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 five 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 and machine learning to identify patterns and predictive factors of student success; (4) constructing temporal knowledge graphs to model academic trajectories and uncover hidden relationships; and (5) synthesizing findings into a data-driven, transferable framework to help institutions support transfer students more effectively.