Pierre Dillenbourg

A former teacher in elementary school, Pierre Dillenbourg graduated in educational science (University of Mons, Belgium). He started his research on learning technologies in 1984. In 1986, he has been on of the first in the world to apply machine learning to develop a self-improving teaching system. He obtained a PhD in computer science from the University of Lancaster (UK), in the domain of artificial intelligence applications for education. He has been assistant professor at the University of Geneva. He joined EPFL in 2002. He has been the director of Center for Research and Support on Learning and its Technologies, then academic director of Center for Digital Education, which implements the MOOC strategy of EPFL (over 2 million registrations). He is full professor in learning technologies in the School of Computer & Communication Sciences, where he is the head of the CHILI Lab: "Computer-Human Interaction for Learning & Instruction ». He is the director of the leading house DUAL-T, which develops technologies for dual vocational education systems (carpenters, florists,...). With EPFL colleagues, he launched in 2017 the Swiss EdTech Collider, an incubator with 80 start-ups in learning technologies. He (co-)-founded 4 start-ups, does consulting missions in the corporate world and joined the board of several companies or institutions. In 2018, he co-founded LEARN, the EPFL Center of Learning Sciences that brings together the local initiatives in educational innovation. He is a fellow of the International Society for Learning Sciences. He currently is the Associate Vice-President for Education at EPFL.

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CS-411: Digital education

This course addresses the relationship between specific technological features and the learners' cognitive processes. It also covers the methods and results of empirical studies on this topic: do student actually learn due to technologies?

LEARN-600: Theoretical Foundations of Learning Sciences 1

How do people learn and how can we support learning? This is part 1 of a two-part course that provides an overview of major theoretical perspectives that attempt to describe how learning works, and serves as an introduction to interpreting education as a means of designing learning environments.

LEARN-601: Theoretical Foundations of Learning Sciences 2

How do people learn and how can we support learning? This is part 2 of a two-part course that provides an overview of major theoretical perspectives that attempt to describe how learning works, and serves as an introduction to interpreting education as a means of designing learning environments.

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

Fashioning the Future: Unlocking the Creative Potential of Deep Generative Models for Design Space Exploration

Richard Lee Davis, Pierre Dillenbourg, Wei Jiang, Kevin Gonyop Kim

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

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

Learning

Learning is the process of acquiring new understanding, knowledge, behaviors, skills, values, attitudes, and preferences. The ability to learn is possessed by humans, animals, and some machines; th

Robot

A robot is a machine—especially one programmable by a computer—capable of carrying out a complex series of actions automatically. A robot can be guided by an external control device, or the contro

Eye tracking

Eye tracking is the process of measuring either the point of gaze (where one is looking) or the motion of an eye relative to the head. An eye tracker is a device for measuring eye positions and eye