Integrating Data Science into Social Work Undergraduate Education

This project aims to serve the national interest by developing undergraduate course modules that model how to apply socially relevant data science and advance the field of social work by cultivating data-driven tools addressing human and social issues. Outcomes of the project are expected to address a critical gap in the infusion of data science in the undergraduate social work curriculum.

The planned modules will prepare students to leverage data science to address complex human and social issues that confront society using an Afrocentric perspective focused on social justice for African Americans and other marginalized groups. The modules created in this project provide actionable impact in undergraduate education by incorporating data science into social science undergraduate courses. Such an approach can also be applied in other courses wanting to include data science.

Frameworks from social work and data science will guide the development of the modules that will equip students with skills to use data-driven insights to address social challenges. The interdisciplinary project team members include both social work and mathematics faculty, guided by an advisory board of data science experts. Four objectives frame the project's effort.

First, is to develop curricular modules for three social work core courses and redesign three social work elective courses to reach students with data analysis skill-building. Second, is to implement a sequence of data science activities, assignments, and assessments within classrooms reaching approximately 60 unique junior and senior social work majors students over the duration of the project.

Third, is to evaluate the modules to determine their impact on students’ capabilities, ambitions, and eventual graduate school/workforce placements. Fourth, is to host a symposium and develop a manual that offers model curricular activities and evidence-based recommendations for peer institutions seeking to integrate data science into existing and new social work or other social science programs.

The project research focuses on understanding the varied impacts of the course modules on different students. Specifically, a mixed-methods approach collecting both quantitative and qualitative data will investigate the effect of data science modules on students’ perception of data science; what is required of faculty to integrate data science into social work courses; and how the new and enhanced modules provide the necessary data science competencies for undergraduate courses in social work.

The research objectives are to measure students' competency with programming, computation, and data analysis; measure differential impacts of course modules on students; and determine the most effective means to infuse existing classes with data science content. The project will inform the broader community on how data science can be incorporated in a variety of undergraduate courses seeking to address socially relevant challenges.

The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through its Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools.

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.