A model for multi-label classification and ranking of learning objects

Authors:
Vivian F. López;Fernando de la Prieta;Mitsunori Ogihara;Ding Ding Wong
Affiliations:
Departament Informática y Automática, University of Salamanca, Plaza de la Merced S/N, 37008 Salamanca, Spain;Departament Informática y Automática, University of Salamanca, Plaza de la Merced S/N, 37008 Salamanca, Spain;Department of Computer Science, University of Miami, 1365 Memorial Drive, Coral Gables, FL 33146, USA;School of Computing and Information Sciences, Florida International University, ECS 251, 11200 SW 8 ST, Miami, FL 33199, USA
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

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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).