How Learners Participate in Connectivist Learning: An Analysis of the Interaction Traces From a cMOOC


  • Zhijun Wang Jiangnan University
  • Terry Anderson
  • Li Chen



participation pattern, cMOOCs, social network analysis, connectivist learning, connectivism, interaction


In this research paper, the authors analyse the collected data output during a 36 week cMOOC. Six-week data streams from blogs, Twitter, a Facebook group, and video conferences were tracked from the daily newsletter and the MOOCs’ hashtag (#Change 11). This data was analysed using content analysis and social network analysis within an interpretative research paradigm. The content analysis was used to examine the technology learners used to support their learning while the social network analysis focused on the participant in different spaces and their participation patterns in connectivist learning.

The findings from this research include: 1) A variety of technologies were used by learners to support their learning in this course; 2) Four types of participation patterns were reveled, including unconnected floaters, connected lurkers, connected participants, and active contributors. The participation of learners displays the participation inequality typical of social media, but the ratio of active contributors is much higher than xMOOCs; 3) There were five basic structures of social networks formed in the learning; and 4) The interaction around topics and topic generation supports the idea of learning as network creation after the analysis of participation patterns that are based on some deep interactive topic. The aim of this study is to gain insight into the behaviors of learners in a cMOOC in an open and distributed online environment, so that future MOOCs designers and facilitators can understand, design and facilitate more effective MOOCs for learners.



How to Cite

Wang, Z., Anderson, T., & Chen, L. (2018). How Learners Participate in Connectivist Learning: An Analysis of the Interaction Traces From a cMOOC. The International Review of Research in Open and Distributed Learning, 19(1).



Research Articles