A SeNA-based study of in-service teachers’ interaction patterns in an online learning community.

AbstractIn-service teachers, constrained by work commitments, adopt online learning methods for their graduate studies, emphasizing the significance of exploring the construction of online learning communities. Analyzing collaborative conversation texts within the online learning process of the “Learning Science” course at a university in Beijing, this study employed social epistemic network analysis (SeNA) to investigate interaction patterns of in-service teachers and propose optimization strategies. The findings of this study show that in asynchronous discussions without intervention, the overall situation of collaborative conversations among in-service teachers tends to be low-level. The social interaction network shows polycentricity, categorizing members into core and peripheral interactors based on distinct social interaction characteristics, highlighting substantial disparities in epistemic networks. Core interactors prioritize engaging with others, yet their contributions primarily echo interactions, reflecting superficial knowledge dimensions. Conversely, specific peripheral interactors prioritize independent reflection, sharing content largely derived from personal experiences or knowledge, signifying a commitment to independent and in-depth thinking within deeper knowledge dimensions. Moreover, learners’ online interactions are influenced by individual characteristics, instructional evaluation and the learning community atmosphere. [ABSTRACT FROM AUTHOR]