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Aim/Purpose: To identify and rectify the learning difficulties of online learners. Background: The major cause of learners’ failure and non-acquisition of knowledge relates to their weaknesses in certain areas necessary for optimal learning. We focus on e-learning because, within this environment, the learner is mostly affected by these vulnerabilities due to the lack of direct contact with the teacher, who would be able to point out the learner’s difficulties and help to rectify them. Methodology: The research sample was 49 learners enrolled in an online course. We focused on three cognitive factors: language, memory, and reasoning. We propose an approach to optimize learners’ performances based on two intelligent agents that model the role of a teacher: the “detector agent” and the “rectifier agent”. Contribution: The intelligent agents beneficially contribute to e-learning enrichment and the development of cognitive skills and solidification of knowledge acquisition. This is achieved by strengthening the memory, the assimilation of lessons by improving language skills, and the reinforcement of problem solving by developing reasoning and analysis capacity. Findings: The results show that the proposed approach efficiently detects the weaknesses of learners and resolves them intelligently. Future Research: The approach toward e-learning performance can be improved by focusing on other factors and intelligent agents that can improve the yield for learners and more effectively optimize system operation for their perceived needs.

Abstract-A major challenge in developing systems of distance learning is the ability to adapt learning to individual users. This adaptation requires a flexible scheme for sequencing the material to teach diverse learners. This is where we intend to contribute to model the personalized learning paths to be followed by the learner to achieve his/her determined educational objective. Our modelling approach of sequencing is based on the pedagogical graph which is called SMARTGraph. This graph allows expressing the totality of the pedagogic constraints under which the learner is submitted in order to achieve his/her pedagogic objective. SMARTGraph is a graph in which the nodes are the learning units and the arcs are the pedagogic constraints between learning units. We shall see how it is possible to organize the learning units and the learning paths to answer the expectations within the framework of individual courses according to the learner profile or within the framework of group courses. To implement our approach we exploit the strength of XLink (XML Linking Language) to define the sequencing graph.

Nowadays, the online interaction has become more necessary and vital in terms of both socio-cognitive and socio-affective levels. It fully plays an important role in the acquisition of knowledge in online learning. However, the way in which these interactions take place remain little regulated and less efficient. Recognizing this, we have developed an approach used to automatically schedule the interactions in the learning process of learners. These interactions will easily help learners to assimilate the obscure concepts and quite difficult to understand. Our goal is achieved basing on intelligent agents modeling of virtual learners. These agents enter into discussion with learners and are widely involved in clarifying these concepts. To affirm the importance of our study in e-learning, we have given a questionnaire to the learners of the Master in French literature. This latter has confirmed that the majority of the learners choose to collaborate and interact with each other to come over the blockage points in their learning process. Furthermore, the empirical result has shown that our approach contributes effectively in online learning. A significant increase in learner results is well noticed.