Planning: An Intermediate Solution to the Problems in Design

  • J. Bravo
  • M. Ortega
  • M. A. Redondo
  • C. Bravo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2722)


A great variety of simulation environments aim at supporting education through a modelling process. However, there are certain problems associated to modelling environments. We intend to build the solution model to every problem through plans based on intermediate languages outlined by the students. These learner plans are abstract solutions to the design problems. Our proposal consists in a tool, Plan Editor, to help the students design a domotic environment, following the intermediate language approach as a first step in the resolution of the problem. In the end, the student will be able to more efficiently simulate the plan proposed.


  1. 1.
    Ajewole, G.A. (1991), “Effects of discovery and expository instructional methods on the attitude of students to biology”. Journal of Research in Science Teaching, 28, 301–409.CrossRefGoogle Scholar
  2. 2.
    Berry, D.C. & Broadbent, D.E. (1984), “Explanation and verbalization in a computer-assisted search tsak”. The Quarterly Journal of Experimental Psychology, 386, 209–231.Google Scholar
  3. 3.
    Bonar, J. G. & Cunningham, R. (1988), “Intelligent Tutoring with Intermediate Representations” ITS-88 Montreal.Google Scholar
  4. 4.
    Bravo Rodríguez, J. (1999), “Design Planning in Simulation Environment for Distance Learning”. Doctoral Dissertation, Madrid.Google Scholar
  5. 5.
    Brennenstuhl, D.C. (1975), “Cognitive versus effective gains in computer simulations”. Simulation & Games, 6, 303–311.CrossRefGoogle Scholar
  6. 6.
    Brown, J.S., (1983), “Process vs product: a perspective on tools for communal and informal electronic learning”, report from “The learning Lab: Education in the electronic age”.Google Scholar
  7. 7.
    Carlsen, D.D. & Andre, T. (1992), “Use of a microcomputer simulation and conceptual change text to overcome students preconceptions about electric circuits”. Journal of Computer-Based Instructions, 19, 105–109.Google Scholar
  8. 8.
    de Jong, T., van Joolingen, W., Pieters, J. & van der Hulst, Anja. (1993), “Why is discovery learning so difficult? and what can we do about it?”. EARLI conference. Aixen-Provence.Google Scholar
  9. 9.
    Duffy, T., Jonassen, D. (1992), “Constructivism and the Technology of instruction”. Lawrence Erlbaum Associates, Hillsdale, New Jersey.Google Scholar
  10. 10.
    Faryniarz, J.V. & Lockwood, L.G. (1992), “Effectiveness of microcomputer simulations in stimulating environmental problem solving by community college students”. Journal of Research in Science.Google Scholar
  11. 11.
    Fishman, B.J., Honebein, P.C., Duffy, T.M. (1991), “Constructivism and the design of learning environments: Context and authentic activities for learning”. NATO Advanced Workshop on the design of Constructivism Learning.Google Scholar
  12. 12.
    Hammond, K.J. (1990), “Case-Based Planning”. In Cognitive Science, vol 14, pp. 385–443.CrossRefGoogle Scholar
  13. 13.
    Lavoie, D.R. & Good, R. (1988), “The nature and use of predictions skills in a biological computer simulation”. Journal of Research in Science Teaching, 25(5), pag. 335–360.CrossRefGoogle Scholar
  14. 14.
    Minton, S. & Zweben (1993), “Learning, Planning and Scheduling: An Overview”. Machine Learning Methods for Planning. Minton (Ed.) Morgan Kaufmann.Google Scholar
  15. 15.
    Paper, S. (1980), “Mindstorms: Children, Computer and Powerful Ideas”. Basic Books Inc., New York.Google Scholar
  16. 16.
    Paper, S. (1987), “Computers in Education:Conceptual Issues”. Saphiro, S., Encyclopaedia of Artificial Intelligence, Edit. Willey, New York.Google Scholar
  17. 17.
    Rivers, R.H. & Vockell, E. (1987), “Computer simulations to stimulate scientific problem solving”. Journal of Research in Science Teaching, 24, 403–415.CrossRefGoogle Scholar
  18. 18.
    Schank, R. Clearcy, C., (1994), “Engines for Education”. Lawrence Erlbaaum Associates, Hillsdale, New Jersey. Scholar
  19. 19.
    Schank, R., Kass, A., “A Goal-Based Scenario for Higher School Students”. Comm. of the ACM, 39(4), 28 (April-1996).Google Scholar
  20. 20.
    Shute, V.J. (1990), “A comparison of inductive and deductive learning environments: Which is better for whon and why?”. Paper presented at the American Educational Research Association (AERA) Annual Meeting, Boston. USA.Google Scholar
  21. 21.
    Soloway, E. (1986), “Learning to Program = Learning to Construct Mechanisms and Explanations”. Communications of the ACM.Google Scholar
  22. 22.
    Wason, P.C. (1966), “Reasoning”. In Foss, B.M. (Ed.). Nes Horizons in Psycology. Harmondsworth, England: Penguin.Google Scholar
  23. 23.
    Wasson, B. (1990), “Determining de Focus if Instruction: Content Planning for Intelligent Tutoring System” Doctoral Thesis, Dep. Computational Science, University of Saskatchewan.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • J. Bravo
    • 1
  • M. Ortega
    • 1
  • M. A. Redondo
    • 1
  • C. Bravo
    • 1
  1. 1.Castilla-La Mancha UniversityCiudad RealSpain

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