Learning Analytics for Primary and Secondary Schools





primary schools, secondary schools, learning analytics adoption


Over the past decade, the increasing use of learning analytics opened the possibility of making data-driven decisions for improving student learning. Driven by the strong university adoption of learning analytics, most early learning analytics research focused on issues specific to tertiary education. With the broader adoption of educational technologies in primary and secondary education and the emergence of new classroom-focused technologies, there has been a growing awareness of the potentials of learning analytics for supporting students and diagnosing their learning progress in pre-university contexts. This special section focused on investigating, developing, and evaluating state-of-the-art learning analytics approaches within primary and secondary school settings. In this editorial, we summarize the papers of the special section and discuss the challenges and opportunities for learning analytics within the school context. We conclude with the discussion around the opportunities for future work and the implications of this special section for the field of learning analytics.


Bienkowski, M. A., Feng, M., & Means, B. (2012). Enhancing teaching and learning through educational data mining and learning analytics: An issue brief. U.S. Department of Education. Retrieved from https://tech.ed.gov/wpcontent/uploads/2014/03/edm-la-brief.pdf

Choi, S. P., Lam, S. S., Li, K. C., & Wong, B. T. (2018). Learning analytics at low cost: At-risk student prediction with clicker data and systematic proactive interventions. Journal of Educational Technology & Society, 21(2), 273–290. https://www.j-ets.net/collection/published-issues/21_2

Cloude, E., Carpenter, D., Dever, D. A., Azevedo, R., & Lester, J. (2021). Game-based learning analytics for supporting adolescents’ reflection. Journal of Learning Analytics, 8(2), 51-72. https://doi.org/10.18608/jla.2021.7371

Few, S. (2013). Information dashboard design: Displaying data for at-a-glance monitoring (2nd Ed.). El Dorado Hills, CA: Analytics Press.

Freeman, A., Adams Becker, S., Cummins, M., Davis, A., & Hall Giesinger, C. (2017). NMC/CoSN Horizon Report: 2017 K–12 edition. The New Media Consortium. Retrieved from https://library.educause.edu/~/media/files/library/2017/11/2017hrk12EN.pdf

Gonski, D., Arcus, T., Boston, K., Gould, V., Johnson, W., O’Brien, L., Perry, L.-A., & Roberts, M. (2018). Through growth to achievement: Report of the review to achieve educational excellence in Australian schools. Department of Education and Training. Retrieved from https://www.dese.gov.au/quality-schools-package/resources/through-growthachievement-report-review-achieve-educational-excellence-australian-schools

Gutiérrez, F., Seipp, K., Ochoa, X., Chiluiza, K., De Laet, T., & Verbert, K. (2020). LADA: A learning analytics dashboard for academic advising. Computers in Human Behavior, 107, 105826. https://doi.org/10.1016/j.chb.2018.12.004

Gunawardena, A. (2017). Brief survey of analytics in K–12 and higher education. International Journal on Innovations in Online Education, 1(1). https://doi.org/10.1615/IntJInnovOnlineEdu.v1.i1.80

Horn, M. B., & Staker, H. (2011). The rise of K–12 blended learning. Innosight Institute. Retrieved from https://www.christenseninstitute.org/wp-content/uploads/2013/04/The-rise-of-K-12-blended-learning.emergingmodels.pdf

Krumm, A. E., Boyce, J., & Everson, H. T. (2021). A collaborative approach to sharing learner event data. Journal of Learning Analytics, 8(2), 73-82. https://doi.org/10.18608/jla.2021.7375

Li, K. C., Lam, H. K., & Lam, S. S. (2015). A review of learning analytics in educational research. In J. Lam, K. K. Ng, S. K. S. Cheung, T. L. Wong, K. C. Li, & F. L. Wang (Eds.), Technology in education: Technology-mediated proactive learning (pp. 173–184). Berlin, Germany: Springer. https://doi.org/10.1007/978-3-662-48978-9_17

Lodge, J. M., Horvath, J. C., & Corrin, L. (2018). Learning analytics in the classroom: Translating learning analytics for teachers. Abingdon-on-Thames, UK: Routledge.

McAlpine, L., Weston, C., Beauchamp, C., Wiseman, C., & Beauchamp, J. (1999). Building a metacognitive model of reflection. Higher Education, 37(2), 105–131. https://doi.org/10.1023/A:1003548425626

McRae, P. (2014). Rebirth of the teaching machine through the seduction of data analytics: This time it’s personal. Revista Intercambio, 6, 28–32. http://revistaintercambio.org/ojsclon/index.php/INTERCAMBIO/article/view/25

Ochoa, X. (2017). Multimodal learning analytics. In C. Lang, G. Siemens, A. F. Wise, & D. Gaševic (Eds.), The handbook of learning analytics (1st ed., pp. 129–141). Beaumont, AB: Society for Learning Analytics Research (SoLAR). http://solaresearch.org/hla-17/hla17-chapter1

Pelanek, R. (2021). Analyzing and visualizing learning data: A system developer’s perspective. Journal of Learning Analytics, 8(2), 93-104. https://doi.org/10.18608/jla.2021.7345

Roberts-Mahoney, H., Means, A. J., & Garrison, M. J. (2016). Netflixing human capital development: Personalized learning technology and the corporatization of K–12 education. Journal of Education Policy, 31(4), 405–420. https://doi.org/10.1080/02680939.2015.1132774

Rodríguez-Triana, M. J., Prieto, L. P., Dimitriadis, Y., de Jong, T., & Gillet, D. (2021). ADA for IBL: Lessons learned in aligning learning design and analytics for inquiry-based learning orchestration. Journal of Learning Analytics, 8(2), 22-50. https://doi.org/10.18608/jla.2021.7357

Sancho, M.-R., Cañabate, A., & Sabate, F. (2015). Contextualizing learning analytics for secondary schools at micro level. Proceedings of the 2015 International Conference on Interactive Collaborative and Blended Learning (ICBL 2015), 9-11 December 2015, Mexico City, Mexico (pp. 70–75) Washington, DC: IEEE Computer Society. https://doi.org/10.1109/ICBL.2015.7387638

Selwyn, N., & Facer, K. (2013). The politics of education and technology: Conflicts, controversies, and connections. New York, NY: Springer. https://doi.org/10.1057/9781137031983

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Trezise, K., de Barba, P. G., Jennens, D., Zarebski, A., Russo, R., & Kennedy, G. (2017). A learning analytics view of students’ use of self-regulation strategies for essay writing. In H. Partridge, K. Davis, & J. Thomas (Eds.), Me, Us, IT! Proceedings of the 34th Annual Conference of the Australasian Society for Computers in Learning in Tertiary Education (ASCILITE 2017), 4–6 December 2017, Toowoomba, Queensland, Australia (pp. 411–421). Australasian Society for Computers in Learning in Tertiary Education. https://2017conference.ascilite.org/wpcontent/uploads/2017/11/ASCILITE-2017-Proceeding.pdf

van Leeuwen, A., Knoop-van Campen, C. A. N., Molenaar, I., & Rummel, N. (2021). How teacher characteristics relate to how teachers use dashboards. Journal of Learning Analytics, 8(2), 6-21. https://doi.org/10.18608/jla.2021.7325

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://misq.org/user-acceptance-of-information-technology-toward-aunified-view.html?SID=tdk2f2ove4s5g7q1h8cfmnhoh5

Voogt, J., Knezek, G., Christensen, R., & Lai, K.-W. (2018). Developing an understanding of the impact of digital technologies on teaching and learning in an ever-changing landscape. In J. Voogt, G. Knezek, R. Christensen, & K.-W. Lai (Eds.), Second handbook of information technology in primary and secondary education (pp. 3–12). Springer International Publishing. https://doi.org/10.1007/978-3-319-71054-9_113

Yacobson, E., Fuhrman, O., Hershkovitz, S., & Alexandron, G. (2021). De-identification is insufficient to protect student privacy, or — What can a field trip reveal? Journal of Learning Analytics, 8(2), 83-92. https://doi.org/10.18608/jla.2021.7353




How to Cite

Kovanovic, V., Mazziotti, C., & Lodge, J. (2021). Learning Analytics for Primary and Secondary Schools. Journal of Learning Analytics, 8(2), 1-5. https://doi.org/10.18608/jla.2021.7543



Special Section: Learning Analytics for Primary and Secondary Schools