Multidisciplinary Data Science Education to Prepare STEM Students for Data Science Careers

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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 the University of Arkansas. Over its five-year duration, this project will fund three-year scholarships to 30 students who have an interest in data science and are pursuing Bachelor of Science degrees in the following STEM disciplines: Data Science; Biological Sciences; Biomedical Engineering; Computer Science and Computer Engineering; Geological Science; Industrial Engineering; and Mathematics. The project takes a novel, multidisciplinary approach that focuses on building data science skills among cohorts of students who have different STEM majors. Scholars will participate in project activities designed to develop data science skills and prepare them for careers in the field, including a summer data science boot camp and a data innovation challenge. The project aims to benefit low-income STEM students and contribute to the data science workforce in Northwest Arkansas. Arkansas is currently ranked 45th per capita in Bachelor of Science degrees in scientific and engineering fields and is unable to meet the State's STEM labor needs. This project aims to help fill workforce gaps, as well as to support Arkansas' goal of becoming a new regional STEM technology center for economic development. The overall goal of this project is to increase STEM degree completion of low-income, high-achieving undergraduates with demonstrated financial need. Scholars will have access to peer mentoring, a weekly social event, and a bi-monthly speaker series will provide opportunities for networking, support, and professional development. In addition, they will participate in a two-week summer boot camp to introduce the academic foundations of data science and take a set of courses designed to develop professional data science skills. Scholars will further develop and apply their data science skills in a data innovation competition, in internships or summer research opportunities, and in a knowledge rotation that allows Scholars to shadow faculty who are using data science in their work. The project includes an educational research study that aims to determine the academic, personal, and/or psychological factors that are most related to program enrollment, retention, graduation time-to-completion, and career outcomes in the field of data science. In addition, the project will study the following two research questions: (1) Do students' perceptions of their math-related ability, data science utility value, interest in data science, and their theory of intelligence beliefs predict enrollment, performance, retention, and graduation in the data science coursework series? and (2) Does participation in project activities account for improved retention, course success, graduation rates, and career/advanced study outcomes? To better understand who enrolls in the program and who persists to completion, the project will assess Scholar interest, perceived ability, utility value, and theory of intelligence. The results obtained in this project will be used to develop a model that can account for variability in retention and graduation of low-income Scholars versus other low-income STEM students. These models can then be applied to develop additional retention programs for low-income STEM students. The knowledge generated by this project has the potential to inform the growing number of data science programs in colleges and universities across the nation and world. 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.