A growing number of studies of professional development and educational reform have begun to illustrate the importance of online (informal) networks and social interactions among educational practitioners (e.g. teachers). However, despite a recent surge of social media use, there is little empirical evidence about the way educational practitioners interact in such (informal) networks online. Consequently, this study aims to contribute to filling this empirical gap and focuses on better understanding the social interactions among educational practitioners in these types of online (informal) networks.
In this context, educational scientists have increasingly acknowledged that the concept of social capital can contribute to our understanding of how (informal) learning networks develop over time. Moreover, social capital has already been used to better understand professional development. Nahapiet and Ghoshal (1998) distinguish between three dimensions of social capital, namely a structural, a cognitive and a relational dimension. Focusing on the structural and cognitive dimensions of social capital, we formulate two main research questions:
1. To what extent does participation in a online (informal) networks contribute to educational practitioners’ formation of:
i. structural social capital?
ii. cognitive social capital?
2. Depending on their network position, to what extent are individuals able to possibly influence the content of the online (informal) network?
In the context of this study, we focus on one particular online (informal) network, namely Twitter. We employ a multimethod approach, combining social network analyses (SNA) and bibliometric analyses. SNA has been widely acknowledged as a valuable tool to assess the structural dimension of social capital. However, in an online realm the distinctive features of commonly used metrics are getting blurred and the explanatory power might be diminished. Consequently, and building upon previous work on topics such as brokerage positions, we propose a new “social brokerage index” (SBI). In the context of our bibliometric analyses, to assess the cognitive dimension of social capital formation, we employ latent semantic analysis (LSA). Additionally, we use a term frequency and weighting algorithm (TF.IDF) to compute and visualize any potential similarity or dissimilarity between contributions. More specifically, we collected data from six international (educational) Twitter hashtag conversations, namely #acps, #caedchat, #edchat, #ntchat, #nyedchat, and #satchat. Using the software tool NodeXL, the data was collected over a period of one year, from the 22nd of May 2014 through to the 21st of May, 2015. The collected data was then imported into R and Pajek to conduct the applicable SNA and bibliometric analyses.
The results of our study suggest that our proposed metric (SBI), has added-value to the analysis of online (informal) network behavior also beyond the scope of Twitter. Moreover, by combining this index with additional bibliometric analyses, we are able to better assess network positions within online (informal) networks. Furthermore, this multimethod approach could possibly allow us to profile conversations in online (informal) networks and better understand what type of discussions draw what type of participants and how the dynamics might be influenced by this.