Recently I have been thinking about how learners manage their time in MOOCs. This thinking was sparked by discussions with Carolyn Rose, Carnegie Mellon University, who has carried out some fascinating computational discourse analysis in Massive Open Online Courses.
At face value its clear that better time management will lead to improved outcomes for learning. There has been a great deal of discussion around time management and students are being advised – see for example the advice from elearningindustry.com and onlinecollege.org However, time management alone is not sufficient for effective learning in open courses. Learning approaches have to be strategic.
In our research on how professionals learn in MOOCs Colin Milligan, Nina Hood and I looked closely at how MOOC learners strategically focus their effort. Effective learning is linked to strategic intent, so the ways effective learners manage their time depends on their underlying motivations for learning.
MOOC learners who are motivated to complete a certificate, but poorly self-regulated can appear to manage their time well (Milligan & Littlejohn, 2014). They set aside time each week to watch all the video lectures and participate in activities. However their focus can become diffuse and they may be overwhelmed with the sheer volume of resources generated through the MOOC, by tutors and by other students.
Self-regulated MOOC learners who are motivated to complete a certificate in a MOOC, for example, will focus attention towards activities they perceive as important for passing assessment. On the other hand, self-regulated learners who want to learn specific concepts focus in on content and activities that help them achieve their goals (Littlejohn, Hood & Milligan, forthcoming). On the surface these seem different, but, on closer examination, when learners are self-regulated they tend to be more strategic about where they focus their attention.
Of course self-regulation is variable. We all are self-regulated in some instances, and less self-regulated in others. Self-regulation depends on a complex mixture of the cognitive, affective and behavioural sub-factors of self-regulation, including interest/ motivation, confidence, persistence etc. So there is opportunity to influence self-regulation either through the design of tools and environments, or through making the learner aware of the impact of specific sub-factors (Littlejohn & Milligan, 2015).
The theories of self-regulation have been developed informal education contexts- principally K-12 settings. Our research has explored these factors in detail in semi-formal (MOOCs) or informal (learning on-the-job) settings. We’ve found a complex interplay of the sub-factors. Though generally we’ve found interest/ motivation, learning strategies, help-seeking and self-satisfaction as being some of the most powerful sub-factors influencing learning (Hood, Littlejohn & Milligan, forthcoming).
Hood, N., Littlejohn, A. & Milligan, C. (forthcoming) Context Counts: Learning in a MOOC
Littlejohn, A.,Hood, N., Milligan, C.& Mustain, P. (forthcoming) Learning in MOOCs: motivations and self-regulated learning in MOOCs
Littlejohn, A. and Milligan, C. (2015) Designing MOOCs for professional learners: Tools and patterns to encourage self-regulated learning, eLearning Papers, Special Issue on Design Patters for Open Online Teaching and Learning, 42 http://www.openeducationeuropa.eu/en/node/170924
Milligan, C. and Littlejohn, A. (2014) Supporting professional learning in a massive open online course. The International Review of Research in Open and Distance Learning 15 (5) 197-213. http://www.irrodl.org/index.php/irrodl/article/view/1855/3113