Ontology-oriented case-based reasoning (CBR) approach for trainings adaptive delivery

Authors:
Dounia Mansouri;Aboubekeur Hamdi-Cherif
Affiliations:
Ferhat Abbas Setif University (UFAS), Faculty of Science, Computer Science Department, Setif, Algeria;Ferhat Abbas Setif University (UFAS), Faculty of Science, Computer Science Department, Setif, Algeria and Qassim University, Computer College, Computer Science Department, Buraydah, Saudi Arabia
Venue:
Proceedings of the 15th WSEAS international conference on Computers
Year:
2011

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Abstract

We propose an approach that adaptively provides the reuse of previous experience of trainings contents to be used by different audiences. The representation of the main building-blocks, or learning objects, that are at the basis of these trainings, is modeled using ontologies. The approach relies on case-based reasoning (CBR) since the trainings adaptation is based on the traces left by previous learning processes. Knowledge is stored in the form of cases, rather than rules. When a new situation is encountered, the CBR system reviews the cases in an attempt to find a match for this particular training. If a match is found, then that specific case can be used to solve the new problem, otherwise it is stored as a new independent problem with a chosen default solution, introduced by the human expert. Following these lines, we develop an adaptation algorithm responsible for the required corrective actions in trainings adaptive delivery destined to diversified learners.