Development of Intelligent Multi-agents System for Collaborative e-learning Support

The aim of this paper is the introduction of intelligence in e-learning collaborative system. In such system, the tutor plays an important role to facilitate collaboration between users and boost less active among them to get more involved for good pedagogical action. However, the problem lies in the large number of platform users, and the tutor tasks become difficult if not impossible. Therefore, we used fuzzy logic technics in order to solve this problem by automating tutor tasks and creating an artificial agent. This agent is elaborate in basing on the learners activities, especially the assessment of their collaborative behaviors. After the implementation of intelligent collaborative system by using Moodle platform, we have tested it. The reader will discover our approach and relevant results

Instrumentation des activités des tuteurs à l’aide d’un système multi-agents d’analyse automatique des interactions

International audienceResearch presented in this article is dedicated to the tutor instrumentation in distance collaborative learning situations. We are particularly interested in the reuse of interaction analysis indicators. In this paper, we present our system SYSAT; a multi-agent system for monitoring the activities of learners. The aim of SYSAT is to reuse indicators (social, cognitive, emotional ...) reported in the literature, in an open and adaptive system. We tested our system on the interaction data from two experiments conducted with two master students of the Ibn Tofail University. The article presents the results and discusses the prospects for Research.Ce travail s'inscrit dans le cadre des recherches sur les Environnements Informatiques pour l'Apprentissage Humain (EIAH), et plus particulièrement dans l’assistance du tuteur dans le suivi des apprenants lors des activités d’apprentissage collaboratives en ligne. Cet article décrit l’architecture du système SYSAT, un système multi-agents d’analyse automatique des interactions. L’objectif de SYSAT est de réutiliser les indicateurs (sociaux, cognitifs, affectifs…) rapportés dans la littérature, au sein d’un système adaptatif et ouvert. Nous avons testé notre système sur les données d’interactions issues de deux expérimentations menées avec les étudiants de deux masters à l’université Ibn Tofail. L’article présente les résultats obtenus et évoque les perspectives de recherche

Instrumentation des activités des tuteurs à l’aide d’un système multi-agents d’analyse automatique des interactions

Research presented in this article is dedicated to the tutor instrumentation in distance collaborative learning situations. We are particularly interested in the reuse of interaction analysis indicators. In this paper, we present our system SYSAT; a multi-agent system for monitoring the activities of learners. The aim of SYSAT is to reuse indicators (social, cognitive, emotional ...) reported in the literature, in an open and adaptive system. We tested our system on the interaction data from two experiments conducted with two master students of the Ibn Tofail University. The article presents the results and discusses the prospects for Research.Ce travail s'inscrit dans le cadre des recherches sur les Environnements Informatiques pour l'Apprentissage Humain (EIAH), et plus particulièrement dans l’assistance du tuteur dans le suivi des apprenants lors des activités d’apprentissage collaboratives en ligne. Cet article décrit l’architecture du système SYSAT, un système multi-agents d’analyse automatique des interactions. L’objectif de SYSAT est de réutiliser les indicateurs (sociaux, cognitifs, affectifs…) rapportés dans la littérature, au sein d’un système adaptatif et ouvert. Nous avons testé notre système sur les données d’interactions issues de deux expérimentations menées avec les étudiants de deux masters à l’université Ibn Tofail. L’article présente les résultats obtenus et évoque les perspectives de recherche

Towards an Adaptive Learning Model using Optimal Learning Paths to Prevent MOOC Dropout

Currently, massive open online courses (MOOCs) are experiencing major developments and are becoming increasingly popular in distance learning programs. The goal is to break down inequalities and disseminate knowledge to everyone by creating a space for exchange and interaction. Despite the improvements to this educational model, MOOCs still have low retention rates, which can be attributed to a variety of factors, including learners’ heterogeneity. The paper aims to address the issue of low retention rates in MOOCs by introducing an innovative prediction model that provides the best (optimal) learning path for at-risk learners. For this purpose, learners at risk of dropping out are identified, and their courses are adapted to meet their needs and skills. A case study is presented to validate the effectiveness of our approach using classification algorithms for prediction and the ant colony optimization (ACO) algorithm to optimize learners’ paths

Development of Intelligent Multi-agents System for Collaborative E-learning Support

The aim of this paper is the introduction of intelligence in e-learning collaborative system. In such system, the tutor plays an important role to facilitate collaboration between users and boost less active among them to get more involved for good pedagogical action. However, the problem lies in the large number of platform users, and the tutor tasks become difficult if not impossible. Therefore, we used fuzzy logic technics in order to solve this problem by automating tutor tasks and creating an artificial agent. This agent is elaborate in basing on the learners activities, especially the assessment of their collaborative behaviors. After the implementation of intelligent collaborative system by using Moodle platform, we have tested it. The reader will discover our approach and relevant results