NSF AI Disclosure Required
NSF requires disclosure of AI tool usage in proposal preparation. Ensure you disclose the use of FindGrants' AI drafting in your application.
Collaborative Research: Creating Rubrics and Related Tools to Assess College Students' Learning of Computational Physics
NSF
About This Grant
This project aims to serve the national interest by modernizing the teaching of physics at the college level. Specifically, the project will enable college instructors to integrate specialized computational physics methods into their classes, and to rate their students' learning against a standard scale. Teaching computational physics is important because these methods are quickly becoming essential for physicists and other scientists around the world. This work will help colleges and universities keep up with these changes. Rating students against a standard scale will enable individual instructors and departments to determine how effective their teaching efforts are, and to make improvements that further benefit their students. Adding computational methods to college classes will enhance the effectiveness of the U.S. scientific workforce, one of the key goals of NSF and the IUSE program. This Engaged Student Learning: Level II project is the first effort to establish standards (and training materials for using the standards) designed specifically for evaluating students' achievement of seven essential learning goals in computational physics. The goals of this project are to expand and improve the teaching of computational physics at five universities in the midwestern U.S: Indiana University Indianapolis, Bradley University, Purdue University, University of Indianapolis, and University of Wisconsin - Stout. The project team will develop seven student learning objectives: 1) use generative AI effectively and ethically, 2) read, understand, and modify existing code, 3) apply common computational tools, 4) test code, 5) explore physics, 6) write clear code, and 7) communicate physics. Each partner institution will have a specific role and responsibility with respect to developing these learning objectives. The project will also develop, test, and improve rubrics related to these objectives that instructors can use to rate students' learning of these methods. Instructors are accustomed to evaluating students within a single class when they assign grades, but this type of rating does not give information about how a student has progressed over years. To measure progress, it is necessary to rate students against objective standards, therefore this project will also produce documents and procedures a department can use to help its members learn to use the rubrics to rate students objectively. This project will also study how students' development as rated by instructors compares to their own view of how much they have learned. Project work and findings will be disseminated through publications, presentations, and workshops for faculty. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Focus Areas
Eligibility
How to Apply
Up to $45K
2028-06-30
One-time $249 fee · Includes AI drafting + templates + PDF export
AI Requirement Analysis
Detailed requirements not yet analyzed
Have the NOFO? Paste it below for AI-powered requirement analysis.