Education for Visualization — Gitta Domik
The subcommittee "Education for Visualization", or EVC, of the ACM SIGGRAPH Education Committee is dedicated "to further development of guidelines and teaching materials for visualization curricula and courses". It has been in existence since 1992. The need for such a committee is rooted in the fact that visualization courses have been offered since the late 1980’s with a variety of topics. With the debut of "Visualization in Scientific Computing", as described in the much cited publication [McCormick, DeFanti, and Brown, 1987], the need to inform about visualization came into existence, but no formal training for educators to teach such courses, nor a common understanding of the "main themes" of visualization was available. This lead to a most unbalanced form of visualization courses, focusing on individual topics, and leaving out important issues such as definitions, goals, or concepts of visualization. Over these last years, our committee has discussed and published specifically on the topics of taxonomy and curricula for visualization. We still continue to explore and improve the contents of visualization courses. At the same time, however, we have also moved on to provide support materials and investigate interactive teaching methods (e.g. over WWW) for visualization courses. In the Loughborough AGOCG workshop in May of 1996, Scott Owen and I are planning to discuss WHAT and HOW to teach in visualization courses. While Scott will concentrate on successful teaching techniques, I am planning to discuss the content and structures of visualization courses and curricula as a result of recent workshops and tutorials.
Main themes for teaching visualization have been identified as the following:
Definitions and Goals of Visualization
For any visualization course it is important to discuss background, definitions, and goals in order to provide a common understanding of visualization. Recommended subtopics are:
• History of (scientific and information) visualization
• Definitions of visualization
• Goals of visualization
Abstract Visualization Concepts
It is necessary to establish a framework for the use of visualization to learn how to make use of concepts and paradigms. Recommended subtopics:
• General visualization models and taxonomy
• Examples of specific visualization models and paradigms
Human Perception Concepts
This section enhances the understanding of how to use graphics tools to support human perception in order to gain insight into phenomena that we seek to interpret. Recommended subtopics:
• The human visual system (biological, psychophysical and cognitive issues, visual phenomena, texture and colour perception)
• Perception theories
• Presenting complex information to the H(V)S (e.g. data exploration, natural computing, integrated displays, using senses additional to vision)
• Practical considerations (e.g. expressiveness, effectiveness, interactivity, annotations, avoiding pitfalls)
• Evaluation methods
Scientific Methods and Concepts
This theme explains the relationship between the 'real world' and the 'models' we have available in order to understand the real world and the 'empirical (data) measurements' we have of the real world. Non-science students have usually little approach to models, data concepts and reality.
• Scientific concepts: what is a model; model vs. acquiring; going from macro-to micro worlds
• Modelling concepts: mathematical methods to represent reality; mathematical concepts; computational models
• Data concepts: how to represent reality; data collections; errors
Aspects of Data
Various aspects of data, such as acquisition, classification, storage and retrieval of data, are to be discussed. Appropriate subtopics are
• Acquisition of data (Simulation vs. measuring devices)
• Discipline-independent classification of information sources
• Data base issues
• Query languages
• Reliability of data
This section discusses the wealth of possibilities for visual representations. This includes 2-d, 3-d and multi-dimensional visualization techniques, such as colour transformations, glyphs for high dimensional data sets, volume visualization, particle tracing, animation, or techniques in virtual environments.
Interaction techniques are fundamental to the design and use of visualization systems. Appropriate subtopics are approaches to interaction issues from the standpoint of ergonometry, HCI and hardware.
Existing Visualization Systems/Tools
Available visualization systems need to be discussed and compared in order to provide a valid basis to make decisions on usability and functionality of such systems.
Aesthetics in Visualization
Appropriate subtopics are:
• Aspects of successful visualizations
• Comparison of good and bad visual representations
A visualization course might include fundamental aspects of mathematics and computer science. The presentation of appropriate subtopics depends on the objectives of the course and the background of the students. Appropriate subtopics may be:
• Mathematical techniques (e.g. vectors, matrices, interpolation approximation, transformations for 2- and 3-d, parametric versus implicit versus explicit representations, curves, surfaces, fractals)
• Computer graphics (e.g. 2-d drawing, clipping, filling; 3-d modelling, rendering, lighting; transparency, translucency; raytracing, radiosity, volume rendering; graphics standards and libraries)
• General computer science (e.g. user interface design; computational geometry; computer hardware architectures, input/output technologies; data structures, data models, data formats, data transfer; programming languages)
Domik, G. O, 1994, Visualization Education, Computer & Graphics, 18(3), pp. 277-280.
Domik, G. O., 1993a, Guidelines for a Curriculum in Scientific Visualization, Computers and Graphics, Vol. 17, No. 2, pp. 185-191.
Domik, G.O., 1993b, An Agenda for Education in Scientific Visualization. Visualization '92 Workshop Report, Computer Graphics, 27:1, p.6, January 1993.
Domik, G., 1993c, Guidelines for a Curriculum in Scientific Visualization, Eurographics Workshop on Graphics and Visualization Education, Eurographics Technical Report Series, ISSN 1017-4656.
Domik, G., 1993d, Education in Scientific Visualization, Proceedings of the IFIP WG3.2 (Computers in University Education) Working Conference, University of California, Irvine.
McCormick, B.H., DeFanti, T.A., and Brown M.D. (eds), 1987, Visualization in Scientific Computing. Computer Graphics 21 (6).
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