A Multi-Layer Computational Framework for Predicting Student Performance Ranges in Higher Education Using Machine Learning

Predicting student academic performance constitutes a strategic priority for higher education institutions seeking to reduce attainment gaps and provide timely, targeted support. Existing approaches predominantly generate single-point performance estimates, overlooking the inherent variability in individual academic trajectories. This paper introduces a novel seven-layer computational framework that predicts student performance as a bounded range, capturing both minimum and maximum expected outcomes rather than as a solitary value. The framework integrates a bespoke imbalanced-data mitigation algorithm, three heuristic feature-selection methods: Genetic Algorithm, Particle Swarm Optimization, and Recursive Feature Elimination, and two complementary model architectures: a Parallel Architecture built upon fourteen supervised learning classifiers, and a Popularity Architecture centered on K-Modes/K-Prototype unsupervised clustering. The framework was validated on a rich, anonymized dataset provided by IBN ZOHR University in Morocco, comprising records from over 200,055 undergraduate students. The proposed framework achieves accuracy of 84%/86% (worst/common-case scenario), representing a 3%/5% improvement over an 81% baseline derived from the ten most relevant prior studies. The unsupervised Popularity Architecture attained peak accuracy of 96.91%, outperforming all supervised configurations. Results further demonstrate that omitting feature selection frequently yields competitive performance, and that increasing the number of hidden layers in neural networks does not significantly alter predictive accuracy in this educational context. The framework is designed for seamless integration into existing student performance dashboard systems, offering the institutions an actionable decision-support tool.