Imbalanced Multi-class Prediction of Student Drop-out and Graduation: A Systematic Literature Review

Student study status prediction, including drop-out and graduation, is a widely studied topic in higher education. Yet, evidence across studies remains difficult to compare due to differences in targets, imbalance treatment, metrics, and validation strategies. This systematic literature review synthesizes 70 peer‑reviewed articles published between 2017 and 2025 that apply machine learning or deep learning to predict study outcomes under class imbalance. Results reveal a strong dominance of binary targets, while multi‑class experiments are relatively rare, though they better reflect institutional categories and expose larger performance gaps across classes. Reported imbalance handling includes data‑level resampling, algorithm‑level class weighting, and ensemble or hybrid designs, but many studies lack sufficient procedural detail. Evaluation practices vary considerably; studies reporting per-class measures and imbalance-aware metrics, such as macro F1 and balanced accuracy, provide more decision-relevant evidence than those relying mainly on accuracy. Validation strategies range from hold‑out and stratified cross‑validation to nested validation, temporal splits, and external testing, shaping the credibility of reported performance for deployment. We propose an integrative taxonomy linking target formulation, imbalance degree, handling strategy, and evaluation design to enhance intervention efficiency through capacity‑aware prioritization, while strengthening responsibility through transparent reporting, defensible validation, and explicit attention to minority class performance.