Un environnement sémantique à base d'agents pour la formation à distance (E-Learning)

Aujourd’hui, les établissements d’enseignement, tels que les universités, de plus en plus offrent des contenus d’E -Learning. Certains de ces cours sont utilisés avec l'enseignement traditionnel (face à face ou présentiel), tandis que d'autres sont utilisés entièrement en ligne. La création de contenu d'apprentissage est une tâche principale dans tous les environnements d'apprentissage en ligne. Les contraintes de réduire au minimum le temps nécessaire pour développer un contenu d'apprentissage, d'augmenter sa qualité scientifique et de l'adapter à de nombreuses situations (contenu adaptatif), ont été un principal objectif et donc plusieurs approches et méthodes ont été proposées. En outre, les caractéristiques intellectuelles et sociales, ainsi que les styles d'apprentissage des individus, peuvent être très différents. Ces différences conduisent les personnes à adapter le contenu d'apprentissage en tenant compte des profils des apprenants et de leurs objectifs et caractéristiques. Cette recherche ouvre des portes pour les systèmes d'apprentissage avancées, qui fournissent aux apprenants immédiatement, des contenus d’apprentissage adaptés selon plusieurs critères de chaque apprenant. Alors que, il ne peut pas être pratique si nous n'avons pas plus d'informations sur l'apprenant et le contenu d'apprentissage (objectifs d'apprentissage, les prérequis, préférences, niveaux ...etc). Par conséquent, nous développons un système collaboratif, où plusieurs auteurs travaillent en collaboration, pour créer et annoter le contenu éducatif en utilisant le système multi-agents. La contribution de notre système est l'hybridation des techniques d'adaptation avec celles de la collaboration et du Web sémantique (ontologie, annotation). Nous représentons les profils des apprenants et le contenu d'apprentissage en utilisant des ontologies et des annotations pour répondre à la diversité et aux besoins individuelles des apprenants. Nous utilisons le paradigme agent, dans notre système, pour bénéficier des points forts de ce paradigme tels que la modularité, autonomie, flexibilité... etc

Une approche basée agent pour la formation à distance

La vie actuelle est caractérisée par la rapidité et les gens sont occupés la plupart du temps (voyage, sport, travail, famille …etc.). Les apprenants ne peuvent pas accéder toujours à l’école à cause de l'occupation. Ces situations ne permettent plus aux apprenants de fréquenter régulièrement une classe de formation. Donc la formation traditionnelle est devenue impossible pour plusieurs apprenants car elle les oblige d'être présents dans des temps prédéfinis (limitation de temps). De même le coté financier consacré pour assurer une bonne formation, est considéré comme un point faible de la formation traditionnelle. Le bouleversement technologique, notamment les technologies de la communication, donne la possibilité d'utiliser une nouvelle forme de la formation dite "formation à distance" "FAD". Dans la FAD la formation est disponibilité dans tous les temps, l'apprenant est libre de choisir le temps de sa formation. Ainsi que ce nouveau mode de formation réduit le temps d'apprentissage. L'adaptation de cours et une stratégie qui s'avère une méthode pertinente pour répondre aux besoins des apprenants selon le profil et les capacités d'intelligence de l'apprenant. On utilise le paradigme "Agent" dans un système éducatif afin de profiter des points forts de ce paradigme tel que la modularité, autonomie, flexibilité …etc. Mots-clés : Formation à distance, système d'adaptation, la technologie de système multi-agent, plateforme de la formation à distance

Agent-Based Approach for E-Learning

The current life knows a remarkable development which changes all aspects of our life such as: our working environment, educational system, traveling patterns, sport activitiesâ?¦etc. The busy lifestyle faced by individuals today and the fast pattern of life may lead to their inability to be involved in a process of education. There is also a considerable technological development, especially the development of computing and communication technology. All these factors led to the idea of the need to reduce the time and to benefit from this technology, particularly in the field of education. The way of the classical education is very slow, and the student is obliged to be present at specific times which may be inappropriate for the majority of learners. Moreover, the substantial funds allocated for the success of classical learning process. Thus, it emerged the so-called E-learning via the Internet; the aims of this new mode of learning across the network (web) are to reduce the time of a process of learning â??education- as well as to erase the drawbacks of the classical way of education. Therefore, in E-learning, the learner is not forced to be present at specific times, but he is free to choose the time of learning, which is appropriate with his schedule. Another point that is the adaptation of the content (courses) with the intellectual and social characteristics of the learner and with his background (previous knowledge), which is the main task for each educational system. The technology of Multiagent system is relevant in this area

Agent-Based Approach for E-Learning

The current life knows a remarkable development which changes all aspects of our life such as: our working environment, educational system, traveling patterns, sport activities…etc. The busy lifestyle faced by individuals today and the fast pattern of life may lead to their inability to be involved in a process of education. There is also a considerable technological development, especially the development of computing and communication technology. All these factors led to the idea of the need to reduce the time and to benefit from this technology, particularly in the field of education. The way of the classical education is very slow, and the student is obliged to be present at specific times which may be inappropriate for the majority of learners. Moreover, the substantial funds allocated for the success of classical learning process. Thus, it emerged the so-called E-learning via the Internet; the aims of this new mode of learning across the network (web) are to reduce the time of a process of learning –education- as well as to erase the drawbacks of the classical way of education. Therefore, in E-learning, the learner is not forced to be present at specific times, but he is free to choose the time of learning, which is appropriate with his schedule. Another point that is the adaptation of the content (courses) with the intellectual and social characteristics of the learner and with his background (previous knowledge), which is the main task for each educational system. The technology of Multiagent system is relevant in this area

Dynamic GSPNs: formal definition, transformation towards GSPNs and formal verification

International audienc

A Bayesian CNN-LSTM Model for Sentiment Analysis in Massive Open Online Courses MOOCs

Massive Open Online Courses (MOOCs) are increasingly used by learn-ers to acquire knowledge and develop new skills. MOOCs provide a trove of data that can be leveraged to better assist learners, including behavioral data from built-in collaborative tools such as discussion boards and course wikis. Data tracing social interactions among learners are especially inter-esting as their analyses help improve MOOCs’ effectiveness. We particular-ly perform sentiment analysis on such data to predict learners at risk of dropping out, measure the success of the MOOC, and personalize the MOOC according to a learner’s behavior and detected emotions. In this pa-per, we propose a novel approach to sentiment analysis that combines the advantages of the deep learning architectures CNN and LSTM. To avoid highly uncertain predictions, we utilize a Bayesian neural network (BNN) model to quantify uncertainty within the sentiment analysis task. Our em-pirical results indicate that: 1) The Bayesian CNN-LSTM model provides interesting performance compared to other models (CNN-LSTM, CNN, LSTM) in terms of accuracy, precision, recall, and F1-Score; and 2) there is a high correlation between the sentiment in forum posts and the dropout rate in MOOCs

Educative and Adaptive System for Personalized Learning: Learning Styles and Content Adaptation

International audienceThe use of electronic environment in our life has become inevitable fact. In the educational field, the learners find many difficulties to attend a classical mode of learning (in class) that obliges them to be present according to daily schedule. Thus, the E-learning allows the process of learning anywhere and any when. However, the absence of the teacher, in the E-learning process, leads to the isolation of the learners. Generally, these learners have different backgrounds, preferences, needs, objectives, intellectual abilities and personal characteristics; and they do not understand and acquire the knowledge using the same courses and the same learning styles. Several aspects influence the effective learning process. Thus, different concepts, views and models of learning styles exist in literature. Many models and classifications of learning styles were proposed according to several angles of view. For this aim, we propose an educational system that provides the accurate learning content and use the most appropriate learning style that meets the needs and preference of each learner (personalized learning). Our system provides several learning styles for the learners; however, the essential task is to find the best course and learner style for each learner according to his characteristics. The adaptation of the content and styles allows the dynamicity in the learning process and gives a new chance for many kinds of learners to enhance their knowledge in personal manner. Moreover, the modeling of our system using multi-agents system increases its interaction and flexibility

A COOPERATIVEMULTI-AGENT APPROACH FOR THE ANNOTATION OF ADAPTIVE CONTENT FOR E-LEARNING

International audienceThe creation of learning content is a main task in any E-learning environment. The constraints of minimizing the time required for developing a learning content and for increasing its scientific quality, have been a principal aim, in the last decade. Thus, several approaches and methods were proposed to reach this aim. Moreover, the intellectual and social characteristics, as well as the learning styles of individuals, can be very different. These differences lead persons to adapt the learning content by taking into account the learners profiles and their objectives. It is indispensable to annotate the learning content using additional information about the learner and the learning content. Therefore, we develop a cooperative system, where several authors work in acooperative manner, to create and edit educational content using multi-agent system. The contributionof our system is the hybridization of adaptation techniques with those of cooperation and indexing(annotation) of the learning content to meet the diversity and individual needs of the learners