Visualizing Wiki-supported knowledge building: Co-evolution of individual and collective knowledge
|Visualizing Wiki-supported knowledge building: Co-evolution of individual and collective knowledge|
|Author(s)||Harrer A., Moskaliuk J., Kimmerle J., Cress U.|
|Published in||WikiSym 2008 - The 4th International Symposium on Wikis, Proceedings|
|Keyword(s)||Co-evolution, Collective knowledge, Knowledge building, Visualization, Wikis (Extra: Co-evolution, Co-evolutionary, Collaborative knowledge, Collective knowledge, Dynamic changes, Knowledge building, Social Network Analysis, Social systems, Temporal development, Theoretical basis, Wikipedia, Biology, Cognitive systems, Electric network analysis, Visualization, Buildings)|
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Visualizing Wiki-supported knowledge building: Co-evolution of individual and collective knowledge is a 2008 conference paper written in English by Harrer A., Moskaliuk J., Kimmerle J., Cress U. and published in WikiSym 2008 - The 4th International Symposium on Wikis, Proceedings.
It is widely accepted that wikis are valuable tools for successful collaborative knowledge building. In this paper, we describe how processes of knowledge building with wikis may be visualized, citing Wikipedia as an example. The underlying theoretical basis of our paper is the framework for collaborative knowledge building with wikis, as introduced by Cress and Kimmerle , , . This model describes collaborative knowledge building as a co-evolution of individual and collective knowledge, or of cognitive and social systems respectively. These co-evolutionary processes may be visualized graphically, applying methods from social network analysis, especially those methods that take dynamic changes into account , . For this purpose, we have undertaken to analyze, on the one hand, the temporal development of an article in the German version of Wikipedia and related articles that are linked to this core article. On the other hand, we analyzed the temporal development of those users who worked on these articles. The resulting graphics show an analogous process, both with regard to the articles that refer to the core article and to the users involved. These results provide empirical support for the co-evolution model. Some implications of our findings and the potential for future research on collaborative knowledge building with wikis and on the application of social network analysis are discussed at the end of the article.
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