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Human-Machine Interaction Efficiency Factors in Flight Simulator Training Towards Chinese Pilots

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Advances in Simulation and Digital Human Modeling (AHFE 2020)

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Abstract

The efficiency of human-machine interaction between Chinese pilots in the simulator is mainly caused by three factors: pilot pressure, the interaction in training, and the application of metacognitive strategies. The sense of pressure is the factor that indirectly affects the training efficiency in simulator training. The interaction in training is a direct influencing factor and a dynamic process. The pilot continuously monitors and evaluates the current status through interaction with the tower and partners to provide adequate information for the execution and handling of the flight process. The pilot continuously receives feedback and corrects operational actions through interaction with the instructor. The application of metacognitive strategy helps pilots to coordinate cognitive resources and gradually form flight experience based on their characteristics. The interaction of several factors plays an essential role in simulator training.

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References

  1. Lancaster, J.A., Khatwa, R., Conner, K.J., Glover, J.H.: Flight simulator evaluation of an airport surface display with indications and alerts (2010)

    Google Scholar 

  2. Guo, H., Pang, J., Han, L., Shan, Z.: Flight data visualization for simulation and evaluation: a general framework. In: Fifth International Symposium on Computational Intelligence & Design. IEEE (2013)

    Google Scholar 

  3. Khatwa, R.: Flight simulator evaluation of pilot performance with the runway awareness and advisory system (RAAS). In: The 23rd Digital Avionics Systems Conference, DASC 2004. IEEE (2004)

    Google Scholar 

  4. Lei, Z., Hongzhou, J., Hongren, L.: PC based high quality and low cost flight simulator. In: IEEE International Conference on Automation & Logistics. IEEE (2007)

    Google Scholar 

  5. Garner, R.: Metacognition and reading comprehension. Int. Rev. Educ. 17(1), 11–26 (2018)

    MathSciNet  Google Scholar 

  6. Zawadzka, K., Simkiss, N., Hanczakowski, M.: Remind me of the context: memory and metacognition at restudy. J. Mem. Lang. 101, 1–17 (2018)

    Article  Google Scholar 

  7. Metzger, K.J., Smith, B.A., Brown, E., Soneral, P.A.G.: Smash: a diagnostic tool to monitor pilot metacognition, affect, and study habits in an undergraduate science course. J. Coll. Sci. Teach. 47 (2018)

    Google Scholar 

  8. Forbes, E.J., Pekala, R.J.: Psychophysiological effects of several stress management techniques. Psychol. Rep. 72(1), 19–27 (1993)

    Article  Google Scholar 

  9. Schneiderman, N., Ironson, G., Siegel, S.D.: Stress and health: psychological, behavioral, and biological determinants. Annu. Rev. Clin. Psychol. 1(1), 607–628 (2005)

    Article  Google Scholar 

  10. Mcewen, B.: 213. Protective and damaging effects of mediators of stress and adaptation: central role of the brain. Brain Behav. Immun. 25 (2011)

    Google Scholar 

  11. Denollet, J., Van Heck, G.L.: Psychological risk factors in heart disease: what type personality is (not) about. J. Psychosom. Res. 51(3), 465–468 (2001)

    Article  Google Scholar 

  12. Kubzansky, L.D., Kawachi, I.: Going to the heart of the matter: do negative emotions cause coronary heart disease? J. Psychosom. Res. 48(4), 323–337 (2000)

    Article  Google Scholar 

  13. Kopp, M., Thege, B.K., Balog, P., Stauder, A., Salavecz, G., Rózsa, S.: Measures of stress in epidemiological research. J. Psychosom. Res. 69(2), 211–225 (2010)

    Article  Google Scholar 

  14. Horowitz, M., Wilner, N., Alvarez, W.: Impact of event scale: a measure of subjective stress. Psychosom. Med. 41(3), 209–218 (1979)

    Article  Google Scholar 

Download references

Acknowledgments

We would like to thank the instructors and the pilot students for participating the interviews, and the Boeing Company.

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Correspondence to Wei Liu .

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Li, Q. et al. (2021). Human-Machine Interaction Efficiency Factors in Flight Simulator Training Towards Chinese Pilots. In: Cassenti, D., Scataglini, S., Rajulu, S., Wright, J. (eds) Advances in Simulation and Digital Human Modeling. AHFE 2020. Advances in Intelligent Systems and Computing, vol 1206. Springer, Cham. https://doi.org/10.1007/978-3-030-51064-0_4

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