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QuEST: Revolutionizing Repository Cross-referencing to Transform Discovery of Teaching Materials

QuEST：彻底改变存储库交叉引用以转变教材的发现

基本信息

批准号：
1043647
负责人：
Min Song
金额：
$ 15万
依托单位：
New Jersey Institute of Technology
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2010
资助国家：
美国
起止时间：
2010-09-15 至 2013-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1043647&HistoricalAwards=false
关键词：
QuEST Revolutionizing Repository Cross referencing

项目摘要

An emerging activity of digital educational repositories is the cross-referencing of resources with national and state standards. The goal of QuEST (Quality Estimate Scoring Technique) is to provide a cross-referencing support system to greatly increase the productivity of the expert human cross-referencer so vastly more resources can be matched with higher accuracy. This mechanism uses information found in NSDL's Strand Maps for both characterization and discrimination analysis to support the scoring of a resource to a particular Strand Map benchmark. QuEST is providing a working cross-referencing support system that can be plugged into any partner digital repository. It is transforming the cross-referencing task from a strictly concept matching effort to a truly representative comparison of what is important to educators. This is reducing the burden on teachers by providing a better filtered set of resources that have been selected based on quality markers used by teachers.By cross-referencing useful content more quickly and effectively, teachers are able to find and select more appropriate content which in turn is helping them deliver high quality education to their students and achieve higher personal satisfaction. This is also leading to greater utilization of the repository. QuEST also supports the NSDL mission by functioning as a learning environment for its project participants, including undergraduate and graduate students, teachers, and librarians. Through their exposure to the larger evaluation and software development components of QuEST, project participants gain increased understanding of the overall digital repository research process.

数字教育知识库的一个新兴活动是资源与国家和州标准的交叉引用。QuEST（Quality Estimate Scoring Technique，质量评估评分技术）的目标是提供一个交叉引用支持系统，以大大提高专家人工交叉引用人员的生产力，从而以更高的准确度匹配更多的资源。该机制使用NSDL的Strand Map中的信息进行特征化和区分分析，以支持根据特定Strand Map基准对资源进行评分。QuEST提供了一个有效的交叉引用支持系统，可以插入任何合作伙伴的数字存储库。它正在将交叉引用任务从严格的概念匹配工作转变为对教育工作者重要的真正代表性比较。这通过提供一组更好的过滤资源来减轻教师的负担，这些资源是根据教师使用的质量标记来选择的。通过更快、更有效地交叉引用有用的内容，教师能够找到并选择更合适的内容，这反过来又帮助他们为学生提供高质量的教育并实现更高的个人满意度。这也导致了对存储库的更大利用。QuEST还支持NSDL的使命，为项目参与者提供学习环境，包括本科生和研究生、教师和图书馆员。通过接触QuEST的更大的评估和软件开发组件，项目参与者对整个数字知识库研究过程有了更多的了解。