Exploring the relationship between interaction patterns and social capital accumulation in connectivist learning

ABSTRACT

To inform the relationships among interaction patterns, social capital accumulation, and learning benefits, based on 29,056 log data from a cMOOC (Connectivist Massive Open Online Course) in China, this study examined the difference in social capital accumulation and content production among different interaction patterns using cluster analysis, lag sequence analysis, social network analysis, and the Kruskal–Wallis test. Five types of interaction patterns in connectivist learning were identified: “creative connected participants”, “active connected participants”, “poorly engaged social participants”, “poorly engaged moderate participants”, and “resource investigating participants”. The results demonstrated that participants’ interaction patterns influence their social capital accumulation and content production and that positive interaction engagement would compensate for the disadvantage of participants’ initial position in social capital accumulation. Furthermore, resource access, social interaction, and content release had different roles in the accumulation of social capital. The pattern with higher engagement in all three kinds of interaction activities was associated with better content production because learners accumulated more bonding capital and bridging capital, which would bring them more returns in content creation. This study highlights the connectivist learning mechanism and effectiveness from the social capital perspective and provides valuable insights into the design and support of connectivist learning.

KEYWORDS:

• Connectivist learning
• cMOOC
• social capital
• interaction pattern
• content production
• social network analysis

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the National Natural Science Foundation of China (NSFC) under grant number 61977011.