Research
My research examines how authentic project-based learning can help computing students connect theory with real software engineering practice. I focus on making projects adoption-ready for instructors, supporting students through technical complexity, and assessing individual transfer-oriented higher-order thinking.
My doctoral work includes FORAP, an expert-validated framework for organizing reusable and adaptable PjBL projects, and a portfolio of 14 NSF-funded computing projects deployed across seven U.S. and Canadian universities. I study these projects through systematic literature review, instructor and student surveys, classroom studies, expert evaluation, and multi-institution deployment.
I also study responsible applications of large language models in computing education and empirical software engineering. This includes LLM-generated design problems for assessment, retrieval-augmented and fine-tuned assistants for project support, and empirical studies of Android logs, privacy disclosures, continuous integration, and bug-report decomposition.