A semantic recommender system for adaptive learning

The ever more complex labor world and the current economic crisis ask learners and workers to continuously update their qualification level to stay relevant on the market. Hence, education and training providers need to adjust their offer in order to cope with such evolving requirements. However, because of the huge number of variables to consider, finding the right learning content able to let an individual fill his/her competency gap may be a task hard to accomplish. In this paper we propose a semantic-based recommender system that is designed to cross heterogeneous information about learners’ and workers’ background as well as advertised job positions with a catalogue of online courses in order to identify the most appropriate learning resources. Experimental observations showed a good agreement between human and automatic recommendations, confirming the applicability of the emerging semantic technology to the generation of user-centered services capable to adapt to individual’s learning needs.