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Roles in social interactions: graphlets in temporal networks applied to learning analytics
Auteur(s)
Charbey, Raphaël  
Brisson, Laurent  
Bothorel, Cécile  
Ruffieux, Philippe  
Garlatti, Serge  
Gilliot, Jean-Marie  
Mallégol, Antoine  
Type
Conférence scientifique
Date de publication
2019
Langue de la référence
Anglais
Unité(s) / centre(s) de recherche hors HEP
IMT Atlantique, Lab-STICC UMR CNRS
Résumé
There is a growing interest in how data generated in learning platforms, especially the interaction data, can be used to improve teaching and learning. Social network analysis and machine learning methods take advantage of network topology to detect relational patterns and model interaction behaviors. Specifically, small induced subgraphs called graphlets, provide an efficient topological description of the way each node is embedded in the meso-scale structure of a network. Here we propose to detect the roles occupied by the different participants, students and teachers, in the successive phases of courses modeled by a sequence of static snapshots. The detected positions, obtained thanks to graphlet enumeration combined with a clustering method, reveal the different roles observed in each snapshot. We also track the role changes through the overall sequence of snapshots. We apply our method to the Sqily platform and describe the mutual skill validation process. The detected roles, the transitions between roles and a overall visualization through Sankey diagrams help interpreting the course dynamics. We found that some roles act like necessary steps to engage students within an active exchange process with their classmates.
Nom de la manifestation
Complex Networks 2019, The 8th International Conference on Complex Networks and their Applications
Date(s) de la manifestation
2019-12-10
Ville de la manifestation
Lisbon
Pays de la manifestation
Portugal
Handle
http://hdl.handle.net/20.500.12162/3146
URL(s) non permanente et complémentaire(s)
https://hal.archives-ouvertes.fr/hal-02317823
Fichier(s)
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ComplexNetwork2019_graphlets_learning_analytics_pre-print.pdf

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570 KB

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Adobe PDF

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09551374157c78cd561404cc8b3de613

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