Massive Numbers, Diverse Learning
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.
- 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
- 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 https://www2.eecs.berkley.edu/Pubs/TechRpts/2013/EECS-2013-109.pdf.
- 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 https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1926431.
- 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
- 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
- Downes, S. (2012). Connectivism and connective knowledge: Essays on meaning and learning networks. Ottawa, Canada: National Research Council Canada. Retrieved from https://pdfs.semanticscholar.org/4718/ee3c1930820e094552f0933cbc3b86548dbc.pdf.
- Eraut, M. (1994). Developing professional knowledge and competence. London: Falmer.Google Scholar
- ESMA. (2016, December 19). European Supervisory Authorities consult on big data. European Securities and Markets Authority. Retrieved from https://www.esma.europa.eu/press-news/esma-news/european-supervisory-authorities-consult-big-data.
- 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
- 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
- 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
- Illeris, K. (2007). How we learn: Learning and non-learning in school and beyond. London: Routledge.Google Scholar
- 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 http://er.educause.edu/articles/2013/6/retention-and-intention-in-massive-open-online-courses-in-depth.
- Milligan, C. (2012). Change 11 SRL-MOOC study initial findings. Blog Learning in the workplace Researching learning among knowledge workers.Google Scholar
- 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
- Morozov, E. (2014, October 13). The planning machine. The New Yorker. Retrieved from www.newyorker.com/magazine/2014/10/13/planning-machine.
- 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
- 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
- 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 http://arxiv.org/abs/1310.3173.
- Rienties, B., & Rivers, B. A. (2014). Measuring and understanding learner emotions: Evidence and prospects. Learning Analytics Review, 1, 1–28.Google Scholar
- Selwyn, N. (2016). Is technology good for education. Cambridge, UK: Polity Books.Google Scholar
- 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 https://www.aclweb.org/anthology/W/W14/W14-41.pdf.
- 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 https://s3.amazonaws.com/academia.edu.documents/37666738/Papers.pdf?AWSAccessKeyId=AKIAIWOWYYGZ2Y53UL3A&Expires=1503231269&Signature=IrKy647r03CIxal0L%2BVnXQFNlkQ%3D&response-content-disposition=inline%3B%20filename%3DDesign_intent_and_iteration_The_HumanMOO.pdf#page=166.
- 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
- 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
- Wang, Y., & Baker, R. (2015). Content or platform: Why do students complete MOOCs? MERLOT, 11(1), 17–30.Google Scholar
- Wegerif, R. (1998). The social dimension of asynchronous learning networks. Journal of Asynchronous Learning Networks, 2(1), 34–49.Google Scholar
- 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
- 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 http://www.academia.edu/11968853/Cognitive_computing_and_data_analytics_in_the_classroom.