Dominique Groux-Leclet

Work place: MIS Laboratory, UPJV, Amiens, France, University of Picardie Jules Verne, 33, rue St Leu, 80039 Amiens-France



Research Interests: Computational Science and Engineering, Computer systems and computational processes, Computer Architecture and Organization, Solid Modeling, Data Structures and Algorithms, Models of Computation


Dominique Groux-Leclet is currently HDR professor of computer science at the MIS Laboratory, Faculty of Sciences, University of Picardie Jules Verne, France.

For twenty years, she has conducted research in Computer Environments for Human Learning. She developed a user-centered participatory design approach, in her habilitation (where interdisciplinary was principal). In 2010, her research focused on personalized navigation in virtual environments, including the design of virtual tours scenarios or the design of educational scenarios for Serious Games. Since 2014, she joined the "autonomous and assisted navigation" axis of the Robotic Perception team. Her research themes are: Context Sensitivity, Adaptation and Personalization, Modeling and Knowledge Representation, 3D Navigation and Uses of digital environments. She has several papers in international conferences and journals.

Author Articles
Dynamic Selection Approach to Overcome the Demotivation of Learners in a Community Learning System

By Dominique Groux-Leclet Ahlame Begdouri Rachid Belmeskine

DOI:, Pub. Date: 8 Jul. 2018

Community of Practice (CoP) is a very rich concept for designing learning systems for adults in relation to their professional development. In particular, for community problem solving. Indeed, Communities of Practice are made up of people who engage in a process of collective learning in a shared domain. The members engage in joint activities and discussions, help each other, and share information. They build relationships that enable them to learn from each other. The most important condition for continuing to learn from a CoP is that the community should live and be active. However, one of the main factors of members demotivation to continue interacting through the CoP is the frequent receipt of a large number of aid requests related to problems that they might not be able to solve. Thing that may lead them to abandon the CoP. In an attempt to overcome this problem, we propose an approach for selecting a group of members who are the most appropriate to contribute to the resolution of a given problem. In this way, the aid request will be sent only to this group. Our approach consists of a static rules-based selection complemented with a dynamic selection based on the ability to solve previous similar problems through analysis of the history of interactions.

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