Relation extraction among learning concepts in intelligent tutoring systems

This paper addresses the question of how to extract the relevance among the learning concepts in an intelligent tutoring system using the mathematical modeling of the search engines. To test the proposed approach, two learning domains have been selected from mathematics. For each domain, five distinct chapters have been quoted from the books written by various authors. After extracting candidate concepts, some feature of them have been determined. After feature extraction, the relationships among the concepts have been detected using context vector models, and finally, the concept maps have been automatically constructed as maximum spanning tree.