The best Open Educational Resources (OER) for each final user can be hard to find. OER come from lots of sources, in many different formats, conforming to a diverse logical structure and each user may present different objectives depending on their role in the teaching/learning process and their context. Previous attempts have only focused on one kind of technologies. A new approach that embraces diversity, may gain from the potential synergies of sharing resources in the development of the final recommendation system and the exploitation of the data. In this work, we aim at identifying the main challenges facing the field of OER recommendation, for a potential architecture model.
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