2024-03-29T10:49:37Zhttps://gredos.usal.es/oai/requestoai:gredos.usal.es:10366/1343682022-02-07T15:34:52Zcom_10366_122575com_10366_4512com_10366_3823col_10366_134243
A model for multi-label classification and ranking of learning objects
López, Vivian
de la Prieta Pintado, Fernando
Ogihara, Mitsunori
Wong, Ding Ding
Computer Science
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).
2017-09-05T11:01:34Z
2017-09-05T11:01:34Z
2012
info:eu-repo/semantics/article
Expert Systems with Applications. Volumen 39 (10), pp. 8878-8884. Elsevier BV.
0957-4174 (Print)
http://hdl.handle.net/10366/134368
en
info:eu-repo/semantics/openAccess
Elsevier BV