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