Simulation Training in Self-Regulated Learning: Investigating the Effects of Dual Feedback on Dynamic Decision-Making Tasks

  • Jung Hyup Kim
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8523)


Self-Regulated Learning (SRL) is a popular concept in the research area of the education. However, most researchers who have studied SRL focus on the theoretical aspects of metacognition or the educational application such as children’s learning and academic performance. The purpose of this research is to investigate the SRL effects of dual feedback (retrospective confident judgments and task performances) in a dynamic task environment. A human-in-the-loop simulation experiment was conducted to collect real-time task performance data from participants and compared the self-regulated learning effects between different feedback conditions. We found that an improvement in the accuracy of their performance prediction might promote an increase in their situation awareness on dynamic decision-making tasks. This research will contribute design faster and more effective training algorism to inexperienced operators in the computer simulation training environment.


Simulation Training Self-Regulated Learning Human-in-the-loop simulation 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Dougherty, M.R., Scheck, P., Nelson, T.O., Narens, L.: Using the past to predict the future. Memory & Cognition 33(6), 1096–1115 (2005)CrossRefGoogle Scholar
  2. 2.
    Dunlosky, J., Hertzog, C.: Training programs to improve learning in later adulthood: Helping older adults educate themselves (1998)Google Scholar
  3. 3.
    Endsley, M.R.: Situation awareness global assessment technique, SAGAT (1988)Google Scholar
  4. 4.
    Hacker, D.J., Bol, L., Horgan, D.D., Rakow, E.A.: Test prediction and performance in a classroom context. Journal of Educational Psychology 92(1), 160 (2000)CrossRefGoogle Scholar
  5. 5.
    Hancock, P., Williams, G., Manning, C.: Influence of task demand characteristics on workload and performance. The International Journal of Aviation Psychology 5(1), 63–86 (1995)CrossRefGoogle Scholar
  6. 6.
    Hawk, T.F., Shah, A.J.: A revised feedback model for task and self-regulated learning. The Coastal Business Journal 7(1), 66–81 (2008)Google Scholar
  7. 7.
    Kim, J.H., Rothrock, L., Tharanathan, A., Thiruvengada, H.: Investigating the effects of metacognition in dynamic control tasks. In: Jacko, J.A. (ed.) Human-Computer Interaction, Part I, HCII 2011. LNCS, vol. 6761, pp. 378–387. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  8. 8.
    Kuiper, R.A., Pesut, D.J.: Promoting cognitive and metacognitive reflective reasoning skills in nursing practice: self-regulated learning theory. Journal of Advanced Nursing 45(4), 381–391 (2004)CrossRefGoogle Scholar
  9. 9.
    Norman, D.A.: The ‘problem’ with automation: inappropriate feedback and interaction, not ‘over-automation’. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 327(1241), 585–593 (1990)CrossRefGoogle Scholar
  10. 10.
    Rothrock, L.: Using time windows to evaluate operator performance. International Journal of Cognitive Ergonomics 5(1), 1–21 (2001)CrossRefGoogle Scholar
  11. 11.
    Zimmerman, B.J., Schunk, D.H.: Self-regulated learning and academic achievement: Theory, research, and practice. Springer-Verlag Publishing (1989)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Jung Hyup Kim
    • 1
  1. 1.Department of Industrial and Manufacturing Systems EngineeringUniversity of MissouriUSA

Personalised recommendations