Broadening Participation Research Project: Research for Social Justice - Broadening Participation through Data Science

The aims of this research project are to increase the participation of minorities and women in STEM through data science. Data science affords students the ability to marry their skills in mathematics, statistics and computer science with their personal passions, be it in health disparities or linguistics. Data science can be used to attract students with such wide-ranging interests, particularly those who want to use STEM as a means to address societal challenges and impact social policies.

A recent article [McGee, Bentley 2017] that analyzed the career motivations of high-achieving Black and Hispanic STEM undergraduate students concluded that minority students are motivated more by what the authors refer to as the “equity ethic” rather than by a big paycheck. These students are seeking STEM careers that let them integrate their STEM expertise with “social justice, empathy, and equity matters." Data Science, therefore, serves as an ideal vehicle to improve minority population perception of the utility of STEM to address social justice and impact social policy changes, thereby helping increase enrollment of minorities and women in STEM.

This proposal builds on the researchers' recently concluded NSF-funded Targeted Infusion Project (PI: Alade Tokuta Award: HRD-1533653), through which they created a Computational and Engineering Mathematics (CEMA) concentration for NCCU Mathematics majors, with focus themes in Data Science, Software Engineering, and Computational Science and a joint 3+2 program with NC State University in Computer Engineering and Electrical Engineering.

The researchers propose a hub-and-spoke model to build multidisciplinary strength to broaden the reach of data science to a broad spectrum of students with wide-varying interests. The hub will be composed of the required skill set in mathematics, statistics and computer science. Each spoke will extend to the student’s chosen field. Through this project, the researchers will establish spokes in social projects on varied topics (poverty, law enforcement, the reach of higher education, etc.) as well as spokes in STEM topics of no (immediate) social relevance, such as problems arising in the fields of scheduling and networking. This design allows for the study of whether and how the application of data science projects grounded in social justice impacts the socio-cognitive factors known to mediate and moderate STEM persistence and success. In addition, the project will document STEM enrollment and retention outcomes.