Collaborative Research: Leveraging Matched Administrative Datasets to Improve Educational Practice and Long Run Life Outcomes: Toward Building a National Interdisciplinary Network

合作研究：利用匹配的管理数据集改善教育实践和长期生活成果：建立国家跨学科网络

• 批准号: 1244328
• 负责人: Kenneth Dodge
• 金额: $ 69.71万
• 依托单位: Duke University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-15 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1244328&HistoricalAwards=false
• 关键词: Collaborative; Research; Leveraging; Matched; Administrative

This proposal intends to develop a national community of researchers that utilizes large scale, longitudinal educational databases that are linked across the spectrum of early childhood to college, workplace and other social sets of data. The research collaborative includes an initial group of scholars in education research, education policy, anthropology, economics, psychology and social sciences from Northwestern and Duke University. These researchers are actively recruiting researchers and educational stakeholders from across the country, who are working with different educational and other data sets to improve capacity to link data together in the expanded data repositories that have been funded by the federal government.The researchers are identifying best practices in the use of data and develop prototypes of their use. Starting from the Florida and North Carolina longitudinal data sets, the researchers are developing prototypes of how these data can be combined to answer pressing educational and social science questions that have heretofore been unable to be addressed. These prototypes are being developed collaboratively across the multiple institutions who are participating in the national community, with Northwestern and Duke serving as the conceptual hubs of the project. The prototypes inform the two national level meetings at which scholars and practitioners to share findings and stimulate the development of such research in other states.The expansion of large scale longitudinal data in education and social sciences has resulted in a demand for new ways to link together data that has typically remained in silos across various state agencies. By developing prototypes of how these data sets should be linked, this research and development is expanding the nature of the questions that the data can inform. These questions include important policy and practice concerns that have been problematic in science, technology, engineering and mathematics education.

该提案旨在建立一个全国性的研究人员社区，利用大规模、纵向的教育数据库，将幼儿期、大学、工作场所和其他社会数据集联系起来。该研究合作团队包括来自西北大学和杜克大学的教育研究、教育政策、人类学、经济学、心理学和社会科学领域的首批学者。这些研究人员正在积极招募来自全国各地的研究人员和教育利益相关者，他们正在处理不同的教育和其他数据集，以提高在联邦政府资助的扩展数据存储库中将数据链接在一起的能力。研究人员正在确定最佳的数据使用的实践并开发其使用的原型。从佛罗里达州和北卡罗来纳州的纵向数据集开始，研究人员正在开发如何组合这些数据的原型，以回答迄今为止无法解决的紧迫的教育和社会科学问题。这些原型正在参与国家社区的多个机构之间合作开发，其中西北大学和杜克大学作为该项目的概念中心。这些原型为两次国家级会议提供了信息，在这两次会议上，学者和从业者可以分享研究结果并刺激其他州此类研究的发展。教育和社会科学领域大规模纵向数据的扩展导致需要新的方式来相互联系通常保留在各个国家机构的孤岛中的数据。通过开发如何链接这些数据集的原型，这项研究和开发正在扩展数据可以揭示的问题的性质。这些问题包括在科学、技术、工程和数学教育中一直存在问题的重要政策和实践问题。