Term of Award
Spring 2025
Degree Name
Master of Science in Computer Science (M.S.)
Document Type and Release Option
Thesis (open access)
Copyright Statement / License for Reuse
This work is licensed under a Creative Commons Attribution 4.0 License.
Department
Department of Computer Science
Committee Chair
Andrew Allen
Committee Member 1
Vijayalakshmi Ramasamy
Committee Member 2
Ryan Florin
Abstract
Eye-tracking technology offers a non-intrusive way to study cognitive processes by tracking where and how long individuals look. In computer science education, it provides valuable insights into how students understand source code—a task that requires intense visual and mental effort. This study investigates how eye-tracking can reveal differences in code comprehension strategies among students in an Introductory Programming course. By analyzing three key metrics—dwell time, gaze entropy, and the K coefficient—the research explores how students engage with code. Dwell time indicates cognitive focus on specific code elements, the K coefficient measures attentional shifts between scanning and focused reading, and gaze entropy reflects how structured or random a student's visual path is. These metrics help characterize student behavior and differentiate performance levels. The results suggest that eye-tracking data can effectively gauge comprehension and potentially support real-time, adaptive teaching strategies tailored to individual learning needs.
Recommended Citation
@article{gauhar2025eyemetric, title={Comparative Analysis of Eye-metric Algorithms for Code Comprehension in Introductory CS Course}, author={Gauhar, Noushin}, year={2025}, organization={Georgia Southern University} }
Research Data and Supplementary Material
No
