How social network analysis can help to measure cohesion in collaborative distance-learning

Abstract : 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 sub-groups 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.
Mots-clés : Réseaux sociaux
Type de document :
Communication dans un congrès
Computer Supported Collaborative Learning, Jun 2003, Bergen, Norway. Kluwer Academic Publishers, pp.343-352, 2003, <http://www.intermedia.uib.no/cscl/>
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https://edutice.archives-ouvertes.fr/edutice-00000422
Contributeur : Florence Galand <>
Soumis le : lundi 19 avril 2004 - 12:14:19
Dernière modification le : mardi 18 octobre 2016 - 01:03:31
Document(s) archivé(s) le : lundi 29 mars 2010 - 18:01:53

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  • HAL Id : edutice-00000422, version 1

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Christophe Reffay, Thierry Chanier. How social network analysis can help to measure cohesion in collaborative distance-learning. Computer Supported Collaborative Learning, Jun 2003, Bergen, Norway. Kluwer Academic Publishers, pp.343-352, 2003, <http://www.intermedia.uib.no/cscl/>. <edutice-00000422>

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