Principles for the Design and Use of Simulations in Science Learning as Exemplified by a Prototype Microworld Article
DAVID F. JACKSON, TAE-KOON KIM, University of Georgia, United States ; Douglas N. Yarger, Peter J. Boysen, Iowa State University, United States
JCMST Volume 19, Number 3, ISSN 0731-9258 Publisher: Association for the Advancement of Computing in Education (AACE), Waynesville, NC USA
This article attempts to contribute to the clarification of princi-ples for the design and use of simulation software in science learning by combining a reflective process of identification of im-portant questions with empirical evidence from limited use of a “microworld” application designed and developed by the first author. We first outline a series of issues, growing out of a criti-cal review of the literature, which we believe remain unresolved or even unaddressed by many researchers, software developers, teachers, and teacher educators in the field of science education. The most salient of these are: (a) the occasional great importance of fine details of the user interface to the practical value of edu-cational software; (b) the distinction between abstract, usually quantitative, computer modeling as a specific means versus a general end of science instruction; (c) the importance of attention to levels of understanding in the curriculum context of simulation use; and (d) approaches to conceptual enhancement of simulation software design, including, but not limited to, the notions of mul-tiple representations and scaffolding and fading.
JACKSON, D.F., KIM, T.K., Yarger, D.N. & Boysen, P.J. (2000). Principles for the Design and Use of Simulations in Science Learning as Exemplified by a Prototype Microworld. Journal of Computers in Mathematics and Science Teaching, 19(3), 237-252. Charlottesville, VA: Association for the Advancement of Computing in Education (AACE). Retrieved July 18, 2018 from https://www.learntechlib.org/primary/p/8071/.
© 2000 Association for the Advancement of Computing in Education (AACE)
- Berger, C.F., Lu, C.R., Belzer, S.J., & Voss, B.E. (1994). Research on the uses of technology in science education. In D.L. Gabel (Ed.), Handbook of research in science teaching and learning (pp. 466-490). New York: Macmillan.
- Bliss, J., Askew, M., & Macrae, S. (1996). Effective teaching and learning: scaffolding revisited. Oxford Review of Education, 22(1), 37-61.
- Bliss, J., & Ogborn, J. (1989). Tools for exploratory learning. Journal of Computer Assisted Learning, 5, 37-50.
- Choi, B., & Gennaro, E. (1987). The effectiveness of using computer simulation experiments on junior high students ’ understanding of the volume displacement concept. Journal of Research in Science Teaching, 24(6), 539-52.
- Clark, R. E. (1985) Confounding in educational computing research. Journal of Educational Computing Research, 1(2), 137-148.
- De Jong, T. (1991). Learning and instruction with computer simulations. Education & Computing, 6, 217-229.
- Dekkers, J., & Donatti, S. (1981). The integration of research studies on the use of simulation as an instructional strategy. Journal of Educational Research, 74(6), 424-427.
- Driver, R., & Scanlon, E. (1988). Conceptual change in science. Journal of Computer Assisted Learning, 5, 25-36.
- Gardner, M., Greeno, J.G., Reif, F., Schoenfeld, A.H., DiSessa, A., & Stage, E. (Eds.) (1990). Towards a scientific practice of science education , Hillsdale, New Jersey: Lawrence Erlbaum.
- Guzdial, M. (1994). Software-realized scaffolding to facilitate programming for science learning. Interactive Learning Environments, 4(1), 1-44.
- Guzdial, M., & Weingarten, F.W. (1996). Setting a computer science research agenda for educational technology. Washington, DC: Computing Research Association.
- Jackson, D.F. (1997). Case studies of microcomputer and interactive video simulations in middle school earth science teaching. Journal of Science Education and Technology, 6, 127-141.
- Jackson, D.F., Edwards, B.J., & Berger, C.F. (1993). The design of software tools for meaningful learning by experience: Flexibility and feedback. Journal of Educational Computing Research, 9, 247-277.
- Johnstone, A.H. (1991). Why is science difficult to learn? Things are seldom what they seem. Journal of Computer Assisted Learning , 7, 75-83.
- Kim, T. (1997). CumulusSim [computer program]. [Online]. Available: URL: http://www.arches.uga.edu/~taekoon/CumulusSim.html.
- Krajcik, J.S., Simmons, P.E., & Lunetta, V.N. (1986). Improving research on computers in science learning. Journal of Research in Science Teaching, 23 (5), 465-470.
- Krajcik, J.S., Simmons, P.E., & Lunetta, V.N. (1988). A research strategy for the dynamics tudy of students’ concepts and problemsolving strategies using science software. Journal of Research in Science Teaching, 25(2), 147-155.
- Lunetta, V.N., & Hofstein, A. (1981). Simulations in science education. Science Education, 65(3), 243-252.
- Millar, R. (1991). Why is science hard to learn? Journal of Computer Assisted Learning, 7, 66-74.
- Njoo, M., & DeJong, T. (1993). Exploratory learning with a computer simulation for control theory: Learning processes and instructional support. Journal of Research in Science Teaching, 30(8), 821-844.
- Peterson, N.S., Jungck, J.R., & Sharpe, D.M. (1987). A design approach to science simulated laboratory: Learning via the construction of meaning. Machine-Mediated Learning, 2, 111-127.
- Reeves, T.C. (1992). Evaluating interactive multimedia. Educational Technology, 32(5), 47-53.
- Richards, J., Barowy, W., & Levin, D. (1992). Computer simulations in the science classroom. Journal of Science Education and Technology, 1, 67-79.
- Rieber, L.P. (1992). Computer-based microworlds: A bridge between constructivism and direct instruction. Educational Technology, Research and Development, 40(1): 93-106.
- Rieber, L.P. (1993). A pragmatic view of instructional technology. In K. Tobin (Ed.), The practice of constructivism in science education (pp. 193-212). Hillsdale, NJ: Lawrence Erlbaum.
- Rivers, R.H., & Vockell, E. (1987). Computer simulations to stimulate scientific problem solving. Journal of Research in Science Teaching, 24 (5), 403-415. Roth, W-M., Woszczyna, C., & Smith, G. (1996). Affordances and constraints
- Snir, J., Smith, C., & Grosslight, L. (1995). Conceptually enhanced simulations: A computer tool for science teaching . In D .N . Perkins , J .L . Schwartz , M.M. West, & M.S. Wiske (Eds.), Software goes to school (pp.106-129). New York: Oxford University Press.
- Steinmetz, A. (1984). The discrepancy evaluation model. In G.F. Madaus, M. Scriven, & D. L. Stufflebeam (Eds.). Evaluation Models. Boston: Kluwer Nijhoff.
- Stratford, S.J. (1997). A review of computer-based model research in precollege science classroom. Journal of Computers in Mathematics and Science Teaching, 16(1), 3-23.
- Woodward, J., Carnine, D., & Gersten, R. (1988). Teaching problem solving through computer simulations. American Educational Research Journal, 25(1), 72-86.
These references have been extracted automatically and may have some errors. If you see a mistake in the references above, please contact firstname.lastname@example.org.
Instructional Design of Scientific Simulations and Modeling Software to Support Student Construction of Perceptual to Conceptual Bridges
Jerry P. Suits, McNeese State University, United States; Moustapha Diack, Southern University-Baton Rouge, United States
EdMedia + Innovate Learning 2002 (2002) pp. 1904–1909
These links are based on references which have been extracted automatically and may have some errors. If you see a mistake, please contact email@example.com.