Semantic Information Retrieval for Personalized E-Learning

We present an approach for personalized retrieval in an e-learning platform, that takes advantage of semantic Web standards to represent the learning content and the user/learner profiles as ontologies, and that re-ranks search results/lectures based on how the contained terms map to these ontologies. One important aspect of our approach is the combination of an authoritatively supplied taxonomy by the colleges, with the data driven extraction (via clustering) of a taxonomy from the documents themselves, thus making it easier to adapt to different learning platforms, and making it easier to evolve with the document/lecture collection. Our experimental results show that the learner´s context can be effectively used for improving the precision and recall in e-learning content retrieval, particularly by re-ranking the search results based on the learner´s past activities.