Examining the Effects of Course Climate, Active Learning, and Intersectional Identities on Undergraduate Student Success in Computing

This project aims to serve the national interest by improving undergraduate student success in computer science courses. To do so, it will study relationships between student success and the course climate and teaching practices of their computer science courses. It will further analyze these relationships for students with intersecting identities, such as women who are first generation college students. Many STEM classrooms feature passive learning approaches and intentionally or unintentionally establish create competitive course climates that inhibit collaboration. Alternative teaching approaches that encourage collaboration and active learning are gaining traction in many undergraduate computing programs. Studies have generally indicated a positive relationship between collaborative active learning and increased student learning outcomes. However, implementation of active learning varies and the results on student learning have been inconsistent. It is possible that the inconsistencies may reflect differential impacts of competitive, passive learning on groups of students with specific intersecting identities. This project aims to explore the relationship between active learning and cooperative course climate on students with intersecting identities, including race, gender, and social class. The knowledge generated by this research has the potential to improve student success in computer science and thus broaden participation of individuals from groups that are not yet equitably represented in computing. This project identifies course structure and sociality as two major components of course climate. Course structure includes features such as the course timeline, expectations for attendance or participation, and how grades are determined. Sociality involves forming and working in supportive communities. The project uses the constructs of course structure and sociality to capture pedagogical components that faculty select and combine in their adoption active learning approaches. Correlations will then be identified between these active learning components and student outcomes, and will be used to explore differential outcomes across intersectional student identities. This knowledge may help instructors select active learning strategies that would lead to more effective student learning, or combine elements of structure and sociality to develop new promising pedagogies in undergraduate computing education. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. This project is in the Engaged Student Learning track, through which the IUSE 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.