OERecommender: um Sistema de Recomendação de REA para MOOC

Ariel Zuquello, Itana Gimenes


One of the key issues of Massive Open Online Courses (MOOC) is to provide mechanisms to support the learning process of their participants. Their population of participants is culturally diverse and the dropout rate is considered high, around 90%. One of the drawbacks of MOOC is the lack of open materials recommendation to support students in their learning process. In order to contribute to the improvement of this scenario, this paper proposes the OERecommender, a recommendation system for MOOC. The OERecommender aims to support students in searching for Open Educational Resources (OER) that can help in their learning process.

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