Proceedings of CSCL '97: The Second International Conference on Computer Support for Collaborative Learning, December 10-14, 1997, University of Toronto, Toronto, Ontario, Canada

By Rogers Hall; Naomi Miyake et al. | Go to book overview

Information Ecology of Collaborations in Educational Settings: Influence of Tool
Mark Guzdial EduTech Institute and GVU Center, College of Computing, Georgia Institute of Technology
Abstract
The information ecology perspective [2] helps to understand information spaces in terms of the creation, searching, and use (consumption) of information. An information ecology perspective of Computer-Supported Collaborative Learning (CSCL) environments describes the flow of information into the ecology (who writes notes? how many and when?) and the use of that information (who reads? how many and when?). From that perspective, use of two CSCL tools is compared to note similarities (e.g., notes written per student) and dissimilarities (e.g., thread length).
I. An Information Ecology Perspective of CSCL
A useful perspective on studying computer-supported collaborative learning is analysis at a high level of aggregation: Multiple group or whole class discussion forums, such as studying an entire CSILE knowledgebase [1] or the newsgroup of an entire class. (I refer to the entire discussion space for a class generically as a forum..) The questions at this level are about the behavior of all the participants in the forum. When do students read notes? When do they write notes? What is the level of participation in the class? Does the kind of computer technology and how it is used impact reading and writing behaviors of students?At such a high level of aggregation it is difficult to make statements about content of notes. We really cannot even determine much about what students are learning and whether they are learning. However, there are benefits to analysis of aggregate behaviors in CSCL forums:
Mediating behaviors. While we cannot determine if individuals are learning or not at the aggregate level, we can determine whether some mediating conditions for learning in a collaborative setting are being met. For example, if a small percentage of students is writing all the notes in the forum, we may suspect that not all students are using the opportunity to articulate their positions and have them reviewed by others. Or, if the forum can be characterized as simply question-and-answer exchanges, in-depth analysis and discussion may not be taking place. By looking at how a whole class reads and writes notes, we can learn about the kind of aggregate behaviors that suggest a successful collaborative learning situation or suggest that there are problems with the learning setting.
Informing designers. Designers of tools for computer-supported collaborative learning can use aggregate behaviors to inform designs and for high-level checks on their designs. Through looking at aggregate behaviors in a variety of settings, we can inform designers about the kinds of aggregate behaviors that can be affected by tool design and which are affected more by kind of use, age of students, or other variables. Further, it is simpler to conduct an aggregate analysis than an in-depth content analysis of a discussion, so an aggregate analysis can provide early, rough estimates on how well use of a new tool is proceeding. For example, if students in a class of 100 only write about two notes each in the first two weeks of a class using a new tool, it would be useful for the designer to know if this is within the range of normal behavior (based on the analysis in this paper, we suspect that it is) or if there is a severe problem that needs immediate attention.
Measuring actual practice. Looking at patterns of use across multiple classes can inform us about what actual classes do, as opposed to carefully controlled experimental groups or

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Proceedings of CSCL '97: The Second International Conference on Computer Support for Collaborative Learning, December 10-14, 1997, University of Toronto, Toronto, Ontario, Canada
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