🤖 AI Summary
Traditional assessments of visualization literacy rely solely on response accuracy, which fails to reveal the underlying cognitive processes and strategic differences users employ when interpreting charts. This study introduces, for the first time, a systematic framework that integrates eye-tracking methodology with standardized testing to construct process-oriented metrics—including component-level attention distribution, frequency of cross-region information integration, and dispersion of gaze trajectories. By moving beyond binary correctness judgments, this approach effectively captures variations in cognitive load and interpretation strategies that are invisible to conventional measures such as accuracy and response time. The proposed method thus enables a paradigm shift from outcome-based evaluation to fine-grained diagnosis of cognitive processes, offering a scalable and nuanced pathway for assessing visualization literacy.
📝 Abstract
Visualization literacy assessments typically rely on correctness to classify performance, providing little evidence about how readers arrive at their answers. We argue that gaze can address this gap as an implicit process signal that complements standardized tests without sacrificing their scalability. Synthesizing findings from visualization and related research, we show that gaze metrics capture cognitive load invisible to accuracy and response time, and reflect strategy differences in attention allocation that track proficiency. We propose assessments that integrate literacy scores with gaze-derived process indicators - component-level attention profiles, integration frequency, and viewing path dispersion - to distinguish fluent comprehension from labored success. This would shift literacy assessment from binary classification toward nuanced characterization of how readers navigate, integrate, and coordinate information across chart components. A roadmap identifies open challenges in empirical grounding, generalizability, assessment design, and practical feasibility.