Collaborative Research: EAGER: Automating HERD Reporting Using Machine Learning and Administrative Data

合作研究：EAGER：使用机器学习和管理数据自动化 HERD 报告

• 批准号: 1547464
• 负责人: Rodolfo Torres
• 金额: $ 17.61万
• 依托单位: University of Kansas Center for Research Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1547464&HistoricalAwards=false
• 关键词: Collaborative; Research; EAGER; Automating; HERD

The National Center for Science and Engineering Statistics (NCSES) Higher Education Research and Development Survey (HERD) data are collected through a survey instrument sent to approximately 900 universities and colleges annually. Each of these institutions collects data to respond to the survey in their own way. Most rely on highly labor-intensive processes to gather and classify information about expenditures and research projects in terms of the character of the work, funding sources and fields of science. The ad-hoc expenditure classification methods employed are dependent on the individuals carrying out the task as they develop the necessary evaluation skills over time. There is potential for a lack of consistency over time and across institutions. This research develop the tools necessary to leverage university administrative data to automate the essential and time-consuming step of classifying projects by science areas, purpose and sponsor type required to respond to the NCSES HERD Survey. The results will provide a better understanding of the similarities/differences in the data reported for HERD and STAR METRICS® and provide suggestions about how data collection for each source might be improved.

国家科学与工程统计中心（NCSES）高等教育研发调查（HERD）数据是通过每年发送给大约900所大学和学院的调查工具来收集的。这些机构中的每一个都收集数据以自己的方式对调查做出回应。大多数人依靠高度劳动密集型的流程来收集和分类有关开支和研究项目的信息，从作品，资助来源和科学领域的特征方面。使用的临时支出分类方法取决于在随着时间的推移发展必要的评估技能时执行任务的个人。随着时间的流逝和整个机构，可能缺乏一致性。这项研究开发了利用大学行政数据所需的工具，以根据科学领域，目的和赞助商类型对项目进行分类的基本且耗时的步骤，以应对NCSES HERD调查。结果将更好地理解有关牛群和StarMetrics®报告的数据的相似性/差异，并提供有关如何改善每个源数据收集的建议。