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Machine Learning for Graduation Prediction in Higher Education: A Systematic Review with a Bio-Inspired Optimization
Andrés Yáñez1,2, Broderick Crawford3, Eric Monfroy2
1Escuela de Ingeniería en Construcción y Transporte, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2147, Valparaíso 2362804, Chile.
Biomimetics (Basel, Switzerland)
|July 27, 2026
Summary
Machine learning models can predict student graduation rates, but current methods lack scalability and diverse optimization techniques. Future research should explore bio-inspired algorithms for more accurate and transferable educational analytics.
Area of Science:
- Educational Data Mining
- Machine Learning in Higher Education
- Predictive Analytics
Background:
- Timely graduation and degree completion are crucial metrics for student success and institutional performance.
- Machine learning (ML) offers potential for predicting student graduation outcomes.
- A systematic review is needed to assess current ML applications in graduation prediction.
Purpose of the Study:
- To systematically review machine learning approaches for graduation prediction.
- To analyze predictive targets, pipeline components, scalability, and the use of bio-inspired optimization.
- To identify research gaps and future directions in educational analytics.
Main Methods:
- A PRISMA-based systematic literature review was conducted.
- Searches were performed in Web of Science Core Collection and Scopus.
- 25 studies published between 2021-2025 were included.
Main Results:
- Most studies used supervised classification with academic performance data and tree-based/ensemble models.
- Feature selection, explainability, and hyperparameter optimization were common.
- Bio-inspired optimization (e.g., PSO, GA, ACO) was underutilized, and scalability was limited.
Conclusions:
- There is a significant opportunity for Bio-Inspired Educational Analytics.
- Future work should focus on scalable feature selection, efficient hyperparameter optimization, and multi-objective metaheuristics.
- Developing accurate, interpretable, fair, and transferable graduation prediction systems is essential.