A Machine-Learning Approach To Predictive Quality Assurance And Accreditation Monitoring In Nigerian Universities

Programme accreditation by the National Universities Commission (NUC) is the main external quality assurance mechanism in Nigerian universities, but it is periodic and retrospective: weaknesses are usually discovered during the visit rather than before it. This study aimed to develop and evaluate a machine-learning (ML) framework that predicts accreditation outcomes from routinely collected pre-visit indicators so that institutions can remediate deficiencies early. Because programme-level accreditation records are not publicly available, we built a reproducible synthetic panel of 4,321 accreditation visits to 1,515 programmes in 149 simulated universities (2014–2024), with outcomes (full, interim, denied) generated by NUC-style scoring rules. Fifteen indicators covering staffing, curriculum, facilities, library, funding, research and employer rating, together with ownership, discipline and prior status, were used as predictors. Logistic regression (LR), support vector machine, multilayer perceptron, random forest and extreme gradient boosting (XGBoost) were trained on 2014–2021 visits with university-grouped cross-validation and tested on 2022–2024 visits. LR performed best on the temporal test set (accuracy 0.773; macro-F1 0.708, 95% CI 0.669–0.744; area under the curve [AUC] 0.908), exceeding a prior-status rule (macro-F1 0.537). For the binary task of identifying programmes at risk of not receiving full accreditation, LR achieved an AUC of 0.900 and good calibration; the 20% of programmes with the highest predicted risk included 52 of the 53 denied programmes. Proportion of PhD-holding staff and laboratory provision were the most influential predictors. ML-based risk scoring could support continuous, pre-emptive quality monitoring, but validation on real NUC data is required before operational use.