Sam Zeini

Combining social network analysis with semantic relations to support the evolution of a scientific community

This paper presents an analytical approach to support organisational learning in terms of the evolution of a scientific community based on a combination of social network analysis and semantic relations. The primary and direct target of the method is to infer hidden or desirable links between subgroups in a networked community. The data source for these inferences comprises memberships in teams and thematic subgroups. The approach has been applied in a case study to a large scientific network on technology enhanced learning.

Evaluation of communication in web-supported learning communities an analysis with triangulation research design

In current educational practice, web-based environments are an established means to support learning scenarios. In the study presented in this article, we use them as supporting tools for presence-learning scenarios and investigate the effects of electronic communication forms in this environment. Following the idea of triangulation, we utilised qualitative methods, statistical analysis and Social Network Analysis. Qualitative methods were used to classify the usage types of communication in Wiki and discussion forums. Based on these categories, we selected project groups with maximal variance of their communication behaviour and conducted Social Network Analysis to explore communication structures in detail. This research design is supported by the fact that no single communication form proved to be superior: combined usage produced better results with respect to the final scores. We concluded that more aspects of the respective communication forms should be taken into account to be able to compare them properly.

Ontology facilitated community navigation – who is interesting for what i am interested in?

Networks have been a common way to show pathways for supporting communities. Usually the local focus of the actors in these networks does not allow them to perceive the broader context of their current interest. In this paper the authors propose an ontology-based approach to link people who have no explicit relation in the network despite potential common interests. With respect to the results we extend the concept of integrating ontologies into social networks to shared information spaces.

Towards a flexible model for computer-based analysis and visualization of collaborative learning activities

The definition of appropriate interaction analysis methods is a major research topic in Computer Supported Collaborative Learning. Analysis methods can be totally or partially supported by computer-based tools that provide for better and more efficient analysis processes. The current research in this field shows that most interaction analysis tools have been based on unstable prototypes, and are highly dependant on the learning environments and research goals for which they were defined. As a consequence, it is not possible to use them in authentic CSCL settings with real users. The goal of this European Research Team therefore is to utilize the synergies of experience in manual interaction analysis with computer-based analytical methods. In this article we present an approach that embeds standardized computer-supported techniques into a semi-formal analysis process model which can be utilized and adapted in a flexible way according to the cases and environments to be analysed.

Integrated Representation and Visualisation of the Dynamics in Computer-mediated Social Networks

In this paper we will demonstrate the potential of processing and visualising the dynamics of computer-mediated communities by means of Social Network Analysis. According to the fact that computer-mediated community systems are manifested also as structured data, we use data structures like e-mail, discussion boards, and bibliography sources for an automatic transformation into social network data formats. Currently our developed converter DMD (Data Multiplexer Demultiplexer) supports GraphML, UCINET, and Pajek formats besides our own data formats which are used for real-time analysis of CSCL (Computer Supported Collaborative Learning) activities. In the case of communication data our converters utilize conversation graphs reflecting aspects of speech act and conversational theory to produce directed graphs in the cases where one-mode person networks are desired. The paper will demonstrate a 3-dimensional visualisation of an author community based on Bibtex bibliography data converted into GraphML. Based on this dataset we visualise publications network with a tool called Weaver, which is developed in our research group. According to Lothar Krempel’s algorithm, Weaver uses the first two dimensions to embed the network structure within a common solution space. The third dimension is used for representing the time axis and thus the dynamics of co-authorship relations.Concluding we aim to discuss potential issues and problems of our approach and the possibilities especially concerning the appropriate visualisation and segmentation of long term communications, such as mailing lists.

The effects of electronic communication support on presence learning scenarios

This paper investigates in the effects of using electronic communication forms in webbased environments. Following the idea of triangulation, we used qualitative methods, statistical analysis and Social Network Analyses to explore the patterns of communication within one selected case of a mixed presence/web-based university course. The results show that while an isolated perspective does not suffice to explain the complex processes, taking more perspectives into account in a combined and integrated way provides a better understanding of technology enabled communication and interaction.

Visualisation of the Dynamics for Longitudinal Analysis of Computer-mediated Social Networks-concept and Exemplary Cases

In this paper we will demonstrate the potential of processing and visualising the dynamics of computer-mediated communities by means of Social Network Analysis. According to the fact that computer-mediated community systems are manifested also as structured data, we use data structures like e-mail, discussion boards, and bibliography sources for an automatic transformation into social network data formats. The paper will demonstrate a 3-dimensional visualisation of two cases: the first presents an author community based on bibliography data converted into GraphML. Based on this dataset we visualise publications networks with a tool called Weaver, which is developed in our research group. According to Lothar Krempel’s algorithm, Weaver uses the first two dimensions to embed the network structure within a common solution space. The third dimension is used for representing the time axis and thus the dynamics of co-authorship relations. The second case describes recent research in open source communities and highlights how our visualization approach can be used as a complement to more traditional approaches, such as content analysis and statistics based on specific SNA indices.

The fourth man: supporting self-organizing group formation in learning communities

In this paper we propose an approach based on social network analysis facilitated by ontologies for the support of learning group formation in computer supported collaborative and blended learning scenarios. This approach allows us to generate new ties between learners who are interested in similar topics. The identification of similar topics is elaborated dynamically by using a shared workspace environment which supports visual editing and modeling of topic relationships.

Task and Time Aware Community Detection in Dynamically Evolving Social Networks

Abstract The temporal analysis of the community structure in dynamically evolving networks requires that the nodes and connections between them be sampled into a time series of successive networks by shifting capturing intervals of typically equal width in time. The size of such time windows affects the outcome of community detection in certain ways possibly depending also on the detection method. In this paper we propose a systematic approach to identify time window sizes so that community detection methods produce meaningful results. For that purpose we investigate several simple indicators, which can help to sample an evolving network depending on the analysis task and the community detection method.