Institutional Analytics for Transformation
Learning analytics—the systematic use of educational data to understand and improve learning—has emerged as a promising approach to addressing persistent challenges in higher education, including student dropout, uneven learning outcomes, and structural inequities. This article examines how learning analytics can support institutional improvement, with particular attention to multi-campus universities and contexts where such approaches remain underdeveloped. Drawing on examples from higher education and K–12 systems worldwide, the article adopts a socio-technical perspective, arguing that the effective use of analytics depends as much on human practices, governance, and ethics as on technological capability.
The article reviews applications of learning analytics across key stakeholder groups. Instructors use dashboards to identify at-risk students and inform pedagogical decisions; students receive personalized feedback, nudges, and course recommendations that support persistence; digital learning platforms adapt content and detect disengagement; advisors prioritize outreach through integrated data systems; and academic leaders use analytics to identify curricular gaps and instructional weaknesses. Across these applications, the article foregrounds core ethical principles—transparency, privacy, consent, fairness, de-biasing, and accountability—as essential foundations for responsible analytics practice.
At the same time, the article highlights significant implementation challenges that frequently undermine analytics initiatives. These include insufficient data infrastructure, alert fatigue when predictions are not embedded in actionable workflows, limited instructor and IT engagement, and difficulties scaling pilot projects across diverse campuses and programs. Addressing these obstacles requires framing learning analytics not as a technical add-on but as a form of organizational change.
To support sustainable implementation, the article advocates for iterative, evidence-informed design processes grounded in the learning sciences, human–computer interaction, and universal design. Key enabling conditions include feedback loops for continuous improvement, professional development in data literacy, clear governance structures with cross-campus representation, defined roles for learning designers and implementation coordinators, and robust data stewardship practices. For multi-campus institutions, balancing standardization with local adaptation is critical to ensuring equitable impact.
The article concludes by considering emerging challenges associated with generative AI and by outlining practical next steps for institutional leaders. By treating learning analytics as a long-term, ethically grounded commitment centered on equity and student agency, universities can harness data to support meaningful improvement and sustain public trust in the responsible use of educational technology.
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Adetutu, O. M., & Lawal, H. B. (2022). Applications of item response theory models to assess item properties and students’ abilities in dichotomous responses items. Open Journal of Educational Development (ISSN: 2734-2050), 3(1), 01–19. DOI: https://doi.org/10.52417/ojed.v3i1.304
Alfredo, R., Echeverria, V., Zhao, L., Lawrence, L., Fan, J. X., Yan, L., … & Martinez-Maldonado, R. (2024). Designing a human-centred learning analytics dashboard in-use. Journal of Learning Analytics, 11(3), 62–81. DOI: https://doi.org/10.18608/jla.2024.8487
Anderson, J. R., Corbett, A. T., Koedinger, K. R., & Pelletier, R. (1995). Cognitive tutors: Lessons learned. The Journal of the Learning Sciences, 4(2), 167–207. DOI: https://doi.org/10.1207/s15327809jls0402_2
Arnold, K. E., & Pistilli, M. D. (2012). Course signals at Purdue: Using learning analytics to increase student success. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, 267–270.
Arnold, K. E., & Pistilli, M. D. (2012). Course signals at Purdue: Using learning analytics to increase student success. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (LAK ’12) (pp. 267–270).
Arnold, K. E., & Pistilli, M. D. (2012, April). Course signals at Purdue: Using learning analytics to increase student success. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (pp. 267–270). DOI: https://doi.org/10.1145/2330601.2330666
Baker, R. S., & Siemens, G. (2013). Learning analytics and educational data mining: Towards communication and collaboration. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, 252–254. DOI: https://doi.org/10.1145/2330601.2330661
Baker, R., Lindrum, D., Lindrum, M. J., & Perkowski, D. (2015). Analyzing early at-risk factors in higher education e-learning courses. Proceedings of the 8th International Conference on Educational Data Mining, 150–155.
Baker, R. S. (2025). Big data and education (9th ed.). University of Pennsylvania.
Baker, R. S., Gowda, S. M., & Salamin, E. (2018). Modeling the learning that takes place between online assessments. Proceedings of the 26th International Conference on Computers in Education, 21–28.
Baker, R. S., Liu, X., Shah, M., Pankiewicz, M., Kim, Y. J., Lee, Y., & Porter, C. (in press). Generative AI as a teaching assistant. In V. Vincent-Lancrin (Ed.), OECD Digital Education Outlook 2026.
Baker, R. S., Wagner, A. Z., Corbett, A. T., & Koedinger, K. R. (2004). The social role of technical personnel in the deployment of intelligent tutoring systems. CMU Technical Report CMU-HCII-04-100. DOI: https://doi.org/10.1007/978-3-540-30139-4_75
Baker, R. S. J. d., & Ocumpaugh, J. (2014). Interaction-based affect detection in educational software. In R. A. Calvo, S. K. D’Mello, J. Gratch, & A. Kappas (Eds.), The Oxford handbook of affective computing. Oxford University Press.
Baker, R. S. J. d., & Yacef, K. (2009). The state of educational data mining in 2009: A review and future visions. Journal of Educational Data Mining, 1(1), 3–17.
Bodily, R., Kay, J., Aleven, V., Jivet, I., Davis, D., Xhakaj, F., & Verbert, K. (2018, March). Open learner models and learning analytics dashboards: A systematic review. In Proceedings of the 8th International Conference on Learning Analytics and Knowledge (pp. 41–50). DOI: https://doi.org/10.1145/3170358.3170409
Bradberry, C., Ray, A., Wayman, M., Dhami, J., Charnock, J., & Pittges, J. (2017). Explaining and predicting first year student retention via card swipe systems. Proceedings of the International Conference on Semantics, Ontologies, Intelligence and Intelligent Systems (SIGODIS).
Brown, A., Basson, M., Axelsen, M., Redmond, P., & Lawrence, J. (2023). Empirical evidence to support a nudge intervention for increasing online engagement in higher education. Education Sciences, 13(2), 145. DOI: https://doi.org/10.3390/educsci13020145
Buckingham Shum, S., & Shibani, A. (2019). Learning analytics growing pains: Socio-technical infrastructure changes as LA tools mature. Australian Learning Analytics Summer Institute.
Calo, T., & Maclellan, C. (2024, July). Towards educator-driven tutor authoring: Generative AI approaches for creating intelligent tutor interfaces. In Proceedings of the Eleventh ACM Conference on Learning@ Scale (pp. 305–309). DOI: https://doi.org/10.1145/3657604.3664694
Campbell, J. P., DeBlois, P. B., & Oblinger, D. G. (2007). Academic analytics: A new tool for a new era. EDUCAUSE Review, 42(4), 40–57.
Cosyn, E., Uzun, H., Doble, C., & Matayoshi, J. (2021). A practical perspective on knowledge space theory: ALEKS and its data. Journal of Mathematical Psychology, 101, 102512. DOI: https://doi.org/10.1016/j.jmp.2021.102512
Esbenshade, L., Baker, R. S., & Vitale, J. (2023, June). From a prediction model to meaningful reports in school. In Education Leadership Data Analytics (ELDA) 2023 Conference (New York, NY).
Feng, M., & Heffernan, N. T. (2006). Informing teachers live about student learning: Reporting in the Assistment system. Technology, Instruction, Cognition and Learning, 3(1/2), 63.
Gašević, D., Dawson, S., & Siemens, G. (2015). Let’s not forget: Learning analytics are about learning. TechTrends, 59(1), 64–71. DOI: https://doi.org/10.1007/s11528-014-0822-x
Google. (2025). AI tutoring can safely and effectively support students: An exploratory RCT in UK classrooms. https://storage.googleapis.com/deepmind-media/LearnLM/learnLM_nov25.pdf
Hooshyar, D., Tammets, K., Ley, T., Aus, K., & Kollom, K. (2023). Learning analytics in supporting student agency: A systematic review. Sustainability, 15(18), 13662. DOI: https://doi.org/10.3390/su151813662
Iraj, H., Fudge, A., Khan, H., Faulkner, M., Pardo, A., & Kovanovic, V. (2021). Narrowing the feedback gap: Examining student engagement with personalized and actionable feedback messages. Journal of Learning Analytics, 8(3), 101–116. DOI: https://doi.org/10.18608/jla.2021.7184
Jayaprakash, S. M., Moody, E. W., Lauría, E. J. M., Regan, J. R., & Baron, J. D. (2014). Early alert of academically at-risk students: An open source analytics initiative. Journal of Learning Analytics, 1(1), 6–47. DOI: https://doi.org/10.18608/jla.2014.11.3
Ju, S., Zhou, G., Barnes, T., & Chi, M. (2020). Pick the moment: Identifying critical pedagogical decisions using long-short term rewards. Proceedings of the International Conference on Educational Data Mining.
Kaliisa, R., Misiejuk, K., López-Pernas, S., Khalil, M., & Saqr, M. (2024, March). Have learning analytics dashboards lived up to the hype? A systematic review of impact on students’ achievement, motivation, participation and attitude. In Proceedings of the 14th Learning Analytics and Knowledge Conference (pp. 295–304). DOI: https://doi.org/10.1145/3636555.3636884
Khachatryan, G. A. (2020). Instruction modeling: Developing and implementing blended learning programs. Oxford University Press. DOI: https://doi.org/10.1093/oso/9780190910709.001.0001
Khalil, M., & Ebner, M. (2016). De-identification in learning analytics. Journal of Learning Analytics, 3(1), 129–138. DOI: https://doi.org/10.18608/jla.2016.31.8
Khosravi, H., Shabaninejad, S., Bakharia, A., Sadiq, S., Indulska, M., & Gašević, D. (2021). Intelligent learning analytics dashboards: Automated drill-down recommendations to support teacher data exploration. Journal of Learning Analytics, 8(3), 133–154. DOI: https://doi.org/10.18608/jla.2021.7279
Khosravi, H., Shum, S. B., Chen, G., Conati, C., Tsai, Y. S., Kay, J., … & Gašević, D. (2022). Explainable artificial intelligence in education. Computers and Education: Artificial Intelligence, 3, 100074. DOI: https://doi.org/10.1016/j.caeai.2022.100074
Koedel, C., & Rockoff, J. E. (2015). Value-added modeling: A review. Economics of Education Review, 47, 180–195. DOI: https://doi.org/10.1016/j.econedurev.2015.01.006
Krumm, A., Means, B., & Bienkowski, M. (2018). Learning analytics goes to school: A collaborative approach to improving education. Routledge. DOI: https://doi.org/10.4324/9781315650722
Kulik, J. A., & Fletcher, J. D. (2016). Effectiveness of intelligent tutoring systems: A meta-analytic review. Review of Educational Research, 86(1), 42–78. DOI: https://doi.org/10.3102/0034654315581420
Kyte, S. B., Atkins, C., Collins, E., & Deil-Amen, R. (2023). Understanding the impact of data-driven tools on advising practice and student support. Journal of Postsecondary Student Success, 2(4), 63–82. DOI: https://doi.org/10.33009/fsop_jpss132841
Lee, D., Arnold, M., Srivastava, A., Plastow, K., Strelan, P., Ploeckl, F., … & Palmer, E. (2024). The impact of generative AI on higher education learning and teaching: A study of educators’ perspectives. Computers and Education: Artificial Intelligence, 6, 100221. DOI: https://doi.org/10.1016/j.caeai.2024.100221
Lin, J., Rao, J., Zhao, Y., Wang, Y., Gurung, A., Barany, A., Ocumpaugh, J., Baker, R. S., & Koedinger, K. R. (2025). Automatic large language models creation of interactive learning lessons. Proceedings of the 20th European Conference on Technology Enhanced Learning. DOI: https://doi.org/10.1007/978-3-032-03870-8_18
Lockyer, L., Heathcote, E., & Dawson, S. (2013). Informing pedagogical action: Aligning learning analytics with learning design. American Behavioral Scientist, 57(10), 1439–1459. DOI: https://doi.org/10.1177/0002764213479367
Long, P., & Siemens, G. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE Review, 46(5), 31–40.
Milliron, M. D., Malcolm, L., & Kil, D. (2014). Insight and action analytics: Three case studies to consider. Research & Practice in Assessment, 9, 70–89.
Molenaar, I., Horvers, A., Dijkstra, R., & Baker, R. (2019). Designing dashboards to support learners’ self-regulated learning. Companion Proceedings of the 9th International Learning Analytics and Knowledge Conference.
Murray, T., Blessing, S., & Ainsworth, S. (Eds.). (2013). Authoring tools for advanced technology learning environments: Toward cost-effective adaptive, interactive and intelligent educational software. Springer Science & Business Media.
Outerbridge, S., & Taub, M. (2025, March). Towards a teacher- and student-facing dashboard to scaffold SRL and motivation for improved learning outcomes. In Society for Information Technology & Teacher Education International Conference (pp. 2864–2870). Association for the Advancement of Computing in Education (AACE).
Pankiewicz, M., & Baker, R. S. (2023). Large language models (GPT) for automating feedback on programming assignments. Proceedings of the 31st International Conference on Computers in Education. DOI: https://doi.org/10.58459/icce.2023.950
Pardo, A., & Siemens, G. (2014). Ethical and privacy principles for learning analytics. British Journal of Educational Technology, 45(3), 438–450. DOI: https://doi.org/10.1111/bjet.12152
Pelánek, R. (2017). Bayesian knowledge tracing, logistic models, and beyond: An overview of learner modeling techniques. User Modeling and User-Adapted Interaction, 27(3), 313–350. DOI: https://doi.org/10.1007/s11257-017-9193-2
Poquet, O., Kitto, K., Jovanovic, J., Dawson, S., Siemens, G., & Markauskaite, L. (2021). Transitions through lifelong learning: Implications for learning analytics. Computers and Education: Artificial Intelligence, 2, 100039. DOI: https://doi.org/10.1016/j.caeai.2021.100039
Queiroga, E. M., Batista Machado, M. F., Paragarino, V. R., Primo, T. T., & Cechinel, C. (2022). Early prediction of at-risk students in secondary education: A countrywide K–12 learning analytics initiative in Uruguay. Information, 13(9), 401. DOI: https://doi.org/10.3390/info13090401
Rose, D. (2000). Universal design for learning. Journal of Special Education Technology, 15(4), 47–51. DOI: https://doi.org/10.1177/016264340001500407
Rust, M. M., & Motz, B. A. (2025). Incorporating an LMS learning analytic into proactive advising: Validity and use in a randomized experiment. The Internet and Higher Education, 101057. DOI: https://doi.org/10.35542/osf.io/sjw2b_v2
Saqr, M., & López-Pernas, S. (2024). Learning analytics methods and tutorials: A practical guide using R (p. 736). Springer Nature. DOI: https://doi.org/10.1007/978-3-031-54464-4
Sclater, N., Peasgood, A., & Mullan, J. (2016). Learning analytics in higher education: A review of UK and international practice. Jisc.
Slade, S., & Prinsloo, P. (2013). Learning analytics: Ethical issues and dilemmas. American Behavioral Scientist, 57(10), 1510–1529. DOI: https://doi.org/10.1177/0002764213479366
Smolansky, A., Cram, A., Raduescu, C., Zeivots, S., Huber, E., & Kizilcec, R. F. (2023, July). Educator and student perspectives on the impact of generative AI on assessments in higher education. In Proceedings of the Tenth ACM Conference on Learning@ Scale (pp. 378–382). DOI: https://doi.org/10.1145/3573051.3596191
Stahl, M., Biermann, L., Nehring, A., & Wachsmuth, H. (2024, June). Exploring LLM prompting strategies for joint essay scoring and feedback generation. In Proceedings of the 19th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2024) (pp. 283–298).
Stavrinides, P., & Zuev, K. M. (2023). Course-prerequisite networks for analyzing and understanding academic curricula. Applied Network Science, 8(1), 19. DOI: https://doi.org/10.1007/s41109-023-00543-w
Susnjak, T. (2024). Beyond predictive learning analytics modelling and onto explainable artificial intelligence with prescriptive analytics and ChatGPT. International Journal of Artificial Intelligence in Education, 34(2), 452–482. DOI: https://doi.org/10.1007/s40593-023-00336-3
Verbert, K., Ochoa, X., De Croon, R., Dourado, R. A., & De Laet, T. (2020, March). Learning analytics dashboards: The past, the present and the future. In Proceedings of the Tenth International Conference on Learning Analytics & Knowledge (pp. 35–40). DOI: https://doi.org/10.1145/3375462.3375504
Volkwein, J. F. (1999). The foundations and evolution of institutional research. New Directions for Institutional Research, 1999(104), 5–20. DOI: https://doi.org/10.1002/ir.10401
Weil, D., Kendall, C., & Snyder, R. (2023). A modern framework for institutional analytics. EDUCAUSE Review.
Yu, R., Pardos, Z., Chau, H., & Brusilovsky, P. (2021, June). Orienting students to course recommendations using three types of explanation. In Adjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization (pp. 238–245). DOI: https://doi.org/10.1145/3450614.3464483
