Towards a Road to Success: The Development of the Integrated Process Model

Chapter
Part of the Understanding Complex Systems book series (UCS)

Abstract

Consistent with the objective of any training program, method, tool or initiative, i.e., to produce successful decision-makers, in this chapter we attempt to develop an integrated process model that accounts for key factors responsible for successful delivery of ILE-based training sessions.

Keywords

Prior knowledge Learning mode Dynamic task Decision strategy Decision-making Dynamic decision-making Education Event-oriented perspective Feedback-oriented view Cognitive apprenticeship Cognitive effort Designer’s logic Task knowledge Structural knowledge Heuristics knowledge Human facilitation Pre-task facilitation In-task facilitation Post-task facilitation Training Transfer learning Integrated process model Interactive learning environments Cognitive effort Decision time Operator’s logic 

References

  1. 1.
    Bakken, B.E.: Learning and transfer of understanding in dynamic decision environments. Ph.D. Dissertation, MIT, Boston (1993)Google Scholar
  2. 2.
    Barnett, S., Ceci, S.: When and where do we apply what we learn? A taxonomy for far transfer. Psychol. Bull. 128(4), 612–637 (2002)CrossRefGoogle Scholar
  3. 3.
    Berry, D.C., Broadbent, D.E.: Interactive tasks and the implicit-explicit distinction. Br. J. Psychol. 79, 251–271 (1988)CrossRefGoogle Scholar
  4. 4.
    Breuer, K., Hajovy, H.: Adaptive instructional simulations to improve learning of cognitive strategies. Educ. Technol. 29–32 (1987)Google Scholar
  5. 5.
    Collins, A.: Cognitive apprenticeship and instructional technology. In: Idol, L., Jones, B.F. (eds.), Educational Values and Cognitive Instruction: Implication for Reform, pp. 11–139 (1991)Google Scholar
  6. 6.
    Conant, R., Ashby, W.: Every good regulator of a system must be a model of the system. Int. J. Syst. Sci. 1, 89–97 (1970)CrossRefMATHMathSciNetGoogle Scholar
  7. 7.
    Cox, R.J.: Exploratory learning from computer-based systems. In: Dijkstra, S., Krammer, H.P.M., van Merrienboer, J.J.G. (eds.) Instructional Models in Computer-Based Learning Environments, pp. 405–419. Springer, Heidelberg (1992)CrossRefGoogle Scholar
  8. 8.
    Davidsen, P.I., Spector, J.M.: Cognitive complexity in system dynamics based learning environments. In: Barlas, Y., Diker, V.G., Polat, S. (eds.) Systems Dynamics Proceedings: Systems Approach to Learning and Education in the 21st Century, vol. 2, pp. 757–760. Bogaziçi University, Istanbul (1997)Google Scholar
  9. 9.
    Dörner, D.: The logic of failure: Why things go wrong and what we can do to make them right (trans: Kimber R, Kimber R). Metropolitan Books, New York (Original work published in 1989) (1996)Google Scholar
  10. 10.
    Druckman, D., Bjork, R.A.: Learning, Remembering, Believing: Enhancing Human Performance. National Academy Press, Washington (1994)Google Scholar
  11. 11.
    Forrester, J.W.: Industrial Dynamics. Productivity Press, Cambridge (1961)Google Scholar
  12. 12.
    Funke, J.: Experimental research on complex problem solving. In: Frensch, P., Funke, J. (eds.) Complex Problem Solving: the European Perspective, pp. 3–25. Lawrence Erlbaum Associates Publishers, NJ (1995)Google Scholar
  13. 13.
    Goodyear, P.: The provision of tutorial support for learning with computer-based simulations. In: Corte, E., Lin, M., Mandal, H., Verschaffel, L. (eds.) Computer-Based Learning Environments and Problem Solving, pp. 391–409. Springer, Berlin (1992)CrossRefGoogle Scholar
  14. 14.
    Gröbler, A., Maier, F.H., Milling, P.M.: Enhancing learning capabilities by providing transparency in transparency. Simul. Gaming 31(2), 257–278 (2000)CrossRefGoogle Scholar
  15. 15.
    Hogarth, R.M.: Beyond discrete biases: functional and dysfunctional aspects of judgmental heuristics. Psychol. Bull. 9(2), 197–217 (1981)CrossRefGoogle Scholar
  16. 16.
    Huber, O.: Complex problem solving as multistage decision making. In: Frensch, P., Funke, J. (eds.) Complex Problem Solving: the European Perspective, pp. 151–173. Lawrence Erlbaum Associates Publishers, NJ (1995)Google Scholar
  17. 17.
    Issacs, W., Senge, P.: Overcoming limits to learning in computer-based learning environments. In: Morecroft, J., Sterman, J. (eds.) Modeling for Learning Organizations, pp. 267–287. Productivity Press, Portland (1994)Google Scholar
  18. 18.
    Jansson, A.: Strategies in dynamic decision making: Does teaching heuristic strategies by instructors affect performance? In: Caverni, J., Bar-Hillel, M., Barron, F., Jungermann, H. (eds.) Contributions to Decision Making-I. Elsevier, Amsterdam (1995)Google Scholar
  19. 19.
    Kintsch, W.: The use of knowledge in discourse processing: a construction-integration model. Psychol. Rev. 95, 163–182 (1988)CrossRefGoogle Scholar
  20. 20.
    Langley, P.A., Morecroft, J.D.W.: Learning from microworld environments: a summary of the research issues. In: Richardson, G.P., Sterman, J.D. (eds.) System Dynamics’ 96. System Dynamics Society, Cambridge (1995)Google Scholar
  21. 21.
    Malloy, T.E., Mitchel, C., Gordon, O.E.: Training cognitive strategies underlying intelligent problem solving. Percept. Mot. Skills 64, 1039–1046 (1987)CrossRefGoogle Scholar
  22. 22.
    Merril, M.D.: The new component design theory: instructional design for courseware authoring. Instr. Sci. 16, 19–34 (1987)CrossRefGoogle Scholar
  23. 23.
    Mirjana Kljajić Borštnar, K., Kljajić, M., Škraba, A., Kofjača, A., Rajkoviča, V.: The relevance of facilitation in group decision making supported by a simulation model. Syst. Dyn. Rev. 27(3), 270–293 (2011)Google Scholar
  24. 24.
    Moxnes, E.: Misperceptions of basic dynamics: the case of renewable resource management. Syst. Dyn. Rev. 20, 139–162 (2004)CrossRefGoogle Scholar
  25. 25.
    Putz-Osterloh, W., Bott, B., Koster, K.: Modes of learning in problem solving—are they transferable to tutorial systems. Comput. Hum. Behav. 6, 83–96 (1990)CrossRefGoogle Scholar
  26. 26.
    Qudrat-Ullah, H.: Debriefing can reduce misperceptions of feedback hypothesis: an empirical study. Simul. Gaming 38(3), 382–397 (2007)CrossRefGoogle Scholar
  27. 27.
    Qudrat-Ullah, H.: Perceptions of the effectiveness of system dynamics-based interactive learning environments: an empirical study. Comput. Educ. 55, 1277–1286 (2010)CrossRefGoogle Scholar
  28. 28.
    Reigeluth, C.M., Schwartz, E.: An instructional theory for the design of computer-based simulations. J. Comput. Based Instr. 16(1), 1–10 (1989)Google Scholar
  29. 29.
    Schmidt, R.A., Bjork, R.A.: New conceptualizations of practice: common principles in three paradigms suggest new concepts of training. Psychol. Sci. 3, 207–217 (1992)CrossRefGoogle Scholar
  30. 30.
    Sengupta, K., Abdel-Hamid, T.: Alternative concepts of feedback in dynamic decision environments: An experimental investigation. Manage. Sci. 39(4), 411–428 (1993)CrossRefGoogle Scholar
  31. 31.
    Sfard, A.: On two metaphors for learning and dangers of choosing just one. Educ. Res. 27(2), 4–12 (1998)CrossRefGoogle Scholar
  32. 32.
    Spector, J.M.: System dynamics and interactive learning environments: lessons learned and implications for the future. Simul. Gaming 31(4), 528–535 (2000)CrossRefGoogle Scholar
  33. 33.
    Sternberg, R.J.: Expertise in complex problem solving: a comparison of alternative conceptions. In: Frensch, P., Funke, J. (eds.) Complex Problem Solving: the European Perspective, pp. 3–25. Lawrence Erlbaum Associates Publishers, NJ (1995)Google Scholar
  34. 34.
    Sterman, J.D.: Business Dynamics: Systems Thinking and Modeling for a Complex World. McGraw-Hill, New York (2000)Google Scholar
  35. 35.
    Wolf, J.: The evaluation of computer-based business games. In: Gentry, J. (ed.) Guide to Business Gaming and Experiential Learning, pp. 279–300. Nichols, London (1990)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.School of Administrative StudiesYork UniversityTorontoCanada

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