International Journal of Artificial Intelligence in Education, (2000), 11, 221-241 221 Analysing student interaction processes in order to improve collaboration. The DEGREE approach Beatriz Barros & M. FelisaVerdejo Dep. de Ingenieria Electrica, Electronica y Control, Escuela Técnica Superior de Ingenieros Industriales (U.N.E.D), Ciudad Universitaria s/n, 28040 Madrid, Spain. Email {bbarros,felisa}@ieec.uned.es Abstract: Computer mediated collaborative learning allows the recording of a large amount of data about the interaction processes and the task performance of a group of students. This empirical data is a very rich source to mine for a variety of purposes. Some purposes are of practical nature like, for instance, the improvement of peer awareness on the on-going work. Other purposes are of a more long-term and fundamental scope such as to understand socio- cognitive correlations between collaboration and learning. Manual approaches to fully monitor and exploit these data are out of the question. A mixture of computational methods to organise and extract information from all this rough material together with partial and focused in-depth manual analysis seems a more feasible and scalable framework. In this paper we present an approach to characterise group and individual behaviour in computer-supported collaborative work in terms of a set of attributes. In this way a process-oriented qualitative description of a mediated group activity is given from three perspectives: (i) a group performance in reference to other groups, (ii) each member in reference to other members of the group, and (iii) the group by itself. In our approach collaboration is conversation-based. Then we propose a method to automatically compute these attributes for processes where joint activity and interactions are carried out by means of semi-structured messages. The final set of attributes has been fixed through an extensive period of iterative design and experimentation. Our design approach allows us to extract relevant information at different levels of abstraction. Visualization and global behavior analysis tools are discussed. Shallow analyses as presented in this paper are needed and useful to tackle with a large amount of information, in order to enhance computer- mediated support. INTRODUCTION Collaborative learning research has paid close attention to the study of pupils interactions during peer-based work in order to analyse and identify the cognitive advantages of joint activity (Dillenbourg, Baker, Blaye, & O’Malley, 1996). As Crook (1996) points out, the benefit of the collaborative approach for learning lies in the processes of articulation, conflict and co- construction of ideas occurring when working closely with a peer. Participants in a problem- solving situation have to make their ideas explicit (assertions, hypothesis, denials..) to other collaborators, disagreements prompt justifications and negotiations, helping students to converge to a common object of shared understanding. The computer provides opportunities to support and enhance this approach in a number of ways, for instance offering computer-based problem spaces for jointly creating and exploiting structures of common knowledge and shared reference. Moreover, networks make possible opening the collaborative framework to distributed communities providing remote access to these spaces as well as computer-mediated communication to support interpersonal exchange and debate. An increasing number of collaborative learning environments for open and closed virtual groups have been built for a range of learning tasks (Scardamalia & Bereiter, 1994) (Edelson, Pea & Gomez, 1996 ) (Wan & Johnson, 1994) (Suthers & Jones, 1997), and