Online user studies of visualizations, visual encodings, and interaction techniques are ubiquitous in visualization research. Yet, designing, conducting, and analyzing studies effectively is still a major burden. Although various packages support such user studies, most solutions address only facets of the experiment life cycle, make reproducibility difficult, or do not cater to nuanced study designs or interactions. We introduce reVISit 2, a software framework that supports visualization researchers at all stages of designing and conducting browser-based user studies. ReVISit supports researchers in the design, debug & pilot, data collection, analysis, and dissemination experiment phases by providing both technical affordances (such as replay of participant interactions) and sociotechnical aids (such as a mindfully maintained community of support). It is a proven system that can be (and has been) used in publication-quality studies-which we demonstrate through a series of experimental replications. We reflect on the design of the system via interviews and an analysis of its technical dimensions. Through this work, we seek to elevate the ease with which studies are conducted, improve the reproducibility of studies within our community, and support the construction of advanced interactive studies.

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.

Visualization items-factual questions about visualizations that ask viewers to accomplish visualization tasks-are regularly used in the field of information visualization as educational and evaluative materials. For example, researchers of visualization literacy require large, diverse banks of items to conduct studies where the same skill is measured repeatedly on the same participants. Yet, generating a large number of high-quality, diverse items requires significant time and expertise. To address the critical need for a large number of diverse visualization items in education and research, this paper investigates the potential for large language models (LLMS) to automate the generation of multiple-choice visualization items. Through an iterative design process, we develop the VILA (Visualization Items Generated by Large LAnguage Models) pipeline, for efficiently generating visualization items that measure people's ability to accomplish visualization tasks. We use the VILA pipeline to generate 1,404 candidate items across 12 chart types and 13 visualization tasks. In collaboration with 11 visualization experts, we develop an evaluation rulebook which we then use to rate the quality of all candidate items. The result is the VILA bank of ~1, 100 items. From this evaluation, we also identify and classify current limitations of the VILA pipeline, and discuss the role of human oversight in ensuring quality. In addition, we demonstrate an application of our work by creating a visualization literacy test, VILA-VLAT, which measures people's ability to complete a diverse set of tasks on various types of visualizations; comparing it to the existing VLAT, VILA-VLAT shows moderate to high convergent validity (R = 0.70). Lastly, we discuss the application areas of the VILA pipeline and the VILA bank and provide practical recommendations for their use. All supplemental materials are available at https://osf.io/ysrhq/.

Visualization literacy is an essential skill for accurately interpreting data to inform critical decisions. Consequently, it is vital to understand the evolution of this ability and devise targeted interventions to enhance it, requiring concise and repeatable assessments of visualization literacy for individuals. However, current assessments, such as the Visualization Literacy Assessment Test (VLAT), are time-consuming due to their fixed, lengthy format. To address this limitation, we develop two streamlined computerized adaptive tests (CATs) for visualization literacy, A-VLAT and A-CALVI, which measure the same set of skills as their original versions in half the number of questions. Specifically, we (1) employ item response theory (IRT) and non-psychometric constraints to construct adaptive versions of the assessments, (2) finalize the configurations of adaptation through simulation, (3) refine the composition of test items of A-CALVI via a qualitative study, and (4) demonstrate the test-retest reliability (ICC: 0.98 and 0.98) and convergent validity (correlation: 0.81 and 0.66) of both CATs via four online studies. We discuss practical recommendations for using our CATs and opportunities for further customization to leverage the full potential of adaptive assessments. All supplemental materials are available at https://osf.io/a6258/.

Graphical perception studies typically measure visualization encoding effectiveness using the error of an "average observer", leading to canonical rankings of encodings for numerical attributes: e.g., position area angle volume. Yet different people may vary in their ability to read different visualization types, leading to variance in this ranking across individuals not captured by population-level metrics using "average observer" models. One way we can bridge this gap is by recasting classic visual perception tasks as tools for assessing individual performance, in addition to overall visualization performance. In this article we replicate and extend Cleveland and McGill's graphical comparison experiment using Bayesian multilevel regression, using these models to explore individual differences in visualization skill from multiple perspectives. The results from experiments and modeling indicate that some people show patterns of accuracy that credibly deviate from the canonical rankings of visualization effectiveness. We discuss implications of these findings, such as a need for new ways to communicate visualization effectiveness to designers, how patterns in individuals' responses may show systematic biases and strategies in visualization judgment, and how recasting classic visual perception tasks as tools for assessing individual performance may offer new ways to quantify aspects of visualization literacy. Experiment data, source code, and analysis scripts are available at the following repository: https://osf.io/8ub7t/?view_only=9be4798797404a4397be3c6fc2a68cc0.

Visualizations today are used across a wide range of languages and cultures. Yet the extent to which language impacts how we reason about data and visualizations remains unclear. In this paper, we explore the intersection of visualization and language through a cross-language study on estimative probability tasks with icon-array visualizations. Across Arabic, English, French, German, and Mandarin, n=50 participants per language both chose probability expressions - e.g. likely, probable - to describe icon-array visualizations (Vis-to-Expression), and drew icon-array visualizations to match a given expression (Expression-to-Vis). Results suggest that there is no clear one-to-one mapping of probability expressions and associated visual ranges between languages. Several translated expressions fell significantly above or below the range of the corresponding English expressions. Compared to other languages, French and German respondents appear to exhibit high levels of consistency between the visualizations they drew and the words they chose. Participants across languages used similar words when describing scenarios above 80% chance, with more variance in expressions targeting mid-range and lower values. We discuss how these results suggest potential differences in the expressiveness of language as it relates to visualization interpretation and design goals, as well as practical implications for translation efforts and future studies at the intersection of languages, culture, and visualization. Experiment data, source code, and analysis scripts are available at the following repository: https://osf.io/g5d4r/.