Moving Through MOOCS: Pedagogy, Learning Design and Patterns of Engagement
- 3.5k Downloads
Massive open online courses (MOOCs) are part of the lifelong learning experience of people worldwide. Many of these learners participate fully. However, the high levels of dropout on most of these courses are a cause for concern. Previous studies have suggested that there are patterns of engagement within MOOCs that vary according to the pedagogy employed. The current paper builds on this work and examines MOOCs from different providers that have been offered on the FutureLearn platform. A cluster analysis of these MOOCs shows that engagement patterns are related to pedagogy and course duration. Learners did not work through a three-week MOOC in the same ways that learners work through the first three weeks of an eight-week MOOC.
KeywordsLearning analytics Learner engagement patterns Learning design Moocs Pedagogy
We thank FutureLearn, The Open University, the University of Birmingham, the University of Edinburgh and the University of Leeds for allowing us access to the data used in this study. We also thank the FutureLearn Academic Network for establishing connections between researchers with an interest in this area.
- 1.Clow, D.: MOOCs and the funnel of participation. In: Learning Analytics and Knowledge 2013 (LAK 13), pp. 185–189. ACM, New York (2013)Google Scholar
- 2.Downes, S.: Connectivism and connective knowledge (2012). http://www.downes.ca/files/books/Connective_Knowledge-19May2012.pdf
- 3.Downes, S.: Like reading a newspaper (2014). http://halfanhour.blogspot.co.uk/2014/03/like-reading-newspaper.html
- 4.Ferguson, R., Clow, D.: Examining engagement: analysing learner subpopulations in massive open online courses (MOOCs). In: Learning Analytics and Knowledge 2015 (LAK 15), pp. 51–58. ACM, New York (2015)Google Scholar
- 6.Jordan, K.: MOOC completion rates: the data. http://www.katyjordan.com/MOOCproject.html
- 7.Kizilcec, R., Piech, C., Schneider, E.: Deconstructing disengagement: analyzing learner subpopulations in massive open online courses. In: Learning Analytics and Knowledge 2013 (LAK 13), pp. 170–179. ACM, New York (2013)Google Scholar
- 10.Littlejohn, A.: Understanding Massive Open Online Courses. CEMCA, New Delhi (2013)Google Scholar
- 11.Pask, G.: Conversation Theory: Applications in Education and Epistemology. Elsevier, New York (1976)Google Scholar
- 13.Siemens, G.: Connectivism: a learning theory for the digital age (2004). http://www.elearnspace.org/Articles/connectivism.htm
- 14.SoLAR: Open learning analytics: an integrated & modularized platform (2011). White Paper, http://solaresearch.org/OpenLearningAnalytics.pdf
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 2.5 International License (http://creativecommons.org/licenses/by-nc/2.5/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.