, Volume 59, Issue 1, pp 64–71 | Cite as

Let’s not forget: Learning analytics are about learning

  • Dragan GaševićEmail author
  • Shane Dawson
  • George Siemens


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.


educational research Learning analytics learning sciences learning technology self-regulated learning 


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  1. 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 CrossRefGoogle Scholar
  2. 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 CrossRefGoogle Scholar
  3. Bayne, S., & Ross, J. (2014). The pedagogy of the Massive Open Online Course: the UK view. The Higher Education Academy. Retrieved from
  4. 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
  5. 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
  6. 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 CrossRefGoogle Scholar
  7. 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
  8. 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
  9. 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
  10. 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 CrossRefGoogle Scholar
  11. 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 CrossRefGoogle Scholar
  12. Hattie, J., & Timperley, H. (2007). The Power of Feedback. Review of Educational Research, 77(1), 81–112. doi: 10.3102/003465430298487 CrossRefGoogle Scholar
  13. 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
  14. 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
  15. 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.CrossRefGoogle Scholar
  16. 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 CrossRefGoogle Scholar
  17. 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 CrossRefGoogle Scholar
  18. 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
  19. 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 CrossRefGoogle Scholar
  20. 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.CrossRefGoogle Scholar
  21. OECD. (2013). Education at a Glance 2013: OECD Indicators. Retrieved from 10.1787/eag-2013-en
  22. 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 CrossRefGoogle Scholar
  23. Siemens, G., & Gašević, D. (2012). Special Issue on Learning and Knowledge Analytics. Educational Technology & Society, 15(3), 1–163.Google Scholar
  24. 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 CrossRefGoogle Scholar
  25. 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 CrossRefGoogle Scholar
  26. 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 CrossRefGoogle Scholar
  27. Wilson, T. D. (1999). Models in information behaviour research. Journal of Documentation, 55(3), 249–270. doi: 10.1108/EUM0000000007145 CrossRefGoogle Scholar
  28. 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 CrossRefGoogle Scholar
  29. 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
  30. 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 CrossRefGoogle Scholar

Copyright information

© Association for Educational Communications and Technology 2014

Authors and Affiliations

  • Dragan Gašević
    • 1
    Email author
  • Shane Dawson
    • 2
  • George Siemens
    • 3
  1. 1.University of EdinburghEdinburghUnited Kingdom
  2. 2.University of South AustraliaAdelaideAustralia
  3. 3.University of TexasArlingtonUSA

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