探索OCW存储库评估资源中元数据质量度量的来源和准确性

Audrey Romero Pelaez, Veronica Segarra-Faggioni, P. Alarcón
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引用次数: 8

摘要

开放教育是一项旨在将知识带到弱势部门并为所有人打开更好教育机会之门的运动。开放教育资源(OER),如开放课件(OCW)已经成为教育环境中的支持资源。由于这个原因,存储库的质量由其元数据的质量来突出。元数据是存储库的一个特征,它方便了教育资源的搜索和发现任务,从而确保了教育资源的重用。因此,为了确保对高质量知识的普遍访问,有一些元数据质量的度量来评估OCW存储库元数据:完整性、一致性、一致性、准确性、来源和可访问性。在本研究中,准确性和来源被优先考虑为元数据质量评估的质量指标,因为它们支持潜在用户搜索和选择开放式课程资源。本文使用语义搜索引擎Serendipity存储库中的一组数据来评估所选的质量元数据指标,应用余弦距离技术和计算来源元数据的完整性,因为它是验证ICT工具质量标准的一个重要方面。当获得研究指标的相应值时,对获得的结果进行分析。有两个重要的发现。首先,余弦技术是寻找资源与其元数据之间语义距离的重要工具。其次,PAV本体提供了OCW的来源属性,有助于获得资源的来源质量。本文仅限于Serendipity语义搜索引擎中可用的数据集,然而,如果我们使用特定的存储库,则认为对结果的解释不会发生变化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Exploring the provenance and accuracy as metadata quality metrics in assessment resources of OCW repositories
Open Education is a movement that was born with the purpose of bringing knowledge to disadvantaged sectors and opening the doors to better education opportunities for all. Open Educational Resources (OER) such as Open CourseWare (OCW) has become support resources in the educational environment. For this reason, the quality of the repositories is highlighted by the quality of their metadata. The metadata is one characteristic of the repositories, that facilitates the search and discovery tasks of educational resources, in this way ensures their reuse. Consequently, to assure universal access to quality knowledge there are some metrics of metadata quality to evaluate OCW repository metadata: completeness, consistency, coherence, accuracy, provenance, and accessibility. In this study, accuracy and provenance are prioritized as quality metrics for metadata quality evaluation because they support potential users in the search and selection of OCW resources. This paper uses a set of data from the repository of semantic search engine Serendipity in order to evaluate the selected quality metadata metrics, applying cosine distance technique and calculation of completeness of provenance metadata because it is an important aspect of validating quality criteria for ICT tools. When the corresponding values of the study metrics are obtained, the analysis of the obtained results is performed. Two important findings are identified. First, the cosine technique is an important tool to find the semantic distance between the resource and its metadata. Second, the PAV ontology provides provenance properties of an OCW that helps in obtaining the provenance quality of a resource. This paper is limited to the dataset available in Serendipity semantic search engine, however, it is considered that the interpretation of the findings does not vary if we use a specific repository.
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