CER: Forming a Networked Improvement Community to Study and Support Course Alignment and Student Success at Critical Transition Points along Computing Education Pathways

Many students who study computer science in college never complete their degrees, often because their high school or community college computing experiences do not prepare them for college-level coursework. This project’s goal is to address those gaps, focusing on two points along the path to a computing degree where students frequently struggle. The first occurs when high school students who earn early college credit through Advanced Placement courses, dual credit, or dual enrollment move on to college computing courses. The second occurs when community college students transfer to a four-year university to continue their computing studies. Both paths are intended to prepare students for college-level computing degree programs while shortening the time and lowering the cost of those programs. However, when this preparation does not line up in content, teaching style, or the programming languages used, students can fall behind even when they have worked hard and earned good grades. By studying how well each step prepares students for the next, this project aims to make computing pathways smoother and fairer. The project will bring together the Chicago Public Schools, City Colleges of Chicago, DePaul University, Loyola University Chicago, and University of Illinois Chicago to find where students lose ground and to test practical ways to help them succeed. Strengthening these pathways will help more students finish computing degrees, supporting a skilled national workforce and expanding opportunity across society. This project investigates course alignment and student preparedness at two junctures in the computing education pathway, using a mixed methods design that links longitudinal administrative records with qualitative student interviews. The first research activity examines how different forms of early college credit, including Advanced Placement Computer Science Principles, Advanced Placement Computer Science A, dual credit, and dual enrollment, prepare students for introductory college programming courses. The second research activity compares community college transfer students with students who begin at a four-year institution, focusing on performance in a data structures course and on whether the programming language used in earlier and later courses stays the same or changes. Because students are grouped within instructors and instructors within institutions, the analysis employs three-level hierarchical linear models that separate student, instructor, and institutional effects while testing whether programming language transitions affect transfer students more than others. This work extends programming language transfer theory, drawn from research on cognitive load and second language learning, to a large urban education system spanning one school district, a seven-campus community college system, and three universities. A central contribution is the formation of a Networked Improvement Community, a structured collaboration in which researchers and practitioners jointly define problems of practice, develop and test improvement strategies, and interpret findings through repeated cycles of inquiry grounded in improvement science. Student interviews, co-designed with participating educators, clarify the specific difficulties learners encounter at each transition. An external evaluation assesses how the community develops its theory of improvement, builds its measurement systems, and applies improvement science methods. Findings will inform course design, transfer policy, advising, and student support, and will provide a transferable model for studying and improving educational transitions in computing. 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.