What is visualization literacy and how should we measure it

De Lily Wanqian Ge

Apparaît dans la collection : 2026 - T1 - WS1 - Rigorous Illustrations - Their creation and evaluation for mathematical research

Researchers have proposed many definitions of visualization literacy, targeting various aspects of the term. But we have yet to fully capture what it really means to be literate in visualizations, which has important downstream implications, such as how to effectively teach visualization skills to younger generations. Despite not having a clear sense of what it is, we must design tests that measure this ability in order to run empirical studies and evaluate interventions to make progress within the visualization community. This tension between not fully understanding visualization literacy as a construct while still needing to measure it is what makes the study of it so challenging. We ran a one-day workshop at CHI 2024 to facilitate critical conversations around understanding, measuring, and improving visualization literacy. This workshop led to a multi-institutional collaborative autoethnography on the topic of visualization literacy measurements. Based on our reflections on the complexity and fluidity of visualization literacy, we propose several calls to action from the conceptual, operational, and methodological perspectives such as broadening test scopes and modalities, improving test ecological validity, and seeking interdisciplinary collaboration.

Informations sur la vidéo

Données de citation

  • DOI 10.57987/IHP.2026.T1.WS1.010
  • Citer cette vidéo Ge, Lily Wanqian (21/01/2026). What is visualization literacy and how should we measure it. IHP. Audiovisual resource. DOI: 10.57987/IHP.2026.T1.WS1.010
  • URL https://dx.doi.org/10.57987/IHP.2026.T1.WS1.010

Bibliographie

  • Lily W. Ge, Maryam Hedayati, Yuan Cui, Yiren Ding, Karen Bonilla, Alark Joshi, Alvitta Ottley, Benjamin Bach, Bum Chul Kwon, David N. Rapp, Evan Peck, Lace M. Padilla, Michael Correll, Michelle A. Borkin, Lane Harrison, and Matthew Kay. 2024. Toward a More Comprehensive Understanding of Visualization Literacy. In Extended Abstracts of the CHI Conference on Human Factors in Computing Systems (CHI EA '24). Association for Computing Machinery, New York, NY, USA, Article 494, 1–7. https://doi.org/10.1145/3613905.3636289
  • Lily W Ge, Anne-Flore Cabouat, Karen Bonilla, Yuan Cui, Yiren Ding, Noëlle Rakotondravony, Mackenzie Michael Creamer et al. "An Autoethnography on Visualization Literacy: A Wicked Measurement Problem." IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS (2025): 1-11.

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