BAYESIAN MODELING OF STUDENT ACADEMIC PERFORMANCE: EVALUATING MOTIVATIONAL FACTORS
Received: 05th May 2026, Revised: 11th May 2026, Accepted: 08th June 2026, Date of Publication: 30th July 2026
DOI:
https://doi.org/10.20319/dv.10.4351Keywords:
Bayesian Modeling, Educational Data Mining, Student Success Prediction, Age-Stratified Analysis, Chinese Language Learning, Attendance Tracking, Predictive Analytics, Online EducationAbstract
Over the past 10 years, according to source [1], most of the supplementary education in the CIS countries and around the world has been moving to online platforms. Many private schools and public institutions are interested in creating a tool that not only tracks student satisfaction and progress but also predicts their academic performance and success in completing courses. In this regard, they collect information about students, their academic and non-academic activities, assessment results, teacher activity, and the performance of other staff. This paper proposes a method based on working with students of various ages studying Chinese, using Bayesian modeling to analyze their productivity and success during the learning process. The main attempt of this paper is to analyze and predict learners’ performance through online conferences to better understand their marks and predict students’ success. The results of this study are going to be presented for international language school and adopted through the usage of methodology.
References
Priyanka, Gupta, A., & Kohli, S. (2021). Supplementary education: A global perspective. Central University of Jammu.
Saeedi, S., Božanić, D., & Safa, R. (2024). Strategic analytics for predicting students' academic performance using cluster analysis and Bayesian networks. Education Science and Management, 2(4), 197–214.
https://doi.org/10.56578/esm020402
Deviatiarova, E., Fadeev, S., & Dukhanov, A. (2023). Analyzing proficiency patterns in test results using clustering and augmentation algorithms. Procedia Computer Science, 229, 254–264.
https://doi.org/10.1016/j.procs.2023.12.027
Sundar, P. V. P. (2013). A comparative study for predicting student's academic performance using Bayesian network classifiers. IOSR Journal of Engineering, 3(2), 37–42.
https://doi.org/10.9790/3021-03213742
Nudelman, Z., Moodley, D., & Berman, S. (2019). Using Bayesian networks and machine learning to predict computer science success. In International Conference on Artificial Intelligence in Education (pp. 14-22). Springer.
https://doi.org/10.1007/978-3-030-05813-5_14
Osmanbegović, E., & Suljić, M. (2012). Data mining approach for predicting student performance. Journal of Economics and Business, 10(1), 3-12.
Liang, F. (2005). Bayesian neural networks for non-linear time series forecasting. Statistics and Computing, 15, 13–29.
https://doi.org/10.1007/s11222-005-4786-8
Xing, W., Guo, R., Petakovic, E., & Goggins, S. (2015). Participation-based student final performance prediction model through interpretable genetic programming: Integrating learning analytics, educational data mining and theory. Computers in Human Behavior, 47, 168–181.
https://doi.org/10.1016/j.chb.2014.09.034
Heckerman, D. (2008). A tutorial on learning with Bayesian networks. In Innovations in Bayesian networks (pp. 33-82). Springer.
https://doi.org/10.1007/978-3-540-78666-7_2
Pearl, J. (2009). Causality: Models, reasoning, and inference (2nd ed.). Cambridge University Press.
Murphy, K. P. (2012). Machine learning: A probabilistic perspective. MIT Press.
Bishop, C. M. (2006). Pattern recognition and machine learning. Springer.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
