Readability

DIS PWiP 2026 acceptance “Bridging Educational Theories of Cognitive Load to Visualization Design and Evaluation”

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Our paper “Structural Interaction: Shifting the Focus of User Interface Design” was accepted to the DIS Provocation and Works-in-Progress track. Vincent Cavez will present this work at DIS ’26 in Singapore. Continue reading DIS PWiP 2026 acceptance “Bridging Educational Theories of Cognitive Load to Visualization Design and Evaluation”

Readability as a multi-measure construct in data visualization

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In this paper, we argue that readability cannot be meaningfully discussed without considering multiple complementary measures, and that relying on a single measure constitutes an epistemological choice that constrains the conclusions that can be drawn.

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EduVIS 2025 acceptance “Bridging Educational Theories of Cognitive Load to Visualization Design and Evaluation”

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Our workshop paper “Bridging Educational Theories of Cognitive Load to Visualization Design and Evaluation” was accepted at the EduVIS 2025 workshop. I will present this work in Vienna during IEEE VIS week 2025. Continue reading EduVIS 2025 acceptance “Bridging Educational Theories of Cognitive Load to Visualization Design and Evaluation”

PREVis: Perceived Readability Evaluation for Visualizations

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PREVis website
Download PREVis questionnaire and guidelines
Download research article (full paper with appendices)
See github code repository
See article on HAL archives


Although readability is recognized as an essential quality of data visualizations, so far there has not been a unified definition of the construct in the context of visual representations. As a result, researchers often lack guidance for determining how to ask people to rate their perceived readability of a visualization. To address this issue, we engaged in a rigorous process to develop the first validated instrument targeted at the subjective readability of visual data representations. Our final instrument consists of 11 items across 4 dimensions: understandability, layout clarity, readability of data values, and readability of data patterns.

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A cognitive load approach to designing and evaluating data visualizations

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Visual representations of data are increasingly prevalent, but we lack a detailed theoretical framework to explain what factors make easy or difficult to read and understand; nor do we know how such factors can impact data visualizations’ efficiency as learning material.

In this Master’s Thesis, I address this gap by exploring the validity and applicability of Cognitive Load Theory, an educational and cognitive science theoretical framework, for designing and evaluating data visualizations. Beyond empirical findings, I also contribute an interdisciplinary perspective on the cognitive processing of visualizations, and I discuss implications in assessing readability in visualization studies.

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