Learning Analytics in Higher Education: a Decade in Systematic Literature Review

In the last decade, Learning Analytics (LA) has evolved in a positive way, considering that the term emerged in 2011 through the Society for Learning Analytics Research (SoLAR). This area of data analytics can be identified as a specialization of Educational Data Mining (EDM). LA emphasizes student learning outcomes. In addition to, a better understanding of student learning behavior and processes. While EDM focuses on helping teachers and students with the analysis of the learning process using popular data mining methods. The purpose of this research is to explore the first decade of work with the application of Learning Analytics in Higher Education Institutions (HEI) in the context of Tutoring Information Systems (TIS), with the intention of supporting institutions, teachers and students to decrease dropout rates. This article presents a systematic literature review (SLR) with 17 primary studies, comprised between 2014 and 2024. The findings reflect the use of LA in improving or optimizing learning using student academic history obtained through Learning Management Systems (LMS), noting the scarcity of works with a focus on tutoring or academic advising. Ultimately, a gap is opened to apply LA in HEI, with information from Institutional Tutoring Program (PIT), integrated with information from an LMS, to contribute to student permanence.

1. A. Salas Martinez and A. Ramirez Martinell, Application of learning analytics in higher education institutions: A systematic literature review, in 11th International Conference in Software Engineering Research and Innovation (CONISOFT) (2023).

2. G. Siemens and R. S. J. d. Baker, Learning analytics and educational data mining: towards communication and collaboration, in Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (2012), p. 252–254.

3. D. Gasevic, Y. Tsai, S. Dawson, and A. Pardo, How do we start? an approach to learning analytics adoption in higher education, International Journal of Information and Learning Technology 4, 342 (2019).

4. G. Siemens and P. Long, Penetrating the fog: Analytics in learning and education, EDUCAUSE Review 5, 30 (2011).

5. S. J. Aguilar, S. A. Karabenick, S. D. Teasley, and C. Baek, Associations between learning analytics dashboard exposure and motivation and self-regulated learning, Computers & Education 162 (2021).

6. C. Schumacher and D. Ifenthaler, Features students really expect from learning analytics, Computers in Human Behavior, 397 (2018).

7. S. Charleer, A. Moere, J. Klerkx, K. Verbert, and T. D. Laet, Learning analytics dashboards to support adviser-student dialogue, IEEE Transactions on Learning Technologies 11, 389 (2018).

8. M. Waheed, K. Kaur, N. Ain, and N. Hussain, Perceived learning outcomes from moodle: An empirical study of intrinsic and extrinsic motivating factors, Information Development 32, 1001 (2016).

9. O. Talbi and A. Ouared, Goal-oriented student motivation in learning analytics: How can a requirements-driven approach help? Education and Information Technologies 27 (2022).

10. F. S. Kia, S. D. Teasley, M. Hatala, S. A. Karabenick, and M. Kay, How patterns of students dashboard use are related to their achievement and self-regulatory engagement, LAK 2020 Conference Proceedings – Celebrating 10 years of LAK: Shaping the Future of the Field – 10th International Conference on Learning Analytics and Knowledge, 340 (2020).

11. I. Jivet, M. Scheffel, H. Drachsler, and M. Specht, Awareness is not enough. pitfalls of learning analytics dashboards in the educational practice, Data Driven Approaches in Digital Education: 12th European Conference on Technology Enhanced Learning 10474, 12, (2017).

12. B. Kitchenham and P. Brereton, A systematic review of systematic review process research in software engineering, Information and Software Technology, 2049 (2013).

13. H. Zhang, M. A. Babar, and P. Tell, Identifying relevant studies in software engineering, Information and Software Technology 53 (2011).

14. P. O. Silva-Vasquez, V. Y. Rosales-Morales, and E. Benitez-Guerrero, Automatic code generation of user-centered serious games: A decade in review, Program. Comput. Softw. 48, 685–701 (2022).

15. S. I. Fernandez Gregorio, L. G. Montane-Jimenez, C. M. Godoy, and V. Y. Rosales-Morales, Architecture for groupware oriented to collaborative medical activities in the rehabilitation of strokes, Program. Comput. Softw. 49, 643–656 (2024).

16. J. A. Reyes, The skinny on big data in education: Learning analytics simplified, TechTrends 59, 75 (2015).

17. R. Bodily and K. Verbert, Review of Research on Student-Facing Learning Analytics Dashboards and Educational Recommender Systems, IEEE Transactions on Learning Technologies 10, 405 (2017).

18. P. Rojas-Castro, Learning Analytics: una revisiA˜de la literatura, EducaciA˜y Educadores 20, 106 (2017).

19. D. T. Tempelaar, B. Rienties, and Q. Nguyen, Towards actionable learning analytics using dispositions, IEEE Transactions on Learning Technologies 10, 6 (2017).

20. O. Viberg, M. Hatakka, O. B¨alter, and A. Mavroudi, The current landscape of learning analytics in higher education, Computers in Human Behavior 89, 98 (2018).

21. C. Herodotou, B. Rienties, A. Boroowa, Z. Zdrahal, and M. Hlosta, A large-scale implementation of predictive learning analytics in higher education: the teachers’ role and perspective, Educational Technology Research and Development 67, 1273 (2019).

22. J. Guerra, M. Ortiz-Rojas, M. A. Zuniga-Prieto, E. Scheihing, A. Jimenez, T. Broos, T. De Laet, and K. Verbert, Adaptation and evaluation of a learning analytics dashboard to improve academic support at three Latin American universities, British Journal of Educational Technology 51, 973 (2020).

23. S. Ranjeeth, T. P. Latchoumi, and P. V. Paul, A Survey on Predictive Models of Learning Analytics, Procedia Computer Science 167, 37 (2020).

24. T. De Laet, M. Millecamp, M. Ortiz-Rojas, A. Jimenez, R. Maya, and K. Verbert, Adoption and impact of a learning analytics dashboard supporting the advisor – Student dialogue in a higher education institute in Latin America, British Journal of Educational Technology 51, 1002 (2020).

25. C. Guzman-Valenzuela, C. Gomez-Gonzalez, A. Rojas-Murphy Tagle, and A. Lorca-Vyhmeister, Learning analytics in higher education: a preponderance of analytics but very little learning? International Journal of Educational Technology in Higher Education 18, 23 (2021).

26. A. Rafique, M. S. Khan, M. H. Jamal, M. Tasadduq, F. Rustam, E. Lee, P. B. Washington, and I. Ashraf, Integrating learning analytics and collaborative learning for improving student’s academic performance, IEEE Access 9, 167812 (2021).

27. C. J. Perez Sanchez, F. Calle-Alonso, and M. A. Vega-Rodrıguez, Learning analytics to predict students’ performance: A case study of a neurodidactics-based collaborative learning platform, Education and Information Technologies (2022).

28. R. Kaliisa, B. Rienties, A. I. Mørch, and A. Kluge, Social learning analytics in computer-supported collaborative learning environments: A systematic review of empirical studies, Computers and Education Open 3 (2022).

29. H. Huang, L. Jew, and D. Qi, Take a MOOC and then drop: A systematic review of mooc engagement pattern and dropout factor, Heliyon 9, e15220 (2023).

30. P. Prinsloo, M. Khalil, and S. Slade, Learning analytics as data ecology: a tentative proposal, Journal of Computing in Higher Education, 1 (2023).

31. K. Kaur and O. Dahiya, Role of educational data mining and learning analytics techniques used for predictive modeling, in 2023 3rd International Conference on Innovative Practices in Technology and Management (ICIPTM) (2023) pp. 1–6.

32. M. A. Chatti, A. L. Dyckhoff, U. Schroeder, and H. Thüs, A reference model for learning analytics, Int. J. Technol. Enhanc. Learn. 4, 318–331 (2012).

33. F. Gutierrez, K. Seipp, X. Ochoa, K. Chiluiza, T. De Laet, and K. Verbert, Lada: A learning analytics dashboard for academic advising, Computers in Human Behavior 107, 105826 (2020).

34. J. Kim, I. Jo, and Y. Park, Effects of learning analytics dashboard: Analyzing the relations among dashboard utilization, satisfaction, and learning achievement, Asia Pacific Education Review 17, 13 (2016).