As a learning theory that reveals a new learning in the Internet environment, connectivism has become a popular academic topic at the forefront of online learning. The MOOC Research Team at the Distance Education Research Centre at Beijing Normal University designed and developed the first massive open online course, adapting a connectivist (cMOOC) approach in China. Using the data collected from six offerings of the cMOOC over 3 years, the big data paradigm was used for data analysis including complex network analysis, content analysis, text mining, behaviour sequence analysis, epistemic network analysis, and statistical and econometric models. This paper summarizes the findings of the patterns of connectivist learning, including a) the basic characteristics and evolutional patterns of complex networks, b) the characteristics and modes of knowledge production, c) the patterns of instructional interactions, and d) the relationships between pipe and content and between facilitators and learners. It is expected that the outcome of this study could make contributions to understanding the changes of online learning in depth and further promote the theoretical development and practical application of a connectivist approach.
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.
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