From Reality to Recognition: Evaluating Visualization Analogies for Novice Chart Comprehension

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Date
2025
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
Novice learners often have difficulty learning new visualization types because they tend to interpret novel visualizations through the mental models of simpler charts they have previously encountered. Traditional visualization teaching methods, which usually rely on directly translating conceptual aspects of data into concrete data visualizations, often fail to attend to the needs of novice learners navigating this tension. To address this, we conducted an empirical exploration of how analogies can be used to help novices with chart comprehension. We introduced visualization analogies: visualizations that map data structures to real-world contexts to facilitate an intuitive understanding of novel chart types. We evaluated this pedagogical technique using a within-subject study (N=128) where we taught 8 chart types using visualization analogies. Our findings show that visualization analogies improve visual analysis skills and help learners transfer their understanding to actual charts. They effectively introduce visual embellishments, cater to diverse learning preferences, and are preferred by novice learners over traditional chart visualizations. This study offers empirical insights and open-source tools to advance visualization education through analogical reasoning.
Description

CCS Concepts: Human-centered computing → Visualization design and evaluation methods; Empirical studies in visualization

        
@inproceedings{
10.2312:eved.20251004
, booktitle = {
EuroVis 2025 - Education Papers
}, editor = {
Aurisano, Jillian
and
Laramee, Robert S.
and
Nobre, Carolina
}, title = {{
From Reality to Recognition: Evaluating Visualization Analogies for Novice Chart Comprehension
}}, author = {
Huang, Oliver
and
Lee, Patrick Yung Kang
and
Nobre, Carolina
}, year = {
2025
}, publisher = {
The Eurographics Association
}, ISBN = {
978-3-03868-273-8
}, DOI = {
10.2312/eved.20251004
} }
Citation