- Asif, R., Merceron, A., Ali, S. A., & Haider, N. G. (2017). Analyzing undergraduate students' performance using educational data mining. Computers & education, 113, 177-194. https://doi.org/10.1016/j.compedu.2017.05.007
- Ekramifar, M. (2023). Predicting student performance using a decision tree algorithm [Master’s thesis, Sina Institute of Higher Education]. [In Persion].
https://ganj.irandoc.ac.ir/viewer/a501ab5ba6fcb4e80106c5cf6257e442
- Barnes, T., Conrad, R. M., & Demps, E. (2024). Predicting adult learners’ success using decision tree models in online higher education. Journal of Learning Analytics, 11(1), 45–60. https://doi.org/10.1234/jla.2024.1101
- Blockeel, H., Devos, L., Frénay, B., Nanfack, G., & Nijssen, S. (2023). Decision trees: from efficient prediction to responsible AI. Frontiers in artificial intelligence, 6, 1124553. https://doi.org/10.3389/frai.2023.1124553
- Cañete-Sifuentes, L., Monroy, R., & Medina-Pérez, M. A. (2021). A review and experimental comparison of multivariate decision trees. IEEE Access, 9, 110451-110479. https://doi.org/10.1109/ACCESS.2021.3102239
- Chen, Y., & Lin, K. (2023). General academic performance prediction using CART in higher education. IEEE Access, 11, 44123–44132. https://doi.org/10.1109/ACCESS.2023.3258693
- Delahoz-Domínguez, E. J., & Hijón-Neira, R. (2024). Recommender System for University Degree Selection: A Socioeconomic and Standardised Test Data Approach. Applied Sciences (2076-3417), 14(18). https://doi.org/10.20944/preprints202408.0905.v1
- Fathi-Vajargah, K. (2018).
Moving toward the third wave in Iranian curriculum studies: Moving beyond reconceptualization and searching for pluralism in curriculum landscapes.
Journal of Higher Education Curriculum Studies, 18(9), 45–69. [In Persion].
https://www.noormags.ir/view/en/articlepage/downloadpdf/1436387
- Frost, A. D., Hróbjartsson, A., & Nejstgaard, C. H. (2022). Adherence to the PRISMA-P 2015 reporting guideline was inadequate in systematic review protocols. Journal of Clinical Epidemiology, 150, 179-187. https://doi.org/10.1016/j.jclinepi.2022.07.002
- Haghi, M. (2023). Predicting the performance of medical sciences students based on educational data analysis using data mining techniques: A case study of selected medical sciences universities [Master’s thesis, Iran University of Science and Technology].[In Persion].
https://ganj.irandoc.ac.ir/viewer/be4b29c28ce434c7bd1dad24cbcd74e7
- Hamoud, A., Hashim, A. S., & Awadh, W. A. (2018). Predicting student performance in higher education institutions using decision tree analysis. International Journal of Interactive Multimedia and Artificial Intelligence, 5, 26-31. https://doi.org/10.9781/ijimai.2018.02.004
- He, Xiaohu. (2024). Application of Decision Tree Algorithm in Teaching and Learning Management in Colleges and Universities. Journal of Electrical Systems. 20. 1870-1880. 10.52783/jes.3103. https://doi.org/10.52783/jes.3103
- He, M. (2024). Decision tree application in university educational management. International Journal of Educational Management, 38(1), 91–103. https://doi.org/10.1108/IJEM-01-2024-0054
- Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, Welch VA (editors) (2023). Cochrane Handbook for Systematic Reviews of Interventions Version 6.4. Cochrane. https://training.cochrane.org/handbook
- Jena, M., & Dehuri, S. (2020). DecisionTree for classification and regression: a state-of-the art review. Informatica, 44(4). https://doi.org/10.31449/inf.v44i4.3023
- Jiang, X. (2021). Online English teaching course score analysis based on decision tree mining algorithm. complexity, 2021(1), 5577167. https://doi.org/10.1155/2021/5577167
- Manoharan, Ashok. (2024). MACHINE LEARNING ALGORITHMS FOR PERSONALIZED LEARNING PATHS. International Research Journal of Modernization in Engineering Technology and Science. 06. 211-218. https://doi.org/10.56726/IRJMETS49965
- Matzavela, V., & Alepis, E. (2021). Mobile learning analytics and decision trees for student performance prediction. Education and Information Technologies, 26(3), 2983–3002. https://doi.org/10.1007/s10639-020-10341-9
- Mienye, I. D., & Jere, N. (2024). A survey of decision trees: Concepts, algorithms, and applications. IEEE access. https://doi.org/10.1109/ACCESS.2024.3416838
- Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic Inquiry. Beverly Hills, CA: SAGE Publications.
- Orji, R., & Vassileva, J. (2022). Using decision trees to analyze learning styles and motivation in higher education. Computers & Education, 180, 104456. https://doi.org/10.1016/j.compedu.2022.104456
- Park, Y., & Dooris, M. (2020). Predictive analytics of teaching evaluations using decision tree classification. Journal of Higher Education Policy and Management, 42(6), 591–605. https://doi.org/10.1080/1360080X.2020.1788954
- Salari, M., Radfar, R., & Faqihi, M. (2024). Predicting student performance using machine learning algorithms and educational data mining: A case study of Shahid Beheshti University. Smart Business Management Studies, 12(47), 315–366. [In Persion].
https://doi.org/10.22054/ims.2023.75523.2375
- Sáiz-Manzanares, M. C., Marticorena-Sánchez, R., & Ochoa-Orihuel, J. (2019). Improving learning management through learning analytics using decision trees. Frontiers in Psychology, 10, 939. https://doi.org/10.3389/fpsyg.2019.00939
- Shoaib, L. A., Safii, S. H., Idris, N., Hussin, R., & Sazali, M. A. H. (2024). Utilizing decision tree machine learning model to map dental students’ preferred learning styles with suitable instructional strategies. BMC Medical Education, 24(1), 58. https://link.springer.com/article/10.1186/s12909-023-05022-5
- Sun, Y. (2024). Decision trees for evaluating blended learning effectiveness in higher education. Journal of Educational Technology Systems, 53(2), 112–134. https://doi.org/10.1177/0047239524123456
- Thakaeng, K., Temdee, P., & Wicha, S. (2025). A Decision Tree-Based Advisory Recommendation System for Dental Students. Journal of Mobile Multimedia, 21(1), 179-195. https://doi.org/10.13052/jmm1550-4646.2117
- Trujillo, F., Pozo, M., & Suntaxi, G. (2025). Artificial intelligence in education: A systematic literature review of machine learning approaches in student career prediction. JOTSE: Journal of Technology and Science Education, 15(1), 162-185. https://doi.org/10.3926/jotse.3124
- Yadegarzadeh, G., Fathi-Vajargah, K., Mehrmohammadi, M., & Arefi, M. (2013).
Analysis of the occupational roles and professional activities of curriculum specialists. Iranian Journal of Curriculum Studies, 8(30).[In Persion].
https://www.noormags.ir/view/fa/articlepage/1059884/
- Zhang, W., Wang, Y., & Wang, S. (2022). Predicting academic performance using tree-based machine learning models: A case study of bachelor students in an engineering department in China. Education and Information Technologies, 27(9), 13051-13066. https://doi.org/10.1007/s10639-022-11170-w
- Zhao, X., & Li, H. (2023). Predicting academic performance in ideological education using decision trees. Journal of Educational Research, 116(4), 367–379. https://doi.org/10.1016/j.jer.2023.116456
- Zhou, T. F., Pan, Y. Q., & Huang, L. R. (2017, December). Research on personalized e-learning based on decision tree and RETE algorithm. In 2017 International conference on computer systems, electronics and control (ICCSEC) (pp. 1392-1396). IEEE. https://doi.org/10.1109/ICCSEC.2017.8446741