In-depth Exploration of Engagement Patterns in MOOCs

  • Lei Shi
  • Alexandra I. Cristea
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11234)


With the advent of ‘big data’, various new methods have been proposed, to explore data in several domains. In the domain of learning (and e-learning, in particular), the outcomes lag somewhat behind. This is not unexpected, as e-learning has the additional dimensions of learning and engagement, as well as other psychological aspects, to name but a few, beyond ‘simple’ data crunching. This means that the goals of data exploration for e-learning are somewhat different to the goals for practically all other domains: finding out what students do is not enough, it is the means to the end of supporting student learning and increasing their engagement. This paper focuses specifically on student engagement, a crucial issue especially for MOOCs, by studying in much greater detail than previous work, the engagement of students based on clustering students according to three fundamental (and, arguably, comprehensive) dimensions: learning, social and assessment. The study’s value lies also in the fact that it is among the few studies using real-world longitudinal data (6 runs of a course, over 3 years) from a large number of students.


E-learning Learning analysis Behavioral analysis Clustering analysis K-means MOOCs 


  1. 1.
    Antonenko, P.D., et al.: Using cluster analysis for data mining in educational technology research. Educ. Tech. Res. Dev. 60(3), 383–398 (2012)CrossRefGoogle Scholar
  2. 2.
    Cristea, A.I., et al.: Earliest predictor of dropout in MOOCs: a longitudinal study of FutureLearn courses. Presented at the 27th International Conference on Information Systems Development (ISD2018), Lund, Sweden, 22 August 2018Google Scholar
  3. 3.
    Cristea, A.I., et al.: How is learning fluctuating? FutureLearn MOOCs fine-grained temporal analysis and feedback to teachers and designers. Presented at the 27th International Conference on Information Systems Development (ISD2018), Lund, Sweden, 22 August 2018Google Scholar
  4. 4.
    Csiksczentmihalyi, M., et al.: Flow: the psychology of optimal experience. Aust. Occup. Ther. J. 51(1), 3–12 (2004)CrossRefGoogle Scholar
  5. 5.
    Ferguson, R., Clow, D.: Examining engagement: analysing learner subpopulations in massive open online courses (MOOCs). Presented (2015)Google Scholar
  6. 6.
    Guo, P.J., et al.: How video production affects student engagement: an empirical study of MOOC videos. Presented (2014)Google Scholar
  7. 7.
    Henrie, C.R., et al.: Measuring student engagement in technology-mediated learning: a review. Comput. Educ. 90, 36–53 (2015)CrossRefGoogle Scholar
  8. 8.
    Kodinariya, T.M., Makwana, P.R.: Review on determining number of cluster in K-means clustering. Int. J. 1(6), 90–95 (2013)Google Scholar
  9. 9.
    Laurillard, D.: Rethinking University Teaching: A Conversational Framework for the Effective Use of Learning Technologies. London, RoutledgeFalmer (2002)Google Scholar
  10. 10.
    MacQueen, J., et al.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Oakland, CA, USA, pp. 281–297 (1967)Google Scholar
  11. 11.
    Marks, H.M.: Student engagement in instructional activity: patterns in the elementary, middle, and high school years. Am. Educ. Res. J. 37(1), 153–184 (2000)CrossRefGoogle Scholar
  12. 12.
    Al Mamun, M.A., et al.: Factors affecting student engagement in self-directed online learning module. In: The Australian Conference on Science and Mathematics Education (Formerly UniServe Science Conference), p. 15 (2017)Google Scholar
  13. 13.
    Shernoff, D.J., et al.: Student engagement in high school classrooms from the perspective of flow theory. In: Csikszentmihalyi, M. (ed.) Applications of Flow in Human Development and Education: The Collected Works of Mihaly Csikszentmihalyi, pp. 475–494. Springer, Dordrecht (2014). Scholar
  14. 14.
    Shi, L., et al.: Towards understanding learning behavior patterns in social adaptive personalized E-learning systems. In: The 19th Americas Conference on Information Systems, pp. 1–10. Association for Information Systems, Chicago (2013)Google Scholar
  15. 15.
    Shi, L., Cristea, A.I.: Demographic indicators influencing learning activities in MOOCs: learning analytics of FutureLearn courses. Presented at the 27th International Conference on Information Systems Development (ISD2018), Lund, Sweden, 22 August 2018Google Scholar
  16. 16.
    Sinatra, G.M., et al.: The challenges of defining and measuring student engagement in science. Educ. Psychol. 50(1), 1–13 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.University of LiverpoolLiverpoolUK
  2. 2.Durham UniversityDurhamUK

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