Learning Analytics in Higher Education: Opportunities and Challenges for Student Success
DOI:
https://doi.org/10.54518/nes.4.1.2026.1444Keywords:
Learning Analytics, Higher Education, Student Success, Educational Data Mining, Artificial Intelligence, Student EngagementAbstract
Learning analytics has become an important data-driven approach for improving teaching, learning, and student success in higher education. This study aims to examine the opportunities and challenges of learning analytics through a Systematic Literature Review (SLR) using the PRISMA 2020 framework. Literature published between 2020 and 2025 was collected from major academic databases and analyzed thematically. The findings indicate that learning analytics enhances student success by improving academic performance, student engagement, personalized learning, early intervention, and institutional decision-making. Technologies such as artificial intelligence, educational data mining, and learning management systems further strengthen these capabilities. However, implementation remains constrained by ethical concerns, data privacy, technological infrastructure, faculty readiness, and organizational capacity. The study concludes that effective governance and responsible implementation are essential for maximizing the educational benefits of learning analytics in higher education.
References
Alam, A. (2023, May). Improving learning outcomes through predictive analytics: Enhancing teaching and learning with educational data mining. In 2023 7th International Conference on Intelligent Computing and Control Systems (ICICCS) (pp. 249–257). IEEE.
Alfredo, R., Echeverria, V., Jin, Y., Yan, L., Swiecki, Z., Gašević, D., & Martinez-Maldonado, R. (2024). Human-centred learning analytics and AI in education: A systematic literature review. Computers and Education: Artificial Intelligence, 6, 100215.
Alzahrani, A. S., Tsai, Y. S., Iqbal, S., Marcos, P. M. M., Scheffel, M., Drachsler, H., & Gašević, D. (2023). Untangling connections between challenges in the adoption of learning analytics in higher education. Education and Information Technologies, 28(4), 4563–4595.
Bergdahl, N., Bond, M., Sjöberg, J., Dougherty, M., & Oxley, E. (2024). Unpacking student engagement in higher education learning analytics: A systematic review. International Journal of Educational Technology in Higher Education, 21(1), 63.
Caspari-Sadeghi, S. (2023). Learning assessment in the age of big data: Learning analytics in higher education. Cogent Education, 10(1), 2162697.
Elmoazen, R., Saqr, M., Khalil, M., & Wasson, B. (2023). Learning analytics in virtual laboratories: A systematic literature review of empirical research. Smart Learning Environments, 10(1), 23.
Feng, G., Fan, M., & Chen, Y. (2022). Analysis and prediction of students' academic performance based on educational data mining. IEEE Access, 10, 19558–19571.
Guzmán-Valenzuela, C., Gómez-González, C., Rojas-Murphy Tagle, A., & Lorca-Vyhmeister, A. (2021). Learning analytics in higher education: A preponderance of analytics but very little learning? International Journal of Educational Technology in Higher Education, 18(1), 23.
Ifenthaler, D., Schumacher, C., & Kuzilek, J. (2023). Investigating students' use of self-assessments in higher education using learning analytics. Journal of Computer Assisted Learning, 39(1), 255–268.
Ismail, S. N., Hamid, S., Ahmad, M., Alaboudi, A., & Jhanjhi, N. (2021). Exploring students' engagement towards the learning management system using learning analytics. Computer Systems Science & Engineering, 37(1), 73–87.
Johar, N. A., Kew, S. N., Tasir, Z., & Koh, E. (2023). Learning analytics on student engagement to enhance students' learning performance: A systematic review. Sustainability, 15(10), 7849.
Linnenluecke, M. K., Marrone, M., & Singh, A. K. (2020). Conducting systematic literature reviews and bibliometric analyses. Australian Journal of Management, 45(2), 175–194.
Liu, Q., & Khalil, M. (2023). Understanding privacy and data protection issues in learning analytics using a systematic review. British Journal of Educational Technology, 54(6), 1715–1747.
Márquez, L., Henríquez, V., Chevreux, H., Scheihing, E., & Guerra, J. (2024). Adoption of learning analytics in higher education institutions: A systematic literature review. British Journal of Educational Technology, 55(2), 439–459.
Mohammadi, M., Tajik, E., Martinez-Maldonado, R., Sadiq, S., Tomaszewski, W., & Khosravi, H. (2025). Artificial intelligence in multimodal learning analytics: A systematic literature review. Computers and Education: Artificial Intelligence, 8, 100426.
Ncube, M. M., & Ngulube, P. (2025). Leveraging learning analytics to personalise academic library services for enhanced student success: A systematic review. The Journal of Academic Librarianship, 51(6), 103160.
Okewu, E., Adewole, P., Misra, S., Maskeliunas, R., & Damasevicius, R. (2021). Artificial neural networks for educational data mining in higher education: A systematic literature review. Applied Artificial Intelligence, 35(13), 983–1021.
Oyedotun, S. A., Ejenarhome, O. P., & Oise, G. P. (2025). Learning analytics and predictive modeling: Enhancing student success through data-driven insights. Journal of Science Research and Reviews, 2(3), 42–51.
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., et al. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71.
Rabelo, A., Rodrigues, M. W., Nobre, C., Isotani, S., & Zárate, L. (2024). Educational data mining and learning analytics: A review of educational management in e-learning. Information Discovery and Delivery, 52(2), 149–163.
Sahni, J. (2023). Is learning analytics the future of online education? Assessing student engagement and academic performance in the online learning environment. International Journal of Emerging Technologies in Learning, 18(2), 33–49.
Salas-Pilco, S. Z., Xiao, K., & Hu, X. (2022). Artificial intelligence and learning analytics in teacher education: A systematic review. Education Sciences, 12(8), 569.
Shafiq, D. A., Marjani, M., Habeeb, R. A. A., & Asirvatham, D. (2022). Student retention using educational data mining and predictive analytics: A systematic literature review. IEEE Access, 10, 72480–72503.
Silva, G., Godwin, G., & Jayanagara, O. (2024). The impact of AI on personalized learning and educational analytics. International Transactions on Education Technology, 3(1), 36–46.
Sloan-Lynch, J., & Morse, R. (2024). Equity-forward learning analytics: Designing a dashboard to support marginalized student success. In Proceedings of the 14th Learning Analytics and Knowledge Conference (pp. 1–11).
Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104, 333–339.
Tzimas, D., & Demetriadis, S. (2021). Ethical issues in learning analytics: A review of the field. Educational Technology Research and Development, 69(2), 1101–1133.
Ungerer, L., & Slade, S. (2022). Ethical considerations of artificial intelligence in learning analytics in distance education contexts. In Learning Analytics in Open and Distributed Learning: Potential and Challenges (pp. 105–120). Springer.
Vashishth, T. K., Sharma, V., Sharma, K. K., Kumar, B., Panwar, R., & Chaudhary, S. (2024). AI-driven learning analytics for personalized feedback and assessment in higher education. In Using Traditional Design Methods to Enhance AI-Driven Decision Making (pp. 206–230). IGI Global.
Zhidkikh, D., Heilala, V., Van Petegem, C., Dawyndt, P., Järvinen, M., Viitanen, S., et al. (2024). Reproducing predictive learning analytics in CS1: Toward generalizable and explainable models for enhancing student retention. Journal of Learning Analytics, 11(1), 132–150.
