Abstract
Existing learning object repositories are adopting strategies for quality assessment and recommendation of materials that rely on information provided by their community of users, such as ratings, comments, and tags. In this direction, Connexions has implemented an innovative approach for quality assurance where resources are socially endorsed by distinct members and organizations through the use of the so-called lenses. This kind of evaluative information constitutes a referential body of knowledge that can be used to create profiles of endorsed learning resources that, in their turn, can be further used in the process of automated quality assessment. The present paper explores the development of endorsed learning resources profiles based on intrinsic features of the resources, and initially evaluates the use of these profiles on the creation of automated models for quality evaluation.
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Cechinel, C., Sánchez-Alonso, S., Sicilia, MÁ., de Azevedo Simões, P.W.T. (2011). Exploring the Development of Endorsed Learning Resources Profiles in the Connexions Repository. In: García-Barriocanal, E., Cebeci, Z., Okur, M.C., Öztürk, A. (eds) Metadata and Semantic Research. MTSR 2011. Communications in Computer and Information Science, vol 240. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24731-6_2
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DOI: https://doi.org/10.1007/978-3-642-24731-6_2
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