About

Nada Basit is a full-time Associate Professor in the Computer Science Department at the University of Virginia. She received her PhD and MS degrees in Computer Science from George Mason University and her BS in Computer Science from the University of Mary Washington. She also holds a Graduate Certificate in Biometrics from the Volgenau School of Engineering at George Mason University. Her research interests include Computer Science Education, Machine Learning, Bioinformatics, Data Mining, and Pattern Recognition. Her current research broadly focuses on improving the student experience in the classroom, enhancing the teaching assistant experience, and engaging students through hands-on activities and educational tools. She has been teaching at the University of Virginia since 2013, delivering both undergraduate and graduate courses in computer science and data science. She co-organized the CAPWIC conference at UVA in 2024 and has received multiple awards for her teaching excellence, including the All-University Teaching Award in 2022 and the Hartfield Excellence in Teaching award in 2019.

Research topics

• Computer Science
• Artificial Intelligence
• Machine Learning
• Information Retrieval
• Multimedia
• Medicine
• World Wide Web
• Psychiatry
• Pedagogy
• Computer hardware
• Data science
• Psychology
• Human–computer interaction
• Bioinformatics
• Mathematics
• Statistics

Selected publications

• Can LLMs Reason About Attention? Towards Zero-Shot Analysis of Multimodal Classroom Behavior

  ArXiv.org · 2026-04-03

  articleOpen accessSenior author

  Understanding student engagement usually requires time-consuming manual observation or invasive recording that raises privacy concerns. We present a privacy-preserving pipeline that analyzes classroom videos to extract insights about student attention, without storing any identifiable footage. Our system runs on a single GPU, using OpenPose for skeletal extraction and Gaze-LLE for visual attention estimation. Original video frames are deleted immediately after pose extraction, thus only geometric coordinates (stored as JSON) are retained, ensuring compliance with FERPA. The extracted pose and gaze data is processed by QwQ-32B-Reasoning, which performs zero-shot analysis of student behavior across lecture segments. Instructors access results through a web dashboard featuring attention heatmaps and behavioral summaries. Our preliminary findings suggest that LLMs may show promise for multimodal behavior understanding, although they still struggle with spatial reasoning about classroom layouts. We discuss these limitations and outline directions for improving LLM spatial comprehension in educational analytics contexts.

  PublisherOA PDF

• Can LLMs Reason About Attention? Towards Zero-Shot Analysis of Multimodal Classroom Behavior

  arXiv (Cornell University) · 2026-04-03

  preprintOpen accessSenior author

  Understanding student engagement usually requires time-consuming manual observation or invasive recording that raises privacy concerns. We present a privacy-preserving pipeline that analyzes classroom videos to extract insights about student attention, without storing any identifiable footage. Our system runs on a single GPU, using OpenPose for skeletal extraction and Gaze-LLE for visual attention estimation. Original video frames are deleted immediately after pose extraction, thus only geometric coordinates (stored as JSON) are retained, ensuring compliance with FERPA. The extracted pose and gaze data is processed by QwQ-32B-Reasoning, which performs zero-shot analysis of student behavior across lecture segments. Instructors access results through a web dashboard featuring attention heatmaps and behavioral summaries. Our preliminary findings suggest that LLMs may show promise for multimodal behavior understanding, although they still struggle with spatial reasoning about classroom layouts. We discuss these limitations and outline directions for improving LLM spatial comprehension in educational analytics contexts.

  PublisherDOI

• ASCI: AI-Smart Classroom Initiative

  2025-02-12 · 1 citations

  articleOpen access1st authorCorresponding

  The Artificial Intelligence Smart Classroom Initiative (ASCI) presents a re-imagined set of online course tools, designed primarily to support growing computer science classes. The system has four primary tools: an office hours queue, an automatic student grouping algorithm, a course-specific local large-language model (LLM), and administration tools for detecting students and TAs that need support. These tools interoperate to improve the quality of one another (e.g., LLM conversations support students directly in the office hours queue) and are enhanced by synchronizing data from multiple external sources such as Piazza, Gradescope, and Canvas. The system has been deployed in multiple courses over the past three semesters: initially as a FIFO queue, then supporting manual grouping and smart grouping of office hour attendees, and recently including LLM support. Preliminary results indicate that students who were grouped using the tool were more likely to return to the queue more than twice as often (on average) than those who were not. However, while grouping in office hours has the potential to decrease student wait times, teaching assistants and students tend to favor one-on-one meetings over group meetings. This might be improved in the future with updates to the software, TA training, and incorporation of other supporting tools (e.g., LLM technology). The other, newer, tools will be more thoroughly evaluated in future semesters.

  PublisherDOI

• How many words does it take to understand a low-resource language?

  2025-01-01

  articleOpen accessSenior author

  Emily Chang, Nada Basit. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 4: Student Research Workshop). 2025.

  PublisherOA PDFDOI

• Towards More Efficient Office Hours for Large Courses: Using Cosine Similarity to Efficiently Construct Student Help Groups

  2024-03-14 · 2 citations

  articleSenior author

  As undergraduate enrollment in computer science rises, instructors continue to investigate methods to improve the student experience at scale. One aspect commonly used in courses at scale is queue-driven office hours, in which students join an online queue and meet with teaching assistants on a first-come, first-serve basis (FIFO).

  PublisherDOI

• Providing a Choice of Time Trackers on Online Assessments

  2023-03-02 · 2 citations

  articleOpen access

  Online assessments allow instructors to facilitate exams and quizzes in both virtual and large classes. Having a clear online timer during these assessments is vital to help students manage their time. However, these same timers can be a cause of anxiety, affecting student performance. Our goals were to determine (i) which types of visualizations are currently in use, (ii) which styles of online timer were preferred by students, and (iii) if providing students a choice of timer impacted their performance.

  PublisherOA PDFDOI

• Analyzing Student Experience of Time Trackers on Assessments

  Proceedings of the 53rd ACM Technical Symposium on Computer Science Education V. 2 · 2022 · 2 citations

  Senior authorCorresponding
  • Computer Science
  • Computer Science
  • Artificial Intelligence

  Visualizing time limits during online assessments is a cause of anxiety, affecting student performance. An initial survey of 34 students across two Computer Science courses found that time-tracking devices produced anxiety for 67.7% of students. While students differed on timer color preference, a majority preferred a count-down display showing time remaining with the ability to hide the timer. In a small pilot study across five exams, we employed multiple time-tracking displays. Preliminary data suggests that students presented with a count-down grayscale timer performed better on average than those presented with a green-yellow-red (GYR) version. Other displays, such as text-only digital count-down timer or elapsed time progress bars, did not elicit as large a difference in performance. These findings indicate the need for further study.

  PublisherDOI

• Meme Magic: Project in Sprints

  ACM eBooks · 2022-02-03

  book

  Meme Magic is a series of six assignments intended to provide progressive exposure to programming in Java using a popular and recent concept: Memes. Memes utilize an image conveying a concept or feeling with a caption provided by the Meme author. The series of assignments, designed as sprints in the context of a larger project, begin with the design and scaffolding of Java classes needed to write a program to produce text-based Memes and end with a fully-functional graphical user interface. For a detailed list of learning goals, please see the Learning Goals section. In the first sprint, students depict the overall project structure of a text-based meme application using Unified Markup Language (UML) and write method stubs in Java. In each of the next two sprints, students implement half of the specified functionality and integrate those components to a fully working application. Students are asked to add Comparators to sort memes to their application in sprint 4 and to unit test all of their code using JUnit in sprint 5. In the final sprint, students extend the functionality once more to a graphical user interface to experience event-driven programming. Once the full sequence is completed, students will be able to generate and save graphical memes. Steps and learning concepts include designing the project structure using UML diagrams, implementing that design, unit testing with JUnit, and event driven programming using Swing.

  PublisherDOI

• Ranking Actionable Mental Health Advice through a Personalized Video Recommendation System

  2022 · 2 citations

  Senior authorCorresponding
  • Computer Science
  • Information Retrieval
  • Computer Science

  In order to improve the quality of life in today’s digital culture, one of the most important areas of health to consider is mental health. Over the past several years, mental health has become increasingly important as the digital landscape has gotten more complicated, and as more and more people seek ways to relax, connect with others, and find mental balance in their life. While there have been advances in technology, particularly in information retrieval and recommendation systems, very few of these advances have been applied to the mental health industry. In this paper, we bridge this gap by introducing a ranking system that suggests personalized videos for users with the intention of helping them improve their mental health. By updating the relative ranking of videos by marginal improvement for different users, then aligning the distribution of videos depending on the areas that the user needs the most help with, the website helps provide personalized and actionable mental health advice.

  PublisherDOI

• Empowering Diabetes Patients by Providing Machine Learning-Driven Predictions and Personalized Visualization Results

  2022 · 1 citations

  Senior authorCorresponding
  • Computer Science
  • Machine Learning
  • Artificial Intelligence

  Although millions of patients have diabetes, it is often challenging to interpret symptoms that historically lead to the condition. To solve this disparity, we created an end-to-end platform that uses a Random Forest model that predicts early-stage diabetes with 95.6% accuracy, then visualizes patient data for those with similar symptoms. After users enter their data for the five most strongly-correlated diabetes symptoms, the model predicts whether the user has diabetes. As a result, this project transforms how patients communicate about their own data, thereby serving as a mechanism to start important conversations with their doctors or others around the world.

  PublisherDOI

Frequent coauthors

• John R. Hott

  University of Virginia

  4 shared

• Abdeltawab Hendawi

  University of Rhode Island

  3 shared

• Harry Wechsler

  George Mason University

  3 shared

• Mark Floryan

  University of Virginia

  2 shared

• Nour Goulmamine

  University of Virginia

  2 shared

• Ankit Gupta

  Manipal Academy of Higher Education

  2 shared

• Ella Truslow

  University of Virginia

  2 shared

• Yijun He

  Shanghai Jiao Tong University

  1 shared

Education

• PhD, Computer Science

  George Mason University

Awards & honors

• All-University Teaching award University of Virginia, May 20…
• The Seven Society Honoree University of Virginia, 2021
• The Seven Society Honoree University of Virginia, 2020
• The Jefferson Scholars Foundation “Hartfield Excellence in T…