Learning analytics

I have contributed to the field of learning analytics since 2009. The Society for Learning Analytics Research (SOLAR) defines learning analytics as

the collection, analysis, interpretation and communication of data about learners and their learning that provides theoretically relevant and actionable insights to enhance learning and teaching

(SoLAR, 2025)

In addition to applied learning analytics projects, I have also contributed conceptual work to this field, including writing about the value of systems models, and the challenges that institutions face in trying to implement learning analytics.

Selected examples of my work include:

“The IKEA project”: Development of a build-it-yourself LMS data dashboard kit for educators (2023)

I collaborated with colleague Alison Myers to develop a custom teacher-facing learning analytics dashboard, using learner activity data from the Canvas LMS. We proposed this pragmatic DIY for educators in contexts where the institution is struggling to establish LA solutions at scale” (Macfadyen & Myers, 2023).

Curriculum analytics: Using analytic methods to enrich curriculum review

I explored ways of integrating learning analytics and data visualization methods into curriculum review, to augment more traditional qualitative review of materials and outcomes with data revealing actual patterns of course enrollment, program completion, student performance across thematic areas, learner demographics, and thematic content of courses (Macfadyen, 2020a; Macfadyen, 2020b; Rashtian et al., 2020).

Proof of concept: The predictive power of LMS data

In the late 2000s, several small studies had suggested positive links between learner activity metrics in learning management systems (LMSs) and their eventual learning outcomes. Together with colleague Shane Dawson (Adelaide University, Australia), I undertook a systematic data analysis project exploring data from online courses. Our highly cited 2010 paper laid the groundwork for ongoing learning analytics research in this area.

References

Macfadyen, L. P., & Myers, A. (2023). The “IKEA Model” for pragmatic development of a custom learning analytics dashboard. In T. Cochrane, V. Narayan, C. Brown, K. MacCallum, E. Bone, C. Deneen, R. Vanderburg, & B. Hurren (Eds.), People, partnerships and pedagogies. Proceedings ASCILITE 2023 (pp. 482-486). ASCILITE. https://doi.org/10.14742/apubs.2023.465

Macfadyen, L. P. (2020a). Content analytics for curriculum review: A learning analytics use case for exploration of learner context. Proceedings, ASCILITE 2020: 37th International Conference on Innovation, Practice and Research in the Use of Educational Technologies in Tertiary Education, 30 November-1 December 2020 (Virtual) (pp. 42-47). ASCILITE. https://ascilite.org/wp-content/uploads/ASCILITE-2020-Conference-Proceedings-Draft-Version.pdf

Macfadyen, L. P. (2020b). Using Quantext for curriculum analysis. In J. McDonald, A. Moskal, C. Gunn, & I. Elgort (Eds.), Quantext pilot study. Project report. Ako Aotearoa. https://ako.ac.nz/assets/Knowledge-centre/Quantext-For-rapid-analysis-of-student-responses-to-short-answer-questions/Quantext.pdf

Macfadyen, L. P., & Dawson, S. (2010). Mining LMS data to develop an ‘‘early warning system” for educators: A proof of concept. Computers & Education, 54(2), 588-599. https://doi.org/10.1016/j.compedu.2009.09.008

Rashtian, H., Hashemi, A., & Macfadyen, L. P. (2020). Harnessing natural language processing to support curriculum analysis. In Proceedings, iCERi 2020: 13th annual international conference of education, research and innovation, pp. 1779-1784. International Academy of Technology, Education and Development (IATED). https://doi.org/10.21125/iceri.2020.0445

References

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