Addressing Preparation Gaps and Promoting Culturally Relevant Teaching to Support Diverse Groups in Computing Courses

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This project aims to serve the national interest by preparing a large, modern, and diverse computing workforce. The project team will investigate how its learning materials (known as a BRIDGES system) contribute to closing the preparation cap of college computing students. The BRIDGES system aims to improve engagement and motivation of first and second-year computer science students in foundational course. However, because studies have shown that students from underrepresented groups derive fewer benefits from the BRIDGES system, the system will be extended in this project to serve a broader and more diverse population. The project will develop learning materials to bridge preparation gaps among students and adapt to the skills, interests, and values of all learners. Additionally, the project will expand the use of the BRIDGES system through outreach, enhanced instructor support, and a cloud-based solution to ease adoption. The project aims to have a significant impact on data structures and algorithm analysis courses, which are foundational courses that underlie most college computer science degree programs.The project will develop evidence-based guidelines to customize learning materials for students from underrepresented groups in STEM to improve student engagement. Outcomes will be explored across gender, ethnicity, and institution types. The project team will investigate how to design a collection of assignments that match the same learning outcomes, while being rooted in different contexts, values, and applications. Surveys will be administered to understand the effectiveness of these approaches and assess the resulting student outcomes. Regression analyses will be used to determine if the pretest scores or the gains in scores from pretest to post-test predict responses to the BRIDGES assignments, survey, and project or course grades. Chi square tests will be used for items with response choices while regression analysis will be used with scale scores. The NSF IUSE: EHR 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.