Abstract
► We use multi-label classification methods for search tagged learning objects (LOs). ► The methodology illustrates the task of multi-label mapping of LOs into types queries. ► We use of multi-label classification algorithm using only the LOs features. ► We also did experiments using web classification with text features. ► Multi-label classifiers such as RAKEL was very effective.
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).
