Automatic Personalization of Learning Scenarios Using SVM

Ouraiba, El Amine; Chikh, Azeddine; Taleb‐Ahmed, Abdelmalik; Yebdri, Zeyneb El

doi:10.1109/icalt.2009.72

Cited by 4 publications

(4 citation statements)

References 4 publications

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“…Plusieurs solutions sont possibles pour générer des parcours d'apprentissage personnalisés à partir d'algorithmes et de techniques provenant de l'intelligence artificielle et du web sémantique telles que l'optimisation par colonie de fourmis [25], les réseaux bayésiens [24], les machines à vecteur de support (SVM) [16], les ontologies [27], etc.…”

Section: Génération De Parcours Pédagogiquesunclassified

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: Génération De Parcours Pédagogiquesunclassified

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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“…Current methods for personalization of learning can be divided into three groups: (i) oriented activities approaches [14]: where the learning process is represented by a graph in which the activities are identified and decomposed; (ii) oriented resources approaches [1,6,19]: where the learning process returns to select, assemble and present contents, (iii) oriented objectives approaches [2,18]: where the learning process is seen as a process of satisfaction of pedagogical objectives already defined. These approaches use a set of algorithms and techniques from artificial intelligence and web semantics such as ant colony optimization [7,8,17,20], Bayesian networks [2], the algorithm of support vector machines (SVM) [15], ontologies [5,16], web services technology [11], etc.. However, these methods are quite limited in term of handling uncertain and imprecise data.…”

Section: Related Workmentioning

confidence: 99%

Personalization of learning activities within a virtual environment for training based on fuzzy logic theory

Mohamed¹,

Abdeslam²,

Lahcen³

2019

Math. Nat. Sci.

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The development of computers and multimedia technology has opened up new possibilities for training based on virtual reality. Virtual reality is the most powerful extension of simulation based systems. In virtual reality there is a move to three dimensional, multi-sensory interfaces. A virtual environment for training (VET) can be defined as a computer-generated environment based on virtual reality, to simulate the real world. Learning through a VET can personalize learning needs for learners to promote the quality of learning. However, learners can't be provided with appropriate learning activities because often there is no personalized service to respond to each learner's particular needs. The obvious solution is to generate learning activities based on each learner's profile. Yet it is a complex process, especially with the inaccuracy of data that may contains a learner's profile. The main goal of this paper is to associate suitable learning activities to each learner based on his profile, to do so, we propose to employ fuzzy logic technique, and the fuzzy inference system to handle reasoning under uncertainty and inaccuracy which is one major issue of great concern in learner model design.

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“…Many authors stress that personalised learning improves learners' achievements and increases learning efficiency (Bennane, 2013;Beres, Maguar, & Turcsanyj-Szabo, 2012;Biletskiy, Baghi, Keleberda, & Fleming, 2009;Dorca, Lima, Fernandes, & Lopes, 2012;Joo, Kim, & Park, 2013;Lubchak, Kupenko, & Kuzikov, 2012;Mulvenna, Anand, & Büchner, 2000;Nekrasova, 2011;Ouraiba, Chikh, et al, 2009). Personalisation can be put into practice from two perspectives, namely, teachers' and learners'.…”

Section: Introductionmentioning

confidence: 96%