More Expert-like Eye Gaze Movement Patterns are Related to Better X-ray Reading
This work addresses expertise acquisition in visual search for dental training, offering incremental insights to inform AI-assisted learning interventions.
The study investigated how undergraduate dentistry students' eye-gaze movements relate to diagnostic performance when reading dental radiographs over time, finding that network metrics like transition entropy negatively correlated with scores while nodes, edges, and average PageRank positively correlated with performance.
Understanding how novices acquire and hone visual search skills is crucial for developing and optimizing training methods across domains. Network analysis methods can be used to analyze graph representations of visual expertise. This study investigates the relationship between eye-gaze movements and learning outcomes among undergraduate dentistry students who were diagnosing dental radiographs over multiple semesters. We use network analysis techniques to model eye-gaze scanpaths as directed graphs and examine changes in network metrics over time. Using time series clustering on each metric, we identify distinct patterns of visual search strategies and explore their association with students' diagnostic performance. Our findings suggest that the network metric of transition entropy is negatively correlated with performance scores, while the number of nodes and edges as well as average PageRank are positively correlated with performance scores. Changes in network metrics for individual students over time suggest a developmental shift from intermediate to expert-level processing. These insights contribute to understanding expertise acquisition in visual tasks and can inform the design of AI-assisted learning interventions.