Mining Teacher Informal Online Learning Networks: Insights From Massive Educational Chat Tweets. (March 2023)

• Record Type: Journal Article
• Authors: Du, Hanxiang; Xing, Wanli; Zhu, Gaoxia
• Is Part Of: Journal of educational computing research. Volume 61:Number 1(2023)
• Page Start: 127
• Page End: 150
• Publication Date: 2023-03
• Subjects: Teacher professional development -- Informal learning -- Social media -- Learning communities -- Social network analysis -- Online community; Computer literacy -- Periodicals; Computer-assisted instruction -- Periodicals; Computer managed instruction -- Periodicals; Education -- Data processing -- Periodicals; 371.334
• DOI: 10.1177/07356331221103764
• Languages: English
• ISSNs: 0735-6331

Abstract

Social-media-based teacher learning networks have the affordance to grant flexibility of time and space for teachers' professional learning, support the development and sustainability of social networking, and meet their just-in-time needs for exchanging knowledge, negotiating meaning and accessing resources. However, most existing research on teacher online learning networks relies on qualitative methods and self-report data. There is a lack of study using quantitative methods to study large networks, especially using authentic data from social media. This work adds to the literature through mining teacher informal online learning networks using authentic data retrieved from Twitter. Specifically, we collected around half a million tweets and developed a network with the data. Then, various social network analysis techniques were utilized to explore the network structure and characteristics, participants' behavioral patterns and how individuals connected with each other. We found that members of massive teacher informal online learning networks tended to communicate more with others of similar characteristics forming homogeneous communities, while hub participants connected many small communities which are significantly from one another, and hence, are the key to degree heterogeneity in a large network.