Pierre Dillenbourg

Biographie

Ancien instituteur primaire, Pierre Dillenbourg obtient un master en Sciences de lEducation (Université de Mons, Belgique). Dans son projet de master en 1986, il est l'un des premiers au monde à appliquer les méthodes de 'machine learning' à l'éducation, afin de développer un 'self-improving teaching system'. Ceci lui permettra de débuter une thèse de doctorat en informatique à l'Université de Lancaster (UK) dans le domaine des applications éducatives de lintelligence artificielle. Il a été Maître dEnseignement et de Recherche à lUniversité de Genève. Il rejoint l'EPFL en 2012, où Il fut le directeur du Centre de Recherche sur l'Apprentissage, la formation et ses technologies(CRAFT), puis académique du Centre pour l'Education à l'Ere Digitale (CEDE) qui met en oeuvre la stratégie MOOC de l'EPFL (plus de 2 millions d'inscriptions). Il est actuellement professeur ordinaire en technologies de formation aux sein de la faculté Informatique et Communications et dirige laboratoire d'ergonomie éducative (CHILI). Depuis 2006, il a aussi été le directeur de DUAL-T, la 'leading house' dédiée aux technologies pour les systèmes de formation professionnelle duale. Il a fondé plusieurs start-ups dans l'éducation et rejoint plusieurs conseils d'administration. En 2017, Il a créé avec des collègues le 'Swiss EdTech Collider', un incubateur qui rassemble 80 start-ups dans le domaine des technologies éducatives. En 2018, ils ont lancé LEARN, le centre EPFL pour les sciences de l'apprentissage, lequel regroupe les initiatives locales en innovation éducative. Pierre est un 'inaugural fellow of the International Society of Learning Sciences'. Il est actuellement le Vice-Président Associé pour l'Education à l'EPFL.

Official source

https://people.epfl.ch/pierre.dillenbourg?lang=fr

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Publications associées (368)

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Personnes menant des recherches similaires (102)

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Evaluation of teachers’ orchestration tools usage in robotic classrooms

Barbara Bruno, Pierre Dillenbourg, Aditi Kothiyal, Sina Shahmoradi

Teachers’ self-efficacy in managing classrooms is an important consideration when it comes to bringing educational robots to classrooms. Orchestration tools support teachers by providing awareness indicators of students’ progress as well as levers to control the flow of the lesson. We designed and evaluated the impact of an orchestration tool for a series of robot-based learning activities to teach a basic concept in mathematics to children, aged 7-10. Six teachers in primary schools across Switzerland used the orchestration tool to manage the activities in six sessions involving a total of ninety-one students. We observed teachers’ usage of the orchestration tool during the sessions and interviewed them after the sessions about the usefulness of these functionalities. Our findings show that even though teachers used the tool in different ways from each other, in general, it supported them in their classroom orchestration practices, mainly to manage the activity sequence and get aware of the robot technical failures and, to a lesser degree, get aware of students’ progress for the purpose of activity transitions and enriching class discussions. We discuss the theoretical implications of these results, relating our findings to the literature on classroom orchestration tool design, especially highlighting the importance of educational level and the type of learning technology as contextual factors affecting teachers’ usage of orchestration tools. We also provide implications for designing orchestration tools, focusing on the necessity of providing different types of awareness indicators and multiple options for activity management to fulfil the variety of teachers’ orchestration needs.

2023

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This paper investigates the potential impact of deep generative models on the work of creative professionals, specifically focusing on fashion design. We argue that current generative modeling tools lack critical features that would make them useful creativity support tools, and introduce our own tool, generative.fashion, which was designed with theoretical principles of design space exploration in mind. Through qualitative studies with fashion design apprentices, we demonstrate how generative.fashion supported both divergent and convergent thinking, and compare it with a state-of-the-art diffusion model, Stable Diffusion. In general, the apprentices preferred generative.fashion over Stable Diffusion, citing the features explicitly designed to support ideation. We conclude that the exploration and development of novel interfaces and interaction modalities that are theoretically aligned with principles of design space exploration is crucial for unlocking the creative potential of generative AI and advancing a new era of creativity.

Association for Computing Machinery2023

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A game-based approach for evaluating and customizing handwriting training using an autonomous social robot

Barbara Bruno, Daniel Carnieto Tozadore, Pierre Dillenbourg, Chenyang Wang

Handwriting learning is a long and complex process that takes about ten years to be fully mastered. Nearly one-third of all children aged 4-12 experiences handwriting difficulties and, sadly, most of them are left to fight them on their own, due to the scarcity of tools for the detection and remediation of such difficulties. Building on state-of-the-art digital solutions for automated handwriting assessment and the training of specific handwriting-related skills, in this article we discuss requirements, rationale, and architecture of a system for handwriting training, which relies on a social robot as a mediator agent, offering personalized training and suggestions. The system is envisioned to operate autonomously and to support long-term interactions via personalization. Preliminary validation of the system in an experiment with 31 children showed its potential not only for autonomously guiding handwriting training sessions but also for its inclusion in the teachers' practice.

2022

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