Intelligent Tutoring Systems Based on the Multi-Agent Systems (ITS-MAS): The Dynamic and Incremental Case Based Reasoning (DICBR) Paradigm and the Inverse Longest Common Sub-Sequence (ILCSS) in the CEHL

The major problem of e-learning is often stopped during training. Due to the fact that it is necessary to ensure an individualized and continuous learner’s follow-up during the learning process. 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 a learner. When interacting with the platform, every learner leaves his/her traces in the machine. These traces are stored in the memory bank, this operation enriches collective past experiences. Via 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/or it avoids possible dropping out. The system can support any learning subject. The success of a casebased reasoning system depends critically on the performance of the retrieval step used and, more particularly, on similarity measure used to retrieve source cases that are similar to the learners' traces (traces in progress). We propose a dynamic retrieving method based on a complementary similarity measure, named Inverse Longest Common Sub-Sequence (ILCSS). To guide and help the learner, the system is equipped with combination of human and virtual tutors.