Personalized links recommendation based on data mining in adaptive educational hypermedia systems

In this paper, we describe a personalized recommender system that uses web mining techniques for recommending a student which (next) links to visit within an adaptable educational hypermedia system. We present a specific mining tool and a recommender engine that we have integrated in the AHA! system in order to help the teacher to carry out the whole web mining process. We report on several experiments with real data in order to show the suitability of using both clustering and sequential pattern mining algorithms together for discovering personalized recommendation links