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

TL;DR

This paper presents a multi-agent system utilizing dynamic case-based reasoning with a novel similarity measure, ILCSS, to provide real-time, individualized learner follow-up in e-learning environments, enhancing support and reducing dropout rates.

Contribution

It introduces a new similarity measure, ILCSS, for case retrieval and integrates multi-agent dynamic CBR with virtual and human tutors for personalized learner monitoring.

Findings

The system effectively detects learner difficulties in real-time.

ILCSS improves the accuracy of case retrieval.

The approach supports various learning subjects.

Abstract

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.