Board 410: Tracing the Evolution of NSF REU Research Priorities and Trends

Yanxia Jia; Tiantian Wang; Chaomei Chen; Yu-Fang Jin

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Conference: 2024 ASEE Annual Conference & Exposition
Location: Portland, Oregon
Publication Date: June 23, 2024
Start Date: June 23, 2024
End Date: June 26, 2024
Conference Session: NSF Grantees Poster Session
Tagged Topic: NSF Grantees Poster Session
Page Count: 16
DOI: 10.18260/1-2--46999
Permanent URL: https://peer.asee.org/46999
Download Count: 71

Paper Authors

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Yanxia Jia Arcadia University orcid.org/0000-0002-6148-8535

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Dr. Yanxia Jia is an Associate Professor of Computer Science in the Department of Computer Science and Mathematics at Arcadia University. She earned her doctoral degree in Computing Science from University of Alberta, Canada. Dr. Jia’s research interests include data science, machine learning, computer science education and computer networks.

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Tiantian Wang The University of Texas at San Antonio

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Tiantian Wang is currently a PhD student in the Department of Electrical and Computer Engineering at the University of Texas at San Antonio (UTSA). Her research interests are in the field of single-cell analysis, specifically in identifying and analyzing cell types. By integrating advanced technologies and computational methods, she aims to uncover new insights into cellular functions and interactions, which could potentially lead to breakthroughs in biological research and personalized health control.

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Chaomei Chen Drexel University

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Dr. Chaomei Chen is a Professor of Information Science in the College of Computing and Informatics at Drexel University in the USA. He is the Editor-in-Chief of Information Visualization and the Field Chief Editor of Frontiers in Research Metrics and Analytics. He is the author of a series of books on visualizing the evolution of scientific knowledge, including Representing Scientific Knowledge: The Role of Uncertainty (Springer 2017), The Fitness of Information: Quantitative Assessments of Critical Information (Wiley, 2014), Turning Points: The Nature of Creativity (Springer, 2011), Information Visualization: Beyond the Horizon (Springer 2004, 2006), Mapping Scientific Frontiers: The Quest for Knowledge Visualization (Springer 2003, 2013). He is the creator of the widely used visual analytics software CiteSpace for visualizing and analyzing structural and temporal patterns in scientific literature.

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Yu-Fang Jin The University of Texas at San Antonio orcid.org/0000-0002-7421-527X

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Dr. Yufang Jin got her Ph.D from University of Central Florida in 2004. After her graduation, she joined the University of Texas at San Antonio (UTSA). Currently, she is a Professor with the Department of Electrical and Computer Engineering at UTSA. Her research interest focus on applications of artificial intelligence, interpretation of deep learning models, and engineering education.

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Abstract

The Research Experiences for Undergraduates (REU) program plays a crucial role in fostering research interests among undergraduate students, motivating them to pursue advanced degrees in Science, Technology, Engineering, and Mathematics (STEM) fields, and developing a diverse, skilled workforce for STEM careers. Annually, the National Science Foundation (NSF) awards approximately 170-190 REU grants. The funding for REU sites often reflects current trends in research. Our study aims to examine REU sites’ contributions in terms of scholarly publications and student training over the past six years. Additionally, we explore the research themes of these REU sites and compare them with those in the Web of Science (WoS) database.

The NSF award database provides details about 3,500 REU awards, including project titles, abstracts, funding periods, and NSF directories. All REU award information is reformatted into the WoS citation format for thorough analysis using a literature analysis tool CiteSpace. Utilizing CiteSpace, we create and visualize topic clusters based on terms and keywords of REU titles and abstracts. Outcome data of REU sites is extracted from the 'Disclaimer/Publications' sections found in the Project Outcomes Reports on NSF award webpages. Quantifiable metrics are extracted, including the number of REU trainees and underrepresented and/or minority students, the number of publications produced, and the number of students who advanced to graduate studies.

Distribution of REU awards across various NSF directories is summarized, highlighting the emphasized areas of REU programs. Examining the quantified outcomes of the REU projects, such as the number of trainees, underrepresented trainees, publications, and students joining graduate school, facilitates a quantitative evaluation of the impact of REU programs and verifies REU sites’ efforts to meet the goals of NSF REU program. Research themes of REU awards and engineering, science and technology-related publications from WoS are represented through the creation of topic clusters. Shared research themes from REU programs and WoS publications suggest that REU sites are keeping pace with the current and emerging trends in scientific research and that the REU program is an effective vehicle for contributing new knowledge to the research community. The analysis of the REU outcome data shows that REU sites made effective efforts in increasing the percentage of underrepresented students.

This study represents the first systematic and quantitative analysis of REU grants in terms of their research trends and outcomes. The insights gained will provide valuable information on the evolution of REU research areas and the scholarly impacts of REU programs, benefiting and aspiring REU principal investigators, grant administrators, and a broader range of researchers.

Citation
Format

Jia, Y., & Wang, T., & Chen, C., & Jin, Y. (2024, June), Board 410: Tracing the Evolution of NSF REU Research Priorities and Trends Paper presented at 2024 ASEE Annual Conference & Exposition, Portland, Oregon. 10.18260/1-2--46999

TY - CPAPER
AB - The Research Experiences for Undergraduates (REU) program plays a crucial role in fostering research interests among undergraduate students, motivating them to pursue advanced degrees in Science, Technology, Engineering, and Mathematics (STEM) fields, and developing a diverse, skilled workforce for STEM careers. Annually, the National Science Foundation (NSF) awards approximately 170-190 REU grants. The funding for REU sites often reflects current trends in research. Our study aims to examine REU sites’ contributions in terms of scholarly publications and student training over the past six years. Additionally, we explore the research themes of these REU sites and compare them with those in the Web of Science (WoS) database.

The NSF award database provides details about 3,500 REU awards, including project titles, abstracts, funding periods, and NSF directories. All REU award information is reformatted into the WoS citation format for thorough analysis using a literature analysis tool CiteSpace. Utilizing CiteSpace, we create and visualize topic clusters based on terms and keywords of REU titles and abstracts. Outcome data of REU sites is extracted from the &#39;Disclaimer/Publications&#39; sections found in the Project Outcomes Reports on NSF award webpages. Quantifiable metrics are extracted, including the number of REU trainees and underrepresented and/or minority students, the number of publications produced, and the number of students who advanced to graduate studies.

Distribution of REU awards across various NSF directories is summarized, highlighting the emphasized areas of REU programs. Examining the quantified outcomes of the REU projects, such as the number of trainees, underrepresented trainees, publications, and students joining graduate school, facilitates a quantitative evaluation of the impact of REU programs and verifies REU sites’ efforts to meet the goals of NSF REU program. Research themes of REU awards and engineering, science and technology-related publications from WoS are represented through the creation of topic clusters. Shared research themes from REU programs and WoS publications suggest that REU sites are keeping pace with the current and emerging trends in scientific research and that the REU program is an effective vehicle for contributing new knowledge to the research community. The analysis of the REU outcome data shows that REU sites made effective efforts in increasing the percentage of underrepresented students.

This study represents the first systematic and quantitative analysis of REU grants in terms of their research trends and outcomes. The insights gained will provide valuable information on the evolution of REU research areas and the scholarly impacts of REU programs, benefiting and aspiring REU principal investigators, grant administrators, and a broader range of researchers.

AU - Yanxia Jia
AU - Tiantian Wang
AU - Chaomei Chen
AU - Yu-Fang Jin
CY - Portland, Oregon
DA - 2024/06/23
PB - ASEE Conferences
TI - Board 410: Tracing the Evolution of NSF REU Research Priorities and Trends
UR - https://peer.asee.org/46999
DO - 10.18260/1-2--46999
ER -