用Clavy工具从医学数据中自动生成可复用的多媒体学习资源
Generation of reusable learning objects from digital medical collections: An analysis based on the MASMDOA framework
- 基于MASMDOA框架分析Clavy工具生成可复用学习对象的流程
- 能整合多源医学知识，生成适配不同用户需求的多媒体学习内容
- 支持导出标准格式，适合医学院校和医疗从业者使用
学习对象（Learning Objects）是多种教育场景中结构化教学材料的常用方法。本文基于定性分析，研究Clavy工具如何从多个医学知识源中检索数据，并重构为多样化的多媒体结构与组织形式，进而生成可适应不同教学场景与用户学习需求的可复用学习对象（RLOs）。Clavy还支持通过教育标准规范导出这些学习对象，显著提升其可复用性。分析表明，该工具能有效将现有数字医学资源转化为医疗学生与医护人员可通过主流e-learning平台便捷访问的学习内容。
原文摘要 · Abstract (English)
Learning Objects represent a widespread approach to structuring instructional materials in a large variety of educational contexts. The main aim of this work consists of analyzing from a qualitative point of view the process of generating reusable learning objects (RLOs) followed by Clavy, a tool that can be used to retrieve data from multiple medical knowledge sources and reconfigure such sources in diverse multimedia-based structures and organizations. From these organizations, Clavy is able to generate learning objects which can be adapted to various instructional healthcare scenarios with several types of user profiles and distinct learning requirements. Moreover, Clavy provides the capability of exporting these learning objects through educational standard specifications, which improves their reusability features. The analysis insights highlight the importance of having a tool able to transfer knowledge from the available digital medical collections to learning objects that can be easily accessed by medical students and healthcare practitioners through the most popular e-learning platforms.
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