Regularized multi-path XSENet ensembler for enhanced student performance prediction in higher education.

Abstract

With the rapid expansion of educational data, institutions face increasing pressure to adopt advanced predictive models that can enhance academic planning, resource allocation, and student support. This study presents a novel educational data mining approach designed to forecast student performance levels categorized as low, medium, and high by analyzing historical and behavioral trends. This work proposes XSEJNet, an innovative hybrid model that integrates ResNeXt architecture with squeeze-and-excitation (SE) attention mechanisms, and employs the Jaya optimization algorithm to refine hyperparameters and boost predictive accuracy and computational efficiency. The model works with structured and unstructured academic data, effectively capturing complex, high-dimensional features to support accurate classification. Through extensive simulations and comparative evaluations, XSEJNet consistently outperforms conventional machine learning models and recent existing techniques such as reinforcement learning co-evolutionary hybrid intelligence (RLCHI), Enhanced AEO-XGBoost, convolution-based deep learning (Conv-DL), and dual graph neural network (DualGNN). The model achieves a high prediction accuracy of 97.98% while also demonstrating faster convergence and reduced computational overhead, making it a scalable and practical solution for real-world educational settings. The findings underscore XSEJNet's ability to support early intervention, strengthen e-learning platforms, and inform institutional decision-making. By advancing predictive capabilities in education, this work makes a meaningful contribution to developing inclusive, data-driven, and sustainable academic systems.