Electronic Visualization
Theoretical Rationale
  • Abstract
  • Discussion
  • References
  • Authors

  • Abstract

        Computers now support the creation of powerful models of complex phenomena. These models provide scientists, mathematicians, engineers, and artists with tools that allow them to explore large and complex systems. Students can take advantage of the same computer power by creating and exploring models of natural and social systems in the classroom. The Internet provides access to models of the global climate, the Greenhouse Effect, the human body, molecules, geometry, and materials science. Teachers can provide these, and other, computer-based models to students and support the exploration and discussion of important subjects around these models. As students construct and manipulate these models, they discuss the models with others and create their own representations of the phenomena they are studying.
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        For thousands of years, people have built models in an effort to better understand the world around them. A model embodies critical aspects or features of something while ignoring or simplifying other aspects. Models take many forms, including maps, blueprints, simulations, and diagrams. Maps have been used for thousands of years to help guide explorers over unfamiliar territory. Blueprints help contractors and builders work together to construct a building. Simulations help scientists and engineers verify and test aspects of systems they are building or studying. Diagrams are used in a variety of fields, from business to computers science, to help people discuss and understand the behavior or structure of systems.

        All these types of models take advantage of visual perception, drawing on the power of symbolic representation. Visual models allow users to supplement language, for words can sometimes be ambiguous or misunderstood. Visual elements can help people reach common ground in their communication and collaborative activities. Models often are visual representations of abstract complex systems, and through the use of color and symbols, complex structures and behaviors can be perceived more clearly. Through visualization, a sophisticated concept or theory can be more accurately understood and discussed. For example, consider a visual model of the solar system represented to scale and how such a model helps students conceptualize the massive distances between planets in space. Likewise, a visual representation of a molecule can help students understand the properties and behaviors of atomic particles when exposed to heat or pressure.

        Perhaps the most prominent use of visualization is in the area of science. In their pursuit of understanding complex phenomena, scientists, mathematicians, engineers, and artists have developed visualizations or representations. These external representations or visualizations capture critical aspects of the phenomena they represent so they can be explained and examined collectively. These visual modeling tools have the power to represent systems in ways that make them more easily understood.

        Scientific visualization is defined as "linking disparate elements from the disciplines of science, computer science, and the visual arts. (Brodie, Carpenter, Earnshaw, Gallop, Hobbold, Mumford , Osland, & Quarendon, 1992; McCormick, DeFanti, & Brown, 1987). Computer-based visualizations are dynamic visual representations that can be designed to accurately mimic the systems they represent. For example, climate visualizations are dynamic models of atmospheric phenomena that can be designed or programmed to represent various climate changes. Visual models of the human body help scientists and physicians repair damaged organs.

    Visualization and Understanding

        A variety of learning environments have been built by researchers to help students learn subject matter by exploring dynamic representations. Software that encourages students to manipulate symbols in corresponding systems within a microworld provides a powerful tool for conceptual learning (Bowers, 1995; Cockburn & Greenberg,1996; Horwitz, Neumann, & Schwartz, 1996; Lewis, McArthur, Bishay, & Chou, 1992; Roschelle & Kaput, 1996). This type of software is designed to allow students to explore a domain and create rich social interactions where the symbol systems are discussed and used to solve real problems.

        "Software is available that juxtaposes mathematical and spatial (or graphical) symbol systems in ways that could help activate students' visual imagery" (Dickson, 1985, p. 34). A microworld is a "representation for restricted domains that can be used to test theories describing the real world" (DiSessa, 1977; Papert, 1980). Microworlds include Logo, the Geometric Supposer, and Interactive Physics, and have been used in a variety of subject areas.

        The use of microworlds is supported by constructivist learning theory. Microworlds allow students to interact with a stimulating environment and then engage in reflective abstraction of the underlying concepts. Concrete illustrations within the microworld provide learning experiences for the abstract world. The link between the objects in the microworld and the objects in the real world allows students to transfer what they have learned in the microworld to real life.

    Benefits of Electronic Visualizations

        According to Gordin and Pea (1995), scientific visualizations are helpful for understanding and learning science because they "provide a powerful inscription that promotes learning conversations, can be flexibly manipulated, and build on existing metaphors and representations" (p. 209). The properties of a visual model that are helpful to students include their ability to manipulate the models and see the effects; to engage in discussions with others about the models and their behavior; and to begin to construct their own representations of the phenomena they are studying. By building and discussing the behavior of these models, students come to better understand them, and their knowledge about these systems is deeper and more robust (Pea, 1992; Pea, 1993; Pea, Edelson, & Gomez, 1994).

        In addition, those students who learn best by coming into contact with visual symbol systems can benefit from alternative representations. Traditional educational material is prominently textual, and while language is an essential tool for learning, the old adage "a picture is worth a thousand words" can be expressive of the power of computer-generated visual images in the classroom.

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    Bowers, J. (1995). An Alternative Perspective for Developing a Mathematical Microworld. Proceedings of the Computer-supported collaborative learning conference '95. Lawrence Erlbaum Associates, Inc. [http://www-cscl95.indiana.edu/cscl95/bowers.html]

    Brodie, K. W., Carpenter, L. A., Earnshaw, R. A., Gallop, J. R., Hubbold, R. J., Mumford, A. M., Osland, C. D., and Quarendon, P. (1992). Scientific Visualization . Berlin: Springer-Verlag.

    Cockburn, A., and Greenberg, S. (1996). Children's Collaboration Styles in a Newtonian MicroWorld . [http://www.cpsc.ucalgary.ca/projects/grouplab/papers/chi96/sg4txt.html].

    Dickson, W. P. (1985). Thought-provoking software: Juxtaposing symbol systems. Educational Researcher, 14(8), 30-38.

    DiSessa, A. A. (1977). On learnable representations of knowledge: A meaning for the computational metaphor. MIT, AI Laboratory, LOGO Memo 47.

    Gordin, D.N. & Pea, R.D. (1995). Prospects for scientific visualization as an educational technology. The Journal of the Learning Sciences, 4(3), 249-279. [http://typhoon.covis.nwu.edu/Papers/Doug-Pea.html]

    Horwitz, P., Neumann, E., & Schwartz, J. (1996). Teaching Science at multiple space time scales. Communications of the ACM, 39(8), 100-102.

    Lewis, M. W., McArthur, D., Bishay, M., and Chou, J. (1992). Object-Oriented Microworlds for Learning Mathematics through Inquiry: Preliminary Results and Directions. Proceedings of the East-West Conference on Emerging Computer Technologies in Education, Moscow, April. [http://www.rand.org/hot/mcarthur/Papers/moscowab.html]

    McCormick, B. H., DeFanti, T. A., & Brown, M. D. (Eds.) (1987). Visualization in scientific computing. Computer Graphics, 21(6).

    Papert, S. (1980). Mindstorms: Children, computers, and powerful ideas. New York: Basic Books.

    Pea, R. D. (1992). Augmenting the discourse of learning with computer-based learning environments. In de Corte, E., Linn, M., Mandl, H., and Verschaffel, L. (Eds.), Computer-Based Learning Environments and Problem-Solving . New York: Springer-Verlag.

    Pea, R. D. (1993). The collaborative visualization project. Communications of the ACM, 36(5), 60-63.

    Pea, R. D., Edelson, D. C., and Gomez, L. M. (1994). Distributed collaborative science learning using scientific visualization and wideband telecommunications. Paper presented at the 160th meeting of the American Association for the Advancement of Science.

    Roschelle, J., & Kaput, J. J. (1996). SimCalc Mathworlds for the Mathematics of Change. Communications of the ACM, 39(8), 97-99.

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