A Recommender System to Provide Adaptive and Inclusive Standard-based Support Along the eLearning Life Cycle

Dynamic support in adaptive inclusive educational systems depends on properly managing the adaptation in the eLearning life cycle by combining design and runtime adaptations and making a pervasive usage of standards along the eLearning life cycle. My Ph.D research focuses on recommender systems for lifelong learning inclusive scenarios, which have particular differences in their need for personalized recommendations. The research presented here makes a proposal for addressing some of the existing challenges. It goes beyond issues that are usually considered when building recommender systems and focuses also on closing the cycle. In particular, I propose a graphical representation that will help to compare the recommenders' performance in eLearning scenarios.