Fostering An Impactful Field of Learning Analytics




Editorial, implementation, rigour, open science, peer review, impact, research, transparency


Over 2019 the breadth of articles published in the Journal of Learning Analytics reflects the diversity of contributions to the field, including quantitative, qualitative and design-based studies and contributions through the different submission types (Research, Practical, Data and Tools, and Book Review). This year we have also published two special sections. The first is a set of empirical papers related to the emerging area of Human-Centered Learning Analytics. In this section, five sets of authors each address the core challenge of how to design and implement learning analytics in ways that are people- rather than technology-centric to achieve impact in the field. The second is a new format for the journal, an invited dialogue around a critical community issue. In this section Neil Selwyn asks the intentionally provocative question “what’s the problem with learning analytics?”, which is engaged with by four respondents from the community, representing a variety of different perspectives. Both of these special sections tackle, in their own way, a central issue for the learning analytics community: how we ensure that the work we do has positive impact in the world.


Kitto, K., Buckingham, S. S., & Gibson, A. (2018). Embracing imperfection in learning analytics. In Proceedings of the 8th International Conference on Learning Analytics and Knowledge (LAK '18). ACM, New York, NY, USA, 451-460.

Stuart, H. H., Levine, R. A., & Utts, J. (2019) Coup de Grâce for a Tough Old Bull: “Statistically Significant” Expires. The American Statistician, 73:sup1, 352-357.

Prager, E. M., Chambers, K. E., Plotkin, J. L., McArthur, D. L., Bandrowski, A. E., Bansal, N., Martone, M. E., Bergstrom, H. C., Bespalov, A. & Graf, C. (2019), Improving transparency and scientific rigor in academic publishing. Brain and Behavior, 9(1).

Tennant, J.P., Dugan, J.M., Graziotin, D. et al. (2017) A multi-disciplinary perspective on emergent and future innovations in peer review [version 3; peer review: 2 approved]. F1000Research, 6:1151

Tracy, S. J. (2010). Qualitative quality: Eight “big-tent” criteria for excellent qualitative research. Qualitative inquiry, 16(10), 837-851.

Wise, A. F., Knight, S., & Ochoa, X. (2018). When Are Learning Analytics Ready and What Are They Ready For. Journal of Learning Analytics, 5(3), 1-4.




How to Cite

Knight, S., Wise, A., & Ochoa, X. (2019). Fostering An Impactful Field of Learning Analytics. Journal of Learning Analytics, 6(3), 1–4.

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