Naseri, Md Yunus; Lohani, Vinod K.; Jha, Manoj Kumar; Biswas, Gautam; Snyder, Caitlin; Jiang, S. X.; & Sear, C. B.(2025). Integration of data science modules across interdisciplinary courses at multiple institutions: Analysis of student and faculty perspectives. ASEE Annual Conference and Exposition, Conference Proceedings. https://doi.org/10.18260/1-2–56860
This National Science Foundation Improving Undergraduate STEM Education funded project integrated data science into six undergraduate STEM courses at a public university, a private university, and a Historically Black College and University. A research–practice partnership involving faculty, graduate students, and an evaluation team developed 12 discipline-specific data science modules that were implemented multiple times and reached over 1,000 students. The modules used real-world datasets, including environmental and traffic data, to embed data science concepts into existing STEM courses and better prepare students for data-driven careers.
Using a mixed methods approach, the team collected instructor interviews, student surveys, pre and post data, and assessment results. Instructors valued the flexibility to tailor content, emphasizing hands-on learning, data visualization, and statistical analysis. Challenges included varied student backgrounds and adjustments during COVID-19.
Students reported increased interest, confidence, and skills in data science after completing the modules. They appreciated real-world applications but requested more guidance and time for complex tools and topics. Overall, the project demonstrates that embedding discipline-specific data science into existing STEM courses is an effective and sustainable way to strengthen data literacy.
