• Review
  • July 13, 2023
  • Open access

Lines of research in the area of educational data mining in higher education: A theoretical review


The paper provides an overview of foreign English-language research literature on pedagogy, the aim of which is to identify the most relevant lines of research in the area of educational data mining in modern higher education. The review considers the factors that have caused the development of educational data mining (hereinafter EDM) and learning analytics (hereinafter LA) in the context of digital transformation processes in modern society. The paper discusses the potential, problems and directions of implementing EDM and LA in higher education in general, as well as in the field of academic performance and students’ behavior, educational programs development and improving the education system efficiency. Scientific novelty of the review lies in identifying the most relevant tasks for EDM research and defining advanced research directions in this area for all actors of educational process in higher education. As a result, the authors analyzed research papers on the described subject area published in the period from 2017 to 2023 and described issues related to personal data ethics and privacy in the context of EDM implementation, the relevant methods of EDM, the experience of EDM implementation in higher education.


  1. Ahmad Z., Shahzadi E. Prediction of Students’ Academic Performance Using Artificial Neural Network // Bulletin of Education. 2018. Vol. 40. Iss. 3.
  2. Ahuja R., Jha A., Maurya R., Srivastava R. Analysis of Educational Data Mining // Advances in Intelligent Systems and Computing. 2018. Vol. 741.
  3. Aleem A., Gore M. M. Educational Data Mining Methods: A Survey // 2020 IEEE 9th International Conference on Communication Systems and Network Technologies (CSNT) (Gwailor, India, 2020). 2020. DOI: 10.1109/CSNT48778.2020.9115734
  4. Alyahyan E., Düştegör D. Predicting Academic Success in Higher Education: Literature Review and Best Practices // International Journal of Educational Technology in Higher Education. 2020. Vol. 17. Iss. 3.
  5. Ang L., Ge F., Seng K. Big Educational Data & Analytics: Survey, Architecture and Challenges // IEEE Access. 2020. Vol. 8.
  6. Attaran M., Stark J., Stottler D. Opportunities and Challenges for Big Data Analytics in American Higher Education – A Conceptual Model for Implementation // Industry and Higher Education. 2018. Vol. 32. Iss. 3.
  7. Azcona D., Hsiao I.-H., Smeaton A. F. Detecting Students-at-Risk in Computer Programming Classes with Learning Analytics from Students’ Digital Footprints // User Modeling and User-Adapted Interaction. 2019. Vol. 29. Iss. 2.
  8. Baker R. S., Corbett A. T., Koedinger K. R. Detecting Student Misuse of Intelligent Tutoring Systems // Proceedings of the 7th International Conference on Intelligent Tutoring Systems (Maceio, Alagoas, Brazil, August 30 – September 3, 2004). 2004. DOI: 10.1007/978-3-540-30139-4_50
  9. Baker R. S., Inventado P. S. Educational Data Mining and Learning Analytics // Learning Analytics / ed. by J. Larusson, B. White. N. Y., 2014.
  10. Bakhshinategh B., Zaiane O. R., ElAtia S., Ipperciel D. Educational Data Mining Applications and Tasks: A Survey of the Last 10 Years // Education and Information Technologies. 2018. Vol. 23. Iss. 1.
  11. Beck J., Woolf B. High-Level Student Modeling with Machine Learning // Proceedings of the 5th International Conference of Intelligent Tutoring Systems (Germany, Berlin, June 19-23, 2000). 2000. DOI: 10.1007/3-540-45108-0_62
  12. Brown M., DeMonbrun R. M., Teasley S. Taken Together: Conceptualizing Students’ Concurrent Course Enrollment Across the Post-Secondary Curriculum Using Temporal Analytics // Journal of Learning Analytics. 2018. Vol. 5. Iss. 3.
  13. Daniel B. K. Big Data and Data Science: A Critical Review of Issues for Educational Research // British Journal of Educational Technology. 2019. Vol. 50. Iss. 1.
  14. Dawson S., Poquet O., Colvin C., Rogers T., Pardo A., Gasevic D. Rethinking Learning Analytics Adoption through Complexity Leadership Theory // LAK’18 Proceedings of the 8th International Conference on Learning Analytics and Knowledge (Australia, Sydney, March, 2018). 2018. DOI: 10.1145/3170358.3170375
  15. Dhankhar A., Solanki K. State of the Art of Learning Analytics in Higher Education // International Journal of Emerging Trends in Engineering Research. 2020. Vol. 8. Iss. 3.
  16. Dimić G., Predić B., Rančić D., Petrović V., Maček N., Spalević P. Association Analysis of Moodle E-Tests in Blended Learning Educational Environment // Computer Applications in Engineering Education. 2017. Vol. 26. Iss. 3.
  17. Gottipati S., Shankararaman V. Competency Analytics Tool: Analyzing Curriculum Using Course Competencies // Education and Information Technologies. 2018. Vol. 23. Iss. 10.
  18. Guan X., Feng X., Islam A. The Dilemma and Countermeasures of Educational Data Ethics in the Age of Intelligence // Humanities and Social Sciences Communications. 2023. Vol. 10.
  19. Handbook of Learning Analytics / ed. by C. Lang, G. Siemens, A. Wise, D. Gašević. 1st ed. Society for Learning Analytics Research (soLAR), 2017. DOI: 10.18608/hla17
  20. Heileman G. L., Slim A., Hickman M., Abdallah C. T. Characterizing the Complexity of Curricular Patterns in Engineering Programs // ASEE Annual Conference & Exposition (Columbus, Ohio, June 24-28, 2017). 2017. DOI: 10.18260/1-2--28029
  21. Hernández de Menéndez M., Morales-Menendez R., Escobar C., Ramirez-Mendoza R. Learning Analytics: State of the Art // International Journal on Interactive Design and Manufacturing (IJIDeM). 2022. Vol. 16. Iss. 2.
  22. Hilliger I., Aguirre C., Miranda C., Celis S., Pérez-Sanagustín M. Lessons Learned from Designing a Curriculum Analytics Tool for Improving Student Learning and Program Quality // Journal of Computing in Higher Education. 2022. Vol. 34.
  23. Hilliger I., Aguirre C., Miranda C., Celis S., Pérez-Sanagustín M. Design of a Curriculum Analytics Tool to Support Continuous Improvement Processes in Higher Education // LAK’20: Proceedings of the Tenth International Conference on Learning Analytics & Knowledge (Frankfurt, Germany, March 23-27, 2020). 2020. DOI: 10.1145/3375462.3375489
  24. Hoel T., Chen W. Privacy and Data Protection in Learning Analytics Should Be Motivated by an Educational Maxim – towards a Proposal // Research and Practice in Technology Enhanced Learning. 2018. Vol. 13. Iss. 20.
  25. Hooda M., Rana C. Learning Analytics Lens: Improving Quality of Higher Education // International Journal of Emerging Trends in Engineering Research. 2020. Vol. 8. Iss. 5.
  26. Ifenthalter D., Yau J. Reflections on Different Learning Analytics Indicators for Supporting Study Success // International Journal of Learning Analytics and Artificial Intelligence for Education. 2020. Vol. 2. Iss. 2.
  27. Juhaňák L., Zounek J., Rohlíková L. Using Process Mining to Analyze Students’ Quiz-Taking Behavior Patterns in a Learning Management System // Computers in Human Behavior. 2019. Vol. 92.
  28. Kitto K., Sarathy N., Gromov A., Liu M., Musial K., Shum S. B. Towards Skills-Based Curriculum Analytics: Can We Automate the Recognition of Prior Learning? // LAK’20: Proceedings of the Tenth International Conference on Learning Analytics & Knowledge (Frankfurt, Germany, March 23-27, 2020). 2020. DOI: 10.1145/3375462.3375526
  29. Kousa P., Niemi H. AI Ethics and Learning: EdTech Companies’ Challenges and Solutions // Interactive Learning Environments. March 1, 2022. DOI: 10.1080/10494820.2022.2043908
  30. Kularski C. M., Martin F. Online Student Privacy in Higher Education: A Systematic Review of the Research // American Journal of Distance Education. 2021. Vol. 36. Iss. 3.
  31. Li Y., Chen X., Sun D., Zhu Y., Zhai X. From “Transparent People” to “Practitioner”: Challenges and Responses to Information Security in Higher Education: Implications from 2021 EDUCAUSE Horizon Report // Journal of Distance Education. 2021. Vol. 3.
  32. Magdy A., Dony C. 1st ACM SIGSPATIAL Workshop on Geo Computational Thinking in Education. 2019. URL: https://www.sigspatial.org/wp-content/uploads/special-issues/11/3/Report02_GeoEd.pdf
  33. Mandinach E., Jimerson J. Data Ethics in Education: A Theoretical, Practical, and Policy Issue // Studia Paedagogica. 2021. Vol. 26. Iss. 4.
  34. Merceron A., Yacef K. A Web-Based Tutoring Tool with Mining Facilities to Improve Learning and Teaching // 11th International Conference on Artificial Intelligence in Education / ed. by F. Verdejo and U. Hoppe. Sydney: IOS Press, 2003.
  35. Moscoso-Zea O., Sampedro A. P., Luján-Mora S. Datawarehouse Design for Educational Data Mining // 2016 15th International Conference on Information Technology Based Higher Education and Training (ITHET) (Turkey, Istanbul, 8-10 September, 2016). 2016. DOI: 10.1109/ITHET.2016.7760754
  36. Musso M. F., Hernández C. F. R., Cascallar E. C. Predicting Key Educational Outcomes in Academic Trajectories: A Machine-Learning Approach // Higher Education. 2020. Vol. 80.
  37. Namoun A., Alshanqiti A. Predicting Student Performance Using Data Mining and Learning Analytics Techniques: A Systematic Literature Review // Applied Sciences. 2021. Vol. 11. Iss. 1.
  38. Romero C., Ventura S. Data Mining in Education // Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 2013. Vol. 3. Iss. 1.
  39. Romero C., Ventura S. Educational Data Mining: A Review of the State of the Art // IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews). 2010. Vol. 40. Iss. 10.
  40. Romero C., Ventura S. Educational Data Mining: A Survey from 1995 to 2005 // Expert Systems with Applications. 2007. Vol. 33. Iss. 1.
  41. Romero C., Ventura S. Educational Data Mining and Learning Analytics: An Updated Survey // WIREs Data Mining and Knowledge Discovery. 2020. Vol. 10. Iss. 3.
  42. Rosé C. P., McLaughlin E. A., Liu R., Koedinger K. R. Explanatory Learner Models: Why Machine Learning (Alone) Is Not the Answer // British Journal of Educational Technology. 2019. Vol. 50. Iss. 6.
  43. Sekli M., De la Vega I. Adoption of Big Data Analytics and Its Impact on Organizational Performance in Higher Education Mediated by Knowledge Management // Journal of Open Innovation: Technology, Market and Complexity. 2021. Vol. 7. Iss. 4.
  44. Shorfuzzaman M., Hossain M. S., Nazir A., Muhammad G., Alamri A. Harnessing the Power of Big Data Analytics in the Cloud to Support Learning Analytics in Mobile Learning Environment // Computers in Human Behaviour. 2019. Vol. 92.
  45. Siemens G. Learning Analytics: The Emergence of a Discipline // American Behavioral Scientist. 2013. Vol. 57. Iss. 10.
  46. Solem M., Dony C., Herman T., Leon K., Magdy A., Nara A., Ray W., Rey S., Russel R. Building Educational Capacity for Inclusive Geocomputation: A Research-Practice Partnership in Southern California // Journal of Geography. 2021. Vol. 120. Iss. 4.
  47. Sonderlund A., Hughes E., Smith J. R. The Efficacy of Learning Analytics Interventions in Higher Education – a Systematic Review // British Journal of Educational Technology. 2018. Vol. 50.
  48. Tang H., Zhang J. Limits of Big Data Application in Education // Journal of East China Normal University (Educational Sciences). 2020. Vol. 38. Iss. 10.
  49. Tang T., McCalla G. Smart Recommendation for an Evolving E-Learning System: Architecture and Experiment // International Journal on E-Learning. 2005. Vol. 4. Iss. 1.
  50. Tasmin R., Muhammad N. R., Aziati A. Big Data Analytics Applicability in Higher Learning Educational System // IOP Conference Series: Materials Science and Engineering (Kota Bharu, Kelantan, 2020). 2020. DOI: 10.1088/1757-899X/917/1/012064
  51. Tsai Y., Rates D., Moreno-Marcos P., Merino P., Jivet I., Scheffel M., Drachsler H., Delago-Kloos C., Gasevic D. Learning Analytics in European Higher Education – Trends and Barriers // Computers & Education. 2020. Vol. 155.
  52. Vieira C., Parsons P., Byrd V. Visual Learning Analytics of Educational Data: A Systematic Literature Review and Research Agenda // Computers & Education. 2018. Vol. 122.
  53. Waheed H., Hassan S. U., Aljohani N. R., Hardman J., Alelyani S., Nawaz R. Predicting Academic Performance of Students from VLE Big Data Using Deep Learning Models // Computers in Human Behavior. 2020. Vol. 104.
  54. West D., Luzeckyj A., Toohey D., Vanderlelie J., Searle B. Do Academics and University Administrators Really Know Better? The Ethics of Positioning Student Perspectives in Learning Analytics // Australasian Journal of Educational Technology. 2020. Vol. 36. Iss. 2.
  55. Zaïane O. Web Usage Mining for a Better Web-Based Learning Environment. 2001. URL: https://webdocs.cs.ualberta.ca/~zaiane/postscript/CATE2001.pdf
  56. Zeng Z., Li Y., Cao Y., Zhao Y., Zhong J., Sidorov D., Zeng X. Blockchain Technology for Information Security of the Energy Internet: Fundamentals, Features, Strategy and Application // Energies. 2020. Vol. 13. Iss. 4.
  57. Zhang L., Li K. F. Education Analytics: Challenges and Approaches // 32nd International Conference on Advanced Information Networking and Applications Workshops (WAINA) (Krakow, Poland, 2018). 2018. DOI: 10.1109/WAINA.2018.00086

Author information

Irina Arturovna Semyonkina


Moscow Polytechnic University

Polina Valentinovna Prusakova

Moscow Polytechnic University

About this article

Publication history

  • Received: May 12, 2023.
  • Published: July 13, 2023.


  • анализ образовательных данных
  • аналитика обучения
  • высшее образование
  • анализ успеваемости и поведения обучающихся
  • субъекты образовательного процесса
  • этика и конфиденциальность личных данных
  • educational data mining
  • learning analytics
  • higher education
  • analytics in the field of academic performance and students’ behavior
  • actors of educational process
  • personal data ethics and privacy


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