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Quality assurance for digital learning object repositories: issues for the metadata creation process

机译:数字学习对象存储库的质量保证:元数据创建过程中的问题

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摘要

Metadata enables users to find the resources they require, therefore it is an important component of any digital learning object repository. Much work has already been done within the learning technology community to assure metadata quality, focused on the development of metadata standards, specifications and vocabularies and their implementation within repositories. The metadata creation process has thus far been largely overlooked. There has been an assumption that metadata creation will be straightforward and that where machines cannot generate metadata effectively, authors of learning materials will be the most appropriate metadata creators. However, repositories are reporting difficulties in obtaining good quality metadata from their contributors, and it is becoming apparent that the issue of metadata creation warrants attention. This paper surveys the growing body of evidence, including three UK-based case studies, scopes the issues surrounding human-generated metadata creation and identifies questions for further investigation. Collaborative creation of metadata by resource authors and metadata specialists, and the design of tools and processes, are emerging as key areas for deeper research. Research is also needed into how end users will search learning object repositories.
机译:元数据使用户能够找到所需的资源,因此它是任何数字学习对象存储库的重要组成部分。为了确保元数据的质量,学习技术社区已经做了大量工作,重点是元数据标准,规范和词汇的开发以及它们在存储库中的实现。迄今为止,元数据创建过程一直被忽略。有一种假设认为,元数据的创建将是直接的,并且在机器无法有效生成元数据的情况下,学习材料的作者将是最合适的元数据创建者。但是,存储库报告难以从其贡献者那里获取高质量的元数据,并且显而易见的是,元数据创建问题值得关注。本文调查了越来越多的证据,包括三个基于英国的案例研究,对围绕人类生成的元数据创建的问题进行了范围划分,并确定了有待进一步研究的问题。由资源作者和元数据专家共同创建元数据以及工具和流程的设计正在成为更深入研究的关键领域。还需要研究最终用户将如何搜索学习对象存储库。

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