Transfer Student Success in CS: Modeling Pathways and Outcomes

Abstract

My dissertation focuses on understanding the experiences of transfer students in computer science (CS), identifying how institutions can better support them, and uncovering concerning patterns and emerging themes to better conceptualize this student population. The project is organized into four phases: (1) conducting a systematic literature review to synthesize current research and highlight gaps; (2) designing and deploying surveys to both pre-transfer community college students and post-transfer university students; (3) applying data analytics, machine learning, and knowledge graphs to identify patterns and predictive factors of student success; and (4) synthesizing findings into a data-driven, transferable framework to help institutions support transfer students more effectively. Ultimately, the goal is to promote academic success for transfer students, ensuring equitable opportunities regardless of where students begin their educational journeys.