Our System IDCBR-MAS: from the Modelisation by AUML to the Implementation under JADE Platform

This paper presents our work in the field of Intelligent Tutoring System (ITS), in fact there is still the problem of knowing how to ensure an individualized and continuous learners follow-up during learning process, indeed among the numerous methods proposed, very few systems concentrate on a real time learners follow-up. Our work in this field develops the design and implementation of a Multi-Agents System Based on Dynamic Case Based Reasoning which can initiate learning and provide an individualized follow-up of learner. This approach involves 1) the use of Dynamic Case Based Reasoning to retrieve the past experiences that are similar to the learner’s traces (traces in progress), and 2) the use of Multi-Agents System. Our Work focuses on the use of the learner traces. When interacting with the platform, every learner leaves his/her traces on the machine. The traces are stored in database, this operation enriches collective past experiences. The traces left by the learner during the learning session evolve dynamically over time; the case-based reasoning must take into account this evolution in an incremental way. In other words, we do not consider each evolution of the traces as a new target, so the use of classical cycle Case Based reasoning in this case is insufficient and inadequate. In order to solve this problem, we propose a dynamic retrieving method based on a complementary similarity measure, named Inverse Longest Common Sub-Sequence (ILCSS). Through monitoring, comparing and analyzing these traces, the system keeps a constant intelligent watch on the platform, and therefore it detects the difficulties hindering progress, and it avoids possible dropping out. The system can support any learning subject. To help and guide the learner, the system is equipped with combined virtual and human tutors

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

International audienceIn E-learning, there is still the problem of knowing how to ensure an individualized and continuous learner"s follow-up during learning process, indeed among the numerous tools proposed, very few systems concentrate on a real time learner"s follow-up. Our work in this field develops the design and implementation of a Multi-Agents System Based on Dynamic Case Based Reasoning which can initiate learning and provide an individualized follow-up of learner. When interacting with the platform, every learner leaves his/her traces in the machine. These traces are stored in a basis under the form of scenarios which enrich collective past experience. The system monitors, compares and analyses these traces to keep a constant intelligent watch and therefore detect difficulties hindering progress and/or avoid possible dropping out. The system can support any learning subject. The success of a case-based reasoning system depends critically on the performance of the retrieval step used and, more specifically, on similarity measure used to retrieve scenarios that are similar to the course of the learner (traces in progress). We propose a complementary similarity measure, named Inverse Longest Common Sub-Sequence (ILCSS). To help and guide the learner, the system is equipped with combined virtual and human tutors