Multi-Agents Dynamic Case Based Reasoning and The Inverse Longest Common Sub-Sequence And Individualized Follow-up of Learners in The CEHL
This addresses the challenge of personalized learner monitoring in e-learning, though it appears incremental as it builds on existing case-based reasoning methods with a new similarity measure.
The paper tackles the problem of providing real-time, individualized follow-up for learners in e-learning by developing a multi-agent system based on dynamic case-based reasoning, which uses a new similarity measure called Inverse Longest Common Sub-Sequence (ILCSS) to retrieve similar learner scenarios and detect difficulties to prevent dropout.
In 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.