It has been argued that cohesion plays a central role in collaborative learning. In face-to-face classes, it can be reckoned from several visual or oral cues. In a Learning Management System or CSCL environment, such cues are absent. In this paper, we show that Social Network Analysis concepts, adapted to the collaborative distance-learning context, can help measuring the cohesion of small groups. Working on data extracted from a 10-week distance-learning experiment, we computed cohesion in several ways in order to highlight isolated people, active subgroups and various roles of the members in the group communication structure. We argue that such processing, embodied in monitoring tools, can display global properties both at individual level and at group level and efficiently assist the tutor in following the collaboration within the group. It seems to be more appropriate than the long and detailed textual analysis of messages and the statistical distribution of participants' contributions.

Abstract. We describe a situation of distance learning based on collaborative production occurring within groups over a significant time span. For such a situation, we suggest giving priority to monitoring and not to guiding systems. We also argue that we need models which are easily computable in order to deal with the heterogeneous and the large scale amount of data related to interactions, i.e. models relying on theoretical assumptions which characterise the structures of groups and of interactions. Social Network Analysis is a good candidate we applied to our experiment in order to compute communication graphs and cohesion factors in groups. This application represents an essential part of a system which would enable tutors to detect a problem or a slowdown of group interaction.

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In order to make replication possible for interaction analysis in online learning, the French project named Mulce (2007Mulce ( -2010 and its team worked on requirements for research data to be shareable. We defined a learning and teaching corpus (LETEC) as a package containing the data issued from an online course, the contextual information and metadata, necessary to make these data visible, shareable and reusable. These human, technical and ethical requirements are presented in this paper. We briefly present the structure of a corpus and the repository we developed to share these corpora. Related works are also described and we show how conditions evolved between 2006 and 2011. This leads us to report on how the Mulce project was faced with four particular challenges and to suggest acceptable solutions for computer scientists and researchers in the humanities: both concerned by data sharing in the Technology Enhanced Learning community.