An Autoethnography on Visualization Literacy: A Wicked Measurement Problem

We contribute an autoethnographic reflection on the complexity of defining and measuring visualization literacy (i.e., the ability to interpret and construct visualizations) to expose our tacit thoughts that often exist in-between polished works and remain unreported in individual research papers. Our work is inspired by the growing number of empirical studies in visualization research that rely on visualization literacy as a basis for developing effective data representations or educational interventions. Researchers have already made various efforts to assess this construct, yet it is often hard to pinpoint either what we want to measure or what we are effectively measuring. In this autoethnography, we gather insights from 14 internal interviews with researchers who are users or designers of visualization literacy tests. We aim to identify what makes visualization literacy assessment a "wicked" problem. We further reflect on the fluidity of visualization literacy and discuss how this property may lead to misalignment between what the construct is and how measurements of it are used or designed. We also examine potential threats to measurement validity from conceptual, operational, and methodological perspectives. Based on our experiences and reflections, we propose several calls to action aimed at tackling the wicked problem of visualization literacy measurement, such as by broadening test scopes and modalities, improving test ecological validity, making it easier to use tests, seeking interdisciplinary collaboration, and drawing from continued dialogue on visualization literacy to expect and be more comfortable with its fluidity.

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Publication Details

Journal
IEEE Transactions on Visualization and Computer Graphics
Published
2025-11-20
DOI
https://doi.org/10.1109/tvcg.2025.3634792
Primary Topic
Data Visualization and Analytics
Type
article
Field-Weighted Citation Impact
0.00
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article

An Autoethnography on Visualization Literacy: A Wicked Measurement Problem

Yuan Cui, Maryam Hedayati, Petra Isenberg, Angela Locoro et al.
IEEE Transactions on Visualization and Computer Graphics
Data Visualization and Analytics
article

An Autoethnography on Visualization Literacy: A Wicked Measurement Problem

Yuan Cui, Maryam Hedayati, Petra Isenberg, Angela Locoro, Karen Bonilla, Michael Correll, Yiren Ding, Noëlle Rakotondravony, Lily W. Ge, Jasmine Otto, Mackenzie Creamer, Bum Chul Kwon, Lane Harrison, Matthew Kay, Anne-Flore Cabouat
article en

Abstract

We contribute an autoethnographic reflection on the complexity of defining and measuring visualization literacy (i.e., the ability to interpret and construct visualizations) to expose our tacit thoughts that often exist in-between polished works and remain unreported in individual research papers. Our work is inspired by the growing number of empirical studies in visualization research that rely on visualization literacy as a basis for developing effective data representations or educational interventions. Researchers have already made various efforts to assess this construct, yet it is often hard to pinpoint either what we want to measure or what we are effectively measuring. In this autoethnography, we gather insights from 14 internal interviews with researchers who are users or designers of visualization literacy tests. We aim to identify what makes visualization literacy assessment a "wicked" problem. We further reflect on the fluidity of visualization literacy and discuss how this property may lead to misalignment between what the construct is and how measurements of it are used or designed. We also examine potential threats to measurement validity from conceptual, operational, and methodological perspectives. Based on our experiences and reflections, we propose several calls to action aimed at tackling the wicked problem of visualization literacy measurement, such as by broadening test scopes and modalities, improving test ecological validity, making it easier to use tests, seeking interdisciplinary collaboration, and drawing from continued dialogue on visualization literacy to expect and be more comfortable with its fluidity.

IEEE Transactions on Visualization and Computer GraphicsVol. 32(1)
Worcester Polytechnic Institute (US), Northwestern University (US), Northeastern University (US), Centre National de la Recherche Scientifique (FR), IBM (United States) (US), University of California, Santa Cruz (US), Northwestern University (PH), University of Northwestern (US), Centre Inria de Saclay (FR), Eastern University (BD), University of Brescia (IT)
Openalex Percentile: Top 64%
Data Visualization and Analytics
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