Collaborative Research: Developing a Computational Model of "Quality" for Educational Digital Libraries

合作研究：为教育数字图书馆开发“质量”计算模型

• 批准号: 0534515
• 负责人: Mary Marlino
• 金额: $ 14.48万
• 依托单位: University Corporation For Atmospheric Res
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-01 至 2009-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0534515&HistoricalAwards=false
• 关键词: Collaborative; Research; Developing; Computational; Model

Prop ID: IIS-0534581, IIS-0534515PI: Sumner, Tamara R., Marlino, Mary.Institution: University of Colorado at Boulder/UCARTitle: Collaborative Research: Developing a Computational Model of "Quality" for Educational Digital LibrariesThis project will develop and evaluate machine-learning derived statistical models of quality for digital library contents for science and engineering education applications. Developing a computational model of quality that approximates expert human judgments is a foundational requirement for developing interfaces and tools that can optimize and scaffold human judgments on quality. The research will investigate: a) characteristics of digital learning resources and library collections that serve as key markers of quality for experts engaged in resource selection and collection curation; B) machine learning and natural language processing techniques with sufficient discrimination to approximate human-decision making; and c) how quality markers might be modeled computationally and requisite design considerations.The research will be conducted by a collaborative partnership between investigators at the University of Colorado at Boulder and the Digital Library for Earth System Education (DLESE) Program Center at UCAR. Quality has emerged as a dominant yet poorly understood concern within national educational digital library efforts such as the National Science Digital Library (NSDL) and DLESE and other large-scale efforts. Evaluating quality involves making complex, time-consuming, and variable human judgments and it is not clear that human-intensive practices will scale to meet anticipated future library growth. Computational models for supporting human quality judgments can thus play a critical role in building future diverse data repositories and networks of these. Primary outcomes from this work will include a conceptual model of expert quality evaluation processes and a corresponding computational model that formalizes the conceptual model and can be empirically validated. Secondary outcomes include the documentation of best practices for collection curation and preliminary guidelines for the design of tools to scaffold curation processes around quality. This research extends current theories in collaborative systems in the design and optimization of the flow of information about quality to support human judgments in distributed and evolving digital library environments.

项目ID：IIS-0534581，IIS-0534515 PI：Sumner，Tamara R.， Marlino，玛丽.机构：科罗拉多大学博尔德分校/UCAR标题：合作研究：开发一个计算模型的“质量”的教育数字图书馆这个项目将开发和评估机器学习派生的质量统计模型的数字图书馆内容的科学和工程教育应用。 开发一个近似专家人类判断的质量计算模型是开发界面和工具的基本要求，这些界面和工具可以优化和支撑人类对质量的判断。该研究将调查：a）数字学习资源和图书馆馆藏的特征，这些特征是从事资源选择和馆藏管理的专家的质量关键标志; B）机器学习和自然语言处理技术，具有足够的区分力，以近似人类决策;以及c）质量标记如何通过计算建模以及必要的设计考虑因素。该研究将由以下合作伙伴进行：研究人员在科罗拉多大学博尔德分校和数字图书馆地球系统教育（DLESE）计划中心在UCAR。 在国家教育数字图书馆的工作中，质量已经成为一个占主导地位但知之甚少的问题，如国家科学数字图书馆（NSDL）和DLESE以及其他大规模的工作。 质量评估涉及到复杂、耗时和多变的人工判断，目前尚不清楚人力密集型实践是否能满足预期的未来图书馆增长。 因此，支持人类质量判断的计算模型可以在构建未来多样化的数据存储库和网络中发挥关键作用。 这项工作的主要成果将包括一个概念模型的专家质量评价过程和相应的计算模型，正式的概念模型，并可以通过经验验证。次要成果包括收集策展的最佳实践文件，以及围绕质量构建策展流程的工具设计的初步指南。这项研究扩展了当前的协同系统的设计和优化的质量信息流的理论，以支持在分布式和不断发展的数字图书馆环境中的人类判断。