Despite their potential value for learning purposes, e-discussions do not necessarily lead to desirable results, even when moderated. The study of the moderator's role, especially in synchronous, graphical e-discussions, and the development of appropriate tools to assist moderators are the objectives of the ARGUNAUT project. This project aims at unifying awareness and feedback mechanisms in e-discussion environments, presently implemented on two existing platforms. This system is primarily directed to a human moderator and facilitating moderation, but might also help the students monitor their own interactions. At the heart of system are the interrelations between an off-line AI analysis mechanism and an online monitoring module. This is done through a collaboration of technological and pedagogical teams, showing promising preliminary results.

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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 predefined 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.