From Features to Fairness: A Thematic Review of Optimized Machine Learning for Student Placement

Ridwan Kamaldeen, Badamasi Imam Ya’u, Kabiru Musa Ibrahim

Abstract


Machine learning (ML) is increasingly deployed in academic placement and student transition prediction, yet adoption remains constrained by persistent challenges: high-dimensional and redundant feature spaces, opaque model behaviour, and the frequent absence of disciplined baseline-versus-optimized comparisons. This review synthesizes theoretical foundations and empirical evidence across ML-based educational prediction, with focused attention on feature selection strategies, explainable artificial intelligence (XAI), and the inherent trade-off between predictive accuracy and interpretability. The paper further identifies critical open challenges, including limited dataset generalizability, class imbalance, algorithmic fairness, and the absence of standardized evaluation frameworks, and concludes by proposing actionable directions for future inquiry. 


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