Developing inclusive, interdisciplinary undergraduate data science curricula in computing and social science

Data science is a rapidly growing field that is having a significant impact on how we live, work, and interact. The goal of this project is to create a cross-cutting data science curriculum at the intersection of computer science and behavioral and social science. This curriculum will provide training in both core computing and programming concepts as well as core quantitative behavioral and social science methods. The curriculum will be designed to be multidisciplinary, culturally relevant, and rigorous. The core proposed course development activity aims to transform computer science education through a collaborative approach to curricular creation that involves faculty from diverse disciplines, diverse institutions, and diverse backgrounds. Tribal colleges, HBCUs, and California Community Colleges, will be invited to curriculum symposia events and supported in adopting these materials. Classroom materials will be designed as a scaffolded collection of instructional lecture videos, computing labs, guided discussions, projects, and concept assessments that can be adopted partly or wholly at institutions across the nation. The open-source curriculum will consist of standalone course modules grounded in modern socio-technical systems and data. Investigators will design and test a hybrid instructional delivery mechanism to ensure broad accessibility to instructors nationwide. Continuous assessment and research will inform investigators on how the project activities will promote further study in interdisciplinary computing education amongst students. The project will identify aspects of the curriculum that build learner confidence in computational thinking and data science and will contribute best practices for computer science and data science education. 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.