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

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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.