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How Metacognitive Monitoring Feedback Influences Workload in a Location-Based Augmented Reality Environment

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Engineering Psychology and Cognitive Ergonomics (HCII 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12767))

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Abstract

This research aims to investigate the impact on workload caused by metacognitive monitoring feedback (MCMF) in a location-based augmented reality (AR) learning environment. MCMF helps learners to monitor and control their cognitive processes and influences their learning behaviors. However, it should be studied further how MCMF affects student workload while using the AR system. In this study, we conducted an experiment to compare perceived mental workload between two groups (with MCMF vs. without MCMF). The results show that MCMF does not increase students’ workload. It means that MCMF can be used effectively without workload increment while learning. The current study advanced our understanding of metacognitive strategies on subject interaction in a location-based AR environment. Furthermore, the study outcomes could develop better metacognitive strategies without increasing learners’ workload in the AR environment.

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References

  1. Hacker, D.J., Dunlosky, J., Graesser, A.C.: Handbook of Metacognition in Education. Routledge, London (2009)

    Book  Google Scholar 

  2. Dunlosky, J., Tauber, S.U.K.: The Oxford Handbook of Metamemory. Oxford University Press, Oxford (2016)

    Google Scholar 

  3. Shaughnessy, M.F., Veenman, M., Kennedy, C.K.: Meta-Cognition: A Recent Review of Research, Theory, and Perspectives. Nova Publishers, New York (2008)

    Google Scholar 

  4. Evans, J.R., Fisher, R.P.: Eyewitness memory: balancing the accuracy, precision and quantity of information through metacognitive monitoring and control. Appl. Cogn. Psychol. 25(3), 501–508 (2011)

    Article  Google Scholar 

  5. Schraw, G.: A conceptual analysis of five measures of metacognitive monitoring. Metacogn. Learn. 4(1), 33–45 (2009)

    Article  Google Scholar 

  6. Kim, J.H.: The effect of metacognitive monitoring feedback on performance in a computer-based training simulation. Appl. Ergon. 67, 193–202 (2018)

    Article  Google Scholar 

  7. Kim, J.H.: The impact of metacognitive monitoring feedback on mental workload and situational awareness. In: Harris, D. (ed.) EPCE 2018. LNCS (LNAI), vol. 10906, pp. 32–41. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-91122-9_3

    Chapter  Google Scholar 

  8. Galy, E., Cariou, M., Mélan, C.: What is the relationship between mental workload factors and cognitive load types? Int. J. Psychophysiol. 83(3), 269–275 (2012)

    Article  Google Scholar 

  9. Hart, S.G.: NASA-task load index (NASA-TLX); 20 years later. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting. Sage Publications (2006)

    Google Scholar 

  10. Cao, A., Chintamani, K.K., Pandya, A.K., Ellis, R.D.: NASA TLX: software for assessing subjective mental workload. Behav. Res. Methods 41(1), 113–117 (2009). https://doi.org/10.3758/BRM.41.1.113

    Article  Google Scholar 

  11. Caldwell, J.A.: Fatigue in aviation. Travel Med. Infect. Dis. 3(2), 85–96 (2005)

    Article  Google Scholar 

  12. Wiegmann, D.A., Shappell, S.A.: Human error analysis of commercial aviation accidents: application of the Human Factors Analysis and Classification System (HFACS). Aviat. Space Environ. Med. 72(11), 1006–1016 (2001)

    Google Scholar 

  13. Palinko, O., et al.: Estimating cognitive load using remote eye tracking in a driving simulator. In: Proceedings of the 2010 Symposium on Eye-Tracking Research & Applications. ACM (2010)

    Google Scholar 

  14. Lauer, T.R., et al.: The Masses of Nuclear Black Holes in Luminous Elliptical Galaxies and Implications for the Space Density of the Most Massive Black Holes Based on observations made with the NASA/ESA Hubble Space Telescope, obtained at the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS 5-26555. These observations are associated with GO and GTO proposals 5236, 5446, 5454, 5512, 5943, 5990, 5999, 6099, 6386, 6554, 6587, 6633, 7468, 8683, and 9107. Astrophys. J. 662(2), 808 (2007)

    Google Scholar 

  15. Hwang, S.-L., et al.: Application control chart concepts of designing a pre-alarm system in the nuclear power plant control room. Nucl. Eng. Des. 238(12), 3522–3527 (2008)

    Article  Google Scholar 

  16. Erzberger, H.: Automated conflict resolution for air traffic control (2005)

    Google Scholar 

  17. Guo, W., Kim, J.H.: How augmented reality influences student workload in engineering education. In: Stephanidis, C., et al. (eds.) HCII 2020. LNCS, vol. 12425, pp. 388–396. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-60128-7_29

    Chapter  Google Scholar 

  18. Flavell, J.H.: Metacognition and cognitive monitoring: a new area of cognitive–developmental inquiry. Am. Psychol. 34(10), 906 (1979)

    Article  Google Scholar 

  19. Nelson, T.O.: Metamemory: a theoretical framework and new findings. In: Psychology of Learning and Motivation, pp. 125–173. Elsevier (1990)

    Google Scholar 

  20. Livingston, J.A.: Metacognition: An Overview (2003)

    Google Scholar 

  21. Dunlosky, J., Metcalfe, J.: Metacognition. Sage Publications, Thousand Oaks (2008)

    Google Scholar 

  22. Fiorella, L., Vogel-Walcutt, J.J.: Metacognitive prompting as a generalizable instructional tool in simulation-based training. In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting. SAGE Publications (2011)

    Google Scholar 

  23. Rhem, J.: Using Reflection and Metacognition to Improve Student Learning: Across the Disciplines, Across the Academy. Stylus Publishing, LLC (2013)

    Google Scholar 

  24. Bannert, M., et al.: Short-and long-term effects of students’ self-directed metacognitive prompts on navigation behavior and learning performance. Comput. Hum. Behav. 52, 293–306 (2015)

    Article  Google Scholar 

  25. Schraw, G., Moshman, D.: Metacognitive theories. Educ. Psychol. Rev. 7(4), 351–371 (1995)

    Article  Google Scholar 

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Correspondence to Wenbin Guo .

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Guo, W., Kim, J.H. (2021). How Metacognitive Monitoring Feedback Influences Workload in a Location-Based Augmented Reality Environment. In: Harris, D., Li, WC. (eds) Engineering Psychology and Cognitive Ergonomics. HCII 2021. Lecture Notes in Computer Science(), vol 12767. Springer, Cham. https://doi.org/10.1007/978-3-030-77932-0_14

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  • DOI: https://doi.org/10.1007/978-3-030-77932-0_14

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  • Print ISBN: 978-3-030-77931-3

  • Online ISBN: 978-3-030-77932-0

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