Enhancing Student Success Prediction with FeatureX: A Fusion Voting Classifier Algorithm with Hybrid Feature Selection

Monitoring students' academic progress is vital for ensuring timely completion of their studies and supporting at-risk students. Educational Data Mining (EDM) utilizes machine learning and feature selection to gain insights into student performance. However, many feature selection algorithms lack performance forecasting systems, limiting their ability to predict future academic success accurately. To address this, we propose FeatureX, a hybrid approach aiming to select high-performing features that impact student quality and reduce dropout rates. FeatureX integrates filter-based and wrapper-based methods to identify relevant features for predicting student performance. This approach enhances educational experiences by optimizing resource allocation and support services. Additionally, the Confidence-Weighted Fusion Voting Classifier (CWFVC) Algorithm supplements feature selection with performance forecasting capabilities, improving accuracy by combining diverse machine learning classifiers. The research evaluates FeatureX using Decision Trees, Random Forests, Support Vector Machines, and Neural Networks. Performance metrics, including accuracy, precision, recall, and F1-score, measure FeatureX's effectiveness. Results show that FeatureX achieves the highest accuracy with a subset of ten features, effectively identifying influential predictors. The CWFVC Algorithm further enhances performance forecasting accuracy, enabling timely identification of at-risk students and reducing dropout rates to foster inclusive education. Our research addresses the demand for data-driven approaches in education, offering an innovative method for predicting student performance and enhancing educational outcomes for diverse students. FeatureX and the CWFVC Algorithm provide valuable tools for educators and administrators to optimize resources, tailor support services, and create a more inclusive learning environment. Leveraging EDM and performance forecasting, educational institutions can proactively support students and promote academic success, contributing to an equitable and effective educational system.