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Group Cognition as a Foundation for the New Science of Learning

  • Gerry Stahl
Chapter

Abstract

In the new global world, it is time to recognize that knowledge is primarily produced socially and progressively, not just by spontaneous acts of isolated minds. Individuals participate in this as knowledge learners, knowledge users and knowledge builders, predominantly through their interactions in small groups. The cognitive work of small groups can be distinct from individual thinking and community knowledge processing.

We need a new science of group learning to complement our sciences of individual learning and community knowledge building. In particular, we need a science that will help us to realize the potential of computer networking to foster the formation of virtual groups and computer-supported collaborative learning (CSCL).

The construct of group cognition provides a theoretical and practical foundation for developing the needed science, for analyzing the work of small groups and for designing effective collaboration software. The Virtual Math Teams (VMT) Project provides a model of such scientific research. Along with related explorations of group processes, it has already started to produce concrete analyses of group cognition and to develop a multi-faceted online educational environment.

A science of group cognition can systematically provide findings that are objective, reliable and generalizable through its interpretive case studies. With its focus on the group as the unit of description, group cognition joins other post-cognitive theories, which extend the analysis of cognition beyond the psychological individual. As the pivotal middle ground in which knowledge is primordially co-constructed, the group provides a foundation for sciences of learning at the individual, small group and community levels.

Keywords

Learning Science Knowledge Building Chat Room Group Cognition Data Session 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.College of Information Science & TechnologyDrexel UniversityPhiladelphiaUSA

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