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Introduction Research collaboration has been explored and discussed in several research areas (Liu et al, 2005; Katz & Martin, 1997; Subramanyam, 1983). Research collaboration can be defined as: working together of researchers to achieve the common goal of producing new scientific knowledge (Katz & Martin, 1997). Commonly used metrics for measuring research collaborative activity include the co-authored publications (concerned with who works with whom) and the citations (concerned with who cites who) (Bukvova, 2010). On the other hand, research analytics is a research field that utilizes mathematical and algorithmic methods for analysing the way research takes place and it can provide meaningful data for researchers (Harmelen & Workman, 2012). Research analytics are also used for analyzing research collaboration towards identifying meaningful patterns and insights. More specifically, co-authorship has been explored as social networks and it is analyzed by using social network analysis (SNA), whereas citations are analyzed by using bibliometrics (Harmelen & Workman, 2012). Co-authorship networks fall under an important class of social networks, which are structured through the process of co-authoring a paper between two or more authors (Liu et al., 2005). Co-authorship networks outline the collaboration between researchers and visualize such collaborations in graphs. Analysis of these networks can reveal features of research communities which facilitate in understanding how research collaboration is realized in different research field. Within this context, co-authorship network analysis has attracted the attention of research community for several research fields. Early research works have focused on defining elements (e.g., network structure and typology, as well as positioning of certain authors) that are meaningful to be analyzed within these networks (Moody, 2004; Newman, 2001; Barabasi & Albert, 1999). Recent research works have mainly focused on analyzing these elements within specific co-authorship networks that are developed by papers co-authored for proceedings of scientific conferences or for scientific journals addressing various research fields (Erfanmanesh et al., 2012; Erman & Todorovski, 2010; Velden et al., 2010; Yan & Ding, 2010). In the field of Technology enhanced Learning (TeL), there is some studies that aim to analyze co-authorship networks (Pham et al., 2012; Ochoa et al., 2009; Kienle, & Wessner, 2005). However, we can notice that these studies are limited and they have been mainly based on studying conferences related to the TeL research field. Thus, one issue to investigate is to study the co-authorship networks developed by prominent scientific journals, which include more mature research results and they could reflect the current state of research collaboration realized in TeL in a more robust way. To this end, in this paper we focus on the co-authorship network of researchers who have published their joint work in the Educational Technology & Society (ETS) Journal from 1999 to 2012. The purpose of our study is to utilize SNA metrics to reveal useful patterns and features within this co-authorship network, as well as to identify the expected evolution of this network. The results of our study could provide a first step towards gaining a thorough insight on how research collaboration is realized in TeL The paper is structured as follows. Following this introduction, the next section presents background information about how co-authorship network analysis can be achieved by using SNA metrics, namely graph metrics and vertex specific metrics and we present those metrics exploited in our study. Afterwards, we discuss related works from the literature that deal with co-authorship networks analysis in the field of TeL. Next, we present the method of analysis of the co-authorship network developed by the ETS Journal and we present the results of this analysis. …
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