BPC-DP: A Spatial Approach to Reduce Inequities in Early Access to CS Education

Lack of exposure to computer science is one of the major reasons cited by teachers behind the underrepresentation of women and students of color in computing fields. This suggests that educators observe interest among all student types and that broadening exposure and access might help drive greater involvement in computing from underrepresented populations. Through this broadening participation in computing demonstration project, researchers at Georgia State University aim to examine a multitude of factors, such as inequitable allocation of resources, that can lead to schools and districts with unequal opportunities to engage students in computing. The multidisciplinary project team will develop a method to identify areas in Georgia school districts that are at a higher risk for lack of exposure to CS education. Government entities, non-profits, and industry can then leverage this platform to place limited resources where they are most needed and can have the greatest impact. This project develops 1) a Co-SEA (Computer Science Education Access) Risk Index to quantify the need for exposure to computing education; 2) a new methodology to identify spatial regions, called RICS (Racially Inequitable Computer Science) Deserts, that are at risk for lack of access to CS education; and 3) a customizable computational framework for decision-makers to determine placement of resources and interventions within given constraints. This novel interdisciplinary approach considers structural factors that collectively limit access to CS education by integrating multi-source data for the region of interest. The project team at Georgia State University take a holistic approach by considering demographic, socio-economic, civil rights, transportation, and school enrollment data to develop the concept of RICS Deserts and provide a means for planners to determine where to place their interventions. The methodology will incorporate sensitivity analysis to evaluate potential biases introduced by spatial uncertainty. The computational framework developed will be scalable for application to any area of interest, whether that be at the community, county, state, regional or national level. 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.