Education

Editors: Gitta Domik and Scott Owen

How Visualization Courses Have Changed over the Past 10 Years G. Scott Owen Georgia State University

Holly Rushmeier Yale University

Gitta Domik University of Paderborn

Beatriz Sousa Santos University of Aveiro

David S. Ebert Purdue University

Daniel Weiskopf University of Stuttgart

Jörn Kohlhammer Fraunhofer IGD Darmstadt

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he past 10 years have seen profound changes in visualization algorithms, techniques, methodologies, and applications. For example, we’re seeing

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extensive use of GPUs, improved algorithms for f low or volume visualization, emphasis on highly interactive visual interfaces, the advent and increasing importance of visual analytics, an increase in nontechnical students in our courses, greater need for professional use of visualization in the workplace, and evaluation frameworks for effective visualization.

All this forces alterations to our visualization courses, especially what, how, or whom we teach. A basic problem has always been that we couldn’t rely on standard textbooks to frame the mandatory knowledge in this field. This situation is unlike that of computer graphics, in which the community widely acknowledges several standard textbooks. Visualization curricula suggestions— for example, ACM Siggraph’s Education Committee recommendations (www.upb.de/cs/vis) or the Visual Analytics Digital Library (http://vadl. cc.gatech.edu)—are partly outdated or incomplete. Computer science curricula guidelines, such as 14

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from the IEEE and ACM, also lag in their recommendations of content for this novel, dynamic knowledge area. Outdated course content recommendations, together with profound changes in the underlying technology and methodology, produce an unstable ground for educators at a time when visual representations have gained great importance in economics, science, and many other areas of society. To address this issue, under the auspices of the ACM Siggraph Education Committee, we held meetings or workshops at Siggraph 2011 and 2012 and a panel and workshop at Eurographics 2012. At the panel, called “The Changes We Have Made to our Visualization Courses over the Last 10 Years,” Holly Rushmeier, Jörn Kohlhammer, David Ebert, Beatriz Sousa Santos, and Daniel Weiskopf discussed how they’ve changed their courses to reflect current problems and practical solutions.1 (Slides are at www.upb.de/cs/vis.) Each panelist has many years’ experience teaching courses covering topics such as scientific visualization, data visualization, information visualization, visualization techniques, and visual analytics. Here, we examine the insights gathered at the panel, workshops, and meetings.

Visualization in a Liberal Education Rushmeier teaches the course Visualization: Data, Pixels, and Ideas in the context of Yale’s liberal

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arts education. Liberal education is, according to the Association of American Colleges and Universities, “a philosophy of education that empowers individuals with broad knowledge and transferable skills, and a strong sense of value, ethics, and civic engagement.”2 Consequently, Rushmeier has a mixed audience with technical and nontechnical backgrounds for which she must design meaningful course content and assignments. Because of her students’ mixed background, her course requires no programming or advanced mathematics. The students’ goals are to ■■ ■■

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understand visualization’s basic components, understand computer graphics tools for producing visualizations, recognize bad visualizations, and use visualization effectively in discovery and communication.

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spatial visualization and projections (3D to 2D); motion; interaction; communication best practices—for example, Edward Tufte’s principles; and scientific and information visualization.

(The courses of the other educators we’ll be looking at also include many or most of these topics.) Class assignments focus on the students’ future work life needs. This means Rushmeier must find engaging, manageable datasets on political, historical, or other issues in the humanities. It also means having the students experiment with tools that allow flexibility in designing visualizations without requiring programming skills. Because there’s no programming, the course uses tools such as Excel, Matlab, and a VRML (Virtual Reality Modeling Language) viewer. Geographic information systems are another tool the students learn to use. These systems are used increasingly in the humanities, including in subjects such as comparative literature (see Figure 1).

A Perspective between Research and Business Careers Kohlhammer sees an increase in students’ motivation to learn visualization aspects for their business careers. As in Rushmeier’s course, assignments and projects in his Information Visualization and Visual Analytics course at Technische Universität Darmstadt focus on students’ later work life by providing hands-on experience with

Figure 1. Students in Pericles Lewis’s Ulysses seminar at Yale University mapped major events in the novel using the addresses in Google, crossreferenced with a map of Dublin in the time of James Joyce.

real-world datasets and data types. His courses have a mix of computer science, business informatics, and mathematics students, with some psychology and engineering students. They all have solid programming skills and an affinity for computer graphics. His course has evolved to include more practical exercises and a strong connection to industry, dealing with areas such as business intelligence, finance (for example, risk analysis), and security. He encourages and supports student involvement in using real-world datasets in global competitions such as the VAST Challenge (Visual Analytics Science and Technology; http://vacommunity.org/ VAST+Challenge+2013). Figure 2 shows a Web-based visual search system for time-oriented research data that Kohlhammer’s students developed.3

Teaching Visual Analytics: Leveraging Multidisciplinarity Ebert and Elmqvist teach Introduction to Visual Analytics to students with diverse backgrounds, so the course requires no programming expertise.4 The students are expected to have a knowledge of one or more of these areas: data analysis, knowledge management, statistics, computer graphics, or visualization. The course consists of group discussions of papers, lectures by the instructors (the course is team-taught), projects, and student presentations of papers. The projects, which might be individual or group, are particularly important. At least five projects have resulted in conference submissions. Figure 3 shows an example project. IEEE Computer Graphics and Applications

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Figure 2. Jörn Kohlhammer’s students at Technische Universität Darmstadt developed a Web-based visual search system for time-oriented research data. First, they created a visual catalog of daily temperature patterns based on the self-organizing-maps algorithm (not shown). Then, they presented a results list of documents based on a detailed selection. Metadata facets allowed interactive drill-downs in the results set.

This course, the only one in this article that focuses on visual analytics, has become a model for courses at many universities. Ebert finds a challenge in the fact that field of visualization has become too broad to cover in 15 weeks. So, the university complements this course with other courses—for example, Visualization Techniques, which covers in detail such topics as volume and flow visualization.

Changes in Beatriz Sousa Santos’s Visualization Courses Over the past decade, Sousa Santos has added more material on human characteristics (beyond visual perception), distributed and collaborative visualization, and displays. She also now teaches information visualization courses. Her students read more research papers and perform more evaluation experiments. Her courses include both undergraduate and graduate students with backgrounds in computer science, engineering, and management information systems. They do practical assignments using the Visualization Toolkit.5 Sousa Santos and her colleagues have also integrated user studies into their courses.6

Teaching Visualization at the University of Stuttgart The University of Stuttgart, where Weiskopf 16

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teaches, offers a variety of courses in computer graphics, geometric modeling, image synthesis, and visualization. In particular, there are courses on scientific visualization and information visualization. Additionally, the university uses a twosemester visualization-centered project to teach software engineering.7 The university also offers the outreach course Introduction to Visualization in Science and Engineering. Students in that course have limited programming experience, so the course is heavily tool based. The program aims to generate a common basis for computer graphics, visualization, and computer vision and to complement computer science students’ typical mathematical and theoretical education. A challenge is the growing need for background knowledge from diverse fields such as mathematics, computer science, human-computer interaction, psychology, data mining, machine learning, and application-specific domains.

The Emerging Areas From the panel, workshops, and meetings, three distinct areas emerged: ■■

scientific or data visualization, in which the data dimensions usually coincide with physical di-

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mensions, such as in medical or remote-sensing scalar or flow data; information visualization, with typically multidimensional data, such as in finance, business intelligence, or large databases; and visual analytics, with massive, multisource, multiscale, heterogeneous, and streaming data.

Data preprocessing (for example, filtering, normalizing, and linguistic analysis) and subsequent visual presentations (for example, line graphs, line-integral-convolution images, and cone trees), which both depend on data syntax and semantics, might be different for these areas but also overlap considerably. All three areas share some learning objectives. Students should be able to ■■ ■■ ■■

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understand visualization techniques; recognize good versus misleading visualizations; select appropriate visualization techniques and visual attributes on the basis of the data and task; explain selected algorithms underlying visualization techniques for, for example, 2D data, 3D scalar or vector data, time-dependent data, multivariate data, hierarchically structured data, graphs and networks, or data with other structures; discuss the handling of unstructured data; understand the appropriate manipulation of data before mapping (which differs between data visualization, information visualization, and visual analytics); understand the limitations and capacity of human information processing; group and describe visualization techniques by some order (for example, by domain, data characteristics, or tasks); discuss how scaling (of the data or display) influences visualization techniques; and understand the theory and application of evaluation techniques to prove a visualization or interaction technique’s success.

Figure 3. This project, by Ahmad Razip, a student in Niklas Elmqvist and David Ebert’s Introduction to Visual Analytics course at Purdue University, correlated bus stops and crime incidence distribution.

components to visual attributes and the interactivity between users and data as well as between users and visualizations.

Visual Presentation This includes a wealth of visualization solutions, sorted by data characteristics, application domain, or task and described by their various parameters. Instructors can present this theme at the breadth level by showing and discussing (interactive) visualizations. They can provide breadth-level training by using the available tools, in-depth training by developing interactive visualization techniques on a GPU, or training at any stage in between, depending on the students’ qualifications.

Interaction Techniques Although the three areas have distinct data domains, courses in them must cover the following themes.

The User This theme includes human information-processing limitations and capabilities as well as an understanding of the tasks users bring to visualization problems.

The Design Stage This stage describes a careful mapping of data

Interaction techniques are a requirement for visual analytics. They’re also becoming increasingly necessary for data and information visualization, in which GPU techniques can reach the necessary processing speed.

Communication Visual analytics in particular has stressed production, presentation, and dissemination as part of the visualization process. However, these topics are also important for data visualization and information visualization. IEEE Computer Graphics and Applications

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Collaboration Interactivity aids collaboration, especially in synchronous and local situations. However, collaboration among stakeholders can also involve aspects that are asynchronous and distributed, such as Web-based collaboration technologies.

Evaluation Evaluation is continuous. It starts with requirements analysis of the visualization problem. It continues with the human-in-the-loop’s constant awareness of the software processes proceeding toward the visualization goal. It ends with evaluation to ensure reaching the goal for the specific visualization problem.

At the panel, both Rushmeier and Ebert remarked that visualization has become too broad of a field to cover in one semester in suitable depth. So, instructors must decide between the breadth and depth of topics or offer one or more complementary visualization courses.

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o help educators respond to the changes occurring in visualization courses, we’ve compiled a set of materials they can use to update their courses:

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One challenge is that more nontechnical students are interested in the courses because of visualization’s increasing use in business and industry. Displays

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the complete set of slides of the panelists and coauthors in this article, previously published articles by Ebert, Weiskopf, and Sousa Santos on their visualization courses (all from IEEE CG&A’s Education department), and related articles from IEEE CG&A’s Education department and other sources.

Links to these materials are at www.upb.de/ cs/vis.

The variety of different displays’ capabilities (size and spatial and tonal resolution) poses problems for visualization techniques, interactivity, and communication. These capabilities must be addressed at least at the mapping or design stage.

Acknowledgments

Challenges Identified

References

One challenge is that more nontechnical students are interested in the courses because of visualization’s increasing use in business and industry. This necessitates the use of tools and real-world cases and datasets. If a course teaches both technical and nontechnical students, this potential difficulty could actually be an opportunity to approach a visualization problem from the viewpoints of multiple disciplines.8 For computer science students, the courses should add newer developments such as shader programming and computer vision. One benefit of this is that some student projects might be worthy of conference publication, as has been the case with Elmqvist and Ebert’s visualanalytics course. Another challenge, as Ebert and Weiskopf stated in the panel discussion, is the need for instructors to update their own knowledge in diverse background fields ranging from math, to humancomputer interaction and perception, to shader programming. So, this article’s references include a few textbooks we use.9–12

1. G. Domik et al., “Visualization Curriculum Panel— or the Changes We Have Made to Our Visualization Courses over the Last 10 Years,” Eurographics 2012— Education Papers, 2012; www.cs.uni-paderborn.de/ fileadmin/Informatik/AG-Domik/VisCurriculum/ folien/eg2012-panel-Domik-2.pdf. 2. “Liberal Education,” Assoc. of Am. Colleges and Universities, 2013; w w w.aacu.org/resources/ liberaleducation/index.cfm. 3. J. Bernard et al., “Irina: A Visual Digital Library Approach for Time-Oriented Scientific Primary Data,” Int’l J. Digital Libraries, vol. 11, no. 2, 2011, pp. 111–123. 4. N. Elmqvist and D.S. Ebert, “Leveraging Multi­ disciplinarity in a Visual Analytics Graduate Course,” IEEE Computer Graphics and Applications, vol. 32, no. 3, 2012, pp. 84–87. 5. P. Dias, J. Madeira, and B. Sousa Santos, “Education: Teaching 3D Modelling and Visualization Using VTK,” Computers and Graphics, vol. 32, no. 3, 2008, pp. 363–370. 6. B. Sousa Santos et al., “Integrating User Studies

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Thanks to Riccardo Scateni for providing the rooms for the Eurographics 2012 workshop at the University of Cagliari.

into Computer Graphics-Related Courses,” IEEE Computer Graphics and Applications, vol. 31, no. 5, 2011, pp. 94–96. 7. C. Müller et al., “Large-Scale Visualization Projects for Teaching Software Engineering,” IEEE Computer Graphics and Applications, vol. 32, no. 4, 2012, pp. 14–19. 8. G. Domik, “Fostering Collaboration and SelfMotivated Learning: Best Practices in a OneSemester Visualization Course,” IEEE Computer Graphics and Applications, vol. 32, no. 1, 2012, pp. 87–91. 9. C. Ware, Information Visualization: Perception for Design, 3rd ed., Morgan Kaufmann, 2012. 10. M.O. Ward, G. Grinstein, and D. Keim, Interactive Data Visualization, AK Peters, 2010. 11. J.J. Thomas and K.A. Cook, eds., Illuminating the Path: The Research and Development Agenda for Visual Analytics, IEEE, 2005. 12. M. Bailey and S. Cunningham, Graphics Shaders: Theory and Practice, 2nd ed., AK Peters, 2011. G. Scott Owen is professor emeritus at Georgia State University’s Department of Computer Science. Contact him at [email protected]. Gitta Domik is a professor at the University of Paderborn’s

Faculty for Electrical Engineering, Computer Science, and Mathematics. Contact her at [email protected]. David S. Ebert is the Silicon Valley Professor of Electrical and Computer Engineering at Purdue University’s School of Electrical and Computer Engineering. Contact him at [email protected]. Jörn Kohlhammer heads the Competence Center for Information Visualization and Visual Analytics at Fraunhofer IGD Darmstadt and is a member of the Interactive Graphics Systems Group at Technische Universität Darmstadt. Contact him at [email protected]. Holly Rushmeier is a professor and the chair of computer science at Yale University. Contact her at [email protected]. Beatriz Sousa Santos is an associate professor in the University of Aveiro’s Department of Electronics, Telecommunications, and Informatics. Contact her at [email protected]. Daniel Weiskopf is a professor of computer science at the University of Stuttgart. Contact him at weiskopf@visus. uni-stuttgart.de. Contact department editors Gitta Domik at domik@ uni-paderborn.de and Scott Owen at [email protected].

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How visualization courses have changed over the past 10 years.

The past 10 years have seen profound changes in visualization algorithms, techniques, methodologies, and applications. These changes are forcing alter...
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