A Chaotic Educational Competition Optimizer with an Explainable SVC for Risk-Aware Student Performance Prediction

Abstract

Predicting student performance has emerged as an essential element of contemporary learning assessment, allowing educational organizations to determine problematic students and offer early intellectual assistance. Many machine learning (ML) methodologies prioritize predicted accuracy at the expense of interpretability and practical insights. This paper provides a framework for predicting student performance that is both risk aware and explainable utilizing a chaotic educational competition optimizer (ECO) in conjunction with a support vector classifier (SVC) to overcome existing challenges. The ECO serves as a metaheuristic feature selection technique for selecting the most significant features from a multivariate educational dataset consisting of 1195 students and 29 behavioral, demographic, and academic characteristics. Experimental findings demonstrate that ECO effectively condenses the feature space to 11 essential indications and improves generalization of model while maintaining classification robustness. Utilizing the chosen features, the ECO–SVC model attains a complete classification accuracy of 87.03%, with F1-scores of 0.92, 0.69, and 0.82 for high-, medium-, and low-performance student categories, respectively, surpassing other benchmark ML methods. The proposed framework incorporates explainable artificial intelligence (XAI) to improve transparency by utilizing local explanations and permutation-driven feature significance. The XAI research verifies that institutional support, learner engagement, and previous academic success are the most important contributing factors to predictive results. Notably the ECO functions as a classifier-independent feature selection mechanism; however, the support vector classifier (SVC) is adopted in this study due to its strong generalization capability and effectiveness in exploiting the optimized feature space. The findings are analyzed using a semiotic-linguistic framework, wherein certain qualities are correlated with symbolic, indexical, and temporal educational signs, converting numerical significance into substantive pedagogical insights. Furthermore, an initial academic risk profile strategy is established by utilizing SVC decision confidence and elucidating feature contributors. The consequent risk ratings accurately categorize students into low-, medium-, and high-risk categories, facilitating the detection of at-risk learners beyond mere final score assessment. The proposed risk-aware and explainable ECO–SVC framework enhances learning outcomes assessment by integrating interpretability, high accuracy, and proactive academic reasoning, rendering it suitable for real-life educational decision-support systems.