Flexible scripting in net-based learning groups
Zu finden in: Scripting Computer-Supported Collaborative Learning, 2007
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In diesem Kapitel schlägt Jörg Haake vor, collaboration scripts mit endlichen Automaten zu modellieren.Von Beat Döbeli Honegger, erfasst im Biblionetz am 22.01.2007
CSCL scripts facilitate cooperative learning by constraining the activities of co-learners and thereby supporting coordination between distributed colearners as well as guiding co-learners through the collaborative learning process. So far, such scripts have been encoded in CSCL environments and their tools. This made flexible adaptations of scripts an expensive task, which hinders experience-based improvements of CSCL scripts. In this paper, we present a formal model of CSCL scripts and show how it can be used to help teachers and designers develop, adapt and experiment with CSCL scripts. In our approach, a script is represented as an extended finite state automaton, which is used to control the user interface and the possible activities in a webbased CSCL environment. We distinguish between atomic scripts, which support a specific collaborative learning activity, and composite scripts, which support a complex collaborative learning task through a sequence of atomic or composite scripts. Scripts can be created by a two-step process: defining atomic CSCL scripts, and linking existing scripts into a composite script for the overall learning activity. This approach enables the definition and reuse of CSCL scripts as well as their adaptation to learning groups and learning situations.Von Jörg M. Haake, Hans-Rüdiger Pfister im Buch Scripting Computer-Supported Collaborative Learning (2007) im Text Flexible scripting in net-based learning groups
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|R. Abelson, Pierre Dillenbourg, Frank Fischer, Patrick Jermann, Paul A. Kirschner, Heinz Mandl, Martin Muehlenbrock, Martin Mühlpfordt, Werner Müller, Hans-Rüdiger Pfister, R. C. Shank, A. Soller, Armin Weinberger|
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|ArgueGraph, collaboration scriptcollaboration script, CSCLComputer-Supported Collaborative Learning, IMS LD, Lernenlearning|