Name
Title | ||
|---|---|---|
Intelligent tutoring systems founded on the multi-agent incremental dynamic case based reasoning |
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-Agent Systems 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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/CIST.2012.6388066 | Information Science and Technology |
Keywords | Field | DocType |
case-based reasoning,information retrieval,intelligent tutoring systems,multi-agent systems,collective past experience,complementary similarity measure,e-learning,human tutors,intelligent tutoring systems,inverse longest common sub-sequence,multiagent incremental dynamic case-based reasoning,retrieval performance,virtual tutors,Computer Environment for Human Learning (CEHL),Dynamic Case-Based Reasoning,Intelligent Tutoring Systems,Inverse Longest Common Sub-Sequence (ILCSS),Multi-Agent Systems,Scenarios and Ontologies,Traces,similarity measure | Similarity measure,Computer science,Multi-agent system,Artificial intelligence,Case-based reasoning,Reasoning system | Conference |
ISSN | ISBN | Citations |
2327-185X | 978-1-4673-2724-4 | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Abdelhamid Zouhair | 1 | 2 | 4.11 |
| El Mokhtar En-Naimi | 2 | 4 | 10.57 |
| Benaissa Amami | 3 | 4 | 2.85 |
| Hadhoum Boukachour | 4 | 13 | 4.88 |
| Patrick Person | 5 | 13 | 4.55 |
| Cyrille Bertelle | 6 | 81 | 20.42 |
| En-Naimi, E.M. | 7 | 1 | 1.04 |