MCA: Cultivating a Leader in Statistics and Data Science Education through Strategic Partnerships

This project aims to serve the national interest by identifying essential competencies for data science literacy and how undergraduate college students learn these data science fundamentals in introductory data science course for diverse, general audiences. The project is significant because it addresses the growing need for data literacy among students who are not majoring in data science, yet will benefit from the ability to interpret and use data in various aspects of their lives and careers.

Data science literacy requires a combination of competencies including statistical thinking, critical thinking, computational skills, data management, data visualization, and ethical reasoning. However, it is challenging to cover all these skills in an introductory course. This project will investigate which competencies should be prioritized and carefully investigate the pedagogical choices and learning pathways that support the development of these skills for students who do not plan to pursue a degree with a heavy emphasis on data analysis.

The broader significance of this project lies in its potential to advance data science education, making it more accessible and effective for students of all majors. This aligns with NSF's mission to promote the progress of science and enhance STEM education.

The research will to provide a comprehensive understanding of competencies and learning trajectories through a mixed-methods design, integrating both qualitative and quantitative data. The integration of these data types will allow for a robust analysis of the current state of data science education. The project will use surveys to gather quantitative data on student experiences and competencies, and semi-structured interviews to collect qualitative insights from instructors and students, which will be analyzed using thematic content analysis.

Additionally, content analysis of syllabi will provide context on current curricular approaches. Guided by resource theory, expectancy-value theory, and grounded theory, the research aims to identify core competencies to prioritize in introductory courses and refine learning trajectories for a diverse audience. The findings will contribute to the development of more effective data science curricula and teaching methods, ultimately enhancing student outcomes and expanding access to data science education.

This project is supported by the Mid-Career Advancement program that offers opportunities for scientists and engineers to substantively enhance and advance their research program through synergistic and mutually beneficial partnerships. This project is also supported by the NSF IUSE: EDU program, which supports research and development projects to improve the effectiveness of STEM education for all student, and the by the HSI Program, which aims to enhance undergraduate STEM education, broaden participation in STEM, and increase capacity to engage in the development and implementation of innovations to improve STEM teaching and learning at Hispanic Serving Institutions.

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.