Analysis of Academic Performance using Learning Management System (LMS) Data and Bayesian Network
DOI:
https://doi.org/10.37134/jsml.vol14.2.2.2026Keywords:
Bayesian Network, Learning Management System (LMS), Learning Analytics, Academic Performance , Mathematics EducationAbstract
Learning Management Systems (LMS) have become integral tools in higher education, generating vast amounts of data that can be leveraged to analyze and enhance academic performance. Despite the abundance of this data, effectively harnessing it to understand complex relationships between learning activities and student outcomes remains a challenge. This paper explores the application of Bayesian Network (BN), a powerful technique in Educational Data Mining (EDM) to model and predict student outcomes using LMS data. BN provides a probabilistic framework to explore how various learning analytics variables influence academic success. Using LMS data from an online undergraduate Mathematics course, the model investigated the impact of student engagement, resource utilization, and participation on exam grades. The results show that consistent attendance (88%), active participation in lecturing sessions (85%), and involvement in online mathematical laboratory activities (62%), despite lower engagement in other areas such as assessments and gamification, are strongly associated with favourable final exam outcomes (62% achieving ‘Good’ or ‘Excellent’ grades). Numerical simulations were conducted to explore future student outcomes by manipulating key variables, demonstrating the potential of improved learning strategies such as full participation, improved prior knowledge and complete utilization of digital resources. This study highlights the utility of BN in analyzing LMS data to inform educational practices and ultimately enhance academic performance in higher education.
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References
Alalawi K, Athauda R, Chiong R. (2025). An extended learning analytics framework integrating machine learning and pedagogical approaches for student performance prediction and intervention. International Journal of Artificial Intelligence in Education, 35, 1239-1287. doi:10.1007/s40593-024-00429-7
Alier M, Pereira J, García-Peñalvo FJ, Casañ MJ, Cabré J. (2025). LAMB: An open-source software framework to create artificial intelligence assistants deployed and integrated into learning management systems. Computer Standards & Interfaces, 92, 103940. doi:10.1016/j.csi.2024.103940
Aljaloud AS, Uliyan DM, Alkhalil A, Abd Elrhman M, Alogali AF, Altameemi YM, Altamimi M, Kwan P. (2022). A deep learning model to predict Student learning outcomes in LMS using CNN and LSTM. IEEE Access, 10, 85255-85265. doi:10.1109/ACCESS.2022.3196784
Baker RSJD (2010). Data mining for education. International Encyclopedia of Education, 7(3), 112-118.
Cantabella M, Martínez-España R, Ayuso B, Yáñez JA, Muñoz A. (2019). Analysis of student behavior in learning management systems through a big data framework. Future Generation Computer Systems, 90, 262-272. doi:10.1016/j.future.2018.08.003
Chen J, Feng J, Hu J, Sun X. (2019). Causal analysis of learning performance based on Bayesian network and mutual information. Entropy, 21(11), 1102. doi:10.3390/e21111102
Cheng L, Guo R, Moraffah R, Sheth P, Candan KS, Liu H. (2024). Evaluation methods and measures for causal learning algorithms. IEEE Transactions on Artificial Intelligence, 3(6), 924-943. doi:10.1109/TAI.2022.3150264
Conijn R, Snijders C, Kleingeld A, Matzat U. (2016). Predicting student performance from LMS data: A comparison of 17 blended courses using Moodle LMS. IEEE Transactions on Learning Technologies, 10(1), 17-29. doi:10.1109/TLT.2016.2616312
Culbertson MJ. (2016). Bayesian networks in educational assessment: The state of the field. Applied Psychological Measurement, 40(1), 3-21. doi:10.1177/0146621615590401
Daniel B. (2015). Big data and analytics in higher education: Opportunities and challenges. British Journal of Educational Technology, 46(5), 904-920. doi:10.1111/bjet.12230
de Carvalho WF, Zárate LE. (2020). A new local causal learning algorithm applied in learning analytics. The International Journal of Information and Learning Technology, 38(1), 103-115. doi:10.1108/IJILT-04-2020-0046
Elfeky AI, Najmi AH, Elbyaly MY. (2024). Effects of big data analytics in learning management systems for improving learners’ academic success. Profesional de la información, 33(1), e330016. doi:10.3145/epi.2024.0016
Ellis B, Wong WH. (2008). Learning causal Bayesian network structures from experimental data. Journal of the American Statistical Association, 103(482), 778-89. doi:10.1198/016214508000000193
Fan J. (2024). A big data and neural networks driven approach to design students management system. Soft Computing, 28(2), 1255-1276. doi:10.1007/s00500-023-09524-8
Genie Modeler. (2025). Pittsburgh, USA, BayesFusion, LLC; [5 Apr 2025]. https://www.bayesfusion.com/genie/
Guo X, Yu K, Liu L, Li J, Liang J, Cao F, Wu X. (2024). Progressive skeleton learning for effective local-to-global causal structure learning. IEEE Transactions on Knowledge and Data Engineering, 36, 9065-9079. doi:10.1109/TKDE.2024.3461832
Heckerman DA. (1998). Tutorial on learning with Bayesian networks. In M.I. Jordan (ed.) Learning in Graphical Models, Springer, Cambridge, UK, 301–354.
Jensen FV, Nielsen TD. (2007). Bayesian Networks and Decision Graphs. Springer Science & Business Media, New York.
Jung E. (2018). An Educational Data Mining with Bayesian Networks for Analyzing Variables Affecting Parental Attachment. In: Park, J., Park, DS., Jeong, YS., Pan, Y. (eds) Advances in Computer Science and Ubiquitous Computing. Lecture Notes in Electrical Engineering, 536, Springer, Singapore. doi:10.1007/978-981-13-9341-9_95
Käser T, Klingler S, Schwing AG, Gross M. (2017). Dynamic Bayesian networks for student modeling. IEEE Transactions on Learning Technologies, 10(4), 450-62. doi:10.1109/TLT.2017.2689017.
Kleimola R, Hirsto L, Ruokamo H. (2025). Promoting higher education students’ self-regulated learning through learning analytics: A qualitative study. Education and Information Technologies, 30(4), 4959-4986. doi:10.1007/s10639-024-12978-4
Koller D, Friedman N. (2009). Probabilistic Graphical Models: Principles and Techniques. MIT Press, Cambridge, MA.
Kondo N, Hatanaka T. (2018). An Approach for Estimation of Students’ Learning States Using Bayesian Network with LMS Log Data. Proceedings of 7th International Congress on Advanced Applied Informatics, Yonago, Japan. doi:10.1109/IIAI-AAI.2018.00102
Kondo, N, Hatanaka T. (2019). Estimation of students’ learning states using bayesian networks and log data of learning management system. International Journal of Institutional Research and Management, 3(2), 60-70. doi:10.52731/ijirm.v3.i2.460
Luan H, Tsai CC.(2021). A review of using machine learning approaches for precision education. Educational Technology & Society, 24(1), 250-266. doi:10.1111/bjet.13204
Maraza-Quispe B, Valderrama-Chauca E, Cari-Mogrovejo, L, Apaza-Huanca J. (2021). Predictive model of student academic performance from LMS data based on learning analytics. Proceedings of the 13th International Conference on Education Technology and Computers, Wuhan, China. doi:10.1145/3498765.3498768
Minn S, Shunkai F. (2023). Fast & Efficient Learning of Bayesian Networks from Data: Knowledge Discovery and Causality. Proceedings of IEEE International Conference on Data Mining Workshops, Shanghai, China. doi:10.1109/ICDMW60847.2023.00128.
Murphy KP. (2002). Dynamic Bayesian Networks: Representation, Inference and Learning. PhD Thesis, University of California, Berkeley, CA.
Mužinić M, Sikavica A, Zelić P, Grubišić A, Šarić-Grgić I. (2024). Predictive modeling of student performance in Moodle LMS using learning analytics. International Conference on Software, Telecommunications and Computer Networks, 1-7. doi:10.23919/SoftCOM62040.2024.10721846
Namoun A, Alshanqiti A. (2021). Predicting student performance using data mining and learning analytics techniques: A systematic literature review. Applied Sciences, 11(1), 237. doi.org/10.3390/app11010237
Naveh, G, Tubin, D, Pliskin, N. (2012). Student Satisfaction with Learning Management Systems: A Lens of Critical Success Factors. Technology, Pedagogy and Education, 21(3), 337-350. doi.org/10.1080/1475939X.2012.720413
Pearl J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Francisco.
Pérez-Suay A, Laparra V, Van Vaerenbergh S, Pascual-Venteo AB. (2024). Learning about student performance from Moodle logs in a higher education context with Gaussian Processes. IEEE Revista Iberoamericana de Tecnologias del Aprendizaje, 19, 169-175. doi:10.1109/RITA.2024.3465035
Qazdar A, Hasidi O, Qassimi S, Abdelwahed EH. (2023). Newly proposed student performance indicators based on learning analytics for continuous monitoring in learning management systems. International Journal of Online & Biomedical Engineering, 9(11). doi:10.3991/ijoe.v19i11.39471
Rienties B, Køhler Simonsen, H, Herodotou C. (2020). Defining the boundaries between artificial intelligence in education, computer-supported collaborative learning, educational data mining, and learning analytics: A need for coherence. Frontiers in Education, 5, 128. doi:10.3389/feduc.2020.00128
Riestra-González M, del Puerto Paule-Ruíz M, Ortin F. (2021). Massive LMS log data analysis for the early prediction of course-agnostic student performance. Computers & Education, 163, 104108. doi:10.1016/j.compedu.2020.104108
Rizwan S, Nee CK, Garfan S. (2025). Identifying the Factors Affecting Student Academic Performance and Engagement Prediction in MOOC using Deep Learning: A Systematic Literature Review. IEEE Access, 13, 18952-18982. doi:10.1109/ACCESS.2025.3533915
Romero C, Ventura S. (2010). Educational data mining: A review of the state of the art. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 40(6), 601-618. doi:10.1109/TSMCC.2010.2053532
Rosé C, Mclaughlin E, Liu R, Koedinger K. (2019). Explanatory learner models: Why machine learning (alone) is not the answer. British Journal of Educational Technology, 50(6), 2943-2958. doi:10.1111/bjet.12858.
Russell SJ, Norvig P. (2020). Artificial Intelligence: A Modern Approach. Pearson, New York.
Sharma A, Vatta S. (2013). Role of learning management systems in education. International Journal of Advanced Research in Computer Science and Software Engineering, 3(6), 997-1002.
Shaun R, De Baker J, Inventado PS. (2014). Educational data mining and learning analytics. Computational Science, 7, 1-16.
Shenoy PP, Shafer G. (1990). Axioms for probability and belief functions propagation. In Shachter R, Levitt T, Lemmer J, Kanal L. (eds.) Uncertainty in Artificial Intelligence. North-Holland, doi:10.1016/B978-0-444-88650-7.50019-6
Shoaib M, Sayed N, Singh J, Shafi J, Khan S, Ali F. (2024). AI student success predictor: Enhancing personalized learning in campus management systems. Computers in Human Behavior, 158, 108301. doi:10.1016/j.chb.2024.108301
Suwawi DDJ, Husni HJ, Laksitowening KA. (2021). Analysis of student performance based on LMS activities with learning analytics approach. JURIKOM (Jurnal Riset Komputer), 8(6), 409-418. doi:10.30865/jurikom.v8i6.3721
Wang C, Han D. (2016). Data Mining Technology Based on Bayesian Network Structure Applied in Learning. Advanced Science and Technology Letters, 137, 67-71. doi:10.14257/astl.2016.137.12
Wong ML, Lee SY, Leung KS. (2004). Data mining of Bayesian networks using cooperative coevolution. Decision Support Systems, 38(3), 451-472. doi:10.1016/S0167-9236(03)00115-5
Wu X, Zhong Y, Ling Z, Yang J, Li L, Sheng W, Jiang B. (2024). Nonlinear learning method for local causal structures. Information Sciences, 654, 119789. doi:10.1016/j.ins.2023.119789
Yang S, Wang H, Yu K, Cao F, Wu X. (2021). Towards efficient local causal structure learning. IEEE Transactions on Big Data, 8(6), 1592-1609. doi:10.1109/TBDATA.2021.3062937
You D, Wu H, Liu J, Yan H, Ma C, Chen Z, Wu X. (2024). Continuous causal structure learning from incremental instances and feature spaces. Information Fusion, 101, 101975. doi:10.1016/j.inffus.2023.101975
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