Let’s Grow Together: Tutorials on Learning Analytics Methods


  • Dragan Gasevic The University of Edinburgh
  • Mykola Pechenizkiy Eindhoven University of Technology, The Netherlands




learning analytics, tutorials, text analysis, social network analysis, epistemic network analysis, microgenetic learning, sequence pattern mining, information visualization


This paper is a guest editorial into a special section that offers a collection of tutorials on methods that can be used in learning analytics. The special section is prepared as a response to the growing need of learning analytics practitioners and researchers to learn and use novel methods. In spite of this need, papers that systematically introduce some of the methods have been underrepresented in the literature. Specifically, the special section features papers that introduce epistemic network analysis, automated content and network analysis of social media, text coherence analysis with Coh-Metrix, microgenetic analysis with sequence pattern mining, and design of visual learning analytics guided by educational theory informed goals.


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. https://doi.org/10.1145/2567574.2567585

Gašević, D., Dawson, S., & Siemens, G. (2015). Let’s not forget: Learning analytics are about learning. TechTrends, 59(1), 64–71. https://doi.org/10.1007/s11528-014-0822-x

Graesser, A. C., McNamara, D. S., & Kulikowich, J. M. (2011). Coh-Metrix Providing Multilevel Analyses of Text Characteristics. Educational Researcher, 40(5), 223–234. https://doi.org/10.3102/0013189X11413260

Shaffer, D. W., Hatfield, D., Svarovsky, G. N., Nash, P., Nulty, A., Bagley, E., … Mislevy, R. (2009). Epistemic Network Analysis: A Prototype for 21st-Century Assessment of Learning. International Journal of Learning and Media, 1(2), 33–53. https://doi.org/10.1162/ijlm.2009.0013

Suthers, D., & Verbert, K. (2013). Learning Analytics As a “Middle Space.” In Proceedings of the Third International Conference on Learning Analytics and Knowledge (pp. 1–4). New York, NY, USA: ACM. https://doi.org/10.1145/2460296.2460298

Verbert, K., Duval, E., Klerkx, J., Govaerts, S., & Santos, J. L. (2013). Learning Analytics Dashboard Applications. American Behavioral Scientist, 57(10), 1500–1509. https://doi.org/10.1177/0002764213479363

Winne, P. H. (2014). Issues in researching self-regulated learning as patterns of events. Metacognition and Learning, 1–9. https://doi.org/10.1007/s11409-014-9113-3

Wise, A. F., & Shaffer, D. W. (2015). Why Theory Matters More than Ever in the Age of Big Data. Journal of Learning Analytics, 2(2), 5–13. https://doi.org/10.18608/jla.2015.22.2




How to Cite

Gasevic, D., & Pechenizkiy, M. (2016). Let’s Grow Together: Tutorials on Learning Analytics Methods. Journal of Learning Analytics, 3(3), 5-8. https://doi.org/10.18608/jla.2016.33.2



Special section: Tutorials in learning analytics (LASI and LAK 2014)

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