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Social Network Analysis, online group communication structure and the measure of cohesion

ContributorChanier Thierry
ContributorReffay Christophe ; Chanier Thierry
CreatorReffay Christophe
dc.date.accessioned2021-07-23T18:44:59Z
dc.date.available2021-07-23T18:44:59Z
Identifiermce-simu-sna-all
Identifierhttp://mulce.univ-bpclermont.fr:8080/PlateFormeMulce/VIEW/PUBLIC/03/VMeta.do?adr=Simuligne%252FCorpus_objets%252Fmce-simu-sna-CP
dc.identifier.urihttps://linghub.org/handle/123456789/675075
DescriptionThis corpus is based on data extracted from the global Learning & Teaching Corpus Simuligne. It provides research data upon which publications here referred to (mainly Reffay, C. & Chanier, T. (2003a)). 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.
Formattext/xml
Formatapplication/pdf
Formatapplication/vnd.ms-excel
Formattext/plain
Formatimage/jpeg
PublisherMulce (MULtimodal Corpus Exchange) ; Universite Blaise Pascal ; Clermont-Ferrand:France ; URL:http://mulce.org
RightsCreative Common License: http://creativecommons.org/licenses/by-nc-sa/2.0/
Rightshttp://lrl-diffusion.univ-bpclermont.fr/mulce/metadata/vdex/mce_licence.xml
RightsRights holders of this corpus are: Thierry Chanier ;Christophe Reffay ; Marie-Laure betbeder ; Maud Ciekanski ; Marie-Noelle Lamy
SubjectComputer-assisted instruction
SubjectStudy and teaching
SubjectData processing
SubjectLanguage and languages
SubjectEducation
Subjectonline collaborative learning ; Social Network Analysis ; group communication structure ; cohesion ; clique ; cluster
TitleSocial Network Analysis, online group communication structure and the measure of cohesion
TypeCollection
TypeDataset
dcterms.accessRightsopen access after registration
dcterms.audienceResearcher or teachers in educational sciences or linguistics
dcterms.bibliographicCitationReffay, C., & Chanier, T. (2009). (editors). Social Network Analysis, online group communication structure and the measure of cohesion. Mulce.org : Clermont Université. [oai : mulce.org:mce-simu-sna-all ; http://repository.mulce.org ]. Corpus linked to the following publication: Reffay, C. & Chanier, T. (2003a). How social network analysis can help to measure cohesion in collaborative distance-learning", in Wasson, B., Ludvigsen, S. & Hoppe, U. (dir.), Designing for change in networked learning environments (Conférence Computer Supported Collaborative Learning conference (CSCL'2003), juin, Bergen, Norway). Dordrecht : Kluwer Acedemic Publisher. pp 343-352, [http://edutice.archives-ouvertes.fr/edutice-00000422 ]
dcterms.conformsToIMS-CP for packaging
dcterms.contributorChanier Thierry
dcterms.contributorReffay Christophe ; Chanier Thierry
dcterms.created2009-05-09
dcterms.creatorReffay Christophe
dcterms.descriptionThis corpus is based on data extracted from the global Learning & Teaching Corpus Simuligne. It provides research data upon which publications here referred to (mainly Reffay, C. & Chanier, T. (2003a)). 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.
dcterms.extent2000 ko
dcterms.formattext/xml
dcterms.formatapplication/pdf
dcterms.formatapplication/vnd.ms-excel
dcterms.formattext/plain
dcterms.formatimage/jpeg
dcterms.identifiermce-simu-sna-all
dcterms.identifierhttp://mulce.univ-bpclermont.fr:8080/PlateFormeMulce/VIEW/PUBLIC/03/VMeta.do?adr=Simuligne%252FCorpus_objets%252Fmce-simu-sna-CP
dcterms.publisherMulce (MULtimodal Corpus Exchange) ; Universite Blaise Pascal ; Clermont-Ferrand:France ; URL:http://mulce.org
dcterms.referencesReffay, C. & Chanier, T. (2003a). How social network analysis can help to measure cohesion in collaborative distance-learning", in Wasson, B., Ludvigsen, S. & Hoppe, U. (dir.), Designing for change in networked learning environments (Conférence Computer Supported Collaborative Learning conference (CSCL'2003), juin, Bergen, Norway). Dordrecht : Kluwer Acedemic Publisher. pp 343-352, http://edutice.archives-ouvertes.fr/edutice-00000422
dcterms.referencesReffay, C. & Chanier, T. (2003c). How social network analysis can help to measure cohesion in collaborative distance-learning", Conférence Computer Supported Collaborative Learning conference (CSCL'2003), juin, Bergen, Norway) [ Slide Presentation ]. http://edutice.archives-ouvertes.fr/edutice-00000422
dcterms.referencesReffay, C. & Chanier, T. (2002) "Social Network Analysis Used for Modelling Collaboration in Distance Learning Groups", Proceeding of Intelligent Tutoring System conference(ITS'02), Juin, France, dans Cerri, S.A. , Guardères, G. & Paraguaçu, F.(dir.) . Intelligent Tutoring System. Springer-Verlag, pp. 31-40. http://edutice.archives-ouvertes.fr/edutice-00000056
dcterms.referenceshttp://edutice.archives-ouvertes.fr/edutice-00000056
dcterms.referenceshttp://edutice.archives-ouvertes.fr/edutice-00000152
dcterms.referenceshttp://edutice.archives-ouvertes.fr/edutice-00000422
dcterms.referencesReffay, C. & Chanier, T. (2003b). "Mesurer la cohésion d'un groupe d'apprentissage en formation à distance.", Actes de la conférence Environnements Informatiques pour l'Apprentissage Humain (EIAH'2003), Avril, Strasbourg, France. INRP : Paris. pp 367-378. http://edutice.archives-ouvertes.fr/edutice-00000152
dcterms.requiresmce.simu.all.all
dcterms.rightsCreative Common License: http://creativecommons.org/licenses/by-nc-sa/2.0/
dcterms.rightshttp://lrl-diffusion.univ-bpclermont.fr/mulce/metadata/vdex/mce_licence.xml
dcterms.rightsRights holders of this corpus are: Thierry Chanier ;Christophe Reffay ; Marie-Laure betbeder ; Maud Ciekanski ; Marie-Noelle Lamy
dcterms.spatial7026232
dcterms.spatialGB
dcterms.spatial7008356
dcterms.spatialFR
dcterms.subjectComputer-assisted instruction
dcterms.subjectStudy and teaching
dcterms.subjectData processing
dcterms.subjectLanguage and languages
dcterms.subjectEducation
dcterms.subjectonline collaborative learning ; Social Network Analysis ; group communication structure ; cohesion ; clique ; cluster
dcterms.temporalname=Simuligne course ; start=2001-04-09; end=2001-07-06
dcterms.titleSocial Network Analysis, online group communication structure and the measure of cohesion
dcterms.typeCollection
dcterms.typeDataset
odrl.Policyhttp://purl.org/net/rdflicense/cc-by-nc-sa2.0


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This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 825182.