Machine Learning for Graduation Prediction in Higher Education: A Systematic Review with a Bio-Inspired Optimization Perspective

Timely graduation, time-to-degree, and degree completion are key indicators of student progression and institutional effectiveness in higher education. This study presents a PRISMA-based systematic literature review of machine learning approaches for graduation-related prediction, with attention to predictive targets, pipeline components, scalability, and bio-inspired optimization. Searches in Web of Science Core Collection and Scopus identified 278 records, of which 25 studies published between 2021 and 2025 met the eligibility criteria. The findings show that most studies formulated graduation prediction as a supervised classification task, relied heavily on academic performance variables, and frequently used tree-based or ensemble models. Feature selection, explainability, and hyperparameter optimization were commonly reported, but bio-inspired optimization was actively implemented in only two studies through Particle Swarm Optimization, Genetic Algorithms, or Ant Colony Optimization. The evidence base also remains limited in scalability, as most studies used single-institution datasets and provided little external validation. These findings identify an opportunity for Bio-Inspired Educational Analytics through scalable feature selection, efficient hyperparameter optimization, model simplification, and multi-objective trade-off analysis. Future research should evaluate whether lightweight, hybrid, and multi-objective metaheuristics can support accurate, interpretable, fair, and transferable graduation prediction systems.