0000000000976341
AUTHOR
Florence Mendes
showing 3 related works from this author
Recommandation de parcours de formation dans un contexte mobile
2013
National audience; Les récentes avancées dans les technologies de l'information et de la communication ont vu naître de nouvelles formes d'enseignement. L'apprentissage à distance classique s'enrichit et se transforme pour donner jour à un apprentissage plus flexible, accessible sur de multiples supports, à toute heure et en tout lieu : l'apprentissage mobile. Nos travaux portent sur la conception d'un système de recommandation basé sur le contenu, modélisé en utilisant les technologies du web sémantique. La recommandation prendra en compte l'objectif de formation, mais également les supports disponibles pour dispenser cette formation, les préférences personnelles de l'apprenant, ou encore …
Pre-processings and Linear-Decomposition Algorithm to Solve the k-Colorability Problem
2004
International audience; We are interested in the graph coloring problem. We studied the effectiveness of some pre-processings that are specific to the k-colorability problem and that promise to reduce the size or the difficulty of the instances. We propose to apply on the reduced graph an exact method based on a linear-decomposition of the graph. We present some experiments performed on literature instances, among which DIMACS library instances.
Context-Aware Adaptive System For M- Learning Personalization
2014
International audience; Context-aware mobile learning is becoming important because of the dynamic and continually changing learning settings in learner's mobile environment, giving rise to many different learning contexts that are difficult to apprehend. To provide personalization of learning content, we aim to develop a recommender system based on semantic modeling of learning contents and learning context. This modeling is complemented by a behavioral part made up of rules and metaheuristics used to optimize the combination of pieces of learning contents according to learner's context. All these elements form a new approach to mobile learning.