More Expert-like Eye Gaze Movement Patterns are Related to Better X-ray Reading

TL;DR

This study uses network analysis of eye-gaze patterns to understand how dental students develop expertise in diagnosing radiographs, revealing key metrics linked to improved diagnostic performance and developmental shifts toward expert visual search strategies.

Contribution

It introduces a novel application of network analysis to model eye-gaze scanpaths and identifies specific network metrics associated with expertise development in dental radiograph diagnosis.

Findings

Transition entropy negatively correlates with performance.

Number of nodes, edges, and PageRank positively correlate with performance.

Students' visual search strategies evolve from intermediate to expert levels.

Abstract

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.