Enhancing education quality with hybrid clustering and evolutionary neural networks in a multi phase framework

Effective student performance evaluation is essential for improving education, especially in higher and technical schools. Data mining helps solve educational and administrative problems. School performance prediction is a key field of Educational Data Mining (EDM), however manual computation and data mining methods struggle with the expanding volume of complicated data from varied sources, leaving research gaps and unresolved challenges. An integrated, multi-phase strategy to these issues is presented in this work. This study uses the Hybrid Probabilistic Ensemble Fuzzy C-Medoids with Feature Selection (HPEFCM-FSP) algorithm to cluster students by academic performance in Phase I to identify those who need extra help. The NeuroEvoClass algorithm mixes evolutionary strategies inspired by swarm intelligence and artificial neural networks (ANN) to improve student performance prediction in Phase II. Particle Swarm Optimization (PSO) optimizes neural network weight assignments, dynamically fine-tuning network topologies depending on the complex student dataset. The algorithm improves prediction power through progressive convergence. The proposed methods outperform traditional models in accuracy, precision, recall, and F1-score, according to this study. Since NeuroEvoClass reliably identifies pupils at risk of academic underperformance, it is promising for Early Warning Systems (EWS) in educational institutions. The study's multi-phase approach helps educators and policymakers make data-driven decisions about student academic achievement. HPEFCM-FSP consistently outperforms K-means and Fuzzy C-means in clustering educational data by getting higher Silhouette Scores and Dunn Index values on benchmark datasets. This algorithm's strong feature selection and clustering help target educational interventions by revealing student learning behaviors. By identifying well-separated groups of high-achieving, above-average, and struggling students, HPEFCM-FSP helps institutions personalize support and interventions. Educational administrators, teachers, and policymakers can use the algorithm to handle huge, heterogeneous educational datasets due to its efficiency and robustness.