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    Título
    A model for multi-label classification and ranking of learning objects
    Autor(es)
    López, Vivian
    de la Prieta Pintado, Fernando
    Ogihara, Mitsunori
    Wong, Ding Ding
    Palabras clave
    Computer Science
    Fecha de publicación
    2012
    Editor
    Elsevier BV
    Citación
    Expert Systems with Applications. Volumen 39 (10), pp. 8878-8884. Elsevier BV.
    Abstract
    This paper describes an approach that uses multi-label classification methods for search tagged learning objects (LOs) by Learning Object Metadata (LOM), specifically the model offers a methodology that illustrates the task of multi-label mapping of LOs into types queries through an emergent multi-label space, and that can improve the first choice of learners or teachers. In order to build the model, the paper also proposes and preliminarily investigates the use of multi-label classification algorithm using only the LO features. As many LOs include textual material that can be indexed, and such indexes can also be used to filter the objects by matching them against user-provided keywords, we then did experiments using web classification with text features to compare the accuracy with the results from metadata (LO feature).
    URI
    http://hdl.handle.net/10366/134368
    ISSN
    0957-4174 (Print)
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