Creating Diverse Data Science Learning Pathways

This project aims to serve the national interest by developing instructional methods and professional development for instructors that will strengthen undergraduate students’ data literacy and skills. Data science is reshaping the US workforce and society at large as organizations generate and look to analyze increasing collections of rich data. The US cannot meet an increasing demand for data literacy without ensuring significant participation in data science education. The inclusion of multiple perspectives in the data science workforce is needed to build flexible solutions that address the most critical needs and serve large, diverse populations. An increasing number of community colleges, colleges, universities and training companies are building data science courses and credentials for undergraduates, but these courses often attract students already in the STEM fields. In order to increase the STEM-enabled workforce, it is critical to involve students who are not yet planning to incorporate data science skills into their careers. This project aims to build data science experiences that attract, retain, support, and empower learners who will be able to bring data skills and practices to fields within or beyond STEM. Current efforts to bring data science learning experiences to students from different backgrounds and disciplines face two major gaps: (1) a need for replicable models to support broader implementation; and (2) a lack of research that demonstrates how models can support data science identity development while attending to students with diverse disciplinary and sociopolitical backgrounds. The goal of this project is to engage students and faculty in the study, testing, and improvement of a model for project-based, workforce-relevant data science education. The model is based on the hypothesis that student choice and agency in a flexible, project-based learning environment will lower barriers to entry and encourage students with diverse backgrounds to explore data science during their undergraduate careers. Project research will draw upon multiple qualitative and quantitative data streams to generate new knowledge about how consideration of students’ identities and attention to student choice and agency can support identity development in data science communities. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the 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.