Increasing Degree-completion for Engineering and Computer Science Scholars

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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 Baylor University. Over its five-year duration, this project will fund four-year scholarships to 22 students who are pursuing Bachelor of Science degrees in Engineering, Electrical and Computer Engineering, Mechanical Engineering and Computer Science. Activities designed for Engineering and Computer Science (ECS) Scholars include an orientation, a monthly seminar series, and required faculty mentoring. Scholars will also participate in existing support services and activities including peer mentoring, study abroad opportunities, alumni mentoring, support and training for undergraduate research, professional development workshops, and tutoring support from the ECS Learning Resource Center. A distinguishing feature of the project is the use of EAB's Navigate, a web-based software platform for tracking student progress, coordinating student care, and employing predictive analytics. The knowledge generated by this project is expected to identify best practices for using predictive analytics to provide timely interventions and improve retention and graduation rates for STEM students. The overall goal of this project is to increase STEM degree completion of low-income, high-achieving undergraduates with demonstrated financial need. The project will use the Navigate predictive analytics platform to track and document ECS Scholar progress in the areas of retention, graduation, internships, undergraduate research experiences, and job placement. The use of predictive analytics has significant potential to improve student success, however best practices for using computer-based predictions and tools to support students are not well developed. This project will investigate whether student and faculty/mentor perceptions of the factors that influence student success align with those identified by the predictive analytics dashboard. The project will also investigate perceptions about the extent to which predictive analytics are helpful for identifying factors that inhibit and promote student success. The project will conduct yearly interviews with students and faculty/mentor focus groups. The discussions will be transcribed and encoded for analysis by qualitative research software. The resulting longitudinal study will generate insights for using predictive analytics to improve student outcomes. Results of the project will be disseminated at regional conferences, national conferences, and among other users of the Navigate platform. 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.