Coding-Driven Exploration for Chemical Education Modernization

This project seeks to serve the national interest by helping undergraduate students learn chemistry in ways that reflect how modern science is practiced. Computation, data analysis, modeling, and simulation are now central to chemistry research and many STEM careers. Yet chemistry courses often emphasize step-by-step procedures and calculations rather than computational problem solving. This gap is especially important in general chemistry, an introductory course that enrolls large numbers of students and influences persistence in STEM pathways. The Coding-Driven Exploration for Chemical Education Modernization (CODECHEM) project will redesign the general chemistry laboratory course sequence at Aurora University to incorporate computation as a tool for students to explore chemical concepts, analyze data, build models, and solve problems. The project will also support instructors in learning to teach computation through professional development and a faculty learning community. By embedding computational learning in required introductory chemistry courses, the project will broaden access to data-intensive STEM practices and help prepare students for future coursework, research opportunities, and STEM careers. The project will also create open-access laboratory activities, instructor guides, computational notebooks, and assessment rubrics that can be adapted by faculty at other institutions seeking to integrate computation into chemistry education. The CODECHEM project will develop, implement, and study a computationally-enriched general chemistry laboratory curriculum at Aurora University. The project is grounded in two complementary frameworks: Computational Thinking for Science (CT-S) and Cognitive Apprenticeship, which guides how instructors model expert reasoning, coach students, scaffold tasks, and gradually fade support. The specific objectives are to design and implement CT-S laboratory activities, examine how students’ self-efficacy, anxiety, perceived usefulness, and prior coding experience shape their engagement with computation, and investigate how instructors adopt Cognitive Apprenticeship practices while teaching computation-rich laboratories. The project will use a design-based research approach across three years. In Year 1, the team will develop and pilot initial laboratory activities. In Year 2, four activities will be implemented in the General Chemistry I and II courses. Students will progress from guided exploration of atomic models to more independent work involving mole calculations, chemical kinetics, and equilibrium modeling. In Year 3, refined activities will be implemented to examine stability, sustainability, and readiness for broader dissemination. Student learning will be investigated through analyses of Jupyter notebooks, code artifacts, written explanations, reflections, survey and focus group responses. Instructor learning will be examined through analyses of survey and interview responses, reflection logs, and professional-development artifacts. Mixed-methods analyses will characterize students’ CT-S learning trajectories, identify relationships between affective factors and computational engagement, and describe how instructor practices evolve. Independent formative and summative evaluations will assess implementation quality, instructor professional development, dissemination, and sustainability. Findings and materials will be shared through peer-reviewed publications, conference presentations, local and regional faculty workshops, virtual seminars, and an open digital repository. The project will contribute research-based design principles, assessment tools, and adaptable instructional resources for integrating computational thinking into undergraduate chemistry laboratories. 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.