Collaborative research: a compelling continuous structures course for computer science majors

The National Science Foundation Division of Undergraduate Education awarded Rochester Institute of Technology $175,871 on October 15, 2026, under the STEM Education program (CFDA 47.076) to develop course materials for an innovative undergraduate computer science course titled Continuous Structures.

The project creates instructional materials in calculus, probability, and statistics designed to prepare U.S. computer science majors for advanced coursework in artificial intelligence, data science, machine learning, simulation, and robotics. The course deliberately mirrors Discrete Structures, the long-established math-preparation course in CS curricula, to emphasize the equal importance of continuous mathematics foundations to discrete mathematics for undergraduate computer scientists competing in an AI-based computing environment. The project investigates whether students' existing programming experience and interest, when applied to real-world application domains, can improve learning outcomes and student perception of relevance in continuous structures coursework. Development is structured in three phases, with coursework advancing students' understanding of symbolic mathematical representations by building on their programming backgrounds.

This is a collaborative research project involving George Washington University, Regis University, and Rochester Institute of Technology. Because the three institutions offer computer science programs with different emphases, the resulting educational materials are likely to be usable across a wide range of U.S. colleges and universities.

Performance occurs at Rochester, NY 14623. The award period runs through September 30, 2029.

Abstract

This project aims to serve the national interest in the age of artificial intelligence and accelerated change by creating materials for an innovative college course titled continuous structures to equip undergraduate U.S. Computer science (CS) students with skills in calculus, probability, and statistics needed to prepare them for advanced coursework in artificial intelligence (AI), data science, machine learning, simulation, and robotics. The course title was intentionally chosen to mirror discrete structures, a long-accepted math-preparation course in CS curricula, to emphasize its equal importance to CS graduates, who need strong foundations in both continuous structures and discrete structures to strengthen U.S. Competitiveness in the new AI- based computing age. This level 1 engaged student learning project directly serves NSF?s education mission to engage undergraduate stem students and improve learning by developing, testing, and using practices and curricular innovations. As the collaborating institutions, george washington university, regis university, and rochester institute of technology, offer computer science programs with different emphases, the resulting educational materials are likely to be usable by a wide range of US colleges and universities. This project plans to investigate whether prior CS students' interest and success in computer programming, when applied to motivational, real-world application domains, can improve learning outcomes in continuous structures. The project will develop coursework that advances students' understanding of symbolic mathematical representations by building on their programming experiences to improve their learning and perceptions of the relevance of continuous structures in modern computing. Course development is structured in three phases: create the materials as multiple self-contained units in each application domain, offer the materials in courses at the collaborating institutions, and revise them as needed to increase their portability and reusability at other institutions. Guiding this course development is a set of computing education research questions that iteratively improve the developed course materials. 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.- subawards are not planned for this award.