Pattern recognition using neural networks: theory and algorithms for engineers and scientists
Pattern recognition using neural networks: theory and algorithms for engineers and scientists
The Theory and Practice of Discourse Parsing and Summarization
The Theory and Practice of Discourse Parsing and Summarization
The convergence of social and technological networks
Communications of the ACM - Remembering Jim Gray
Thread-based analysis of patterns of collaborative interaction in chat
Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology
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This paper discusses a tool to study the relations among group members of a social network that evolve using a virtual environment. Understanding how these relations are built and maintained is essential to foster favorable conditions that can facilitate collective and individual learning. The focus of interest is how inner relations affect both the collective and individual learning of its members. We consider a group as being a set of relations among people and artifacts. As an example, we might determine which relations among members of a Project Team (predominantly goal-oriented) are different from the ones found in Communities of Practice (predominantly learning-oriented), and how they affect the learning of their members. We propose to characterize the relations among members by analyzing text content of social exchanges (e.g. email messages, blog posts, chat sessions, etc). The approach originality relies on taking into account the distinct points of view of its actors. Due to the distributed character of the problem (a virtual group), we intend to develop this tool by using a Multi-Agent System, whose architecture we present in this paper.