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Position paper: A case to study the relationship between data visualization readability and visualization literacy

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Bibliographic Details
Title: Position paper: A case to study the relationship between data visualization readability and visualization literacy
Authors: Cabouat, Anne-Flore, He, Tingying, Cabric, Florent, Isenberg, Tobias, Isenberg, Petra
Contributors: Cabouat, Anne-Flore
Publisher Information: 2024.
Publication Year: 2024
Subject Terms: [SCCO.COMP] Cognitive science/Computer science, concepts and paradigms Readability, Visualization Readability, Visualization Literacy, Assessment tests ACM Reference, Data visualization literacy, Data visualization and analysis, [INFO.INFO-HC] Computer Science [cs]/Human-Computer Interaction [cs.HC], Visualization design and evaluation methods, Visualization theory, Visualization literacy, Empirical studies in visualization
Description: In this position paper we argue that improving visualization literacy evaluation tools is important for defining and understanding the concept of readability in data visualizations. Only with reliable and relevant measures can we assess how a potential factor affects a reader’s performance; accordingly, only with appropriate measuring instruments can we start to investigate the tight web of interactions between individual characteristics, features of the visual design, and reading tasks requirements. As we slowly progress in our understanding of how people process information from data visualization, and based on these improved tools and other developments, we can further develop theoretical foundations in data visualization.
Document Type: Conference object
File Description: application/pdf
Language: English
Access URL: https://hal.science/hal-04523790v2
Rights: CC BY
Accession Number: edsair.dedup.wf.002..b7c9f78385165f6eb42a0c1f89830ef5
Database: OpenAIRE
Description
Abstract:In this position paper we argue that improving visualization literacy evaluation tools is important for defining and understanding the concept of readability in data visualizations. Only with reliable and relevant measures can we assess how a potential factor affects a reader’s performance; accordingly, only with appropriate measuring instruments can we start to investigate the tight web of interactions between individual characteristics, features of the visual design, and reading tasks requirements. As we slowly progress in our understanding of how people process information from data visualization, and based on these improved tools and other developments, we can further develop theoretical foundations in data visualization.