The hybrid recommendation of digital educational resources in a distance learning environment: the case of MOOC

The accompaniment and the follow-up of the learners in an online training aim at helping the learner to carry out his or her training and to guarantee an adapted and quality learning. During a learning process, personalized search and recommendation of digital educational resources form aspects of this accompaniment. This article presents a search engine and a hybrid recommendation of digital educational resources. This engine allows for filtering and personalized search by providing adapted resources to the users' profiles on the one hand; on the other hand, to making a combination of the collaborative, the content-based and the semantic filtering to propose other additional resources. The semantic filtering is based on the exploitation of SPARQL queries from the system that we propose. They are executed on a remote server containing reusable vocabularies and formalized according to the linked data principles and technologies, such as the Lod Cloud. The result obtained is a set of linked terms to the keywords specified in the search query. These terms are then used to extend the search. We used a search test-set by keywords entered via a form and then we manually analyzed the linked terms obtained and the documents returned. The results obtained by our approach are satisfactory.