Metadata for Recommending Primary and Secondary Level Learning Resources

Recommender systems have been used in education to assist users in the discovery of learning resources. Unlike product-oriented recommender systems, the goals and behavior of users in education are influenced by their context; such influence may be stronger in formal scenarios such as primary and secondary education since context is highly regulated. Intuitively, we could assume that a biology teacher may be more interested in biology-related content rather than content from other fields. In this paper we explore such assumption by analyzing the impact of educational metadata that is associated to resources and teachers. We apply hierarchical clustering to determine clusters of interest and using a teacher profile, we classify new teachers and new items in order to predict their preferences. In order to validate our approach, we used a dataset derived from a repository of learning resources widely used by teachers in primary and secondary school in Chile in the role of old users, we also performed an experiment with teachers in training in the role of new users. Our results confirm the diverse impact of metadata on the formation of such clusters and on recommendation.