Explainable Machine Learning-Based Student Academic Performance Prediction

Student academic performance prediction can help educational institutions identify learners who may benefit from timely, targeted support.Machine-learning models can combine carefully selected indicators such as prior assessment results, attendance, assignment completion andlearning-platform engagement to estimate likely performance. However, a prediction that cannot be explained may be difficult for teachers andstudents to trust, and may conceal data quality problems or unfair patterns. This paper presents a conceptual framework for explainablemachine-learning-based student academic performance prediction. The proposed workflow includes data governance, preprocessing, featureengineering, baseline and advanced model comparison, interpretable explanations, fairness checks, and human-led intervention. Candidatemodels include logistic regression, decision trees, random forests and gradient-boosted trees, with SHAP- or permutation-based explanationswhere appropriate. Evaluation should use held-out data, class-sensitive metrics, calibration and subgroup analysis rather than accuracy alone.The framework is designed to support—not replace—professional educational judgement. No original dataset or experimental result is claimed;model performance must be established through a documented local study