Let’s not forget: Learning analytics are about learning
The analysis of data collected from the interaction of users with educational and information technology has attracted much attention as a promising approach for advancing our understanding of the learning process. This promise motivated the emergence of the new research field, learning analytics, and its closely related discipline, educational data mining. This paper first introduces the field of learning analytics and outlines the lessons learned from well-known case studies in the research literature. The paper then identifies the critical topics that require immediate research attention for learning analytics to make a sustainable impact on the research and practice of learning and teaching. The paper concludes by discussing a growing set of issues that if unaddressed, could impede the future maturation of the field. The paper stresses that learning analytics are about learning. As such, the computational aspects of learning analytics must be well integrated within the existing educational research.
Keywordseducational research Learning analytics learning sciences learning technology self-regulated learning
Unable to display preview. Download preview PDF.
- Bayne, S., & Ross, J. (2014). The pedagogy of the Massive Open Online Course: the UK view. The Higher Education Academy. Retrieved from https://www.heacademy.ac.uk/resources/detail/elt/the_pedagogy_of_the_MOOC_UK_view
- Corrin, L., & de Barba, P. (2014). Exploring students’ interpretation of feedback delivered through learning analytics dashboards. In Proceedings of the ascilite 2014 conference. Dunedin, NZ.Google Scholar
- Dawson, S., Gašević, D., Siemens, G., & Joksimovic, S. (2014). Current State and Future Trends: A Citation Network Analysis of the Learning Analytics Field. In Proceedings of the Fourth International Conference on Learning Analytics And Knowledge (pp. 231–240). New York, NY, USA: ACM. doi:10.1145/2567574.2567585
- Gašević, D., Dawson, S., Rogers, T., & Gasevic, D. (2014). Learning analytics should not promote one size fits all: The effects of instructional conditions in predicating learning success. Submitted to The Internet and Higher Education. Google Scholar
- Gašević, D., Mirriahi, N., & Dawson, S. (2014). Analytics of the Effects of Video Use and Instruction to Support Reflective Learning. In Proceedings of the Fourth International Conference on Learning Analytics And Knowledge (pp. 123–132). New York, NY, USA: ACM. doi:10.1145/2567574.2567590
- Gašević, D., Mirriahi, N., Dawson, S., & Joksimovic, S. (2014). What is the role of teaching in adoption of a learning tool? A natural experiment of video annotation tool use. Submitted for Publication to Computers & Education. Google Scholar
- Jayaprakash, S. M., Moody, E. W., Lauria, 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.Google Scholar
- Kovanović, V., Joksimović, S., Gašević, D., Siemens, G., & Hatala, M. (2014). What public media reveals about MOOCs? Submitted for Publication to British Journal of Educational Technology. Google Scholar
- Macfadyen, L. P., & Dawson, S. (2012). Numbers Are Not Enough. Why e-Learning Analytics Failed to Inform an Institutional Strategic Plan. Educational Technology & Society, 15(3).Google Scholar
- OECD. (2013). Education at a Glance 2013: OECD Indicators. Retrieved from http://dx.doi.org/10.1787/eag-2013-en
- Siemens, G., & Gašević, D. (2012). Special Issue on Learning and Knowledge Analytics. Educational Technology & Society, 15(3), 1–163.Google Scholar
- Winne, P. H., & Hadwin, A. F. (1998). Studying as selfregulated learning. In D. J. Hacker, J. Dunlosky, & A. C. Graesser (Eds.), Metacognition in educational theory and practice (pp. 277–304). Mahwah, NJ, US: Lawrence Erlbaum Associates Publishers.Google Scholar