Towards flexible learning object metadata
Towards flexible learning object metadata
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DOI：
10.1504/ijceell.2006.008917
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发表时间：
2006-01-01
影响因子：
1.6
通讯作者：
McCalla, Gord
中科院分区：
文献类型：
--
作者：
Brooks, Christopher;McCalla, Gord
本文概述了我们在获取、描述和使用学习对象元数据方面所做的研究。与IEEE LOM和其他标准化元数据方案不同，我们主张采用一种更灵活的方法来定义和关联元数据与学习对象。这种方法，我们称之为生态方法，将元数据视为对观察到的用户与特定目的的学习对象的交互进行推理的过程。这种方法的核心概念是，支持语义Web的计算代理将提供和使用已收集的关于学习对象的实际使用数据片段，以确定该学习对象对某些新目的的有用性。与传统的“一刀切”方法相比，这是一种元数据创建的进化方法。
This paper outlines the research we are doing in acquiring, describing and using learning object metadata. Instead of the IEEE LOM and other standardised metadata schemes, we argue for a more flexible approach to both defining and associating metadata with learning objects. This approach, which we call the ecological approach, sees metadata as the process of reasoning over observed interactions of users with a learning object for a particular purpose. Central to this approach is the notion that Semantic Web enabled computational agents will both provide and consume pieces of actual usage data that have been collected about a learning object in determining the usefulness of this learning object for some new purpose. This is then an evolutionary approach to metadata creation as compared to move traditional prescriptive `one size fits all' approaches.