MultiAgent case-based reasoning and individualized follow-up of learner in remote learning

In distance learning/training in a Computing Environment for Human Learning (CEHL), among the numerous methods proposed, very few concentrate on a real time follow-up of learner/trainee. Our work develops the design and implementation of a MultiAgent System based on case based reasoning which can initiate learning and provide an individualized monitoring of learner/trainee. When interacting with the platform, every learner/trainee leaves his/her traces in the machine. They are stored in a basis under the form of scenarios thus enriching 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. To help and guide the learner the system is equipped with combined virtual and human tutors.