Educational Data Mining in Online Learning: Data Mining Techniques and Algorithms, Factors, Equity and Accessibility Dimensions (A Systematic Literature Review)
DOI:
https://doi.org/10.70609/g-tech.v10i1.8628Keywords:
Educational Data Mining, Online Learning, Learning Analytics, E-Learning, Systematic Literature ReviewAbstract
This Systematic Literature Review (SLR) examines the application of Educational Data Mining (EDM) in online learning from 2015 to 2025 using the PRISMA approach. Thirty-two studies were analyzed to identify the data mining techniques used, the factors analyzed, and the extent to which the literature considers the equity and accessibility dimensions. The review results indicate that EDM is widely applied to predict academic performance, identify learning behavior patterns, detect at-risk students, and analyze the use of learning resources. The dominant techniques include classification, prediction, sequence analysis, process mining, and clustering. However, the equity and accessibility aspects are rarely discussed explicitly most studies only implicitly address accessibility through digital interaction behavior, while social factors related to equity, such as learning readiness, environmental support, and the digital divide, appear in only a small proportion. Furthermore, the variety of data formats and limited course coverage limit the generalizability of the findings. Overall, this study emphasizes the need for stronger integration between educational analytics and the social dimension for EDM to more effectively support equitable distribution of quality and access to online learning.
References
Akçapinar, G., Altun, A., & Aşkar, P. (2015). Öğrencilerin Akademik Performanslarının Çevrimiçi Öğrenme Ortamındaki Etkileşim Verilerine Göre Modellenmesi. Elementary Education Online, 14(3), 815–824. https://doi.org/10.17051/io.2015.03160
Akcapinar, G., & Bayazit, A. (2018). Investigating video viewing behaviors of students with different learning approaches using video analytics. Turkish Online Journal of Distance Education, 19(4), 116–125. https://doi.org/10.17718/tojde.471907
Akçapınar, G., Altun, A., & Aşkar, P. (2019). Using learning analytics to develop early-warning system for at-risk students. International Journal of Educational Technology in Higher Education, 16(1). https://doi.org/10.1186/s41239-019-0172-z
Akila Devi, T. R., Javubar Sathick, K., Abdul Azeez Khan, A., & Arun Raj, L. (2023). Novel Framework for Improving the Correctness of Reference Answers to Enhance Results of ASAG Systems. SN Computer Science, 4(4). https://doi.org/10.1007/s42979-023-01682-8
Angeioplastis, A., Aliprantis, J., Konstantakis, M., & Tsimpiris, A. (2025). Predicting Student Performance and Enhancing Learning Outcomes: A Data-Driven Approach Using Educational Data Mining Techniques. Computers, 14(3). https://doi.org/10.3390/computers14030083
Araka, E., Oboko, R., Maina, E., & Gitonga, R. (2022). Using Educational Data Mining Techniques to Identify Profiles in Self-Regulated Learning: An Empirical Evaluation. International Review of Research in Open and Distributed Learning, 23(1), 131–162. https://doi.org/10.19173/IRRODL.V22I4.5401
Azizi, H., Amini, M. S., Sulaimany, S., & Mafakheri, A. (2025). Visibility graph analysis for educational data: potentials and a case study of predicting at-risk online students. Scientific Reports, 15(1). https://doi.org/10.1038/s41598-025-17760-1
Behaz, A., Moumen, H., & Nouari, W. (2023). Trace Analysis of Educational Videos to Identify the Groups of Learners with Similar Profiles. Ingenierie Des Systemes d’Information, 28(1), 97–104. https://doi.org/10.18280/isi.280110
Du, J., Song, Y., An, M., An, M., Bogart, C., & Sakr, M. (2022). Cheating Detection in Online Assessments via Timeline Analysis. SIGCSE 2022 - Proceedings of the 53rd ACM Technical Symposium on Computer Science Education, 1, 98–104. https://doi.org/10.1145/3478431.3499368
Feng, S., & Law, N. (2021). Mapping Artificial Intelligence in Education Research: a Network‐based Keyword Analysis. International Journal of Artificial Intelligence in Education, 31(2), 277–303. https://doi.org/10.1007/s40593-021-00244-4
Hayadi, B. H., & Hariguna, T. (2025). Predictive Analytics in Mobile Education: Evaluating Logistic Regression, Random Forest, and Gradient Boosting for Course Completion Forecasting. International Journal of Interactive Mobile Technologies , 19(5), 210–232. https://doi.org/10.3991/ijim.v19i05.52381
Leppänen, L., Leinonen, J., Ihantola, P., & Hellas, A. (2017). Predicting academic success based on learning material usage. SIGITE 2017 - Proceedings of the 18th Annual Conference on Information Technology Education, 13–18. https://doi.org/10.1145/3125659.3125695
Li, C., & Yin, W. (2025). Interactive online learning method for students based on artificial intelligence. Discover Artificial Intelligence, 5(1). https://doi.org/10.1007/s44163-025-00401-x
Li, X., Zhu, X., Zhu, X., Ji, Y., & Tang, X. (2020). Student Academic Performance Prediction Using Deep Multi-source Behavior Sequential Network. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12084 LNAI, 567–579. https://doi.org/10.1007/978-3-030-47426-3_44
López-Pernas, S., & Saqr, M. (2024). The Why, the How and the When of Educational Process Mining in R. In Learning Analytics Methods and Tutorials: A Practical Guide Using R (pp. 467–488). Springer Nature. https://doi.org/10.1007/978-3-031-54464-4_14
López-Pernas, S., Saqr, M., Conde, J., & Del-Río-Carazo, L. (2024). A Broad Collection of Datasets for Educational Research Training and Application. In Learning Analytics Methods and Tutorials: A Practical Guide Using R (pp. 17–66). Springer Nature. https://doi.org/10.1007/978-3-031-54464-4_2
Mai, T. T., Bezbradica, M., & Crane, M. (2022). Learning behaviours data in programming education: Community analysis and outcome prediction with cleaned data. Future Generation Computer Systems, 127, 42–55. https://doi.org/10.1016/j.future.2021.08.026
Mai, T. T., Crane, M., & Bezbradica, M. (2021). Students’ Behaviours in using Learning Resources in Higher Education: How do behaviours reflect success in Programming Education? International Conference on Higher Education Advances, 47–55. https://doi.org/10.4995/HEAd21.2021.12939
Mercado, J., Mendoza-Cardenas, C., Fletscher, L., & Gaviria-Gomez, N. (2025). Learning Analytics in a Non-Linear Virtual Course. Algorithms, 18(5). https://doi.org/10.3390/a18050284
Nacu, D., Martin, C. K., Schutzenhofer, M., & Pinkard, N. (2016). Beyond traditional metrics: Using automated log coding to understand 21st century learning online. L@S 2016 - Proceedings of the 3rd 2016 ACM Conference on Learning at Scale, 197–200. https://doi.org/10.1145/2876034.2893413
Nuankaew, W. S., & Nuankaew, P. (2023). Predictive Model for Clustering Learning Outcomes Affected by COVID-19 Using Ensemble Learning Techniques. International Journal of Educational Methodology, 9(2), 297–307. https://doi.org/10.12973/ijem.9.2.297
Ordoñez-Avila, R., Meza, J., & Ventura, S. (2025). Mining autonomous student patterns score on LMS within online higher education. PeerJ Computer Science, 11. https://doi.org/10.7717/peerj-cs.2855
Rubio, M. A. (2020). Automated Prediction of Novice Programmer Performance Using Programming Trajectories. In I. I. Bittencourt, M. Cukurova, R. Luckin, K. Muldner, & E. Millán (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Vol. 12164 LNAI (pp. 268–272). Springer. https://doi.org/10.1007/978-3-030-52240-7_49
Ruipérez-Valiente, J. A., Gašević, D., Joksimović, S., Kovanović, V., Muñoz-Merino, P. J., & Kloos, C. D. (2017). A data-driven method for the detection of close submitters in online learning environments. 26th International World Wide Web Conference 2017, WWW 2017 Companion, 361–368. https://doi.org/10.1145/3041021.3054161
Ruipérez-Valiente, J. A., Jaramillo-Morillo, D., Joksimović, S., Kovanović, V., Muñoz-Merino, P. J., & Gašević, D. (2021). Data-driven detection and characterization of communities of accounts collaborating in MOOCs. Future Generation Computer Systems, 125, 590–603. https://doi.org/10.1016/j.future.2021.07.003
Sein Minn. (2022). AI-assisted knowledge assessment techniques for adaptive learning environments. Computers and Education: Artificial Intelligence, 3. https://doi.org/10.1016/j.caeai.2022.100050
Spitzer, M. W. H., & Moeller, K. (2022). Predicting fraction and algebra achievements online: A large-scale longitudinal study using data from an online learning environment. Journal of Computer Assisted Learning, 38(6), 1797–1806. https://doi.org/10.1111/jcal.12721
Tan, T. K., & Samavedham, L. (2022). The learning process matter: A sequence analysis perspective of examining procrastination using learning management system. Computers and Education Open, 3. https://doi.org/10.1016/j.caeo.2022.100112
Wang, J., & Yu, Y. (2025). Machine learning approach to student performance prediction of online learning. PLoS ONE, 20(1). https://doi.org/10.1371/journal.pone.0299018
Wen, Y., Tian, Y., Wen, B., Zhou, Q., Cai, G., & Liu, S. (2020). Consideration of the local correlation of learning behaviors to predict dropouts from MOOCs. Tsinghua Science and Technology, 25(3), 336–347. https://doi.org/10.26599/TST.2019.9010013
Wu, S. (2025). Application of artificial intelligence graph convolutional network in classroom grade evaluation. Scientific Reports, 15(1). https://doi.org/10.1038/s41598-025-17903-4
Xu, T. (2023). Using Data Mining Models To Predict Students’ Academic Performance Before the Online Course Start. Journal of Educators Online, 20(1). https://doi.org/10.9743/JEO.2023.20.1.15
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Imalatul Hidayah, Ririen Kusumawati
This work is licensed under a Creative Commons Attribution 4.0 International License.
