Naaz Sibia

Naaz Sibia is an active faculty member in the Department of Computer Science at the University of Toronto, with a robust publication record spanning from 2021 to 2025. Sibia has established themselves as a prominent researcher in computer science education, particularly focusing on student learning experiences in programming contexts. Their work appears consistently in top-tier computer science education venues including ITiCSE, SIGCSE, Koli Calling, and DataEd.

Sibia's research interests center around programming education, with particular emphasis on self-explanation techniques, multiple representations in introductory programming, student interaction in Q&A forums, and the integration of educational technology. Their recent work has increasingly explored the application of large language models in educational contexts, reflecting emerging trends in the field. A significant portion of their research investigates how students learn programming concepts, with attention to factors like student discomfort, isolation, and the impact of different instructional approaches.

Analysis of Sibia's publication trends shows a clear progression from foundational studies on student learning behaviors to more sophisticated investigations incorporating AI technologies. Their 2023-2025 work demonstrates growing interest in large language models for educational applications, voice interfaces for self-explanation, and the impact of multiple representations on novice programmers. The research consistently emphasizes practical applications for improving computer science education at both introductory and advanced levels.

Sibia maintains a strong collaborative network, frequently working with researchers including Angela M. Zavaleta Bernuy, Michael Liut, and Andrew Petersen. These collaborations span multiple institutions and have resulted in numerous joint publications across various computer science education venues. Their work appears to focus primarily on empirical studies of student learning, often employing mixed-methods approaches to investigate educational interventions.

While specific laboratory affiliations aren't detailed in the publication record, Sibia's research suggests involvement with educational technology initiatives at the University of Toronto, particularly those focused on enhancing programming education through innovative pedagogical approaches and technological interventions. Their recent work on large language models indicates engagement with emerging AI technologies in educational contexts.