By Jerry Andriessen, Michael Baker, Dan D. Suthers
Arguing to profit: Confronting Cognitions in Computer-Supported Collaborative studying Environments specializes in how new pedagogical situations, job environments and verbal exchange instruments inside of Computer-Supported Collaborative studying (CSCL) environments can favour collaborative and effective confrontations of principles, proof, arguments and causes, or arguing to learn.
This ebook is the 1st that has assembled the paintings of the world over well known students on argumentation-related CSCL learn. All chapters current in-depth analyses of the techniques through which the interactive war of words of cognitions can result in collaborative studying, at the foundation of a wide selection of theoretical versions, empirical facts and Internet-based tools.
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Additional resources for Arguing to Learn: Confronting Cognitions in Computer-Supported Collaborative Learning Environments
My research strategy of comparing representations does not constitute a commitment to mutual exclusivity of the representations. Each representation has its own strengths and weaknesses, and each may be the best choice for different cognitive tasks, learning objectives, and populations. In fact, recent versions of Belvedere integrate three representational "views" (Graph, Matrix, and a Hierarchy representation not discussed here) of evidence models in one tool, providing an interesting platform for future studies.
This change was driven in part by a refocus on collaborative learning (Koschmann, 1994; Slavin, 1980; Webb & Palincsar, 1996), which led to a major change in how we viewed the role of the interface representations. Rather than being a medium of communication or a formal record of the argumentation process, we came to view the representations as resources (stimuli and guides) for conversation and reasoning (Collins & Ferguson, 1993; Roschelle, 1994). Laboratory and field trials with Belvedere provided many examples of situations in which Belvedere's diagrammatic representations appeared to be influencing learner's argumentation.
Rather, it seeks to identify the kinds of interactions, and therefore learning, that each representational notation encourages. ) After these illustrations, we will be fully prepared to review the studies undertaken with Belvedere. Hypothesis 1: Representational Notations Influence Learners' Ontologies The first hypothesis claims that important guidance for learning interactions comes from ways in which a representational notation limits what can be represented (Reader, unpublished; Stenning & Oberlander, 1995).