Collaborative Network for STEM Undergraduate Education through Computational Training in High Performance Computing

This project aims to serve the national interest by strengthening computational workforce readiness through expanded access to high-performance computing (HPC) education for undergraduate STEM students. As high-performance computing, artificial intelligence, and data-intensive research continue to drive scientific discovery and technological innovation, the project plans to prepare a broader STEM workforce with the computational problem-solving skills needed to contribute across scientific and engineering disciplines. To accomplish this goal, the project plans to establish an interconnected curricular ecosystem across a five-institution consortium in Missouri. This Engaged Student Learning Level 2 project includes developing a flexible, discipline-adaptable suite of plug-and-play instructional modules spanning astronomy, biology, chemistry, computer science, data science, and physics. A distinguishing, innovative aspect of the project is its scalable, low-barrier approach that integrates HPC concepts, ranging from unplugged algorithmic activities to hands-on distributed computing tasks, directly into existing introductory and intermediate coursework without requiring full curricular redesigns. The project also plans to train 40 STEM faculty members through summer workshops and a sustained mentoring network, while simultaneously preparing undergraduate student instructional facilitators to provide peer support. The broader significance and importance of this work are in its potential to democratize advanced computing education, ultimately improving career readiness for up to 800 students in data-intensive STEM sectors and providing a national model for interdisciplinary learning in resource-constrained settings. The scope and research goals of this project focus on generating transferable knowledge regarding the effective integration of modular HPC pedagogy within undergraduate STEM education. The project plans to investigate two primary research topics: first, exploring faculty and student perceptions, beliefs, and discipline-specific considerations regarding advanced computing topics in lower-level coursework; and second, identifying the curriculum design principles and pedagogical strategies that optimize student learning gains and computational mindset development. With a mixed-methods research design, the project team plans to collect quantitative and qualitative data through pre- and post-course surveys, concept assessments, semi-structured interviews, and classroom observations. These research endeavors will be coupled with an independent external evaluation that utilizes annual surveys, faculty focus groups, and implementation fidelity tracking to assess institutional capacity building and student proficiency. Dissemination strategies include sharing all newly developed instructional resources, datasets, and containerized environments openly via a public GitHub repository, as well as publishing findings in STEM education journals and presenting at regional and national computing conferences. Through systematically validating how modular interventions impact interdisciplinary learning outcomes, this project plans to advance the foundational understanding of student learning and curricular innovation. 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.