VisVisual: A Toolkit for Teaching and Learning Data Visualization

Magdalena Boucher Luiz Morais Fateme Rajabiyazdi + 18 lainnya

15 Agustus 2023

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.

Vetria L. Byrd Nicole Dwenger

24 September 2021

In this work, the data visualization activity (DVA) worksheet method for teaching and learning data visualization is presented. The DVA worksheet method consists of a series of activity worksheets developed to guide novice instructors and students through the data visualization process. The activity worksheets help new faculty and visualization instructors, lacking formal training in pedagogy and data visualization, learn the data visualization process, design course work, and develop curriculum. The worksheets can be used for individual activities, or as a collection of activities to support data visualization capacity building. Each worksheet focuses on an individual step in the process, allowing the worksheets to be tailored to discipline-specific data visualization needs. We share the motivation and evolution of the worksheets from paper-based to the semi-automated process utilized in fall 2020. We conclude this work with a discussion and areas for improvement.

Zezhong Wang Lovisa Sundin Dave Murray-Rust + 1 lainnya

18 Januari 2020

This paper introduces the concept of 'cheat sheets' for data visualization techniques, a set of concise graphical explanations and textual annotations inspired by infographics, data comics, and cheat sheets in other domains. Cheat sheets aim to address the increasing need for accessible material that supports a wide audience in understanding data visualization techniques, their use, their fallacies and so forth. We have carried out an iterative design process with practitioners, teachers and students of data science and visualization, resulting six types of cheat sheet (anatomy, construction, visual patterns, pitfalls, false-friends and well-known relatives) for six types of visualization, and formats for presentation. We assess these with a qualitative user study using 11 participants that demonstrates the readability and usefulness of our cheat sheets.

Qi Li

20 Juni 2020

This chapter will first address data visualization and then discuss the relationship between data visualization and aesthetics. It discusses the definition of data and information and the forms and characteristics of traditional data visualization, emphasizes on understanding of meaning of data in effectiveness and efficiency. And then this chapter outlines some key data visualizations, which includes Trees, Scatter plots, Charts, Tables, Diagram, Graphic, Waveform, Simulation and Volume.

V. Nguyen Kwanghee Jung Vibhuti Gupta

29 Oktober 2021

Data visualization blends art and science to convey stories from data via graphical representations. Considering different problems, applications, requirements, and design goals, it is challenging to combine these two components at their full force. While the art component involves creating visually appealing and easily interpreted graphics for users, the science component requires accurate representations of a large amount of input data. With a lack of the science component, visualization cannot serve its role of creating correct representations of the actual data, thus leading to wrong perception, interpretation, and decision. It might be even worse if incorrect visual representations were intentionally produced to deceive the viewers. To address common pitfalls in graphical representations, this paper focuses on identifying and understanding the root causes of misinformation in graphical representations. We reviewed the misleading data visualization examples in the scientific publications collected from indexing databases and then projected them onto the fundamental units of visual communication such as color, shape, size, and spatial orientation. Moreover, a text mining technique was applied to extract practical insights from common visualization pitfalls. Cochran’s Q test and McNemar’s test were conducted to examine if there is any difference in the proportions of common errors among color, shape, size, and spatial orientation. The findings showed that the pie chart is the most misused graphical representation, and size is the most critical issue. It was also observed that there were statistically significant differences in the proportion of errors among color, shape, size, and spatial orientation.