Abstract
Computer simulations provide a challenging opportunity to create learning environments in which learners are free to explore the domain and discover the domain properties themselves. Contemporary theories of learning state that knowledge acquired and constructed within such an exploratory environment will be rooted more deeply within the learner’s knowledge structures. On the other hand, it has also become clear that offering a simulation to the learner without offering any additional support may result in learners getting “lost” in the simulation environment, not learning very much. Therefore, additional support is deemed necessary for simulation learning environments.
The current chapter describes a software instrument (a “hypothesis scratchpad”) that can be offered to the learner in order to support the process of hypothesis formation. The design of this instrument is based on an analysis of hypothesis space, one of the two search spaces from the theory of Klahr and Dunbar [14], who describe discovery as search in two related spaces, a hypothesis space and an experiment space. A study was performed in which the hypothesis scratchpad was used to influence the learners search processes in variable space and relation space, the two subspaces of hypothesis space. The moment of stating hypotheses (before doing experiments or during a series of experiments) and the guidance for variable space search (starting with instances or directly going to general variables) was varied in this study. It appeared that the learners who were prompted to state hypotheses before doing experiments stated more hypotheses while doing experiments with the simulation. Learners who were searching varibale space only at the level of general variables, chose relations at a more precise level than learners searching variable space, starting with instances before going to the general concepts.
Part of this paper is based at research conducted in the project SMISLE. SMISLE is a R&D project partially funded by the CEC under contract D2007 within the main phase of the DELTA programme.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Alessi S.M., & Trollip, S.R. (1985).Computer based instruction, methods and development. Englewood Cliffs, NY: Prentice-Hall.
Dunbar, K. & Klahr, D. (1989). developmental differences in scientific discovery processes, in:Klahr,D., &Kotovsky,K.(Ed.) Complex information processing,The impact of Herbert Simon, (pp. 109–145). Hillsdale, New Jersey: Lawrence Erlbaum associates.
Gorman, M.E., & Gorman, M.E. (1984). A comparison of disconfirmatory, confirmatory and control strategies on Wason’s 2-4-6 task.Quarterly Journal of Experimental Psychology 36A, 629–648.
Gorman, M.E., Stafford, A., & Gorman, M.E. (1987). Disconfirmation and dual hypotheses on a more difficult version of Wason’s 2-4-6 task.The Quarterly Journal of Experimental Psychology, 39A, 1–28.
Greeno, J.G. & Simon, H.A. (1988). Problem solving and reasoning. In R.C. Atkinson, R.J. Herrnstein, G. Lindzey & R.D. Luce (Eds.),Stevens’ handbook of experimental psychology;Vol. 2: Learning and cognition (pp. 589–673 ). New York: Wiley.
Jonassen, D. H . (1991). Objectivism versus constructivism: Do we need a new philosophical paradigm?Educational Technology Research &Development, 39, 5–14
de Jong, T . (1991). Learning and instruction with computer simulations,Education and Computing, 6, 217–230.
de Jong, T., & Njoo, M. (1992). c. In E. de Corte, M. Linn, H. Mandl, & L. Verschaffel (Eds.)Computer-based learning environments and problem solving (NATO ASI series F: Computer and Systems Series). Berling: Springer.
de Jong, T., Tait, K., & van Joolingen, W.R. (1992). Authoring for intelligent simulation based instruction: a model based approach. In S.A. Cerri & J. Whiting (Eds.)Learning Technology in the European Communities (pp. 619–637). Dordrecht: Kluwer Academic Publishers.
van Joolingen, W.R. (1992).QMaPS,Qualitative reasoning for intelligent simulation learning environments. Poster presented at the Qualitative Reasoning workshop, Edinburgh, 1992.
van Joolingen, W.R. (1993).Understanding and facilitating discovery learning in computer-based simulation environments. PhD. Thesis. Eindhoven: Eindhoven University of Technology.
van Joolingen, W.R., & de Jong, T. (1991). Supporting hypothesis formation by learners exploring an interactive computer simulation.Instructional Science, 20, 389–404.
van Joolingen, W.R., & de Jong, T. (1992). Modelling domain knowledge for Intelligent Simulation Learning Environments. Computers & Education, 18, 29–38.
Klahr, D., & Dunbar, K. (1988). Dual space search during scientific reasoning. Cognitive Science, 12, 1–48.
Kulkarni, D., & Simon, H. A. (1988). The processes of scientific discovery: The strategy of experimentation.Cognitive science, 12, 139–175.
Lawson. A.E., McElrrath, C.B., Burton, M.S., James, B.D., Doyle, R.P., Woodward, S.L., Kellerman, L., & Snyder, J.D. (1991). Hypothetico-deductive reasoning skill and concept acquisition: testing a constructivist hypothesis.Journal of research in science teaching, 28, 953–970.
Michael, J.A., Haque, M.M., Rovick, A.A., & Evens, M. (1989). The pathophysiology tutor: a first step towards a smart tutor. In H. Maurer (Ed.),Computer Assisted Learning. Proceedings of the 2 nd International Conference ICCAL (pp. 390–400 ). Berlin: Springer Verlag.
Mynatt, C.R., Doherty, M.E., & Tweney R.D. (1977). Confirmation bias in a simulated research environment: An experimental study of scientific inference.Quarterly Journal of Experimental Psychology, 29, 85–95.
Newell, A. & Simon, H.A. (1972).Human problem solving. Englewood Cliffs, NJ: Prentice-Hall.
Njoo, M., & de Jong, T. (1992). Exploratory learning with a computer simulation for control theory: Learning processes and instructional support.Journal of Research in Science Teaching (in press).
Peterson, N.S., Jungck, J.R., Sharpe, D.M., & Finzer, W.F. (1987). A design approach to sicnce. Simulated laboratories: Learning via the construction of meaning.Machine-mediated Learning, 2 (1), 111–127.
Plötzner, R., Spada, H., Stumpf, M., & Opwis, K. (1990). Learning qualitative reasoning in a microworld for elastic impacts.European Journal of psychology of education, 4, 501–516.
Plötzner, R., & Spada, H. (1992). Analysis-based learning on multiple levels of mental domain representation. In:E de Corte M Linn H Mandl&L Verschaffel (eds.) Computer-based learning environments and problem solving, pp. 103–129. Berlin: Springer.
Popper., KR . (1959).The logic of scientific discovery. New York: Basic Books.
Qin, Y. & Simon, H.A. (1990). Laboratory replication of scientific discovery processes.Cognitive Science, 14, 281–312.
Shute, V., & Glaser, R. (1990). A large scale Evaluation of an Intelligent Discovery World: Smithtown.Interactive Learning Environments 1, 59–77.
Shute, V., Glaser, R., & Raghavan, K. (1989), Discovery and inference in an exploratory laboratory, in P.L. Ackerman, R.J. Sternberg, and R. Glaser (eds.),Learning and Individual Differences, San Francisco: Freeman.
Wason, P.C. (1964). On the failure to eliminate hypotheses in a conceptual task.Quarterly Journal of experimental Psychology, 12, 129–140.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1993 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
van Joolingena, W., de Jong, T. (1993). Exploring a Domain with a Computer Simulation: Traversing Variable and Relation Space with the Help of a Hypothesis Scratchpad. In: Towne, D.M., de Jong, T., Spada, H. (eds) Simulation-Based Experiential Learning. NATO ASI Series, vol 122. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-78539-9_14
Download citation
DOI: https://doi.org/10.1007/978-3-642-78539-9_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-78541-2
Online ISBN: 978-3-642-78539-9
eBook Packages: Springer Book Archive