The University has successfully run two Massive Open Online Courses (MOOCs); one on ‘Inside Cancer’ and one on ‘Sustainability for Professionals’. These MOOCs have attracted different communities of participants, as reported internally at Exchange! 2014.
Social Network Analysis (SNA) is a technique used to identify the characteristics of a network. A variety of SNA metrics can be used to detect the overall character of a network (density, connectedness, ‘small world’ and so on). In addition node-level metrics (one at the level of the individual participant) can be used to identify different types of participant (for example, ‘hubs’, ‘spokes’ and ‘links’).
This project has applied SNA techniques to these two Bath MOOC courses. The study has been mainly descriptive rather than proscriptive. The hypotheses that have been explored are that:
- The differences between the courses leads to measurable differences in the nature of the resulting network;
- These differences could guide the development of new MOOCs.
In order to explore these hypotheses, the courses have be compared using a variety of SNA metrics. The findings show that:
- A pedagogical design encouraging community driven (‘connectivist’) learning does indeed lead to measurable structural differences in a MOOC network. Thus, design of a MOOC needs to take into account the desired learning behaviour of the participants;
- We were able to identify network learning – in which conversations are driven by the community rather than by tutors. Thus, the role of community gatekeepers are key and these individuals can be identified and supported;
- There is some evidence that a more centralized MOOC becomes more community led over time. Thus, it may be important to redesign a MOOC over time to take account of the evolving participant behaviour.