Examining engagement: analysing learner subpopulations in massive open online courses (MOOCs)

Massive open online courses (MOOCs) are now being used across the world to provide millions of learners with access to education. Many learners complete these courses successfully, or to their own satisfaction, but the high numbers who do not finish remain a subject of concern for platform providers and educators. In 2013, a team from Stanford University analysed engagement patterns on three MOOCs run on the Coursera platform. They found four distinct patterns of engagement that emerged from MOOCs based on videos and assessments. However, not all platforms take this approach to learning design. Courses on the FutureLearn platform are underpinned by a social-constructivist pedagogy, which includes discussion as an important element. In this paper, we analyse engagement patterns on four FutureLearn MOOCs and find that only two clusters identified previously apply in this case. Instead, we see seven distinct patterns of engagement: Samplers, Strong Starters, Returners, Mid-way Dropouts, Nearly There, Late Completers and Keen Completers. This suggests that patterns of engagement in these massive learning environments are influenced by decisions about pedagogy. We also make some observations about approaches to clustering in this context