The major disadvantage of the current Learning Management Systems is the lack of learner assistance in their learning processes and, therefore, they can not replace the presence of the teacher who ensures the progress of learning. In fact, we proposed to integrate, for each learner, a metacognitive agent that supported the metacognitive assistance and extracts the defectsin the learning process and strategies. The goal is to invite the learner to correct himself and improve his learning method. Metacognitive questionnaires were distributed to a group of 100 students before, during and after a computer course. The goal is to evaluatethe metacognitive attributes and to determine their influence on the success of learning. Decision trees were used as data analysis tools to extract a set of rules and to discover the influence of these metacognitive attributes on the result obtained by the learners. The results indicate that there are relationships between the different metacognitive attributes and the learners’ success. We note there is the influence of metacognitive incitement on learner outcomes, which reflects the degree of understanding of a learning pedagogical unit by the learner.

This article addresses a multi-agent approach to solving the problem of integrating metacognitive incentives into Learning Management System. The behavior of the teacher in a classroom-teaching situation, where teacher adopting the competency-based approach, is characterized by a set of didactic interventions dynamically adaptable according to the actions-reactions of the learners. These interventions are continually subjected to perfection by experience. In this article, we are interested in modeling the multi-agent system in order to help the learners develop their metacognitive skills in a continuous way. The purpose of this system is to supervise the activities and statements of the learner and communicate them to the metacognitive agent. The latter focuses on the assessment of the learner's metacognitive skills in order to trigger, automatically, metacognitive incentives to provide help messages. Integrating the agent for metacognitive control and assistance, allows learners to maintain motivation and confidence, and elicit their attention to the importance of metacognitive skills during learning activity. The "MaSE" methodology and the "agentTool" are used to model the multi-agent system.

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