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.
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Ali, L., Hatala, M., Gašević, D., & Jovanović, J. (2012). A qualitative evaluation of evolution of a learning analytics tool. Computers & Education, 58(1), 470–489. doi:10.1016/j.compedu.2011.08.030
Arnold, K. E., & Pistilli, M. D. (2012). Course signals at Purdue: using learning analytics to increase student success. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (pp. 267–270). New York, NY, USA: ACM. doi:10.1145/2330601.2330666
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.
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
Elton, L. (2004). Goodhart’s Law and Performance Indicators in Higher Education. Evaluation & Research in Education, 18(1-2), 120–128. doi:10.1080/09500790408668312
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.
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.
Greene, J. A., & Azevedo, R. (2009). A macro-level analysis of SRL processes and their relations to the acquisition of a sophisticated mental model of a complex system. Contemporary Educational Psychology, 34(1), 18–29. doi:10.1016/j.cedpsych.2008.05.006
Hadwin, A. F., Nesbit, J. C., Jamieson-Noel, D., Code, J., & Winne, P. H. (2007). Examining trace data to explore self-regulated learning. Metacognition and Learning, 2(2-3), 107–124. doi:10.1007/s11409-007-9016-7
Hattie, J., & Timperley, H. (2007). The Power of Feedback. Review of Educational Research, 77(1), 81–112. doi:10.3102/003465430298487
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.
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.
Liu, Z., Nersessian, N. J., & Stasko, J. T. (2008). Distributed cognition as a theoretical framework for information visualization. IEEE Transactions on Visualization and Computer Graphics, 14(6), 1173–1180.
Lockyer, L., Heathcote, E., & Dawson, S. (2013). Informing Pedagogical Action Aligning Learning Analytics With Learning Design. American Behavioral Scientist, 57(10), 1439–1459. doi:10.1177/0002764213479367
Lust, G., Elen, J., & Clarebout, G. (2013). Students’ tool-use within a web enhanced course: Explanatory mechanisms of students’ tool-use pattern. Computers in Human Behavior, 29(5), 2013–2021. doi:10.1016/j.chb.2013.03.014
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).
McGill, T. J., & Klobas, J. E. (2009). A task-technology fit view of learning management system impact. Computers & Education, 52(2), 496–508. doi:10.1016/j.compedu.2008.10.002
McNamara, D. S., Graesser, A. C., McCarthy, P. M., & Cai, Z. (2014). Automated Evaluation of Text and Discourse with Coh-Metrix. Cambridge, UK: Cambridge University Press.
OECD. (2013). Education at a Glance 2013: OECD Indicators. Retrieved from http://dx.doi.org/10.1787/eag-2013-en
Reimann, P., Markauskaite, L., & Bannert, M. (2014). e-Research and learning theory: What do sequence and process mining methods contribute? British Journal of Educational Technology, 45(3), 528–540. doi:10.1111/bjet.12146
Siemens, G., & Gašević, D. (2012). Special Issue on Learning and Knowledge Analytics. Educational Technology & Society, 15(3), 1–163.
Tanes, Z., Arnold, K. E., King, A. S., & Remnet, M. A. (2011). Using Signals for appropriate feedback: Perceptions and practices. Computers & Education, 57(4), 2414–2422. doi:10.1016/j.compedu.2011.05.016
Trigwell, K., Prosser, M., & Waterhouse, F. (1999). Relations between teachers’ approaches to teaching and students’ approaches to learning. Higher Education, 37(1), 57–70. doi:10.1023/A:1003548313194
Verbert, K., Duval, E., Klerkx, J., Govaerts, S., & Santos, J. L. (2013). Learning Analytics Dashboard Applications. American Behavioral Scientist, 57(10), 1500–1509. doi:10.1177/0002764213479363
Wilson, T. D. (1999). Models in information behaviour research. Journal of Documentation, 55(3), 249–270. doi:10.1108/EUM0000000007145
Winne, P. H. (2006). How Software Technologies Can Improve Research on Learning and Bolster School Reform. Educational Psychologist, 41(1), 5–17. doi:10.1207/s15326985ep4101_3
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.
Zhou, M., & Winne, P. H. (2012). Modeling academic achievement by self-reported versus traced goal orientation. Learning and Instruction, 22(6), 413–419. doi:10.1016/j.learninstruc.2012.03.004
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Gašević, D., Dawson, S. & Siemens, G. Let’s not forget: Learning analytics are about learning. TECHTRENDS TECH TRENDS 59, 64–71 (2015). https://doi.org/10.1007/s11528-014-0822-x
- educational research
- Learning analytics
- learning sciences
- learning technology
- self-regulated learning