Abstract

Theories of CSCL have often focused on the discourse of student groups and their possible modes of participation in this discourse as definitive of collaborative learning. Lave and Wenger (1991), for instance, analyzed the increasing participation of novices in the discourses of communities of practice. Scardamalia and Bereiter (1996) proposed the use of networked computers to promote literate participation of students in knowledge-building discourses. Many contemporary theorists define their approaches in terms of dialog, communication and interaction. Most recently, Sfard (2008) has analyzed mathematical thinking of students as growing participation in specific discourses. Research methods in CSCL tend to focus on the analysis of traces of communication and other indicators of participation in discourse in order to study phenomena of collaboration and to assess effectiveness of computational supports. Researchers often complain that such analysis is time-consuming and tedious, wishing that computers could take over some of this burden. In their contribution to this issue, Rose and colleagues review the current state of the art of computational linguistics and outline prospects for computer support of discourse analysis. The major limitation of automated processing of natural language—and for that matter of reliable manual coding procedures—is the central role of context in discourse; the determination of the significance of a given utterance depends considerably upon its indexical references to other elements in the discourse context. Kienle and Herrmann present a context-oriented theory of communication and explore through design-based research its implications for the design of technology to support collaborative/discursive learning. They discover that understanding the contextual embeddedness of discourse can be problematic even for human participants, who also can benefit from computer support in CSCL settings. Computer-Supported Collaborative Learning (2008) 3:235–236 DOI 10.1007/s11412-008-9047-3

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