Predicting University Entrance Examination Ranks by Developing a Stacking-Based Ensemble Machine Learning Algorithm

Articles in Press

Document Type : Research Paper

Authors

1 Prof., Department of Industrial Management, Faculty of Management, University of Tehran, Tehran, Iran.

2 Ph.D. Candidate, Department of Industrial Management, Faculty of Management, University of Tehran, Tehran, Iran.

10.22059/ijms.2025.385883.677186

Abstract

A key issue that is important for planning and consulting is the accurate prediction of students’ rankings in important national university entrance exams such as Iran’s nationwide university entrance examination, commonly known as the Konkur. Although machine learning has been increasingly used in educational data mining, most existing models have shown limited accuracy, are inadequately formulated, and lack sufficient optimization for practical application. This study introduces a novel stacking-based ensemble learning model that incorporates XGBoost, LightGBM, and CatBoost as base learners, with a linear regression model as a meta-learner to improve national rank prediction. The proposed model’s important hyperparameters were tuned using the Optuna optimization framework to enhance the performance of each model. The model was trained and validated on a large dataset of over 73,000 student records from Ghalamchi and evaluated using five-fold cross-validation with NRMSE and R² as performance measures. The results showed that the proposed model significantly outperformed baseline models such as Random Forest, Gradient Boosting, and MLP Regressor, achieving NRMSE of 0.0659 and R² of 0.7735. The effective integration of advanced learners with systematic hyperparameter optimization led to this improved performance.

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Interdisciplinary Journal of Management Studies

Articles in Press, Accepted Manuscript
Available Online from 20 October 2025

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