Abstract
Nowadays prevalent learning theories state that in the study process the learner is actively involved in constructing and reconstructing his/her knowledge base. This conclusion is reflected in modern approaches to teaching that have abandoned viewing the learner as an ‘empty box’ into which knowledge could be poured, and stress the active role of the learner and the importance of his/her foreknowledge. Some forms of Computer Assisted Instruction are well suited for this teaching approach. The use of hypertext-like systems, in which learners are encouraged to explore a domain, is such an example. A second example of CAI that elicits exploratory behaviour is simulation-based learning.
It is, however, also evident that exploratory learning puts a high cognitive demand on the learner. Instructional support is needed if learning from simulations is to be effective. In practice this support is often provided by human tutors. The topic of the SIMULATE project is to investigate how this support can be given by a computer learning environment. We have termed environments that combine a simulation with (intelligent) support: Intelligent Simulation Learning Environments (ISLEs).
In our analysis we identified four characteristics of instructional use of simulations: presence of (simulation) models, presence of instructional goals, elicitation of exploratory learning processes and possibility of learner activity. The significance of these characteristics for designing an Intelligent Simulation Learning Environment is assessed by combining these characteristics with the four ‘classical’ design components of Intelligent Tutoring Systems: the domain, learner, instruction, and learner interface component. Combining components and characteristics leads to a descriptive framework in which ingredients necessary for ISLEs can be placed. The present chapter summarises these findings and puts an emphasis on ‘exploratory learning processes’.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Ausubel, D.P., Novak, J.D., & Hanesian, H. (1978). Educational psychology: A cognitive view. New York: Holt, Rinehart & Winston.
Bocker, H.D., Herczeg, J., & Herczeg, M. (1989). ELAB — An electronics laboratory. In D. Bierman, J. Breuker, & J. Sandberg (Eds.), Proceedings of the Fourth International Conference on A1 & Education (pp. 15–25 ). Amsterdam: IOS.
de Jong, T. (1990a). Ontwikkelingen in Computer Ondersteund Onderwijs in het Nederlands Hoger Onderwijs in de jaren 1988/1989. [Developments in computer-assisted instruction in the Netherlands for the years 1988/1989] (OCTO report 90/01). Eindhoven University of Technology.
de Jong, T. ( 1990b, November). Learning and instruction with computer simulations: interface aspects. Paper presented at the NATO AETW ‘Cognitive modelling and interactive environments’, Eindhoven, The Netherlands.
de Jong, T. (1991a). Learning and instruction with computer simulations. Education & Computing, 6, 217–229.
de Jong, T. (Ed.) (in press). Computer simulations in an instructional context. Amsterdam: Elsevier.
de Jong, T., de Hoog, R., & de Vries, F. (1991). SUPER-MIDAS. A computer simulation for learning and instruction decision support systems. In H. Hijne & J. van Berkum (Eds.), Prototype/mock-up of an Integrated Simulation-based Learning Environment (DELTA project SAFE P7061; SAFE/SIM/CE-rep.). Courseware Europe BV.
de Jong, T., Tait, K., & van Joolingen, W.R. (in press). Authoring for intelligent simulation based instruction: A model-based approach. Proceedings of the DELTA and Beyond conference, The Hague, The Netherlands.
Farr, M.J., & Psotka, J. (1989). Introduction. Machine-Mediated Learning, 5, 1–6.
Ferguson-Hessler, M.G.M., & de Jong, T. (1990). Studying physics text. Differences in study processes between good and poor performers. Cognition and Instruction, 7, 41–54.
Friedler, Y., Nachmias, R., & Linn, M. C. (1990). Learning scientific reasoning skills in microcomputer-based laboratories. Journal of Research in Science Teaching, 27, 173–191.
Germann, P. J. (1989). The processes of biological investigations test. Journal of Research in Science Teaching, 26, 609–625.
Hamburger, H., & Lodger, A. (1989). Semantically constrained exploration and heuristic guidance. Machine-Mediated Learning, 5, 81–107.
Hardman, L. (1990). User interface tools to support learning (DELTA project SAFE, working paper SAFE/HYP/OWL-pap/user_i/f_tools). Edinburgh: Office Workstations Ltd.
Hartley, J.R. (1988). Learning from computer based learning in science. Studies in Science Education, 15, 55–76.
Hijne, H., & van Berkum, J. A. (1990, October). Authoring for intelligent simulation learning environments. Paper presented at the DELTA & Beyond conference, The Hague, The Netherlands.
Hijne, H., & de Jong, T. (1989, September). SIMULATE: Simulation authoring tools environment (OCTO report 1989/2). Paper presented at the EARLI Conference, Madrid, Spain.
Hollan, J.D., Hutchins, E. L., & Weitzman, L. (1984). STEAMER: An interactive inspectable simulation-based training system. AI Magazine, 5, 15–27.
Klahr, D., & Dunbar, K. (1988). Dual space search during scientific reasoning. Cognitive Science, 12, 1–48.
Kurland, L.C., & Tenney, Y.J. (1988). Issues in developing an intelligent tutor for a real-world domain: Training in radar mechanics. In J. Psotka, L.D. Massey, & S.A. Mutter (Eds.), Intelligent tutoring systems: Lessons learned (pp. 119–181 ). Hillsdale, NJ: Erlbaum.
Langley, P., Simon, H.A., Bradshaw, G.L., & Zytkow, J.M. (1987). Scientific discovery, computational explorations of the creative process. Cambridge: MIT Press.
Lavoie, D.R., & Good, R. (1988). The nature and use of prediction skills in a biological computer simulation. Journal of Research in Science Teaching, 25, 335–360.
Lesgold, A. (1990). Tying development of intelligent tutors to research on theories of learning. In H. Mandl, E. De Corte, S.N. Bennett, & H.F. Friedrich (Eds.), Learning and Instruction. European research in an international context (vol. 2.1, pp. 321–337 ). Oxford: Pergamon Press.
Linn, M.C. (1990). Perspectives of research in science teaching: Using the computer as a laboratory partner. In H. Mandl, E. De Corte, S.N. Bennett, & H.F. Friedrich (Eds.), Learning and Instruction. European research in an international context (vol. 2.1, pp. 443–460 ). Oxford: Pergamon Press.
Mokros, J.R., & Tinker, R.F. (1987). The impact of microcomputer based labs on children’s ability to interpret graphs. Journal of Research in Science Teaching, 24, 369–383.
Njoo, M., & de Jong, T. (in press). Learning processes of students working with a computer simulation in mechanical engineering. Proceedings of the EARLI conference, Madrid, Spain.
Njoo, M., & de Jong, T. (in press). Stimulating exploratory learning with a computer simulation for control theory: The effect of hints (OCTO Report). Eindhoven University of Technology.
Njoo, M., & de Jong, T. (1991, April). The effect of offering study process planning support, learning process information, and ready-made hypotheses, on learning with a computer simulation on control theory. Paper presented at the AERA conference, Chicago.
Plotzner, R., Spada, H., Stumpf, M., & Opwis, K. (1990). Learning qualitative reasoning in a microworld for elastic impacts (Forschungsbericht nr. 59). Research group on cognitive systems, University of Freiburg.
Reimann, P. (1989). Modelling scientific discovery learning processes with adaptive production systems. In D. Bierman, J. Breuker, & J. Sandberg (Eds.), Artificial intelligence and education; synthesis and reflection. Proceedings of the Fourth International Conference on AI and Education (pp. 218–227 ). Amsterdam: IOS.
Rivers, R.H., & Vockell, E. (1987). Computer simulations to stimulate scientific problem solving. Journal of Research in Science Teaching, 24, 403–415.
Schauble, L., Glaser, R., Raghavan, K., & Reiner, M. (1990, April). Causal models and experimentation strategies in scientific reasoning. Paper presented at the AERA conference, Boston.
Self, J. ( 1989, May). The case for formalising student models (and Intelligent Tutoring Systems generally). Paper presented at the AI & Education conference, Amsterdam, The Netherlands.
Shulman, L.S., & Keislar. E.R. (Eds.) (1966). Learning by discovery: A critical appraisal. Chicago: Rand McNally.
Shute, V.J. ( 1990, April). A comparison of inductive and deductive learning environments: Which is betterfor whom and why? Paper presented at the AERA conference, Boston.
Shute, V.J., & Glaser, R. (1990). A large-scale evaluation of an intelligent discovery world: Smithtown. Interactive Learning Environments, 1, 51–77.
Shute, V. J., Glaser, R., & Raghavan, K. (1989). Inference and discovery in an exploratory laboratory. In P.L. Ackermann, R.J. Sternberg, & R. Glaser (Eds.), Learning and individual differences (pp. 279–326 ). New York: W.H. Freeman.
Swanson, J.H. ( 1990, April). The effectiveness of tutorial strategies: An experimental evaluation. Paper presented at the AERA conference, Boston.
Tait, K. (Ed.) (1990). Towards the specification of support tools for authors constructing simulation-based intelligent learning environments (DELTA project SAFE (P7061), deliverable SIM/22). University of Leeds, Computer-Based Learning Unit.
Towne, D.M., Munro, A., Pizzini, Q.A., Surmon, D.S., Coller, L.D., & Wogulis, J.L. (1990). Model-building tools for simulation-based training. Interactive Learning Environments, I, 33–50.
van Berkum, J.J.A., & de Jong, T. (1991). Instructional environments for simulations. Education & Computing, 6, 305–358.
van Joolingen, W., & de Jong, T. (1991). A prototype scratchpad for hypothesis formation and experimental design. In H. Hijne & J. van Berkum (Ed.), Prototype/mock-up of an Integrated Simulation-based Learning Environment (DELTA project SAFE P7061; SAFE/SIM/CE-rep.). Courseware Europe BV.
Veenman, M.V.J., & Elshout, J.J. (1990). De meerwaarde van een goede probleemaanpak. [The surplus value of a proper problem approach]. Tijdschrift voor Onderwijsresearch, 75, 337–347.
White, B.Y., & Frederiksen, J.R. (1990). Causal model progressions as a foundation for intelligent learning environments. Artificial Intelligence, 42, 99–157.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1992 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
de Jong, T., Njoo, M. (1992). Learning and Instruction with Computer Simulations: Learning Processes Involved. In: De Corte, E., Linn, M.C., Mandl, H., Verschaffel, L. (eds) Computer-Based Learning Environments and Problem Solving. NATO ASI Series, vol 84. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-77228-3_19
Download citation
DOI: https://doi.org/10.1007/978-3-642-77228-3_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-77230-6
Online ISBN: 978-3-642-77228-3
eBook Packages: Springer Book Archive