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Improving Science Students’ Data Visualizations: A STEAM-Based Approach
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A course at the University of Nevada, Reno (UNR) teaches science students an interdisciplinary approach for designing data visualizations, in combination with big data computation and statistical analysis. This 15-week, undergraduate-level course, titled “Computational Skills for Big Data: Analysis, Statistics, and Visualization,” is co-taught by visual communication design, math, and physics faculty, and is an example of a pedagogy rooted in a STEAM-based approach that combines instruction in art or design with instruction in science, technology, engineering or mathematics. This paper introduces the interdisciplinary data visualization typology that formed the basis of this course’s visualization component: data visualization for facilitating analysis and data visualization for sharing knowledge. Data visualization for facilitating analysis is the use of visualization among an expert audience who are intimately familiar with the represented data, and data visualization for sharing knowledge is the use of visualization to represent data in a way that is intelligible to a non-expert audience. The authors argue that most of the extant data visualization education and science education literature can be classified as referring to one or the other of these types, and that this classification can help aid planning and facilitating instruction in data visualization (the design and use of graphical representations of data). This paper outlines the general structure of the course, and its data visualization components, in detail, and then provides an overview of how and why the course was assessed as it was, and finally reflects on its potential reproducibility. An analysis of work produced by students as the course progressed suggests that challenging STEM undergraduate students to distinguish between data visualization for facilitating analysis and data visualization for sharing knowledge is a helpful typology to guide teaching data visualization. The paper also argues for greater collaboration between visual communication design faculty and STEM faculty in undergraduate courses, and posits that their collaborative efforts to teach data visualization is an effective way to do this.