the exact goals and on how to assess the benefits of scaffolding technologies, there is agreement that the new tools make it possible for people to perform and learn in far more complex ways than ever before.
In many fields, experts are using new technologies to represent data in new ways—for example, as three-dimensional virtual models of the surface of Venus or of a molecular structure, either of which can be electronically created and viewed from any angle. Geographical information systems, to take another example, use color scales to visually represent such variables as temperature or rainfall on a map. With these tools, scientists can discern patterns more quickly and detect relationships not previously noticed (e.g., Brodie et al., 1992; Kaufmann and Smarr, 1993).
Some scholars assert that simulations and computer-based models are the most powerful resources for the advancement and application of mathematics and science since the origins of mathematical modeling during the Renaissance (Glass and Mackey, 1988; Haken, 1981). The move from a static model in an inert medium, like a drawing, to dynamic models in interactive media that provide visualization and analytic tools is profoundly changing the nature of inquiry in mathematics and science. Students can visualize alternative interpretations as they build models that can be rotated in ways that introduce different perspectives on the problems. These changes affect the kinds of phenomena that can be considered and the nature of argumentation and acceptable evidence (Bachelard, 1984; Holland, 1995).
The same kinds of computer-based visualization and analysis tools that scientists use to detect patterns and understand data are now being adapted for student use. With probes attached to microcomputers, for example, students can do real-time graphing of such variables as acceleration, light, and sound (Friedler et al., 1990; Linn, 1991; Nemirovsky et al., 1995; Thornton and Sokoloff, 1998). The ability of the human mind to quickly process and remember visual information suggests that concrete graphics and other visual representations of information can help people learn (Gordin and Pea, 1995), as well as help scientists in their work (Miller, 1986).
A variety of scientific visualization environments for precollege students and teachers have been developed by the CoVis Project (Pea, 1993a; Pea et al., 1997). Classrooms can collect and analyze real-time weather data (Fishman and D’Amico, 1994; University of Illinois, Urbana-Champaign, 1997) or 25 years of Northern Hemisphere climate data (Gordin et al., 1994). Or they can investigate the global greenhouse effect (Gordin et al., 1996). As described above, students with new technological tools can communicate across a network, work with datasets, develop scientific models, and conduct collaborative investigations into meaningful science issues.
Since the late 1980s, cognitive scientists, educators, and technologists have suggested that learners might develop a deeper understanding of phenomena in the physical and social worlds if they could build and manipulate