scholarly journals Facebook or fail-book: Exploring “community” in a virtual community of practice

ReCALL ◽  
2020 ◽  
Vol 32 (3) ◽  
pp. 291-306
Author(s):  
Ward Peeters ◽  
Marilize Pretorius

AbstractCreating collaborative working and learning experiences has long been at the forefront of computer-assisted language learning research. It is in this context that, in recent years, the integration of social networking sites and Web 2.0 in learning settings has surged, generating new opportunities to establish and explore virtual communities of practice (VCoPs). However, despite the number of studies on the concept, research remains inconclusive on how learners develop a sense of community in a VCoP, and what effect this may have on interaction and learning. This research project proposes to use social network analysis, part of graph theory, to explore the configuration of a set of VCoPs, and presents an empirical approach to determine how interaction in such communities takes shape. The present paper studies the concept of “community” in two VCoPs on Facebook. Participants (Group 1: N = 123, Group 2: N = 34) in both VCoPs are enrolled in English as a foreign language courses at two Belgian institutions of higher education. Social network analysis is used to show how both learner groups establish and develop a network of peers, and how different participants in those groups adopt different roles. Participation matrices reveal that interaction mainly revolves around a number of active key figures and that certain factors such as the incorporation of online and offline assignments and the inclusion of a teacher online result in varying levels of success when establishing collaborative dialogue within the VCoPs. Recommendations are formulated to inform and improve future practice.

Author(s):  
Enrique Murillo

Social Network Analysis (SNA) provides a range of models particularly well suited for mapping bonds between participants in online communities and thus reveal prominent members or subgroups. This can yield valuable insights for selecting a theoretical sample of participants or participant interactions in qualitative studies of communities. This chapter describes a procedure for collecting data from Usenet newsgroups, deriving the social network created by participant interaction, and importing this relational data into SNA software, where various cohesion models can be applied. The technique is exemplified by performing a longitudinal core periphery analysis of a specific newsgroup, which identified core members and provided clear evidence of a stable online community. Discussions dominated by core members are identified next, to guide theoretical sampling of text-based interactions in an ongoing ethnography of the community.


2019 ◽  
Vol 39 ◽  
pp. 93-112 ◽  
Author(s):  
Mark Warschauer ◽  
Soobin Yim ◽  
Hansol Lee ◽  
Binbin Zheng

AbstractThis paper will review the role of data mining in research on second language learning. Following a general introduction to the topic, three areas of data mining research will be summarized—clustering techniques, text-mining, and social network analysis—with examples from both the broader field and studies conducted by the authors. The application of data mining in second language learning research is relatively new, and more theoretical and empirical support is needed in the appropriate collection, use, and interpretation of data for specific research and pedagogical objectives. The three examples that we introduce illustrate how new data sources accessible in online environments can be analyzed to better understand the optimal instructional context for corpus-based vocabulary learning (clustering technique), characteristics and patterns of collaborative written interaction using Google Docs (text mining and visualizations), and issues of access and community in computer-mediated discussion (social network analysis). Implications of these new techniques for L2 research will be discussed.


2012 ◽  
Vol 4 (3) ◽  
pp. 46-58 ◽  
Author(s):  
Darren Quinn ◽  
Liming Chen ◽  
Maurice Mulvenna

Social Network Analysis is attracting growing attention as social networking sites and their enabled applications transform and impact society. This paper aims to provide a comprehensive review of social network analysis state of the art research and practice. In the paper the authors’ first examine social networking and the core concepts and ingredients of social network analysis. Secondly, they review the trend of social networking and related research. The authors’ then consider modelling motivations, discussing models in line with tie formation approaches, where connections between nodes are taken into account. The authors’ outline data collection approaches along with the common structural properties observed in related literature. They then discuss future directions and the emerging approaches in social network analysis research, notably semantic social networks and social interaction analysis.


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