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

提案ID: IIS-0534581， IIS-0534515PI: Sumner, Tamara R., Marlino, Mary。项目名称：合作研究：开发教育数字图书馆“质量”计算模型该项目将开发和评估用于科学和工程教育应用的数字图书馆内容质量的机器学习统计模型。开发一个近似于专家判断的质量计算模型，是开发能够优化和支撑人类质量判断的接口和工具的基本要求。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衡量质量的关键标志；B)机器学习和自然语言处理技术，具有足够的判别能力，可以近似于人类的决策；c)如何对质量标记进行计算建模和必要的设计考虑。该研究将由科罗拉多大学博尔德分校的研究人员和UCAR地球系统教育数字图书馆（DLESE）项目中心的合作伙伴关系进行。在国家科学数字图书馆（NSDL）和dleses以及其他大规模的努力中，质量已经成为一个主要的但人们知之甚少的问题。评估质量涉及到复杂的、耗时的和可变的人工判断，并且不清楚人工密集型实践是否能够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专家质量评估过程的概念模型和相应的计算模型，该模型形式化了概念模型并可以进行经验验证。次要成果包括收藏管理最佳实践的文档和设计工具的初步指导方针，以支持围绕质量的管理过程。本研究扩展了协作系统在设计和优化质量信息流方面的现有理论，以支持分布式和不断发展的数字图书馆环境中的人类判断。

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.