Analysis of sociodemographic factors influencing students’ data visualization literacy

Mestrado Bolonha em Data Analytics for BusinessThe rapid pace at which data is created has required the creation of new tools to extract information from large amounts of data. Data visualization has proven effective in facilitating access to essential information from a dataset. For this reason, it is critical to examine Data Visualization Literacy (DVL), particularly in the context in which learning occurs, in schools. Studies related to this topic have been consulted, however, the research done so far to understand the influence of sociodemographic factors on the ability to read, interpret, and draw conclusions from data visualizations has not reached a consensus. Therefore, this study aims to bridge the controversy surrounding the topic by examining whether Age, Sex, Field of studies in High School (FSHS), Current level of education (CLE), and Current field of studies (CFS) predict students’ responses to data visualization questions. In this study, data collection was done through an online survey, which not only contained questions about the sociodemographic characteristics of the students, but also a section intended for data visualization questions. The non-probability convenience sampling technique was used and after processing the collected data, a total of 153 responses were obtained. To analyze the data, 6 binary logistic regressions were developed, each referring to one of the 6 data visualization questions contained in the survey, in order to compare the findings of this study with those previously supported by other authors. The results suggest that all variables except CLE were important factors in predicting students’ ability to answer the data visualization questions correctlyinfo:eu-repo/semantics/publishedVersio

A Visual Exploration of Bias in Covid-19 Coverage

During the Covid-19 pandemic, news outlets used information visualizations to convey noteworthy data about different facets of the crisis in a short period of time. Despite claims of neutrality, an information visualization also conveys bias. Exploring bias in visualizations allows us to understand the bias that some news outlets hold. I chose to explore how news outlets conveyed political bias in a visualization. In this study, using the AllSides scale, I first identified ten news outlets of varying political bias. I then collected five Covid-19 visualizations from each news outlet. I analyzed each visualization’s use of information visualization techniques and topics in order to explore the ways political bias manifests visually. It is unsurprising that I found that news outlets were concerned about Covid-19, discussing the spread and number of Covid-19 cases. News outlets were also similar in the types of colors and graphs they used. The news outlets explored the pandemic on both a national and international level. We see that the bias manifests into either accurately exploring the severity of the pandemic or downplaying the severity of the pandemic. No news outlet overstates the concern of Covid-19. By understanding how media bias manifests in information visualizations, we can further understand how to decrease these biases and truly understand what a visualization is trying to convey. Information literacy is one underused method that can help us understand bias in information visualizations. Specifically, visual literacy is essential to determining which visualizations to believe

Who benefits from Visualization Adaptations? Towards a better Understanding of the Influence of Visualization Literacy

The ability to read, understand, and comprehend visual information representations is subsumed under the term visualization literacy (VL). One possibility to improve the use of information visualizations is to introduce adaptations. However, it is yet unclear whether people with different VL benefit from adaptations to the same degree. We conducted an online experiment (n = 42) to investigate whether the effect of an adaptation (here: De-Emphasis) of visualizations (bar charts, scatter plots) on performance (accuracy, time) and user experiences depends on users' VL level. Using linear mixed models for the analyses, we found a positive impact of the De-Emphasis adaptation across all conditions, as well as an interaction effect of adaptation and VL on the task completion time for bar charts. This work contributes to a better understanding of the intertwined relationship of VL and visual adaptations and motivates future research.Comment: Preprint and Author Version of a Short Paper, accepted to the 2022 IEEE Visualization Conference (VIS

Data Visualization, Accessibility and Graphicacy: A Qualitative Study of Communicative Artifacts through SUS Questionnaire

The study presented examines the accessibility of information conveyed through the language of infographics, analyzing the usability by users in the fruition of information content of five Data Visualization artifacts, selected according to the degree of iconicity of representation by Anceschi. Specifically, the study compared the SUS evaluation by two groups [F=100-M=100] homogeneous in educational grade and age but distinguished in owning proven Visual Design competence or not. It is therefore investigated, whether basic soft skill, is sufficient to achieve an optimal level of accessibility or rather, whether Graphicacy competence is discriminated. Therefore, understanding whether infographic language could be considered ad a universal language or no. A three-variable correlation design was therefore constructed: two independent variables, the System Usability Scale (SUS) along with the degree of iconicity of the representation, and one dependent variable, namely the amount of information extracted from the infographic. The results show that in both Group A and B is evident a general difficulty in accessibility of information correlated to the degree of iconicity of the infographic representation. Specifically, in "non designer" group, no infographics achieved the minimum usability rating, which, on the other hand, in "designer" group, is achieved by the only two artifacts with a medium/low degree of iconicity. From the analysis of the data, Graphicacy-acquired within the educational curriculum of Designers-would appear to be a determinate element in the correct decoding of communicative artifacts. The contribution, through existing data and literature, leads, on the one hand, to confirm that Graphicacy has been found to be neglected in comparison to Literacy, Numeracy, and Articulacy and that the complexity and sophistication of infoaesthetic may be incomprehensible without timely data visualization literacy

Educational data comics:What can comics do for education in visualization?

This paper discusses the potential of comics for explaining concepts with and around data visualization. With the increasing spread of visualizations and the democratization of access to visualization tools, we see a growing need for easily approachable resources for learning visualization techniques, applications, design processes, etc. Comics are a promising medium for such explanation as they concisely combine graphical and textual content in a sequential manner and they provide fast visual access to specific parts of the explanations. Based on a first literature review and our extensive experience with the subject, we survey works at the respective intersections of comics, visualization and education: data comics, educational comics, and visualization education. We report on five potentials of comics to create and share educational material, to engage wide and potentially diverse audiences, and to support educational activities. For each potential we list, we describe open questions for future research. Our discussion aims to inform both the application of comics by educators and their extension and study by researchers

Challenges and Opportunities in Data Visualization Education: A Call to Action

This paper is a call to action for research and discussion on data visualization education. As visualization evolves and spreads through our professional and personal lives, we need to understand how to support and empower a broad and diverse community of learners in visualization. Data Visualization is a diverse and dynamic discipline that combines knowledge from different fields, is tailored to suit diverse audiences and contexts, and frequently incorporates tacit knowledge. This complex nature leads to a series of interrelated challenges for data visualization education. Driven by a lack of consolidated knowledge, overview, and orientation for visualization education, the 21 authors of this paper-educators and researchers in data visualization-identify and describe 19 challenges informed by our collective practical experience. We organize these challenges around seven themes People, Goals & Assessment, Environment, Motivation, Methods, Materials, and Change. Across these themes, we formulate 43 research questions to address these challenges. As part of our call to action, we then conclude with 5 cross-cutting opportunities and respective action items: embrace DIVERSITY+INCLUSION, build COMMUNITIES, conduct RESEARCH, act AGILE, and relish RESPONSIBILITY. We aim to inspire researchers, educators and learners to drive visualization education forward and discuss why, how, who and where we educate, as we learn to use visualization to address challenges across many scales and many domains in a rapidly changing world: viseducationchallenges.github.io.Comment: Accepted for publication at VIS 2023 Conference, Melbourne, VI

Mini-VLAT: A Short and Effective Measure of Visualization Literacy

The visualization community regards visualization literacy as a necessary skill. Yet, despite the recent increase in research into visualization literacy by the education and visualization communities, we lack practical and time-effective instruments for the widespread measurements of people's comprehension and interpretation of visual designs. We present Mini-VLAT, a brief but practical visualization literacy test. The Mini-VLAT is a 12-item short form of the 53-item Visualization Literacy Assessment Test (VLAT). The Mini-VLAT is reliable (coefficient omega = 0.72) and strongly correlates with the VLAT. Five visualization experts validated the Mini-VLAT items, yielding an average content validity ratio (CVR) of 0.6. We further validate Mini-VLAT by demonstrating a strong positive correlation between study participants' Mini-VLAT scores and their aptitude for learning an unfamiliar visualization using a Parallel Coordinate Plot test. Overall, the Mini-VLAT items showed a similar pattern of validity and reliability as the 53-item VLAT. The results show that Mini-VLAT is a psychometrically sound and practical short measure of visualization literacy