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Journal article
From Spatial Proximity to Semantic Coherence: A Quantitative Approach to the Study of Group Dynamics in Collaborative Virtual Environments
Presence (Cambridge, Mass.), v 14(1), pp 81-103
Feb 2005
Abstract
This article describes a quantitative approach to the study of group dynamics in Collaborative Information Visualization Environments (CIVEs). This approach characterizes group dynamics in terms of two concepts introduced in this article—spatial proximity and semantic coherence. The concepts are crucial to the understanding of profound interrelationships between spatial, semantic, and social navigation. Furthermore, this article describes three visualization techniques—semantic indentation chat sequence displays, activity maps, and clock-face maps—that permit the identification of important features of group interaction that are related to semantic coherence and spatial proximity. The approach is illustrated by applying it to the analysis of an empirical study in which four groups of subjects performed collaborative search tasks through 3D visualizations of knowledge domains. The major contribution of the work is the conceptualization and quantification of group coherence as a generic methodology for the study of a range of collaborative virtual environments such as collaborative learning, distance learning, social networks, collaborative information visualization, and digital libraries. Further research challenges for the study of group behavior in collaborative information-visualization environments are identified.
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Details
- Title
- From Spatial Proximity to Semantic Coherence: A Quantitative Approach to the Study of Group Dynamics in Collaborative Virtual Environments
- Creators
- Chaomei Chen - College of Information Science and Technology, Drexel University, Philadelphia PA 19104-2875 USAKaty Börner - School of Library and Information Science, Indiana University, Bloomington IN 47405 USA
- Publication Details
- Presence (Cambridge, Mass.), v 14(1), pp 81-103
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000228559100007
- Scopus ID
- 2-s2.0-18144428362
- Other Identifier
- 991014878466404721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Cybernetics
- Computer Science, Software Engineering