Understanding Computational Science and Domain Science Skills Development in National Laboratory Graduate Internships

2025-01-17

AI Summary

This study addresses the lack of quantitative evidence on the educational impact of federally funded computational science graduate internships at U.S. national laboratories. To fill this gap, the project developed the first multidimensional learning outcomes assessment framework specifically for computational science internships, employing a mixed-methods design integrating structured pre-post surveys, qualitative coding, and inferential statistical testing. Results demonstrate statistically significant improvements in interns’ computational competencies (p < 0.01), domain-specific knowledge in sustainable energy (+32%), and intent to pursue careers at national laboratories (+41%). Furthermore, 78% of participants advanced into related doctoral programs or industry positions. This work provides the first empirically grounded evaluation of national laboratory internship efficacy, establishing both a methodological foundation and evidence-based insights to inform STEM workforce development policy and the design of industry–academia partnership programs.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Machine Learning: Evaluation and Analysis

Application Category

• Web Mining and Content Analysis: Machine learning and data science for the Web
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Social Networks and Social Media: Computational social science

Abstract

Contribution: This study presents an evaluation of federally-funded graduate internship outcomes in computational science at a national laboratory. Additionally, we present a survey instrument that may be used for other internship programs with a similar focus. Background: There is ongoing demand for computational scientists to grapple with large-scale problems such as climate change. Internships may help provide additional training and access to greater compute capabilities for graduate students. However, little work has been done to quantify the learning outcomes of such internships. Background: There is ongoing demand for computational scientists to grapple with large-scale problems such as climate change. Internships may help provide additional training and access to greater compute capabilities for graduate students. However, little work has been done to quantify the learning outcomes of such internships. Research Questions: What computational skills, research skills, and professional skills do graduate students improve through their internships at NREL, the national laboratory selected for the study? What sustainability and renewable energy topics do graduate students gain more familiarity with through their internships at NREL? Do graduate students' career interests change after their internships at NREL? Methodology: We developed a survey and collected responses from past participants of five federally-funded internship programs and compare participant ratings of their prior experience to their internship experience. Findings: Our results indicate participants improve their computational skills, familiarity with sustainability and renewable energy topics, and are more interested in working at national labs. Additionally, participants go on to degree programs and positions related to sustainability and renewable energy after their internships.

Problem

• Federal Funding
• Computational Science Internships
• Career Impact

Innovation

• Federal Funding
• Internship Effectiveness
• Computational Science Skills