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Analysing Structured Learning Behaviour in Massive Open Online Courses (MOOCs): An Approach Based on Process Mining and Clustering
ARTICLE English

, Eindhoven School of Education, Eindhoven University of Technology, The Netherlands ; , Eindhoven University of Technology ; , RWTH Aachen University, Germany

IRRODL Volume 19, Number 5, ISSN 1492-3831 Publisher: Athabasca University Press

Abstract

The increasing use of digital systems to support learning leads to a growth in data regarding both learning processes and related contexts. Learning Analytics offers critical insights from these data, through an innovative combination of tools and techniques. In this paper, we explore students’ activities in a MOOC from the perspective of personal constructivism, which we operationalized as a combination of learning behaviour and learning progress. This study considers students’ data analyzed as per the MOOC Process Mining: Data Science in Action. We explore the relation between learning behaviour and learning progress in MOOCs, with the purpose to gain insight into how passing and failing students distribute their activities differently along the course weeks, rather than predict students' grades from their activities. Commonly-studied aggregated counts of activities, specific course item counts, and order of activities were examined with cluster analyses, means analyses, and process mining techniques. We found four meaningful clusters of students, each representing specific behaviour ranging from only starting to fully completing the course. Process mining techniques show that successful students exhibit a more steady learning behaviour. However, this behaviour is much more related to actually watching videos than to the timing of activities. The results offer guidance for teachers.

Citation

van den Beemt, A., Buijs, J. & van der Aalst, W. (2018). Analysing Structured Learning Behaviour in Massive Open Online Courses (MOOCs): An Approach Based on Process Mining and Clustering. The International Review of Research in Open and Distributed Learning, 19(5),. Athabasca University Press. Retrieved December 19, 2018 from .

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References

  1. Bächtold, M. (2013). What do students “construct” according to constructivism in science education? Research in Science Education, 43(6), 2477–2496.
  2. Bogarín, A., Cerezo, R., & Romero, C. (2018). A survey on educational process mining. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(1).
  3. Brooks, C., & Thompson, C. (2017). Predictive modelling in teaching and learning. In C. Lang, G. Siemens, A.F. Wise, & D. Gasevic (Eds.), Handbook of Learning Analytics (pp. 61-68). DOI
  4. Buckingham Shum, S., & Ferguson, R. (2012). Social learning analytics. Educational Technology& Society, 15(3), 3–26. Retrieved from https://www.j-ets.net/ETS/journals/15_3/2.pdf
  5. Chi, M. (2000). Self-explaining: The dual processes of generating inferences and repairing mental models. In R. Glaser (Ed.), Advances in instructional psychology. Mahwah, NJ: Lawrence Erlbaum Associates.
  6. Clow, D. (2013). MOOCs and the funnel of participation. In Proceedings of the Third International
  7. Gillani, N., & Eynon, R. (2014). Communication patterns in massively open online courses. Internet and Higher Education, 23, 18–26.
  8. Kizilcec, R.F., Piech, C., & Schneider, E. (2013). Deconstructing disengagement: analyzing learner subpopulations in massive open online courses. In Proceedings of the Third International
  9. Loyens, S.M.M., & Gijbels, D. (2008). Understanding the effects of constructivist learning environments: Introducing a multi-directional approach. Instructional Science, 36(5–6), 351–357.
  10. Maldonado-Mahauad, J., Pérez-Sanagustín, M., Kizilcec, R.F., Morales, N., & Munoz-Gama, J.(2018). Mining theory-based patterns from big data: Identifying self-regulated learning strategies in massive open online courses. Computers in Human Behavior, 80, 179–196.
  11. Reigeluth, C.M. (2016). Instructional theory and technology for the new paradigm of education. Revista de Educación a Distancia, (50), 1–18.
  12. Trcka, N., Pechenizkiy, M., & Vander Aalst, W.M.P. (2011). Process mining from educational data. In
  13. Vahdat, M., Oneto, L., Anguita, D., Funk, M., & Rauterberg, M. (2015). A learning analytics approach
  14. Vander Aalst, W.M.P. (2016). Process Mmining: Data science in action. Heidelberg: Springer.
  15. Veletsianos, G., & Shepherdson, P. (2016). A systematic analysis and synthesis of the empirical MOOC literature published in 2013–2015. The International Review of Research in Open and Distributed Learning, 17(2). Https://doi.org/10.19173/irrodl.v17i2.2448
  16. Wen, M., & Rose, C.P. (2014). Identifying latent study habits by mining learner behavior patterns in massive open online courses. In Proceedings of the 23rd ACM International Conference on

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