What I do
My research interests include Human-Computer Interaction, Information Visualization and Data Analysis, User Experience Modeling, Reflective technologies, Interpretation systems, or Document and Knowledge Engineering. Application domains are related to education, culture, health, humanities… I focus on interdisciplinary approaches for the study of human-machine coupling and co-development and collaborate with people from sociology, psychology, ergonomy, anthropology, education science, philosophy, design, medecine, focusing on real world activities.
Jan. 19: Official announcement of the opening of our Master’s Degree in Digital Cultures. The interdisciplinary program on Digital Cultures we have been working on for several years will open in sept. 2019 in University of Nantes, in a brand new building at the center of the creative district. Official announcement and program (in french)
Aug. 18: New website!. A brand new website is available. It is based on Jekill, should be readable on any device, has some dynamic flavor. It is the result of an effort to present my various interdisciplinary research activities in a hopefully more comprehensive way than the previous site (still available here).
Aug. 18: Enaction schools websites. Between 2006 and 2009 four summer schools on “Enaction and Cognitive Science” have been organized. The archive of their websites, with a lot of documents, is available here.
Recent publications (all)
Survey of Immersive Analytics (2019) Adrien Fonnet, Yannick Prié. in IEEE Transactions on Visualization and Computer Graphics, 22 pp. (to appear) doi Show abstract Immersive analytics (IA) is a new term referring to the use of immersive technologies for data analysis. Yet such applications are not new, and numerous contributions have been made in the last three decades. However, no survey reviewing all these contributions is available. Here we propose a survey of IA from the early nineties until the present day, describing how rendering technologies, data, sensory mapping, and interaction means have been used to build IA systems, as well as how these systems have been evaluated. The conclusions that emerge from our analysis are that: multi-sensory aspects of IA are under-exploited, the 3DUI and VR community knowledge regarding immersive interaction is not sufficiently utilised, the IA community should focus on converging towards best practices, as well as aim for real life IA systems.
Success prediction in MOOCs: A case study (2019) Antoine Pigeau, Olivier Aubert, Yannick Prié. in EDM 2019, Educational Data Mining Conference, Montreal, Canada, July 2019. Show abstract Success prediction in Massive Open Online Courses (MOOCs) is now tackled in numerous works, but still needs new case studies to compare the solutions proposed. We study here a specific dataset from a French MOOC provided by the OpenClassrooms company, featuring 12 courses. We exploit various features present in the literature and test several classification models.
Prototyping Immersive Analytics: Experiments with Design Students (2019) Adrien Fonnet, Grégoire Cliquet, Yannick Prié. in Workshop on Immersive Analytics “Interaction Design and Prototyping for Immersive Analytics” at CHI 2019, Glasgow, UK, May 2019. Show abstract Immersive analytics is an emerging field of research that explores the use of immersive technologies for data analysis. We argue that immersion is different from the simple use of 3D graphics and therefore requires new methods of representation and interaction with the data. We believe in the benefits of interdisciplinarity, especially with design, to help shape this new field. We describe two workshops that we have organized with design students, and we share lessons learned and key findings.
Leveraging Learners’ Activity Logs for Course Reading Analytics Using Session-Based Indicators (2018) Madjid Sadallah, Benoît Encelle, Azze-Eddine Maredj, Yannick Prié. in International Journal of Technology Enhanced Learning (to appear) Show abstract A challenge that course authors face when reviewing their contents is to detect how to improve their courses in order to meet the expectations of their learners. In this paper, we propose an analytical approach that exploits learners’ logs of reading to provide authors with insightful data about the consumption of their courses. We first model reading activity using the concept of reading-session and propose a new and efficient session identification. We then elaborate a list of indicators computed using learners’ reading sessions that allow to represent their behaviour and to infer their needs. We evaluate our proposals with course authors and learners using logs from a major e-learning platform. Interesting results were found. This demonstrates the effectiveness of the approach in identifying aspects and parts of a course that may prevent it from being easily read and understood, and for guiding the authors through the analysis and review tasks.