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Theses

Détection automatique des interactions entre apprenants dans les jeux sérieux multi-joueurs dédiés à l'apprentissage

Abstract : This thesis is supervised by Mathieu Muratet, Amel Yessad and Vanda Luengo (thesis director). The goal of this work is to allow the automatic detection of peer interactions that could emerge from a multi-player learning game scenario before any use of it. The peer interactions contribute to the motivation and involvement of learners in their learning process. The work undertaken in this thesis propose models and analysis tools to allow game designers to obtain information on the peer interactions that could emerge from their game without requiring players’ traces. Thus, the designers could rely on that information to modify their scenarios to match with their needs towards peer interactions. In order to fulfill this goal, we brought three main contributions. The first contribution is an ontology thanks to which it becomes possible to model multi-player learning games scenarios with various granularity levels. The interactions are often abstractly defined; the second contribution aims to help their formalization thanks to low-level features. Interactions formalized in a such a way become automatically detectable. The third contribution is a set of algorithm dedicated to the analysis of the modeled scenario in order to detect the various interactions that could emerge from it. The ontology has been tested on various serious games scenarios. The two other contributions have been put to the test through an experimentation carried out on a game created in the scope of this thesis.
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Mathieu Guinebert. Détection automatique des interactions entre apprenants dans les jeux sérieux multi-joueurs dédiés à l'apprentissage. Environnements Informatiques pour l'Apprentissage Humain. Sorbonne Université, 2019. Français. ⟨NNT : 2019SORUS130⟩. ⟨tel-02944719⟩

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