Abstract
Starting with the focal question, “what should students know about technology?” we describe and illustrate a way of designing educational technology that is strongly informed by empirical studies of how students actually understand and use a technology. We also have theoretical aspirations in developing what we hope to be general principles that can, along with empirical data, orient design.
The type of technology used to illustrate this design methodology is scientific visualization software, in which spatially distributed data is given form as adjustable and often highly suggestive visual displays. Our primary contention is that what students need to know about this software is precisely those aspects of it that define it as a system of representations. More generally, we advocate representation as an important instructional target, and we examine what students know that can be enhanced by appropriate technology and learning activities.
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REFERENCES
Asbell-Clarke, J. and Barclay, T. (1996). Discovering the scientist within. Hands-On! (TERC) 19(2): 4,5,17.
Azavedo, F. S. (1998). Inventing mapping: Meta-representational competence for spatially distributed data. Paper presented at annual meeting of the American Education Research Association, San Diego, Calfornia.
Barstow, D. and Berenfeld, B. (1996). Data Visualization as an Essential Component of Telecollaborative, Inquiry-based Science Learning. Paper presented at annual meeting of the American Education Research, New York.
diSessa, A. A. (1987). Artificial worlds and real experience. In Lawler, R., and Yazdani, M., (Eds.), Artificial Intelligence and Education, Ablex, Norwood, New Jersey, pp. 55–77.
diSessa, A. A., Hammer, D., Sherin, B., and Kolpakowski, T. (1991). Inventing graphing. Journal of Mathematical Behavior 10: 117–160.
diSessa, A. A. (1992). Images of learning. In De Corte, E., Linn, M. C., Mandl, H., and Verschaffel, L. (Eds.), Computer-Based Learning Environments and Problem Solving, Springer Verlag, Berlin, pp. 19–40.
diSessa, A. A. (1995). The many faces of a computational medium. In diSessa, A., Hoyles, C., Noss, R., and Edwards, L., (Eds.), Computers and Exploratory Learning, Springer-Verlag, Berlin, pp. 337–359.
diSessa, A. A. (1997). Open toolsets: New ends and new means in learning mathematics and science with computers. In E. Pehkonen (Ed.). Proceedings of the 21st Conference of the International Group for the Psychology of Mathematics Education, Vol. 1. Lahti, Finland, pp. 47–62.
Friedman, J. S. (1996). Image Processing in a Science Classroom: A Constructivist Perspective on the Role of Prior Knowledge. Paper presented at annual meeting of the American Education Research, New York.
Gordin, D. N., and Pea, R. D. (1995). Prospects for scientific visualization as an educational technology. Journal of the Learning Sciences 4(3): 249–279.
Greenberg, R., Kolvoord, R. A., Magisos, M., Strom, R. G., and Croft, S. (1993). Image processing for teaching. Journal of the Learning Sciences 2: 469–480.
Hancock, C. (1995). The medium and the curriculum: Reflections on transparent tools and tacit mathematics. In diSessa, A., Hoyles, C., Noss, R., and Edwards, L. (Eds.), Computers and Exploratory Learning, Springer-Verlag, Berlin, pp. 221–240.
Leinhardt, G., Zaslavksy, O., and Stein, M. K. Functions, graphs, and graphing: Tasks, learning, and teaching. Review of Educational Research 60(1): 1–64.
Lillesand, T. M. and Kiefer, R. W. (1994). Remote Sensing and Image Interpretation, Chapter 1. John Wiley and Sons, New York.
Palmer, S. (1977). Fundamental aspects of cognitive representation. In Rosch, E., and Lloyd, B. B. (Eds.), Cognition and Categorization, Erlbaum, Hillsdale, New Jersey, pp. 259–303.
Sherin, B. L. (1998). The elements of representational design. Paper presented at the annual meeting of the American Education Research Association, Chicago.
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Friedman, J.S., diSessa, A.A. What Students Should Know About Technology: The Case of Scientific Visualization. Journal of Science Education and Technology 8, 175–195 (1999). https://doi.org/10.1023/A:1009404212653
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DOI: https://doi.org/10.1023/A:1009404212653