The social life of small graphical chat spaces
Proceedings of the SIGCHI conference on Human Factors in Computing Systems
Conversation Map: An Interface for Very Large-Scale Conversations
Journal of Management Information Systems
Social network analysis in virtual environments
Proceedings of the 20th ACM conference on Hypertext and hypermedia
Goal-oriented visualizations of activity tracking: a case study with engineering students
Proceedings of the 2nd International Conference on Learning Analytics and Knowledge
Discovering habits of effective online support group chatrooms
Proceedings of the 17th ACM international conference on Supporting group work
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Multi-user virtual environments (MUVEs) allow many users to explore the environment and interact with other users as they learn new content and share their knowledge with others. The semi-synchronous communicative interaction within these learning environments is typically text-based Internet relay chat (IRC). IRC data is stored in the form of chatlogs and can generate a large volume of data, posing a difficulty for researchers looking to evaluate learning in the interaction by analyzing and interpreting the patterns of communication structure and related content. This paper describes procedures for the measurement and visualization of chat-based communicative interaction in MUVEs. Methods are offered for structural analysis via social networks, and content analysis via semantic networks. Measuring and visualizing social and semantic networks allows for a window into the structure of learning communities, and also provides for a large cache of analytics to explore individual learning outcomes and group interaction in any virtual interaction. A case study on a learning based MUVE, SRI's Tapped-In community, is used to elaborate analytic methods.