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A view of Computer-Supported Collaborative Learning research today
Conference proceeding   Open access

A view of Computer-Supported Collaborative Learning research today

G Stahl
2011 International Conference on Collaboration Technologies and Systems (CTS), pp 325-325
May 2011
DOI: https://doi.org/10.1109/CTS.2011.5928705
url
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.230.891View
SubmittedOpen Access (License Unspecified),  Open

Abstract

Cognition Collaboration Collaborative work Communities conversation analysis CSCL group cognition Learning systems methodology Presses Terminology theory
The field of Computer-Supported Collaborative Learning (CSCL) explores the design and use of collaboration technologies to support learning systems, such as school classrooms or small groups of people building knowledge together. I hope that CSCL environments can be designed that make possible and encourage groups to think and learn collaboratively. In my research, my colleagues and I look at logs of student groups chatting and drawing about mathematics in order to see how they build on each other's ideas to achieve more than they would individually. There are many theories useful for framing the cognitive work that groups undertake in CSCL and other collaboration settings, and they may in principle not be reducible to a single theory. Collaboration research explores questions involving numerous distinct-though interacting-phenomena at multiple levels of description. It may be most useful to clearly distinguish levels such as individual, small-group and community units of analysis, and to differentiate terminology for discussing these different levels. Seminal theoretical works influential within collaboration research suggest a post-cognitive approach to group cognition as a complement to analyzing cognition of individuals or of communities of practice. The traditional argument between quantitative vs. qualitative approaches may be moot; increasingly, studies mix methods to obtain a fuller picture, using cases studies to describe processes and statistics to generalize models of variables. Since CSCL combines researchers from technical backgrounds, including AI, with those from social sciences, the current tension may be more between those seeking cognitive explanations and those providing situated descriptions. For instance, many researchers want to guide students toward knowledge-building behaviors while others want to understand how students do the work of collaborative learning, without imposing interpretive standards. This may, for instance, involve coding student utterances into categories of pre-defined interest vs. analyzing the conversational moves and interactional practices that uniquely occur in specific traces of online interaction. The results can be as different as confirming hypotheses about general variables vs. exploring the complex network of social and technical phenomena at work in a unique situation of collaboration. A variety of studies of learning systems seem necessary to guide design of collaboration technologies.

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