DocumentCode :
3656422
Title :
Towards a Multi-label Classification of Open Educational Resources
Author :
Marcos Mouriño García;Roberto Pérez Rodríguez; Rifón;Manuel Vilares Ferro
Author_Institution :
Dept. of Telematics Eng., Univ. of Vigo, Vigo, Spain
fYear :
2015
fDate :
7/1/2015 12:00:00 AM
Firstpage :
407
Lastpage :
408
Abstract :
Nowadays, there are a lot of online repositories containing thousands of very useful educational resources for the educational community. To take full advantage of these resources requires a simple, direct and effective access to those resources that are of interest, therefore, it is necessary that those resources are ordered or ranked based on some criteria?-that is to say, they have to be classified. Classification is usually done manually by the resource provider, which directly implies a main problem: the time spent categorising resources. In this paper we propose a solution to this problem through the design and implementation of a multi-label classifier that enables the automatic classification of a set of educational resources in their most suitable category or categories, thus eliminating the need to manually perform this classification. Evaluation results show that the performance of the OER classifier is comparable to classification of a de-facto standard corpus: OHSUMED.
Keywords :
"Support vector machines","Classification algorithms","Standards","Training","Measurement","Algorithm design and analysis","Conferences"
Publisher :
ieee
Conference_Titel :
Advanced Learning Technologies (ICALT), 2015 IEEE 15th International Conference on
Type :
conf
DOI :
10.1109/ICALT.2015.55
Filename :
7265364
Link To Document :
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