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In E-learning, there is still the problem of knowing how to ensure anindividualized and continuous learner's follow-up during learning process,indeed among the numerous tools proposed, very few systems concentrate on areal time learner's follow-up. Our work in this field develops the design andimplementation of a Multi-Agents System Based on Dynamic Case Based Reasoningwhich can initiate learning and provide an individualized follow-up of learner.When interacting with the platform, every learner leaves his/her traces in themachine. These traces are stored in a basis under the form of scenarios whichenrich collective past experience. The system monitors, compares and analysesthese traces to keep a constant intelligent watch and therefore detectdifficulties hindering progress and/or avoid possible dropping out. The systemcan support any learning subject. The success of a case-based reasoning systemdepends critically on the performance of the retrieval step used and, morespecifically, on similarity measure used to retrieve scenarios that are similarto the course of the learner (traces in progress). We propose a complementarysimilarity measure, named Inverse Longest Common Sub-Sequence (ILCSS). To helpand guide the learner, the system is equipped with combined virtual and humantutors.

Multi-Agents Dynamic Case Based Reasoning and The Inverse Longest Common Sub-Sequence And Individualized Follow-up of Learners in The CEHL