Advancing educational data mining for enhanced student performance prediction: a fusion of feature selection algorithms and classification techniques with dynamic feature ensemble evolution

Abstract

The integration of technology into educational institutions has led to the generation of vast data, creating opportunities for Educational Data Mining (EDM) to improve learning outcomes. This study introduces a novel feature selection model, "Dynamic Feature Ensemble Evolution for Enhanced Feature Selection" (DE-FS), which combines traditional methods such as correlation matrix analysis, information gain, and Chi-square with heat maps to select the most relevant features for predicting student performance. The core innovation of DE-FS lies in its dynamic and adaptive thresholding mechanism, which adjusts thresholds based on evolving data patterns, addressing the limitations of static methods and mitigating issues like overfitting and underfitting. This research makes three key contributions: it introduces an advanced ensemble-based feature selection methodology, incorporates dynamic and adaptive thresholding to improve accuracy and flexibility, and demonstrates DE-FS's superior predictive performance across diverse educational datasets. The results highlight DE-FS's ability to adapt to fluctuating data patterns, enabling precise and reliable student performance predictions, supporting targeted interventions, and improving resource allocation to enhance personalized learning experiences.