Actionable Learning Analytics: Predicting University Performance Levels with Interpretable Machine Learning

Higher education institutions need timely, explainable tools to identify students at risk of low performance on large-scale examinations and to guide targeted academic support strategies. In response to this challenge, this study proposes an explainable machine learning framework to predict undergraduate students' performance levels in Colombia's SABER PRO examination. Using student background variables (e.g., gender, region, school type, parental education, and occupation) and SABER 11 standardised test scores (Critical Reading, Mathematics, Citizenship Skills, Science, and English), we formulate a binary classification problem that distinguishes desirable outcomes (levels 3–4) from non-desirable outcomes (levels 1–2). We benchmark baseline models against non-linear learners, including XGBoost, GLMNET, SVM, DT, and LDA, using a 10-fold cross-validation protocol with systematic hyperparameter tuning. Model performance is assessed through confusion matrices and AUC scores. To support educational decision-making, we complement predictive results with explainability analyses, including global feature importance and individual-level explanations via SHAP, enabling transparent identification of the key drivers behind performance levels. The proposed approach provides actionable learning analytics to guide early academic support, promote responsible and transparent educational decision-making, and improve the likelihood of desirable SABER PRO achievement.