Computing in undergraduate biology classrooms: engaging students in applied computing experiences

The National Science Foundation Division of Undergraduate Education awarded the University of Northern Colorado $605,815 on October 1, 2026, under the STEM Education program (CFDA 47.076) to integrate Python programming into undergraduate biology courses and provide professional development for faculty and graduate teaching assistants.

The project will embed computing across the biology curriculum—introductory, intermediate, and upper-level courses—allowing students to engage with computational skills multiple times throughout their academic trajectory. The recipient will develop computing modules aligned with commonly taught biology courses, designed for adaptation and adoption at other institutions. Professional development activities will equip faculty members and graduate TAs to support effective instruction in this interdisciplinary area. The project employs a design and development research approach with iterative refinement of computing materials and instructor support resources.

The award targets preparation of biology majors for careers that integrate biology and computing, an increasingly critical intersection in the STEM workforce. The project emphasizes building student confidence in computational skills, particularly for students without prior computing exposure, and supports broader participation in STEM by connecting students to emerging fields that require data-driven competencies.

Performance occurs at the University of Northern Colorado in Greeley, Colorado. The period of performance extends from October 1, 2026, through September 30, 2029.

Abstract

This project aims to serve the national interest by better preparing biology majors for future careers that integrate biology and computing?an increasingly critical intersection in the science, technology, engineering, and mathematics (stem) workforce. To achieve this, we will integrate computing, using python programming, into the undergraduate biology curriculum, including introductory biology courses as well as intermediate- and upper-level biology courses, allowing students to potentially engage with computing multiple times throughout their academic trajectory. In parallel, we will provide professional development for faculty members and graduate teaching assistants (TAS) to support effective instruction in this interdisciplinary area. By equipping students with computational skills required to engage with rapidly advancing technologies, this level II engaged student learning project supports student preparation in an evolving stem workforce. By introducing computing early and repeatedly across the biology curriculum, students have more opportunities to build confidence, see connections between computing and biology, and explore career paths in emerging fields. This is particularly important for the success of students who have not previously had exposure to computing. Moreover, the project will support faculty members in advancing computing education within the life sciences. The project is also designed for broader adaptation and adoption at other institutions, as the computing modules align with commonly taught biology courses. Overall, this project aligns with national priorities by broadening participation in stem, improving computational skills, and preparing students for success in a data-driven workforce. This project will use a design and development research approach, with research informing iterative development of computing materials and instructor supports. The project includes the following four goals: (1) develop and iteratively refine computing-enriched instructional materials, which include opportunities for students to engage in computational thinking and quantitative reasoning, and purposeful use of artificial intelligence (AI), tailored to existing undergraduate biology courses at multiple levels. (2) design and implement professional development opportunities for biology faculty members and graduate TAS focused on effective pedagogy for integrating computing into biology instruction. (3) conduct research on the impact of the project on student outcomes, including interest and self-efficacy in computing in biology, academic and career planning, and skills in applying computing to biology concepts. (4) investigate how the professional development supports for faculty members and TAS influence their integration of computing into the biology classroom. This project plans to collect and analyze?using both quantitative and qualitative analysis?survey and interview responses from students, TAS, and faculty members as well as student computing assignments. This project plans to use a quasi-experimental design as part of the approach to identify impacts of the computing curriculum, comparing terms prior to versus during integration of computing. In addition to examining student outcomes after engaging in computing in a single class, the project plans to conduct a case study analysis to examine the impact on students of repeatedly engaging in computing across multiple courses in their major. The scope of this project focuses on courses at the university of northern colorado (UNC) and aligns with the UNC department of biology?s commitment to expanding student access to learning experiences emphasizing key skills in biology. The project plans to publish its evaluation and research findings and make curriculum and professional development materials that are developed freely available to the public. Overall, this project plans to advance understanding of how to build teaching capacity for computing in biology and generate new knowledge about how to effectively embed computing across the undergraduate biology curriculum and how to provide biology majors with authentic opportunities to build essential data analysis and computational skills. 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.