International Journal of
Information and Education Technology

Abstract

The choice of a paramedical specialization suited to students’ academic profiles and preferences is a challenge for Moroccan students. To respond to this challenge, this study evaluated seven tree-based algorithms on real data from 889 nursing students from two Moroccan training institutes to determine the best-performing model to predict the students’ paramedical specialization. We implemented Bayesian hyperparameter optimization with Optuna, stratified 5-fold cross-validation, and final evaluation on an independent test set to ensure a robust and reliable evaluation of the models. The resampling techniques (SMOTE, SMOTEENN, ADASYN, SMOTETomek) were applied only in the training folds to avoid data leakage. Ensemble models (Random Forest, Extra Trees, XGBoost, Gradient Boosting, HistGradient Boosting) surpassed the baseline model (Decision Tree), while AdaBoost’s performance remained weak and unstable in cross-validation and on the test set (test macro F1-score as low as 0.1384), confirming its strong sensitivity to resampling-induced noise. On the test set, Random Forest with SMOTEENN achieved the best overall performance (macro F1-score = 0.8836, macro Receiver Operating Characteristic curve and Area Under the Curve (ROC-AUC) = 0.9879, macro Precision-Recall Area Under the Curve (PR-AUC) = 0.9294), whereas XGBoost with SMOTE delivered near-identical predictive performance (macro F1-score = 0.8828) with a faster inference time (0.0060 s), offering the best deployment trade-off. By integrating these two optimal approaches, an orientation system can accurately predict the specialization best suited to a student’s profile and preferences.