Clare: a Computer-supported Collaborative Learning Environment Based on the Thematic Structure of Research and Learning Artifacts

This research concerns the representation issue in collaborative learning environments Our basic claim is that knowledge representation is not only fundamental to machine learning as shown by AI researchers but also essential to human learning in particular human metalearning few existing learning support systems however provide represen tations which are intended to help the learner make sense of and organize the subject content of learning integrate a wide range of classroom activities e g reading review ing writing discussion and compare and contrast various viewpoints from individual learners It is our primary purpose to construct an example instance of such a repre sentation and to show that useful computational manipulations can be performed on it and that the combination of the representation and related computational services can actually lead to the improved learner s performance on selected collaborative learning tasks First we propose a representational scheme called RESRA which characterizes the thematic structure of learning and research materials or artifacts and which might be used to organize learning activities at the levels of summarization evaluation argumentation integration and construction We develop a computer based tool called CLARE that supports the use of RESRA and two of its own aggregates i e threads and perspectives for various learning tasks e g conducting reviews engaging in collaborative writing participating in structured online discussions And nally we design two experiments that will empirically evaluate the e ectiveness of CLARE and provide evidence for our research claims