研究发现，更像专家的眼动模式能提升牙片诊断准确率。
More Expert-like Eye Gaze Movement Patterns are Related to Better X-ray Reading
- 用图网络分析学生看牙片时的眼动路径
- 眼动路径越复杂、节点越多，诊断成绩越好
- 适合医学教育和AI辅助训练系统设计
理解新手如何发展视觉搜索技能对优化培训方法至关重要。本研究通过网络分析方法，分析了多名本科牙科学生在多个学期中诊断牙片时的眼动轨迹。将眼动路径建模为有向图，考察网络指标随时间的变化。利用时间序列聚类识别出不同的视觉搜索策略，并探索其与诊断表现的关系。结果表明，转移熵与成绩呈负相关，而节点数、边数及平均PageRank与成绩呈正相关。个体学生网络指标的动态变化显示，其认知处理方式从初级向专家水平演进。这些发现有助于理解视觉任务中的专业能力形成机制，可为AI辅助学习干预的设计提供依据。
原文摘要 · Abstract (English)
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.
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