Analysing student interaction processes in order to improve collaboration . The DEGREE approach

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.