Abstract
In this paper we describe a novel approach that fosters a strong personalized content-based recommendation of LOs. It gives priority to those LOs that are most similar to the student’s short-term learning goals (the concepts that the student wants to learn in the session) and, at the same time, have a high pedagogical utility in the light of the student’s cognitive state (long-term learning goals). The paper includes the definition of a flexible metric that combines the similarity with the query and the pedagogical utility of the LO.
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Ruiz-Iniesta, A., Jiménez-Díaz, G., Gómez-Albarrán, M. (2009). User-Adaptive Recommendation Techniques in Repositories of Learning Objects: Combining Long-Term and Short-Term Learning Goals. In: Cress, U., Dimitrova, V., Specht, M. (eds) Learning in the Synergy of Multiple Disciplines. EC-TEL 2009. Lecture Notes in Computer Science, vol 5794. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04636-0_62
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DOI: https://doi.org/10.1007/978-3-642-04636-0_62
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