability and robustness in handling noisy and imbalanced data, while XGBoost refines predictions by iteratively correcting errors. By integrating these models through ensemble techniques such as stacking, the method capitalizes on their complementary strengths to achieve superior prediction accuracy. The approach is validated using a comprehensive dataset that includes demographic, academic, behavioral, and interactional attributes. The integrated model demonstrates improved predictive performance, enabling early identification of at-risk students and supporting personalized educational interventions. This work underscores the potential of machine learning in enhancing education outcomes through data-driven insights.
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