Published in IEEE Frontiers in Education Conference (FIE), Nashville, Tennessee, USA, 2025
A global exploration of gender diversity in computer science, based on a systematic review of 260 research papers across regions.
Recommended citation: Nawar Wali and Sara Hooshangi. 2025. Diversity in Computer Science Through a Global Lens. In IEEE Frontiers in Education Conference (FIE), Nashville, TN.
Published in Proceedings of ITiCSE 2025, Nijmegen, Netherlands, 2025
Transfer students play an important role in enhancing diversity within computer science programs. As first-generation college students, minorities, rural residents, and individuals from low-income backgrounds, transfer students represent a demographic critical to fostering innovation and inclusion in the field. However, while significant research has been conducted on transfer students in STEM disciplines, studies specifically addressing their experiences in computer science remain limited. This paper presents a preliminary systematic review of the literature to explore the challenges, successes, and gaps in the support of transfer students in computer science.
Recommended citation: Nawar Wali and Sara Hooshangi. 2025. Transfer Students in Computer Science: Examining Barriers, Success Metrics, and Research Gaps. In *Proceedings of ITiCSE 2025*. DOI:10.1145/3724363.3729118.
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Published in ICER, 2025
My dissertation focuses on understanding the experiences of transfer students in computer science (CS), identifying how institutions can better support them, and uncovering emerging themes to conceptualize this student population.
Recommended citation: Nawar Wali. 2025. Transfer Student Success in CS: Modeling Pathways and Outcomes. In Proceedings of ICER 2025, Charlottesville, Virginia, USA.
Published in ACM Capital Region Celebration of Women in Computing, Washington D.C., United States, 2025
This research explores how we can leverage only CS and math coursework data to better support students in CS3, without relying on sensitive information like demographics.
Recommended citation: Nawar Wali and Sara Hooshangi. 2025. Machine Learning Insights into Academic Success in CS3: The Role of Mathematics and CS Coursework. In ACM Capital Region Celebration of Women in Computing, Washington D.C.
Published in arXiv, 2024
The rapid spread of misinformation on social media, especially during crises, challenges public decision-making. To address this, we propose HierTKG, a framework combining Temporal Graph Networks (TGN) and hierarchical pooling (DiffPool) to model rumor dynamics across temporal and structural scales.
Recommended citation: Mariam Almutairi, Melike Yıldız Akta¸s, Nawar Wali, Shutonu Mitra, and Dawei Zhou. 2024. Enhancing Temporal Link Prediction with HierTKG. doi.org/10.48550/arXiv.2412.12385
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