Collaborative Research: Educational Data Mining Approaches for Digital Libraries

协作研究：数字图书馆的教育数据挖掘方法

• 批准号: 0840738
• 负责人: Sherry Hsi
• 金额: $ 7.5万
• 依托单位: Exploratorium
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-10-01 至 2010-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0840738&HistoricalAwards=false
• 关键词: Collaborative; Research; Educational; Data; Mining

This collaborative research project between the Exploratorium and Utah State's Department of Instructional Technology and Learning Sciences investigates online evaluation approaches and the application of educational data mining to educational digital libraries and services. Much work over the past decades has focused on developing algorithms and methods for discovering patterns in large datasets, known as Knowledge Discovery from Data (KDD). Webmetrics, the application of KDD to web usage mining, is growing rapidly in areas such as e-commerce. Educational Data Mining (EDM) is just beginning to emerge as a tool to analyze the massive, longitudinal user data that are captured in online learning environments and educational digital libraries.This project uses EDM to examine data from the Exploratorium's Learning Resources Collection and the Instructional Architect at Utah State University. The results are combined with more traditional evaluation data (e.g., surveys, interviews) as part of a comprehensive strategy to understand science teachers' behaviors, motivations, and learning experiences with digital library resources The outcomes of this targeted research promise to contribute new knowledge regarding Knowledge Discovery from Data and the application of Educational Data Mining to digital libraries. The project informs improvements in the design of the user experience, as well as tailored teacher professional development, contributing to a growing body of research on teacher learning by using cyber-enabled approaches.

探索馆和犹他州教育技术和学习科学部的这一合作研究项目调查了在线评估方法以及教育数据挖掘在教育数字图书馆和服务中的应用。在过去的几十年里，许多工作都集中在开发在大型数据集中发现模式的算法和方法，即从数据中发现知识(KDD)。Webmetrics是知识发现在Web使用挖掘中的应用，在电子商务等领域发展迅速。教育数据挖掘(EDM)刚刚开始成为一种分析在线学习环境和教育数字图书馆中捕获的海量纵向用户数据的工具。该项目使用EDM来检查来自探索馆学习资源集合和犹他州立大学教学架构师的数据。研究结果与更传统的评估数据(例如，调查、访谈)相结合，作为理解科学教师使用数字图书馆资源的行为、动机和学习经验的综合战略的一部分。这项有针对性的研究成果有望为从数据中发现知识和教育数据挖掘在数字图书馆中的应用贡献新的知识。该项目提供了用户体验设计方面的改进，以及量身定做的教师专业发展，促进了越来越多的关于利用网络方法进行教师学习的研究。