Towards a Multi-label Classification of Open Educational Resources

M. Mouriño-García, Roberto Pérez-Rodríguez, L. Anido-Rifón, M. Ferro
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引用次数: 2

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.
开放教育资源多标签分类研究
现在,有很多在线资源库包含成千上万的教育社区非常有用的教育资源。为了充分利用这些资源,需要对感兴趣的资源进行简单、直接和有效的访问,因此,有必要根据一些标准对这些资源进行排序或排名。也就是说,它们必须是保密的。分类通常由资源提供者手动完成,这直接暗示了一个主要问题:对资源进行分类所花费的时间。在本文中,我们通过设计和实现一个多标签分类器来解决这个问题,该分类器可以将一组教育资源自动分类到最合适的类别中,从而消除了手动进行分类的需要。评估结果表明,OER分类器的性能可与事实上的标准语料库:OHSUMED的分类相媲美。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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