Using Fine-Grained Quantitative and Qualitative Data to Enhance Curricula and Broaden Participation in Computer Science

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This project will contribute to the national need for well-educated scientists, mathematicians, engineers, and technicians by supporting the retention and graduation of high-achieving, low-income students with demonstrated financial need at Florida State University, a Carnegie I Research University. Over its five- year duration, this project will fund four-year scholarships to 33 unique Scholars who are pursuing bachelor’s degrees in computing. The project objectives include: (1) identifying and recruiting students with financial need and academic talent; (2) improving retention through cohort class enrollments, dedicated tutors, and academic support; (3) providing internship and research opportunities to Scholars; and (4) gathering feedback to refine the computing curriculum. A distinguishing feature of this project is the application of natural language processing, machine learning, and traditional analyses to examine fine-grained qualitative and quantitative data related to student success. These analyses are expected provide insights into student retention, the computing curriculum, the effectiveness of current support systems, and how to encourage women and other underrepresented groups to major in computer science. The overall goal of this project is to increase STEM degree completion of low-income, high- achieving undergraduates with demonstrated financial need. Although a growing number of jobs require expertise in computing, only 10% of STEM graduates study computer science. This project seeks to increase the participation of low-income, high- achieving students in computer science. To achieve this goal, the project strategies include high school outreach, dedicated tutors, student support systems, cohort enrollment, and replacing student loans with scholarships. This project will investigate the effectiveness of these activities using randomized control trial experiments. As part of this study, the project will use natural language processing and machine learning approaches to analyze data from Experience Sampling Method surveys to identify and remediate gender and cultural biases in the computing curriculum. The expected outcomes of the project include identification of curriculum changes to encourage diversity and quantification of factors that contribute to student success in computer science. Project evaluation will include annual data collection and analyses of cohort demographics, academic performance, retention, use of support systems, reasons for separation, and placement. The research findings will be published at conferences such as SIGCSE, ASEE, FIE, and AERA. This project is funded by NSF’s Scholarships in Science, Technology, Engineering, and Mathematics program, which seeks to increase the number of low-income academically talented students with demonstrated financial need who earn degrees in STEM fields. It also aims to improve the education of future STEM workers and to generate knowledge about academic success, retention, transfer, graduation, and academic/career pathways of low-income 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.