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The main objective of this paper is to propose and evaluate an architecture that provides, manages, and collects data that permit high levels of adaptability and relevance to the user profiles. In addition, we implement this architecture on a platform called HyperManyMedia. To achieve this objective, an approach for personalized search is implemented that takes advantage of the semantic Web standards (RDF and OWL) to represent the content and the user profiles. The framework consists of the following phases: (1) building the semantic E-learning domain using the known college and course information as concept and sub-concept, (2) generating the semantic user profiles as ontologies, (3) clustering the documents to discover more refined sub-concepts, (4) reranking the user's search results based on his/her profile, and (5) providing the user with semantic recommendations. The implementation of the ontologies models is separate from the design and implementation of the information retrieval system, thus providing a modular framework that is easy to adapt and port to other platforms. Finally, the experimental results show that the user context can be effectively used for improving the precision and recall in E-learning search, particularly by re-ranking the search results based on the user profiles.

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a b s t r a c tMassive Open Online Courses (MOOCs) are becoming an essential source of information for both students and teachers. Noticeably, MOOCs have to adapt to the fast development of new technologies; they also have to satisfy the current generation of online students. The current MOOCs' Management Systems, such as Coursera, Udacity, edX, etc., use content management platforms where content are organized in a hierarchical structure. We envision a new generation of MOOCs that support interpretability with formal semantics by using the SemanticWeb and the online social networks. Semantic technologies support more flexible information management than that offered by the current MOOCs' platforms. Annotated information about courses, video lectures, assignments, students, teachers, etc., can be composed from heterogeneous sources, including contributions from the communities in the forum space. These annotations, combined with legacy data, build foundations for more efficient information discovery in MOOCs' platforms. In this article we review various Collaborative Semantic Filtering technologies for building Semantic MOOCs' management system, then, we present a prototype of a semantic middle-sized platform implemented at Western Kentucky University that answers these aforementioned requirements.

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