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The Impact of Metacognitive Monitoring Feedback on Mental Workload and Situational Awareness

  • Jung Hyup Kim
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10906)

Abstract

The need to develop more effective feedback has become a growing concern in training. Feedback should be designed to provide meaningful information in order to help them improve their performance. On the other hand, the feedback should be designed not to increase the learners’ mental workload even while they maximize the benefits of using such feedback during training. Recently, Kim [1] developed the metacognitive monitoring feedback method. This methodology was tested in a computer-based training environment. The authors’ results showed that metacognitive monitoring feedback significantly improved participants’ performance during two days of a training session. However, the previous study did not investigate the impact of metacognitive monitoring feedback on participants’ mental workload and situational awareness. Hence, in this study, we investigated those needs and found a negative relationship between situational awareness and workload when the trainees observed the metacognitive monitoring feedback.

Keywords

Metacognition Mental workload Situational awareness 

References

  1. 1.
    Kim, J.H.: The effect of metacognitive monitoring feedback on performance in a computer-based training simulation. Appl. Ergon. 67, 193–202 (2018)CrossRefGoogle Scholar
  2. 2.
    Norman, D.A.: The ‘problem’ with automation: inappropriate feedback and interaction, not ‘over-automation’. Philos. Trans. R. Soc. Lond. Ser. B Biol. Sci. 327(1241), 585–593 (1990)CrossRefGoogle Scholar
  3. 3.
    Endsley, M.R.: Toward a theory of situation awareness in dynamic systems. Hum. Factors: J. Hum. Factors Ergon. Soc. 37(1), 32–64 (1995)CrossRefGoogle Scholar
  4. 4.
    Thiruvengada, H., Rothrock, L.: Time windows-based team performance measures: a framework to measure team performance in dynamic environments. Cogn. Technol. Work 9(2), 99–108 (2007)CrossRefGoogle Scholar
  5. 5.
    Kim, J.H.: Developing a Metacognitive Training Framework in Complex Dynamic Systems Using a Self-regulated Fuzzy Index (2013)Google Scholar
  6. 6.
    Dunlosky, J., Metcalfe, J.: Metacognition. Sage Publications, Thousand Oaks (2008)Google Scholar
  7. 7.
    Dougherty, M.R., et al.: Using the past to predict the future. Mem. Cogn. 33(6), 1096–1115 (2005)CrossRefGoogle Scholar
  8. 8.
    Endsley, M.R.: Situation Awareness Global Assessment Technique (SAGAT). IEEE (1988)Google Scholar
  9. 9.
    Nygren, T.E.: Psychometric properties of subjective workload measurement techniques: implications for their use in the assessment of perceived mental workload. Hum. Factors: J. Hum. Factors Ergon. Soc. 33(1), 17–33 (1991)CrossRefGoogle Scholar
  10. 10.
    Hancock, P., Williams, G., Manning, C.: Influence of task demand characteristics on workload and performance. Int. J. Aviat. Psychol. 5(1), 63–86 (1995)CrossRefGoogle Scholar
  11. 11.
    Nelson, T.O., Narens, L.: Metamemory: a theoretical framework and new findings. Psychol. Learn. Motiv. 26, 125–141 (1990)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Industrial and Manufacturing Systems EngineeringUniversity of MissouriColumbiaUSA

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