Combining structural, process-oriented and textual elements to generate awareness indicators for graphical e-discussions

Authors:
Rakheli Hever;Reuma De Groot;Maarten De Laat;Andreas Harrer;Ulrich Hoppe;Bruce M. McLaren;Oliver Scheuer
Affiliations:
The Hebrew University of Jerusalem, Jerusalem, Israel;The Hebrew University of Jerusalem, Jerusalem, Israel;The University of Exeter, Exeter, UK;University of Duisburg-Essen, Duisburg, Germany;University of Duisburg-Essen, Duisburg, Germany;Deutsches Forschungszentrum für Künstliche Intelligenz, Saarbrucken, Germany;Deutsches Forschungszentrum für Künstliche Intelligenz, Saarbrucken, Germany
Venue:
CSCL'07 Proceedings of the 8th iternational conference on Computer supported collaborative learning
Year:
2007

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Abstract

Moderation of e-discussions can be facilitated by online feedback promoting awareness and understanding of the ongoing discussion. Such feedback may be based on indicators, which combine structural and process-oriented elements (e.g., types of connectors, user actions) with textual elements (discussion content). In the ARGUNAUT project (IST-2005027728, partially funded by the EC, started 12/2005) we explore two main directions for generating such indicators, in the context of a synchronous tool for graphical e-discussion. One direction is the training of machine-learning classifiers to classify discussion units (shapes and paired-shapes) into pre-defined theoretical categories, using structural and process-oriented attributes. The classifiers are trained with examples categorized by humans, based on content and some contextual cues. A second direction is the use of a pattern matching tool in conjunction with e-discussion XML log files to generate "rules" that find "patterns" combining user actions (e.g., create shape, delete link) and structural elements with content keywords.