Virginia Tech® home

Current Student Highlight: Taha Hassan

May 1, 2023

Taha Hassan
Taha Hassan

Taha Hassan is a PhD candidate with the Department of Computer Science at Virginia Tech, and a graduate research assistant for learning analytics with Technology-Enhanced Learning and Online Strategies (TLOS). 

He is advised by Scott McCrickard. In his doctoral research, Taha investigates how editorial processes serve as guardrails for a trustworthy user experience of novel information systems, especially of recommender systems in higher education and news media. Editorial processes represent a consensus, formal or informal, of domain stakeholders’ beliefs about ability, authority, utility, safety, and responsibility.

Taha examines (1) how these individual beliefs influence the adoption and the division of editorial labor in recommender systems, and (2) how HCI practitioners can promote explainability and cooperative work in these editorial arrangements. He hopes to design and roll out a recommender system of learning resources for Canvas LMS with the help of his mentors and peers at Virginia Tech division of IT. Previously, Taha helped develop ‘Depth of Use’ (DOU): a multiyear Canvas platform analytics project at TLOS. DOU has seen successful application in TLOS efforts to support instructional design, LMS evangelism, and pandemic response efforts across Virginia Tech.

His work has appeared at the ACM Conference on User Modeling, Adaptation and Personalization (UMAP ’21), and ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE ’20). He was a recipient of the Fulbright Scholarship for his studies at Virginia Tech (Fulbright MS Program, 2013-2015).

Hassan is fundamentally intrigued by the conversation between structure and agency we experience in our daily lives, especially in our use of recommender systems to engage, learn, and cooperate. In domains such as higher education, sacred spaces, and news media, novel recommender systems may have to reckon with unwritten rules of authorship (the ‘structure’) observed by recommendation consumers with unique preferences, biases, and propensities (the ‘agency’). 

Regarding his coursework at Virginia Tech in HCI theory, UX engineering, and industrial-organizational psychology, Taha says “These courses encouraged me to reflect on and combine these perspectives. At TLOS, this integrated perspective has benefited key stakeholder needs analyses and development of novel recommendation tools immensely. In the future, I’ll continue to highlight and improve the editorial processes for trustworthy human-AI interaction in new domains (esp., religion-spiritual storytelling) and for up-and-coming technologies (esp., short-form video and XR).