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Addressing affective issues in the recommendation process has shown their ability to increase the performance of recommender systems in non-educational scenarios. In turn, affective states have been considered for many years in developing intelligent tutoring systems. Currently, there are some works that combine both research lines. In this paper we discuss the benefits of considering affective issues in educational recommender systems and describe the extension of the Semantic Educationaldblp:conf/ectel/SantosB12 fatcat:eiuxarsxobggfbfpwxhugrhcey