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GUINEBERT Mathieu

Tiến sĩ
Nhóm nghiên cứu : MOCAH
Ngày đi : 31-08-2019
Ban lãnh đạo nghiên cứu : Vanda LUENGO
Đồng hướng dẫn : YESSAD Amel, MURATET Mathieu

Automatic Detection of peer interactions in Multi-Player Learning Game

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 those 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.
Bảo vệ luận án : 20-09-2019 - 14h - Campus Jussieu 55-65/211
Hội đồng giám khảo :
Jean-Charles MARTY LIRIS CHAMBERY [Rapporteur]
Sébastien GEORGE IUT LAVAL [Rapporteur]
Amal EL-FALLAH SEGROUCHNI LIP6
Élise LAVOUE LIRIS
Pierre LAGARRIGUE , ICA
Vanda LUENGO LIP6
Amel YESSAD LIP6
Mathieu MURATET LIP6

Bài báo khoa học 2017-2019

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