Towards Automated Evaluation of Learning Resources Inside Repositories

It is known that current Learning Object Repositories adopt strategies for quality assessment of their resources that rely on the impressions of quality given by the members of the repository community. Although this strategy can be considered effective at some extent, the number of resources inside repositories tends to increase more rapidly than the number of evaluations given by this community, thus leaving several resources of the repository without any quality assessment. The present work describes the results of two experiments to automatically generate quality information about learning resources based on their intrinsic features as well as on evaluative metadata (ratings) available about them in MERLOT repository. Preliminary results point out the feasibility of achieving such goal which suggests that this method can be used as a starting point for the pursuit of automatically generation of internal quality information about resources inside repositories.

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