Seeking the Truth: Human-Facilitated ILEs and Hypotheses Development

  • Hassan Qudrat-Ullah
Part of the Understanding Complex Systems book series (UCS)


Following the conceptual exploration and the empirical reflections on decision-making and learning with ILEs in the previous chapters, in this chapter, we attempt to develop a theoretical framework for the alternative designs of ILE(s). Any model or theory albeit to guide decision-making in dynamic tasks , should have viable and testable preposition(s).


Alternative designs of ILEs Cognitive mechanism Decisional aids Learner–learner interactions Learner–facilitator interactions Complex problem solving Dynamic tasks Hypothesized process model Hypotheses development Human-facilitated ILE Co-operative learning Experimental design Structured controversy method Pre-test Prior knowledge Cognitive processes Transfer learning Structural knowledge Heuristics knowledge Task performance Decision strategy Learning mode, and prior knowledge Laboratory experiments Human facilitation Facilitator support Pre-task In-task Post-task Decision heuristics Solution strategies Target goals Cues Debriefing Guidance Learners Mental models Structured group learning Expert solution Consistency Fluctuations Cognitive effort Questionnaires ANOVA Simulated task 


  1. 1.
    Bakken, B.E.: Learning and Transfer of Understanding in Dynamic Decision Environments. Ph.D. Dissertation, MIT: Boston (1993)Google Scholar
  2. 2.
    Berry, D.C., Broadbent, D.E.: On the relationship between task performance and associated verbalized knowledge. Q. J. Exp. Psychol. 36A, 209–231 (1984)CrossRefGoogle Scholar
  3. 3.
    Blazer, W.K., Doherty, M.E., O’Connor, R.: Effects of cognitive feedback on performance. Psychol. Bull. 106(3), 410–433 (1989)CrossRefGoogle Scholar
  4. 4.
    Breuer, K.: Computer simulations and cognitive development. In: Duncan, K.A., Harris, D. (Eds.) The Proceedings of the World Conference on Computers in Education 1985 WCC/85: 239–244. Amsterdam: North Holland (1985)Google Scholar
  5. 5.
    Briggs, P.: Do they know what they are doing? An evaluation of word-processor user’s implicit and explicit task-relevant knowledge, and its role in self-directed learning. Int. J. Man Mach. Stud. 32, 298–385 (1990)CrossRefMathSciNetGoogle Scholar
  6. 6.
    Broadbent, B., Aston, B.: Human control of a simulated economic system. Ergonomics 21, 1035–1043 (1978)CrossRefGoogle Scholar
  7. 7.
    Conant, R., Ashby, W.: Every good regulator of a system must be a model of the system. Int. J. Sys. Sci 1, 89–97 (1970)CrossRefMATHMathSciNetGoogle Scholar
  8. 8.
    Dhawan, R., O’ Conner, M., Borman, M.: The effect of qualitative and quantitative system dynamics training: an experimental investigation. Sys. Dyn. Rev. 27(2), 313–327 (2011)CrossRefGoogle Scholar
  9. 9.
    Ford, D.N., Mccormack, D.E.M.: Effects of time scale focus on system understanding in decision support systems. Simul. Gaming 31(3), 309–330 (2000)CrossRefGoogle Scholar
  10. 10.
    Forrester, J.W.: Industrial Dynamics. Productivity Press, Cambridge (1961)Google Scholar
  11. 11.
    Gonzalez, M., Machuca, J., Castillo, J.: A transparent-box multifunctional simulator of competing companies. Simul. Gaming 31(2), 240–256 (2000)CrossRefGoogle Scholar
  12. 12.
    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
  13. 13.
    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
  14. 14.
    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, pp. 213–253. Elsevier, Amsterdam (1995)Google Scholar
  15. 15.
    Kottermann, E., Davis, D., Remus, E.: Computer-assisted decision making: performance, beliefs, and illusion of control. Organ. Behav. Hum. Decis. Process. 57, 26–37 (1995)CrossRefGoogle Scholar
  16. 16.
    Lane, M., Tang, Z.: Effectiveness of simulation training on transfer of statistical concepts. J. Educ. Comput. Res. 22(4), 383–396 (2000)CrossRefGoogle Scholar
  17. 17.
    Langan-Fox, J., Wirth, A., Code, S., Langfield-Smith, K., Wirth, A.: Analyzing shared and team mental models. Int. J. Ind. Ergon. 28, 99–112 (2001)CrossRefGoogle Scholar
  18. 18.
    Leemkui, H., De Jong, T.: Adaptive advice in learning with a computer-based knowledge management simulation game. Acad. Manage. Learn. Educ. 11(4), 653–665 (2012)CrossRefGoogle Scholar
  19. 19.
    Mayer, W., Dale, K., Fraccastoro, K., Moss, G.: Improving transfer of learning: relationship to methods of using business simulation. Simul. Gaming 42(1), 64–84 (2011)CrossRefGoogle Scholar
  20. 20.
    Moxnes, E.: Misperceptions of basic dynamics: the case of renewable resource management. Sys. Dyn. Rev. 20, 139–162 (2004)CrossRefGoogle Scholar
  21. 21.
    Plate, R.: Assessing individuals’ understanding of nonlinear casual structures in complex systems. Sys. Dyn. Rev. 28(1), 19–33 (2010)CrossRefGoogle Scholar
  22. 22.
    Qudrat-Ullah, H.: Debriefing can reduce misperceptions of feedback hypothesis: an empirical study. Simul. Gaming 38(3), 382–397 (2007)CrossRefGoogle Scholar
  23. 23.
    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
  24. 24.
    Sanderson, P.M.: Verbalizable knowledge and skilled task performance: association, dissociation, and mental model. J. Exp. Psychol. Learn. Mem. Cogn. 15, 729–739 (1989)CrossRefGoogle Scholar
  25. 25.
    Schaffernicht, M., Groesser, N.: Mental models of dynamic systems: taking stock and looking ahead. Sys. Dyn. Rev. 28(1), 46–68 (2012)CrossRefGoogle Scholar
  26. 26.
    Schön, D.: The Reflective Practitioner. Basic Books, New York (1938)Google Scholar
  27. 27.
    Severin, W.J.: Another look at cue summation. ACM Commun. Rev. 15(4), 233–245 (1967)Google Scholar
  28. 28.
    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
  29. 29.
    Tennyson, R.D., Thurlow, R., Breuer, K.: Problem-oriented simulations to develop and improve higher-order thinking strategies. Comput. Hum. Behav. 3, 151–165 (1987)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.School of Administrative StudiesYork UniversityTorontoCanada

Personalised recommendations