Pattern-Based Cross Media Social Network Analysis for Technology Enhanced Learning in Europe

Abstract

Abstract. It is extremely challenging to get an overview of the current state-of-the-art in technology enhanced learning in Europe. Rapid technological and pedagogical innovations, constantly changing markets, a vivid number of small and medium enterprises, complex policy processes, ongoing political and societal debates on the pros and cons of technology enhanced leaning, combined with many languages and different cultures, make it almost impossible for people to be informed. We want to introduce the media base and the measure tools for pattern-based cross media social network analysis, created by the PROLEARN network of excellence in professional learning. The main goal of this endeavour is the reduction of complexity for actors in digital social networks by applying ideas from social software and already successful methods for complexity reduction, such as information visualization, social network analysis and pattern languages.

Social network analysis for technology-enhanced learning: review and future directions

“…Studies that merely analyse networks, rather than also visualising them, include those that study student interactions Dawson, 2010;Yao, 2010;Hamulic and Bijedic, 2009;Aviv et al, 2003), student achievement (Moolenaar et al, 2012;Lomi et al, 2011;Cho et al, 2007), networking patterns (Capuano et al, 2011;Modritscher et al, 2011;Ryymin et al, 2008;Klamma et al, 2006), and communities (Merlo et al, 2010;Reffay and Chenier, 2002). Table 2 provides an overview of the network analysis methods and findings for each of the studies mentioned in the current subparagraph.…”

“…(2007) density, centralisation small groups of 3-4 individuals share more information and knowledge than dyads; small group activity yields higher sense of community and social ability Klamma et al (2006) degree, closeness, betweenness, structural holes identification of the troll role, a person that "aims at drawing attention and starting useless discussions" Posea et al (2006) density, closeness, eigenvector, centralisation n/a Aviv et al (2003) cliques, bridges, role groups, eigenvector centrality, degree, density high cohesion exists in structured asynchronous learning networks Martínez et al (2003) density, centralisation a mixed methods approach can be used to identify networking patterns Reffay and Chenier (2002) cliques, clusters hierarchical cluster analysis is a useful pre-step in cohesion analysis using cliques…”

Enhancing Learning with Visualization Techniques

“…As a second example, there is a rich body of research on the use of social network analysis (SNA) visualizations to provide awareness of co-workers in Computer-Supported Collaborative Work or research networks (Klamma et al, 2006). With the explosive rise of social networks like FaceBook, google+ or Twitter, and tools based on visual representations of these networks (Heer & boyd, 2005), we expect that these tools will be widely leveraged where social software is being deployed in collaborative learning environments as well.…”

A text categorisation tool for open source communities based on semantic analysis

“…Email: toral@esi.us.es Behaviour & Information Technology 2011, 1-13, iFirst article perspective, community members are modelled as the nodes of the network while arcs represent the flow of knowledge among them . Some other studies propose the extension of social network analysis considering not only the activity of the community members but also their patterns of communication (Klamma et al 2006). These patterns allow the identification of different members' profiles in digital social networks, like trolls, spammers, conversationalists, questioners and answering persons.…”