PEGASE, un système tutoriel intelligent générique et adaptatif en environnement virtuel

Buche, Cédric; Querrec, Ronan; Loor, Pierre De; Chevaillier, Pierre; Tisseau, Jacques

doi:10.3166/tsi.28.1051-1076

Cited by 4 publications

(5 citation statements)

References 10 publications

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“…Le cadre de l'apprentissage individuel a été bien décrit dans la littérature. Nous conseillons notamment de lire [1,15]. Rappelons que nous abordons principalement l'apprentissage adaptatif collaboratif dans la mesure où nous voulons lutter contre l'isolement et la perte de motivation des apprenants.…”

Section: éTat De L'artunclassified

Towards a semantic platform for adaptive and collaborative e-learning

Sabeima

Lamolle²,

Anghour³

et al. 2023

Revue Africaine De Recherche en Informatique Et Mathématiques Appliquées

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soumission à Episciences In the world of e-learning, learning systems have sought to adapt the user's profile and the content offered to them. However, from the point of view of collaboration between learners based on adaptation to the learner profile, this adaptation has not been sufficiently explored as an important aspect of the e-learning process. Adaptation will allow users with similar or very similar profiles to be grouped together to learn in harmony while maintaining motivation and commitment to learning. This should increase the success rate of learners. This will also allow us to reuse learning paths with good success rates for future recommendations to users with the same profile. In this paper, we focus on this aspect and propose a learning system that controls learning paths adapted to the users' profile and that allows collaborative learning of users in a synchronous way. After an overview of the existing work in the field of adaptive e-learning, we propose an architecture for the piloting of this type of collaborative adaptive learning based on ontologies and orchestrated by a multi-agent system. The latter is responsible for the piloting of learning paths, the recommendation of paths in collaborative or non-collaborative mode through communication between the different agents involved, and the management of events captured by the system. Dans le monde de la formation en ligne, les systèmes d'apprentissage ont cherché à proposer un contenu adapté à tout utilisateur selon son profil.Cette adaptation a comme objectif général de permettre à l'apprenant de tirer le meilleur parti du contenu exposé des ressources d'apprentissage.Cependant, du point de vue de la collaboration entre apprenants selon l'adaptation au profil des apprenants, cette adaptation n'a pas été suffisamment explorée comme un aspect important du processus d'apprentissage en ligne.L'adaptation va permettre de grouper des utilisateurs de profil similaire ou très proche pour apprendre en harmonie tout en gardant la motivation et l'engagement nécessaire afin d'augmenter le taux de réussite des apprenants. Cet état de fait doit permettre aussi de réutiliser certains parcours d'apprentissage avec de bons taux de réussite pour de futures recommandations aux utilisateurs ayant le même profil. Dans cet article, nous mettons l'accent sur cet aspect et proposons un système d'apprentissage qui recommande des parcours d'apprentissage adaptés au profil des utilisateurs et qui permet un apprentissage collaboratif des utilisateurs de façon synchrone. Après un tour d'horizon de l'existant dans le domaine de l'apprentissage adaptatif en ligne, nous proposerons une architecture pour le pilotage de ce type d'apprentissage adaptatif collaboratif. Cette solution est fondée sur des ontologies et orchestrée par un système multi-agents. Ce dernier est responsable du pilotage des parcours d'apprentissage, de la recommandation de parcours en mode collaboratif ou non par le biais d'une communication entre les différents agents intervenants et de la gestion des événements captés par le système.

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Section: éTat De L'artunclassified

Towards a semantic platform for adaptive and collaborative e-learning

Sabeima

Lamolle²,

Anghour³

et al. 2023

Revue Africaine De Recherche en Informatique Et Mathématiques Appliquées

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“…First category of functionalities are those of ITSs. These are generally composed of several components (modules), modeling and implementing the following instructional and content models (Buche, 2005):…”

Section: Hybrid Its Functionalities and Characteristics For Tutoring ...mentioning

confidence: 99%

Using a Knapsack Model to Optimize Continuous Building of a Hybrid Intelligent Tutoring System

Khemaja

2016

International Journal of Human Capital and Information Technology Professionals

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Intelligent Tutoring Systems (ITS) provide an alternative to the traditional “one size fits all” approach. Their main aim is to adapt learning content, activities and paths to support learners. Meanwhile, during the last decades, advances in lightweight, portable devices and wireless technologies had drastically impacted Mobile and Ubiquitous environments' development which has driven opportunities towards more personalized, context-aware and dynamic learning processes. Moreover, mobile and hand held devices could be advantageous to incremental learning, based on very short and fine grained activities and resources delivery. However, measuring efficiency and providing the most relevant combination/orchestration of learning activities, resources and paths remains and open and challenging problem especially for enterprises where choices and decisions face several constraints as time, budget, targeted core competencies, etc. This paper, attempts to provide a knapsack based model and solution in order to implement ITS's intelligent decision making about best combination and delivery of e-training activities and resources especially in the context of fast changing Information and Communication Technology (ICT) domain and its required skills. An android and OSGi based prototype is implemented to validate the proposal through some realistic use cases.

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“…They are mostly intended to competency and knowledge transfer. ITSs are generally composed of several components (modules), modeling and implementing the following instructional and content models (Buche, C., 2005):…”

Section: Intelligent Tutoring Systems Main Principlesmentioning

confidence: 99%

“…Finally, very recent ITSs have been developed using MASs. We can name for instance MASCARET (Buche, C. 2005), GASPAR (Buche, C., Querrec, R., De Loor, P., & Chevaillier, P. 2003) and GRU (GrundSpenkins, J. Anohina, A. 2005.…”

Section: Intelligent Tutoring Systems Architecturesmentioning

confidence: 99%

Skill oriented Training Activity as a Service

Khemaja

Mastour

2014

International Journal of Human Capital and Information Technology Professionals

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In this paper, the authors address the problem of fast changing Information and Communication Technology (ICT) profession which hampers efficiency of ICT professionals and practitioners as well as businesses. For instance, ICT practitioners increasingly need support and training activitie to adapt their skills or to acquire new ones in order to deal with ICT technology changes. The authors attempt for that aim to provide a generic training Framework based on Intelligent Tutoring Systems' configuration and adaptation to ICT professionals and practitioners needs. In this framework, the Intelligent Tutoring System's components are e-skill oriented training services using corresponding resources all together deployed in the cloud accordingly to a Software as a Service model. More specifically, they proposed Framework adopts the European e-Competence Framework, the European Qualification Framework as well as the Instructional Management System portfolio standard.

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PEGASE, un système tutoriel intelligent générique et adaptatif en environnement virtuel

Cited by 4 publications

References 10 publications

Towards a semantic platform for adaptive and collaborative e-learning

Towards a semantic platform for adaptive and collaborative e-learning

Using a Knapsack Model to Optimize Continuous Building of a Hybrid Intelligent Tutoring System

Skill oriented Training Activity as a Service

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