Abstract
A numerous amount of models supporting automated provision of personalized recommendations to students have been developed lately. Students usually receive recommendations based on outcomes of tests they have already taken. In this paper we explore methods from formal concept analysis for predicting students’ preferences with respect to learning objects going to be suggested to them before they have taken relevant tests.
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Encheva, S. (2012). Preferences Predictions of Learning Objects Supported by Collaborative Recommendations. In: Li, Z., Li, X., Liu, Y., Cai, Z. (eds) Computational Intelligence and Intelligent Systems. ISICA 2012. Communications in Computer and Information Science, vol 316. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34289-9_17
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DOI: https://doi.org/10.1007/978-3-642-34289-9_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34288-2
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