The present paper presents the results of an alternative approach for automatically evaluating quality inside learning object repositories that considers lower-level measures of the resources as possible indicators of quality. It is known that current repositories face a difficult situation, as their amount of resources tends to increase more rapidly than the number of evaluations provided by the community of users and experts. Alternative approaches for automatically assessing quality can relieve human-work and provide temporary quality information before more time and consuming evaluation is performed. We propose a methodology to automatically generate quality information about learning resources inside repositories with Artificial Neural Networks models. For that, we considered 34 low-level measures as possible indicators of quality and we used available evaluative metadata inside two world recognized repositories (MERLOT and Connexions) as baseline information for the establishment of classes of quality. The preliminary findings point out the feasibility of such an approach and can be used as a starting point in the process of automatically generating internal quality information about learning objects inside repositories.

ranking mechanisms, quality assessment, ratings, learning object repositories, artificial neural networks