Special Section on Learning Analytics for Primary and Secondary Schools-Call for Papers




Over the past decade, there has been substantial growth in the adoption of learning analytics and data-driven techniques for improving teaching and learning. Learning analytics have been used for monitoring, supporting, and assessing different forms of learning and teaching and thus opened the possibility to make data-driven decisions of how to improve student learning. By investigating varying online and blended learning modalities and addressing issues around student retention, students at risks and study approaches, most of the previous work, however, has focused on tertiary education and issues that were specific for that educational context. 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.

The focus of this special section is on investigating, developing, and evaluating state-of-the-art learning analytics approaches within the primary and secondary school settings. We specifically invite both research and practitioner contributions that go beyond the pure development of analytics systems by also addressing the specific challenges and opportunities of the school context later target group. We further invite contributions that make connections between analytical systems to contemporary pedagogies and educational theories. Finally, we also invite data and tool report submissions which provide overview of the educational datasets, tools and methods that focus on the use of learning analytics within primary and secondary school context.

Advancing the use of Learning Analytics within Schools

Although the use of educational technologies has been growing in K-12 sector (Horn & Staker, 2011; Voogt et al., 2018), learning analytics has been, for the most part, used to support tertiary learning and teaching (Li et al., 2015; Sancho et al., 2015). While there are many reasons for this, more constrained resources for supporting analytics implementation and limited expertise in data analytics within schools are likely contributing factors. The collection and analysis of data is also far more sensitive topic within the primary and secondary contexts (Gunawardena, 2017), due to significant concerns around privacy and ethical use of data by parents, teachers, legal authorities and social activists (Singer, 2014).

While the adoption of analytics has drawn sharp criticism (McRae, 2014; Roberts-Mahoney et al., 2016; Selwyn & Facer, 2013), there is also a growing realisation of the unique opportunities that analytics provide in supporting contemporary teaching and learning. The 2017 Horizon K–12 report (Freeman et al., 2017) estimated 2–3 years as the time to broader adoption of learning analytics within primary and secondary domains, with main opportunities being to “predict learner outcomes, trigger interventions or curricular adaptations, and even prescribe new pathways or strategies to improve student success” (p. 44). Similar benefits and opportunities were noted by the earlier US Department of Education report (Bienkowski et al., 2012) and also more recent Gonski report in Australia (Gonski et al., 2018), who emphasise the power of data and analytics to provide more personalised learning experiences and improve student learning outcomes. Moreover, there have been substantial developments within the learning analytics field itself; The development of multimodal learning analytics (MMLA)(Ochoa, 2017) as well novel classroom-based analytics systems (Lodge et al., 2018), provided analytical approaches that are far more suitable for primary and secondary school contexts use. Since 2018, a full-day pre-conference event on learning analytics adoption within schools has been running at LAK conference, witnessing strong interest by school teachers, administrators, policy makers, and industry representatives.

The goal of this special issue is to build upon the current momentum around learning analytics use within schools and provide a place for researchers to report their current efforts in this domain. We invite research studies, practitioner reports, and data and tool reports that focus on the use of learning analytics for supporting primary and secondary school learning. The aim is to showcase the latest developments in learning analytics use within schools, and empirical evidence on the effectiveness of its use for supporting learning and teaching in the school context. We also seek studies that aim to foster innovative pedagogical approaches through the adoption of learning analytics as well as improve theoretical understanding of teaching and learning in these spaces. Finally, given the early stage of learning analytics adoption within schools, we especially encourage studies which provide practical suggestions for the implementation of analytical systems in school settings and examination of the barriers of their adoption and areas for future work.


Some areas of interest are, but not limited to, one of the following topics:

  • Analytics tools and systems: Studies that focus on new and state-of-the-art learning analytics tools and platforms for supporting school learning and teaching (e.g., teacher dashboards). 
  • School adoption, implementation and scale-up: Studies that provide insights into the current state of analytics adoption in schools, key challenges and opportunities on an institutional and professional level, as well as reports on the current learning analytics adoption efforts.
  • Practical implications: Studies that focus on developing recommendations for practice using robust empirical evidence around analytics use within primary and secondary schools.
  • Theory building: Contributions that focus on advancing theoretical understanding of learning analytics use for supporting learning and teaching within school settings by building upon existing educational and learning science-related theories.
  • Methods: Contributions that describe methodological approaches and challenges that are grounded in theory, and serve the purpose of combining different multimodal data for using learning analytics within the primary and secondary school context.
  • Critical perspectives: Contributions which provide critical and balanced assessment and examination of learning analytics use and adoption within school settings.

Prospective authors may contact the section editors with queries. Final submissions will take place through JLA’s online submission system at http://learning-analytics.info When submitting a paper, select the section “Special Section: Learning Analytics for Primary and Secondary Schools". All submissions should follow JLA’s standard manuscript guidelines and template available on the journal website, and will undergo double-blind peer review.


  • Full manuscripts due:  September 20, 2020
  • Completion of first review round:  November, 2020
  • Revised/final manuscripts due:  December, 2020
  • Completion of second review round (if needed):  February, 2021
  • Revised/final manuscripts due:  March, 2021
  • Publication of special issue:  July/August 2021, Issue 8(2)


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.

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.

Gonski, D., Arcus, T., Boston, K., Gould, V., Johnson, W., O’Brien, L., Perry, L.-A., & Roberts, M. (2018). Through Growth to Achievement: Review to Achieve Educational Excellence in Australian Schools. Department of Education and Training.

Gunawardena, A. (2017). Brief survey of analytics in K12 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. https://www.christenseninstitute.org/wp-content/uploads/2013/04/The-rise-of-K-12-blended-learning.emerging-models.pdf

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). Springer. https://doi.org/10.1007/978-3-662-48978-9_17

Lodge, J. M., Horvath, J. C., & Corrin, L. (Eds.). (2018). Learning Analytics in the Classroom: Translating Learning Analytics Research for Teachers. Routledge.

McRae, P. (2014). Rebirth of the teaching machine through the seduction of data analytics: This time it’s personal. Revista Intercambio, 6, 28–32.

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). Society for Learning Analytics Research (SoLAR). http://solaresearch.org/hla-17/hla17-chapter1

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

Sancho, M.-R., Cañabate, A., & Sabate, F. (2015). Contextualizing learning analytics for secondary schools at micro level. 2015 International Conference on Interactive Collaborative and Blended Learning (ICBL), 70–75. https://doi.org/10.1109/ICBL.2015.7387638

Selwyn, N., & Facer, K. (2013). The Politics of Education and Technology: Conflicts, Controversies, and Connections. Springer.

Singer, N. (2014). InBloom Student Data Repository to Close. The New York Times Bits Blog. http://bits.blogs.nytimes.com/2014/04/21/inbloom-student-data-repository-to-close/

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