Collaborative Research: Equity of Access to Computer Science: Factors Impacting the Characteristics and Success of Undergraduate CS Majors

This project aims to serve the national interest by improving undergraduate computer science education. It will do so by completing a research study that can reveal potential systemic limitations in access to computer science education by all students. This research study will examine ten-years of undergraduate student application, admissions, and retention data from four institutions. Analysis of these data will describe how students of varying demographics and pre-college preparation are present throughout the computer science talent pipeline. This study will fill an important research gap about factors that affect the flow of students into and through the computer science major. It is well documented that the demographic characteristics of computer science students are highly skewed toward males versus females and have skewed racial/ethnic distributions. What is not yet understood is at what point in the talent pipeline these imbalances are greatest and the degree to which they change as students progress through computer science undergraduate programs. In addition, the current educational disruption caused by COVID-19 provides the important and unique opportunity to determine what effect, if any, the resulting educational changes have had on participation of different groups of students in computer science. Students from underrepresented groups appear to have encountered greater difficulty accessing distance learning and being connected to the full range of educational opportunities presented by these unique circumstances, which are very strongly related to technological know-how. There is legitimate cause for concern that the pandemic will further divide the advantaged from the disadvantaged, further marginalizing the underrepresented groups that the project is studying from opportunities to advance into computer science majors and progress successfully through them. Computer science is an area of critical strategic importance for the nation, and a field in which cultivating domestic talent can have enormous impact. Thus, examining pre- and post- pandemic patterns of participation in computer science have the potential to help the nation meet its growing needs for talent in computer science and related fields, such as cybersecurity and artificial intelligence.

This study will use a large, rich data set compiled from ten years of undergraduate application, admissions, and course-level data from four institutions: Loyola Marymount University, Cal State University Long Beach, the University of California Riverside, and the University of California San Diego. Analysis of these longitudinal data will improve understanding of who has access, who applies, who is admitted, and who succeeds in computer science. Using classical statistical approaches and modern machine learning based approaches to analysis of large data sets, the study seeks to understand how to improve the inclusion of all students in computer science. It will supplement this large-scale quantitative analysis with qualitative analysis of results from targeted focus-groups and interviews. The qualitative analysis, coupled with the quantitative analysis of longitudinal data from four institutions with different student demographics and other characteristics, will provide a deeper analysis of access to and success in computer science than any previous study. The resulting extension of knowledge has the potential to lay the foundation for achieving equitable access to computer science education for all students. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students.

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.