Recommendation of personalized learning contents supported by semantic web technologies

Abstract

Information and Communication Technologies are changing educational processes in several ways and making possible new options for longlife learning. One important issue is to provide mechanisms for the design of contents adapted to the needs and preferences of individuals, that is, providing personalized courses. In this paper we present a recommendation module based on semantic web technologies that combines the semantic modelling of both learning objects and learning objectives. We also describe how the recommendation module has been included in two eLearning systems developed by our research group, for the recommending reinforcement contents and supporting the design of on-demand courses. © Springer-Verlag Berlin Heidelberg 2013.