Sociogram

Abstract: The paper investigates the place of visual tools in mixed-methods research on social networks, arguing that they can not only improve the communicability of results, but also support research at the data gathering and analysis stages. Three examples from the authors' own research experience illustrate how sociograms can be integrated in multiple ways with other analytical tools, both quantitative and qualitative, positioning visualization at the intersection of varied methods and channelling substantive ideas as well as network insight in a coherent way. Visualization also facilitates the participation of a broad range of stakeholders, including among others, study participants and non-specialist researchers. It can support the capacity of qualitative and mixed-methods research to reach out to areas of the social that are difficult to circumscribe, such as hidden populations and informal organisations. On this basis, visualization appears as a unique opportunity for mixing methods in the study of social networks, emphasizing both structure and process at the same time.

Abstract: The broad use of computer-supported collaborative-learning (CSCL) environments (e.g., instant messenger-chats, forums, blogs in online communities, and massive open online courses) calls for automated tools to support tutors in the time-consuming process of analyzing collaborative conversations. In this article, the authors propose and validate the cohesion network analysis (CNA) model, housed within the ReaderBench platform. CNA, grounded in theories of cohesion, dialogism, and polyphony, is similar to social network analysis (SNA), but it also considers text content and discourse structure and, uniquely, uses automated cohesion indices to generate the underlying discourse representation. Thus, CNA enhances the power of SNA by explicitly considering semantic cohesion while modeling interactions between participants. The primary purpose of this article is to describe CNA analysis and to provide a proof of concept, by using ten chat conversations in which multiple participants debated the advantages of CSCL technologies. Each participant's contributions were human-scored on the basis of their relevance in terms of covering the central concepts of the conversation. SNA metrics, applied to the CNA sociogram, were then used to assess the quality of each member's degree of participation. The results revealed that the CNA indices were strongly correlated to the human evaluations of the conversations. Furthermore, a stepwise regression analysis indicated that the CNA indices collectively predicted 54% of the variance in the human ratings of participation. The results provide promising support for the use of automated computational assessments of collaborative participation and of individuals' degrees of active involvement in CSCL environments.

Abstract: This chapter touches on the key historical developments, ideas, and concepts in social network analysis and applies them to social media network examples. Social network theory and analysis is a relatively recent set of ideas and methods largely developed over the past 80 years. It builds on and uses concepts from the mathematics of graph theory, which has a longer history. Using network analysis, one can visualize complex sets of relationships as maps (i.e., graphs or sociograms) of connected symbols and calculate precise measures of the size, shape, and density of the network as a whole and the positions of each element within it. Network analysts see the world as a collection of interconnected pieces. Those studying social networks see relationships as the building blocks of the social world, each set of relationships combining to create emergent patterns of connections among people, groups, and things. The focus of social network analysis is between, not within, people. Social media network data collection, scrubbing, analysis, and display tasks have historically required a remarkable collection of tools and skills. This book focuses on a single tool designed for nonprogrammers, NodeXL, because of its relative ease of use, support for rich visuals and analytics, and integration with the ubiquitous Excel spreadsheet software.

Abstract: Analysing the structure of a social network can help us understand the key factors influencing interaction and collaboration in a virtual learning community ( VLC). Here, we describe the mechanisms used in social network analysis ( SNA) to analyse the social network structure of a VLC for teachers and discuss the relationship between face-to-face and online collaborations. In contrast to previous research applying SNA to analyse measuring indexes alone, we emphasise the mechanisms combining SNA, questionnaires, content analysis and focus group interviews-the key methodology to analyse complex interaction in a VLC. On this basis, we present an analysis model for teachers' VLC and apply it to a teachers' VLC known as ' IRIS'. The study participants comprised 172 K12 teachers aged between 25 and 55 years. This study collected collaboration data from 2006 to 2012 and analysed the social network structure using sociograms, centrality, cohesive subgroups, clique phenomenon, and matrix correlation of SNA. These findings suggest that face-to-face and online collaborations are both indispensable in teaching and in research and continuously supplement and remedy each other in professional development. Moreover, the model succeeded in accessing, describing and analysing the social network structure of a VLC. [ABSTRACT FROM AUTHOR]