Comparison of Logistic Regression and Random Forest Performance in Student Dropout Prediction based on Multi Source Data
Keywords:
Student Dropout Prediction, Machine Learning, Multi-Source Data, Random Forest, Logistic RegressionAbstract
The high rate of student dropouts is one of the important challenges in higher education because it can affect academic quality, learning effectiveness, and the performance of educational institutions. This condition encourages the need for a prediction system that is able to identify students at risk of dropouts early so that preventive measures can be taken appropriately. This study aims to compare the performance of Logistic Regression and Random Forest algorithms in predicting student dropout based on multi-source. The dataset consists of 4,424 student data with 34 attributes covering academic, demographic, socioeconomic, and academic administration aspects. The research stages include data preprocessing, target transformation into binary classification, feature scaling, data sharing using an 80:20 scheme, and handling class imbalances using the Synthetic Minority Oversampling Technique (SMOTE). Furthermore, a modeling process was carried out using Logistic Regression and Random Forest algorithms to predict the risk of student dropout. Model evaluation was carried out using accuracy, precision, recall, F1-score, and Area Under Curve Receiver Operating Characteristic (AUC-ROC). The results showed that Random Forest performed better than Logistic Regression with an accuracy of 0.884, precision of 0.842, recall of 0.785, F1-score of 0.812, and AUC-ROC of 0.930. Meanwhile, Logistic Regression obtained an accuracy of 0.871, precision of 0.780, recall of 0.835, F1-score of 0.806, and AUC-ROC of 0.928. These results show that Random Forest is more effective in handling complex relationships in multi-source data for student dropout predictions
References
[1] A. Igualde-Sáez et al., “University Student Dropout: A Longitudinal Dataset of Demographic, Socioeconomic, and Academic Indicators,” Data, vol. 10, no. 10, pp. 1–12, 2025, doi: 10.3390/data10100162.
[2] M. Vaarma and H. Li, “Predicting student dropouts with machine learning: An empirical study in Finnish higher education,” Technol. Soc., vol. 76, no. December 2023, p. 102474, 2024, doi: 10.1016/j.techsoc.2024.102474.
[3] F. da C. Silva, A. M. Santana, and R. M. Feitosa, “An Investigation Into Dropout Indicators in Secondary Technical Education Using Explainable Artificial Intelligence,” Rev. Iberoam. Tecnol. del Aprendiz., vol. 20, pp. 105–114, 2025, doi: 10.1109/RITA.2025.3566095.
[4] A. M. Rabelo and L. E. Zárate, “A model for predicting dropout of higher education students,” Data Sci. Manag., vol. 8, no. 1, pp. 72–85, 2025, doi: 10.1016/j.dsm.2024.07.001.
[5] N. T. Vo, Q. N. Le, and H. Le, “EAI Endorsed Transactions Artificial Intelligence-Driven Early Prediction of Student Dropout and Academic Outcomes in Higher Education : A Comparative Study of Advanced Machine Learning Approaches,” vol. 13, no. 1, pp. 1–10, 2026, doi: 10.4108/eetinis.131.11758.
[6] M. Segura, J. Mello, and A. Hernández, “Machine Learning Prediction of University Student Dropout: Does Preference Play a Key Role?,” Mathematics, vol. 10, no. 18, pp. 1–20, 2022, doi: 10.3390/math10183359.
[7] J. G. C. Krüger, A. de S. Britto, and J. P. Barddal, “An explainable machine learning approach for student dropout prediction,” Expert Syst. Appl., vol. 233, no. May, p. 120933, 2023, doi: 10.1016/j.eswa.2023.120933.
[8] F. T. Johora, M. N. Hasan, A. Rajbongshi, M. Ashrafuzzaman, and F. Akter, “An explainable AI-based approach for predicting undergraduate students academic performance,” Array, vol. 26, no. February, 2025, doi: 10.1016/j.array.2025.100384.
[9] A. Bettahi, F. Z. Belouadha, and H. Harroud, “A Modular and Explainable Machine Learning Pipeline for Student Dropout Prediction in Higher Education,” Algorithms, vol. 18, no. 10, pp. 1–31, 2025, doi: 10.3390/a18100662.
[10] C. H. Cho, Y. W. Yu, and H. G. Kim, “A Study on Dropout Prediction for University Students Using Machine Learning,” Appl. Sci., vol. 13, no. 21, 2023, doi: 10.3390/app132112004.
[11] A. Zanellati, S. P. Zingaro, and M. Gabbrielli, “Balancing Performance and Explainability in Academic Dropout Prediction,” IEEE Trans. Learn. Technol., vol. 17, pp. 2140–2153, 2024, doi: 10.1109/TLT.2024.3425959.
[12] V. Realinho, J. Machado, L. Baptista, and M. V. Martins, “Predicting Student Dropout and Academic Success,” Data, vol. 7, no. 11, 2022, doi: 10.3390/data7110146.
[13] P. D. Atika, “A Comparative Study of Machine Learning-Based Student Dropout Risk Prediction,” vol. 14, no. 225, pp. 167–174, 2026, doi: 10.33558/piksel.v14i1.12299.
[14] I. K. Nti and S. Ramanayake, “Explainable Machine Learning for Student Dropout Prediction and Tailored Interventions in Online Personalized Education,” 2025, [Online]. Available: https://www.researchsquare.com/article/rs-6615052/v1
[15] N. Mduma, “Data Balancing Techniques for Predicting Student Dropout Using Machine Learning,” Data, vol. 8, no. 3, pp. 0–14, 2023, doi: 10.3390/data8030049.
[16] G. A. Borges, C. Filipa Dos Santos Pedro, J. Cesar Santos Dos Anjos, A. Rodrigues, F. Boavida, and J. Sá Silva, “A Platform for Early Class Dropout Prediction of University Students,” IEEE Access, vol. 13, no. July, pp. 109116–109133, 2025, doi: 10.1109/ACCESS.2025.3581751.
[17] J. O. Q. Quispe, O. C. Toledo, M. C. Toledo, E. E. C. Llatasi, and E. M. R. Saira, “Early Prediction of University Student Dropout Using Machine Learning Models,” Nanotechnol. Perceptions, vol. 20, no. S5, pp. 659–669, 2024, doi: 10.62441/nano-ntp.v20iS5.62.
[18] B. Bennemann, B. Schwartz, J. Giesemann, and W. Lutz, “Predicting patients who will drop out of out-patient psychotherapy using machine learning algorithms,” Br. J. Psychiatry, vol. 220, no. 4, pp. 192–201, 2022, doi: 10.1192/bjp.2022.17.
[19] A. Balboa, A. Cuesta, J. González-Villa, G. Ortiz, and D. Alvear, “Logistic regression vs machine learning to predict evacuation decisions in fire alarm situations,” Saf. Sci., vol. 174, no. February, 2024, doi: 10.1016/j.ssci.2024.106485.
[20] I. Kurt, M. Ture, and A. T. Kurum, “Comparing performances of logistic regression, classification and regression tree, and neural networks for predicting coronary artery disease,” Expert Syst. Appl., vol. 34, no. 1, pp. 366–374, 2008, doi: 10.1016/j.eswa.2006.09.004.
[21] A. S. Aldila et al., “EVALUATING LOGISTIC REGRESSION , SVM , KNN , AND ENSEMBLE MODELS FOR ACCURATE HEART DISEASE RISK PREDICTION,” vol. 11, no. 3, pp. 949–957, 2026, doi: 10.33480/jitk.v11i3.6738.EVALUATING.
[22] D. Opazo, S. Moreno, E. Álvarez-Miranda, and J. Pereira, “Analysis of first-year university student dropout through machine learning models: A comparison between universities,” Mathematics, vol. 9, no. 20, pp. 1–27, 2021, doi: 10.3390/math9202599.
[23] E. Melo, I. Silva, D. G. Costa, C. M. D. Viegas, and T. M. Barros, “On the Use of eXplainable Artificial Intelligence to Evaluate School Dropout,” Educ. Sci., vol. 12, no. 12, 2022, doi: 10.3390/educsci12120845.
[24] A. K. Sharma, L. H. Li, and R. Ahmad, Default Risk Prediction Using Random Forest and XGBoosting Classifier, vol. 314. Springer International Publishing, 2023. doi: 10.1007/978-3-031-05491-4_10.
