Supporting Equitable Collaborations through Pair Programming Experiences in Undergraduate Data Science Courses

This project aims to serve the national interest by implementing and studying evidence-based teaching practices that promote equitable collaboration in undergraduate data science courses at California Polytechnic State University, San Luis Obispo and California State University, Monterey Bay. There is rapidly growing demand for data scientists both nationally and globally. At the same time, many educational approaches that have been studied in computer science, mathematics, statistics, and other areas have not been explored in the interdisciplinary setting of data science. Creating classrooms where every student has the opportunity to learn effectively is integral to growing and sustaining a diverse data science workforce. Students' sense of belonging in their classrooms is directly associated with their likelihood of persisting in STEM, be it in their chosen disciplines or more broadly. Group work is one method of supporting engagement and sense of belonging in undergraduate classrooms. This project plans to investigate students' experiences when collaborating on pair programming tasks in the data science classroom, with a particular focus on students from populations that have historically been underrepresented in data science and broader STEM. Studies have shown that pair programming in computer science courses can either serve as a positive experience for students or can magnify imbalances in power or perceived authority. The project will iteratively design and test tools that will support positive pair programming experiences and improve student learning. The collaborating institutions provide a rich opportunity to study and refine pair programming practices in distinct contexts, as California State Polytechnic University, San Luis Obispo is a predominantly white institution and California State University, Monterey Bay is a Hispanic-Serving Institution. The overarching goal of this project is to understand how small group experiences, specifically pair programming, can be designed to create engaging, inclusive, and effective learning opportunities for all students. There is minimal research on specific ways that instructors might foster equitable student collaborations in the data science classroom, and this project's knowledge generation efforts will address this critical gap. The project will use discourse analysis to investigate the conditions under which students are positioned with power and authority when collaborating with their peers on pair programming tasks in data science. A cross-case analysis will be used to explore project-developed pedagogies in a variety of classes within two demographically different institutions. Through these investigations, the project will identify positive, asset-centered narratives about students and create professional development materials to train educators to enact equitable, effective pair programming approaches for data science education. Each of these project goals should increase and strengthen the future data science workforce. 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 project is also funded by the NSF Hispanic-Serving Institutions (HSI) Program, which aims to enhance undergraduate STEM education, broaden participation in STEM, and build capacity at HSIs. 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.