Massive Numbers, Diverse Learning

  • Allison LittlejohnEmail author
  • Nina Hood
Part of the SpringerBriefs in Education book series (BRIEFSEDUCAT)


MOOCs provide education for millions of people worldwide. Though it is not clear whether everyone can learn in a MOOC. Building on the typology of MOOC participants introduced is in Chap.  3, and we explore the claim that MOOCs are for everyone. We trace the different reasons people participate in MOOCs and the ways they learn. MOOCs tend to be designed for people who are already able to learn as active, autonomous learners. Those with low confidence may be inactive. However, even learners who are confident and able to regulate their learning experience difficulties if they don’t comply with the expectations of the course designers or their peers. For example, if a learner chooses to learn by observing others, rather than contributing, this behaviour can be perceived negatively by tutors and by peers. This indicates that MOOCs sustain the traditional hierarchy between the educators (those that create MOOCs and technology systems) and the learners (those who use these courses and systems). Although this hierarchy is not always visible, since it is embedded within the algorithms and analytics that power MOOC tools and platforms.



The authors wish to thank Vicky Murphy of The Open University for comments and for proofing this chapter.


  1. Abeer, W., & Miri, B. (2014). Students’ preferences and views about learning in a MOOC. Procedia—Social and Behavioral Sciences, 152, 318–323.CrossRefGoogle Scholar
  2. Alario-Hoyos, C., Perez-Sanagustin, M., Cormier, D., & Delgado-Kloos, C. (2014). Proposal for a conceptual framework for educators to describe and design MOOCs. Journal of Universal Computer Science, 20(1), 6–23.Google Scholar
  3. Balakrishnan, G., & Cooetzee, D. (2013). Predicting student retention in Massive Open Online Courses using Markov models (Report No. UCB/EECS-2013-109). Berkley, CA: University of California at Berkeley. Retrieved from
  4. Barron, B. (2006). Interest and self-sustained learning as catalysts of development: A learning ecology perspective. Human Development, 49(4), 193–224.CrossRefGoogle Scholar
  5. Biesta, G. (2009). Good education in an age of measurement: On the need to reconnect with the question of purpose in education. Educational Assessment, Evaluation and Accountability, 21(1), 33–46.CrossRefGoogle Scholar
  6. Boekaerts, M. (1993). Being concerned with well-being and with learning. Educational Psychologist, 28(2), 149–167.CrossRefGoogle Scholar
  7. Boyd, D., & Crawford, K. (2011, September 21). Six provocations for big data. SSRN. Paper presented at A Decade in Internet Time: Symposium on the Dynamics of the Internet and Society, Oxford Internet Institute, Oxford, UK. Retrieved from
  8. Buckingham-Shum, S., & Deakin-Crick, R. (2012, April 29–May 2). Learning dispositions and transferable competencies: pedagogy, modelling and learning analytics. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (pp. 92–101). New York, NY: ACM.Google Scholar
  9. Campbell, J., Gibbs, A., Najafi, H., & Severinski, C. (2014). A comparison of learner intent and behaviour in live and archived MOOCs. International Review of Research in Open and Distributed Learning, 15(5), 234–262.CrossRefGoogle Scholar
  10. Cobb, P., & Bower, J. (1999). Cognitive and situated learning perspectives in theory and practice. Educational Research, 28(2), 4–15.CrossRefGoogle Scholar
  11. Colvin, K., Champaign, J., Liu, A., Zhou, Q., Fredericks, C., & Pritchard, D. (2014). Learning in an introductory physics MOOC: All cohorts learn equally, including an on-campus class. International Review of Research in Open and Distributed Learning, 15(4), 263–283.CrossRefGoogle Scholar
  12. de Waard, I., Abajian, S., Gallagher, M., Hogue, R., Keskin, N., Koutropoulos, A., et al. (2011). Using mLearning and MOOCs to understand chaos, emergence, and complexity in education. International Review of Research in Open and Distance Learning, 12(7), 94–115.Google Scholar
  13. Downes, S. (2012). Connectivism and connective knowledge: Essays on meaning and learning networks. Ottawa, Canada: National Research Council Canada. Retrieved from
  14. Ebben, M., & Murphy, J. S. (2014). Unpacking MOOC scholarly discourse: A review of nascent MOOC scholarship. Learning, Media and Technology, 39(3), 328–345.CrossRefGoogle Scholar
  15. Emanuel, E. (2013). Online education: MOOCs taken by educated few. Nature, 503, 342.CrossRefGoogle Scholar
  16. Eraut, M. (1994). Developing professional knowledge and competence. London: Falmer.Google Scholar
  17. ESMA. (2016, December 19). European Supervisory Authorities consult on big data. European Securities and Markets Authority. Retrieved from
  18. Fischer, G. (2014). Beyond hype and underestimation: Identifying research challenges for the future of MOOCs. Distance Education, 35(2), 149–158.CrossRefGoogle Scholar
  19. Gašević, D., Dawson, S., & Siemens, G. (2015). Let’s not forget: Learning analytics are about learning. TechTrends, 59(1), 64–71.CrossRefGoogle Scholar
  20. Gasevic, D., Kovanovic, V., Joksimovic, S., & Siemens, G. (2014). Where is research on massive open online courses headed? A data analysis of the MOOC research initiative. The International Review of Research in Open and Distributed Learning, 15(5).Google Scholar
  21. Gillani, N., & Eynon, R. (2014). Communication patterns in massively open online courses. The Internet and Higher Education, 23, 18–26.CrossRefGoogle Scholar
  22. Gillani, N., Yasserie, T., Eynon, R., & Hjorth, I. (2014). Structural limitations of learning in a crowd: Communication vulnerability and information diffusion in MOOCs. Scientific Reports, 4, 6447.CrossRefGoogle Scholar
  23. Greeno, J., Collins, A., & Resnick, L. (1996). Cognition and learning. In D. Berliner & R. Calfee (Eds.), Handbook of educational psychology (pp. 15–41). New York, NY: MacMillian.Google Scholar
  24. Hakkarainen, K., & Paavola, S. (2007, February). From monological and dialogical to trialogical approaches to learning. Paper presented at the international workshop “Guided Construction of Knowledge in Classrooms”, Hebrew University, Jerusalem.Google Scholar
  25. Hew, K. (2014). Promoting engagement in online courses: What strategies can we learn from three highly rated MOOCS? British Journal of Educational Technology, 47(2), 320–342.CrossRefGoogle Scholar
  26. Hood, N., Littlejohn, A., & Milligan, C. (2015). Context counts: How learners’ contexts influence learning in a MOOC. Computers & Education, 91, 83–91.CrossRefGoogle Scholar
  27. Illeris, K. (2007). How we learn: Learning and non-learning in school and beyond. London: Routledge.Google Scholar
  28. Jiang, S., Williams, A. E., Warschauer, M., He, W., & O’Dowd, D. K. (2014). Influence of incentives on performance in a pre-college biology MOOC. The International Review of Research in Open and Distributed Learning, 15(5), 99–112.CrossRefGoogle Scholar
  29. Jordan, K. (2014). Initial trends in enrolment and completion of massive open online courses. The International Review of Research in Open and Distributed Learning, 15(1), 133–160.CrossRefGoogle Scholar
  30. Kellogg, S., Booth, S., & Oliver, K. (2014). A social network perspective on peer supported learning in MOOCs for educators. The International Review of Research in Open and Distributed Learning, 15(5), 265–289.CrossRefGoogle Scholar
  31. Koller, D., Ng, A., Do, C., & Chen, Z. (2013). Retention and intention in massive open online courses: In depth. EduCause Review Online, 48(3), 62–63. Retrieved from
  32. Kop, R., Fournier, H., & Mak, J. (2011). A pedagogy of abundance or a pedagogy to support human beings? Participant support on massive open online courses. International Review of Research in Open and Distributed Learning, 12(7), 74–93.CrossRefGoogle Scholar
  33. Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Cambridge, UK: Cambridge University Press.CrossRefGoogle Scholar
  34. Lin, Y. L., Lin, H. W., & Hung, T. T. (2015). Value hierarchy for massive open online courses. Computers in Human Behaviour, 53, 408–418.CrossRefGoogle Scholar
  35. Littlejohn, A., Hood, N., Milligan, C., & Mustain, P. (2016). Learning in MOOCs: Motivations and self-regulated learning in MOOCs. The Internet and Higher Education, 29, 40–48.CrossRefGoogle Scholar
  36. Liyanagunawardena, T., Adams, A., & Williams, S. (2013). MOOCs: A systematic study of the published literature 2008–2012. International Review of Research in Open and Distributed Learning, 14(3), 202–227.CrossRefGoogle Scholar
  37. Margaryan, A., Bianco, M., & Littlejohn, A. (2015). Instructional quality of massive open online courses (MOOCs). Computers & Education, 80, 77–83.CrossRefGoogle Scholar
  38. Milligan, C. (2012). Change 11 SRL-MOOC study initial findings. Blog Learning in the workplace Researching learning among knowledge workers.Google Scholar
  39. Milligan, C., & Littlejohn, A. (2016). How health professionals regulate their learning in massive open online courses. The Internet and Higher Education, 31, 113–121.CrossRefGoogle Scholar
  40. Milligan, C., Littlejohn, A., & Margaryan, A. (2013). Patterns of engagement in connectivist MOOCs. Journal of Online Learning and Teaching, 9(2), 149–161.Google Scholar
  41. Mor, Y., Ferguson, R., & Wasson, B. (2015). Learning design, teacher inquiry into student learning and learning analytics: A call for action. British Journal of Educational Technology, 46(2), 221–229.CrossRefGoogle Scholar
  42. Morozov, E. (2014, October 13). The planning machine. The New Yorker. Retrieved from
  43. Muñoz-Merino, P., Ruiperez-Valiente, J., Alario-Hoyos, C., Perez-Sanagustin, M., & Delgado-Kloos, C. (2015). Precise effectiveness strategy for analyzing the effectiveness of students with educational resources and activities in MOOCs. Computers in Human Behaviour, 47, 108–118.CrossRefGoogle Scholar
  44. Nonaka, I., & Toyama, R. (2003). The Knowledge-creating theory revisited: Knowledge creation as a synthesizing process. Knowledge Management Research and Practice, 1(1), 2–10.CrossRefGoogle Scholar
  45. Pea, R. (1997). Practices of distributed intelligence and designs for education. In G. Salomon (Ed.), Distributed cognitions: Psychological and educational considerations (pp. 47–87). Cambridge, UK: Cambridge University Press.Google Scholar
  46. Piaget, J. (1964). Part I: Cognitive development in children: Piaget development and learning. Journal of Research in Science Teaching, 2(3), 176–186.CrossRefGoogle Scholar
  47. Putnam, R., & Borko, H. (1997). Teacher learning: Implications of new views of cognition. In B. Biddle, T. Good, & I. Goodson (Eds.), The International handbook of teachers and teaching (pp. 1223–1296). Dordrecht, The Netherlands: Kluwer.CrossRefGoogle Scholar
  48. Rayyan, S., Seaton, D., Belcher, J., Pritchard, D., & Chuang, I. (2013, October). Participation and performance in 8.02x Electricity and Magnetism: The first physics MOOC from MITx. Paper presented at Physics Education Research Conference Proceedings, Portland, Oregon, US. Retrieved from
  49. Rienties, B., & Rivers, B. A. (2014). Measuring and understanding learner emotions: Evidence and prospects. Learning Analytics Review, 1, 1–28.Google Scholar
  50. Selwyn, N. (2010). Looking beyond learning: Notes towards the critical study of educational technology. Journal of Computer Assisted learning, 26(1), 65–73.CrossRefGoogle Scholar
  51. Selwyn, N. (2016). Is technology good for education. Cambridge, UK: Polity Books.Google Scholar
  52. Sfard, A. (1998). On two metaphors for learning and the dangers of choosing just one. Educational Researcher, 27(2), 4–13.CrossRefGoogle Scholar
  53. Shen, C., & Kuo, C. (2015). Learning in massive open online courses: Evidence from social media mining. Computers in Human Behavior, 51, 568–577.CrossRefGoogle Scholar
  54. Sinha, T., Li, N., Jermann, P., & Dillenbourg, P. (2014, October 25). Capturing “attrition intensifying” structural traits from didactic interaction sequences of MOOC learners. Paper presented at the 2014 Conference on Empirical Methods in Natural Language Processing. Workshop on Modeling Large Scale Social Interaction in Massively Open Online Courses, Doha, Qatar (pp. 42–49). Taberg, Sweden: Taberg Media Group AB. Retrieved from
  55. Skrypnyk, O., de Vries, P., & Hennis, T. (2015, May 18–20). Reconsidering retention in MOOCs: The relevance of formal assessment and pedagogy. Paper presented at the Third European MOOCs Stakeholders Summit, Université catholique de Louvain, Mons, Belgium. Retrieved from
  56. Tabba, Y., & Medouri, A. (2013). LASyM: A learning analytics system for MOOCs. International Journal of Advanced Computer Science and Applications, 4(5), 113–119.Google Scholar
  57. Vale, K., & Littlejohn, A. (2014). Massive open online course: A traditional or transformative approach to learning. In A. Littlejohn & C. Pegler (Eds.), Reusing open resources: Learning in open networks for work, life and education (pp. 138–153). New York, NY: Routledge.Google Scholar
  58. Vu, D., Pattison, P., & Robins, G. (2015). Relational event models for social learning in MOOCs. Social Networks, 43, 121–135.CrossRefGoogle Scholar
  59. Wang, Y., & Baker, R. (2015). Content or platform: Why do students complete MOOCs? MERLOT, 11(1), 17–30.Google Scholar
  60. Wegerif, R. (1998). The social dimension of asynchronous learning networks. Journal of Asynchronous Learning Networks, 2(1), 34–49.Google Scholar
  61. Williams, R., Karousou, R., & Mackness, J. (2011). Emergent learning and learning ecologies in Web 2.0. The International Review of Research in Open and Distance Learning, 12(3), 39–59.Google Scholar
  62. Williamson, B. (2015, April 15–17). Cognitive computing and data analytics in the classroom. Paper presented at British Sociological Association Annual Conference 2015, Glasgow Caledonian University, Glasgow, UK. Retrieved from
  63. Yang, D., Wen, M., Kumar, A., Xing, E., & Rosé, C. (2014). Towards an integration of text and graph clustering methods as a lens for studying social interaction in MOOCs. International Review of Research in Open and Distributed Learning, 15(5), 214–234.CrossRefGoogle Scholar

Copyright information

© The Author(s) 2018

Authors and Affiliations

  1. 1.Open UniversityMilton KeynesUK
  2. 2.University of AucklandAucklandNew Zealand

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