EDUCATIONAL DATA MINING TECHNOLOGY AND ITS USE IN THE DIGITAL TRANSFORMATION OF ACADEMIC INSTITUTIONS
DOI:
https://doi.org/10.32782/2786-8273/2025-10-5Keywords:
Educational Data Mining, learning analytics, digital transformation, personalization, higher education, academic performance, BI platformsAbstract
Introduction. The rapid digitalization of higher education produces vast data flows in Learning Management Systems (LMS), where each student’s activity leaves a trace. Yet the presence of data alone does not ensure effective decision-making. Many institutions still rely on intuition and fragmented observations, limiting opportunities for personalization and quality improvement. Educational Data Mining (EDM) transforms raw data into actionable knowledge, supporting a systematic and evidence-based approach to educational management. Purpose. The article explores EDM as a driver of digital transformation in academic institutions, emphasizing its essence, methodological foundations, software tools, and applications in teaching, learning, and governance. Methods. The study employs a systematic literature review, comparative analysis of EDM methods (classification, clustering, association rules, social network analysis, sequential pattern mining), and evaluation of software tools, ranging from LMS analytics and specialized research platforms to BI systems and programming languages. A practical case of integrating Microsoft Teams (Grades) with Power BI is also analyzed. Results. Findings show that EDM supports education at three complementary levels: (1) course level – early risk detection, personalized learning paths, quality improvement; (2) faculty level – performance monitoring, curriculum optimization, workload balancing; (3) institutional level – strategic decision-making, retention analysis, and accreditation support. EDM forms a holistic ecosystem of instruments that create a unified evidence base for academic management. Conclusion. EDM fosters a culture of data-driven education, where information becomes a strategic resource for sustainable institutional development and competitiveness. Key challenges include low digital maturity, lack of unified methodological standards, and ethical concerns regarding the use of student data. Future research should prioritize multi-algorithmic integration, development of universal models combining academic and non-academic factors, and closer alignment of EDM with decision-support systems in higher education.
References
Díaz-Choque, M., Chamorro-Atalaya, O., Ortega-Galicio, O. A., Arévalo-Tuesta, J. A., Cáceres-Cayllahua, E., Dávila-Laguna, R. F., Aybar-Bellido, I. E., Siguas-Jerónimo, Y. B. (2023). Contributions of Data Mining to University Education, in the Context of the Covid-19 Pandemic: A Systematic Review of the Literature. International Journal of Online and Biomedical Engineering (iJOE), no. 19(12), pp. 16–33. DOI: https://doi.org/10.3991/ijoe.v19i12.40079
Aldowah, H., Al-Samarraie, H., & Fauzy, W. M. (2019). Educational data mining and learning analytics for 21st century higher education: A review and synthesis. Telematics and Informatics, no. 37, pp. 13-49. DOI: https://doi.org/10.1016/J.TELE.2019.01.007
R. Alarcón, J. Bravo, C. Valdivia and J. Aquino, "Analytical system based on Big Data for digital transformation in Higher Education," 2022 IEEE 2nd International Conference on Advanced Learning Technologies on Education & Research (ICALTER), Lima, Peru, 2022, pp. 1-4. DOI: https://doi.org/10.1109/ICALTER57193.2022.9964613
Chytas, K., Tsolakidis, A., Triperina, E., & Skourlas, C. (2022). Educational data mining in the academic setting: employing the data produced by blended learning to ameliorate the learning process. Data Technol. Appl., no. 57, pp. 366-384. DOI: https://doi.org/10.1108/DTA-06-2022-0252
Antonio Fernández · Beatriz Gómez · Kleona Binjaku · Elinda Kajo Meçe. (2023) Digital transformation initiatives in higher education institutions: A multivocal literature review. Education and Information Technologies. No. 28. pp. 12351–12382. DOI: https://doi.org/10.1007/s10639-022-11544-0
Aulakh, K., Roul, R.K., & Kaushal, M. (2023). E-learning enhancement through educational data mining with Covid-19 outbreak period in backdrop: A review. International Journal of Educational Development, no. 101, pp. 102814-102814. pp. 1-17.
Romero, C., & Ventura, S. (2020). Educational data mining and learning analytics: An updated survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, no. 10(3). DOI: https://doi.org/10.1002/widm.1355
Garcia, F., Valls, C. y Lázaro, J.L. (2022). Estrategias para la transformación digital de un centro educativo: una revisión sistemática. RiiTE Revista interuniversitaria de investigación en Tecnología Educativa, no. 13, pp. 157-172. DOI: https://doi.org/10.6018/riite.533971
Chrisnanto, Y.H., & Abdullah, G. (2021). The uses of educational data mining in academic performance analysis at higher education institutions (case study at UNJANI). Matrix : Jurnal Manajemen Teknologi dan Informatika. Pp.26-35. DOI: https://doi.org/10.31940/MATRIX.V11I1.2330
Beck, J.E., Woolf, B.P. (2000). High-Level Student Modeling with Machine Learning. In: Gauthier, G., Frasson, C., VanLehn, K. (eds) Intelligent Tutoring Systems. ITS 2000. Lecture Notes in Computer Science, vol 1839. Springer, Berlin, Heidelberg. DOI: https://doi.org/10.1007/3-540-45108-0_62
Tang, T., McCalla, G. (2004). Evaluating a Smart Recommender for an Evolving E-learning System: A Simulation-Based Study. In: Tawfik, A.Y., Goodwin, S.D. (eds) Advances in Artificial Intelligence. Canadian AI 2004. Lecture Notes in Computer Science(), vol 3060. Springer, Berlin, Heidelberg. DOI: https://doi.org/10.1007/978-3-540-24840-8_34
George Siemens and Ryan S. J. d. Baker. (2012). Learning analytics and educational data mining: towards communication and collaboration. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (LAK '12). Association for Computing Machinery, New York, NY, USA, pp. 252–254. DOI: https://doi.org/10.1145/2330601.2330661
Khine, M. S. (2024). Educational Data Mining and Learning Analytics. In: Khine, M.S. (eds) Artificial Intelligence in Education. Springer, Singapore. DOI: https://doi.org/10.1007/978-981-97-9350-1_1
Xiao, W., Ji, P., & Hu, J. (2022). A survey on educational data mining methods used for predicting students' performance. Engineering Reports, no. 4(5). DOI: https://doi.org/10.1002/eng2.12482
Barbeiro, L., Gomes, A., Correia, F. B., & Bernardino, J. (2023). A Review of Educational Data Mining Trends. Procedia Computer Science, no. 237, pp. 88-95. DOI: https://doi.org/10.1016/j.procs.2024.05.083
Papadogiannis, I., Wallace, M., & Karountzou, G. (2024). Educational Data Mining: A Foundational Overview. Encyclopedia, no. 4(4), pp. 1644-1664. DOI: https://doi.org/10.3390/encyclopedia4040108
Lin, Y., Chen, H., Xia, W., Lin, F., Wang, Z., & Liu, Y. (2023). A Comprehensive Survey on Deep Learning Techniques in Educational Data Mining. ArXiv. DOI: https://arxiv.org/abs/2309.04761
