Dynamic Evolution Analysis of Social Network in cMOOC Based on RSiena Model


  • Yaqian Xu Beijing Normal University
  • Junlei Du Beijing Normal University




cMOOC, social network, SIENA, evolution, interaction, connectivism


The network is a key concept which has been highly valued in connectivism. Research about the static characteristics of social networks in connectivist learning has been carried out in recent years, however, little knowledge exists regarding the principles of network evolution from a dynamic perspective. This article chose the first connectivist massive open and online course (cMOOC) in China, “Internet plus Education: Dialogue between Theory and Practice” as the research object, using the dynamic analysis method of social networks which is based on stochastic actor-oriented models, to reveal the influence of the individual attributes and network structural attributes on the dynamic evolution of social networks in a cMOOC. We found that: 1) the learners with the same sex, the same social identity, and the same type of behaviour tendency found it much easier to interact with each other; 2) there is a heterogeneous phenomenon with course identity, meaning that compared to communicating with other learners, learners are more inclined to reply to a facilitator; and 3) the reciprocity and transitivity have significant effects on social network evolution. This study is valuable for understanding the network evolution and has implications for the improvement of cMOOC design, in turn improving the online learning experience for cMOOC learners.

Author Biographies

Yaqian Xu, Beijing Normal University

XU Yaqian, is a PhD student majoring in distance education in Beijing Normal University, China. Her research interests include connectivism, online learning analysis, and instructional interaction and she has published 10 papers in journals

Junlei Du, Beijing Normal University

DU Junlei, is a graduate student majoring in software engineering in Beijing Jiaotong University. His research interests include knowledge graph, data analysis and machine learning and he has published 7 papers in journals.


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