Affective issues in Semantic Educational Recommender Systems.
Olga C. Santos, Jesús G. Boticario · 2012
Abstract. 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 Educational Recommender Systems (SERS) approach, which is characterized by its interoperability with e-learning services, to deal with learners ’ affective traits in educational scenarios.