Cognitive Performance Degradation in High School Students as the Response to the Psychophysiological Changes

  • Oleksandr BurovEmail author
  • Evgeniy Lavrov
  • Svitlana Lytvynova
  • Nadiia Pasko
  • Svitlana Dubovyk
  • Olena Orliyk
  • Olga Siryk
  • Vasyl Kyzenko
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1201)


This paper describes experimental study of cognitive performance degradation in high school students as the response to the psychophysiological changes in their activity support. The technique of studying the stability of cognitive abilities of high school students has revealed significant fluctuations in the speed and reliability of simple cognitive test tasks. A strong correlation between subjects’ cognitive test activity and individual properties of their cardiovascular and nervous system, as well as energy regulation and solar physiological parameters (speed and density of solar wind) has been revealed (R = 0.88 … 0.91, p < 0.01). It is articulated that identified features of cognitive activity require further investigation and clarification of the mechanisms of regulation of such activity.


Cognitive activity Physiological support External factors 



This research has been supported by the Institute of Information Technologies of the National Academy of Pedagogic Science.


  1. 1.
    Burov, O.: Day-to-day monitoring of an operator’s functional state and fitness-for-work: a psychophysiological and engineering approach. In: Chebykin, O.Y., Bedny, G., Karwowski, W. (eds.) Ergonomics and Psychology: Developments in Theory and Practice, pp. 107–126. CRC Press, Boca Raton (2008)
  2. 2.
    Zubchenko, T.M., Naumenko, Yu.A., Burov, O.Yu.: ICT for studying the dynamics of school abilities under the influence of external and internal factors. Komp’iuter u shkoli ta sim’ii 1, 3–14 (2017)
  3. 3.
    McLachlan, S., Dube, K., Kyrimi, E.: On behalf of HiKER (Health Informatics and Knowledge Engineering Research) Group: LAGOS: learning health systems and how they can integrate with patient care. BMJ Health Care Inform. (2019).  
  4. 4.
    Lytvynova, S., Melnyk, O.: Professional development of teachers using cloud services during non-formal education. In: Proceedings of the 12th International Conference on ICTERI 2016, Kyiv, Ukraine, 21–24 June 2016. Integration, Harmonization and Knowledge Transfer, vol. 1614, pp. 648–655. (2016).
  5. 5.
    Michalak, D., Rozmus, M.: Methods and tools for acquiring high-quality skills in digital era - innovative practices and results from 3DSPEC and e-MOTIVE projects. In: Karwowski, W., et al. (eds.) AHFE 2019. AISC, vol. 963, pp. 260–270 (2019).  
  6. 6.
    Bedny, G.Z., Karwowski, W.: A systemic-structural activity approach to the design of human-computer interaction tasks. Int. J. Hum.-Comput. Interact. 16(2), 235–260 (2003)
  7. 7.
    Pinchuk, O., Burov, O., Lytvynova, S.: Learning as a systemic activity. In: Karwowski, W., et al. (eds.) AHFE 2019. AISC, vol. 963, pp. 335–342 (2019).  
  8. 8.
    Burov, O.: Life-long learning: individual abilities versus environment and means. In: Proceedings of the 12th International Conference on ICTERI 2016, Kyiv, Ukraine, 21–24 June 2016. Integration, Harmonization and Knowledge Transfer, vol. 1614, pp. 608–619. (2016).
  9. 9.
    Burov, A.: Evaluation of functional state of operators on parameters of mental serviceability. Hum. Physiol. 2, 29–36 (1986)
  10. 10.
    Lavrov, E., Lavrova, O.: Intelligent adaptation method for human-machine interaction in modular E-learning systems. In: Proceedings of the 15th International Conference on ICT in Education, Research and Industrial Applications Integration, Harmonization and Knowledge Transfer. Volume II: Workshops, Kherson, Ukraine, 12–15 June 2019, pp. 1000–1010 (2019)
  11. 11.
    Mulder, L.J.M., Van Roon, A., Veldman, H., Laumann, K., Burov, A., Quispel, L., Hoogeboom, P.J.: How to use cardiovascular state changes in adaptive automation. In: Hockey, G.R.J., Gaillard, A.W.K., Burov, O. (eds.) Operator Functional State. The Assessment and Prediction of Human Performance Degradation in Complex Tasks. NATO Science Series, pp. 260–272. IOS Press, Amsterdam (2004)
  12. 12.
    Burov, O.Yu., Pinchuk, O.P., Pertsev, M.A., Vasylchenko, Y.V.: Use of learners’ state indices for design of adaptive learning systems. Inf. Technol. Learn. Tools 68(6), 20–32 (2018)
  13. 13.
    Rudenko, S.A.: Study of school children chronic fatigue with method of acupuncture diagnostic by Nakatani. In: Seminar Proceedings Psychophysiological Aspects of Giftedness: Theory and Practice, Kyiv, Ukraine, 3 February 2012, pp. 92–97 (2012)

Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021

Authors and Affiliations

  • Oleksandr Burov
    • 1
    Email author
  • Evgeniy Lavrov
    • 2
  • Svitlana Lytvynova
    • 1
  • Nadiia Pasko
    • 3
  • Svitlana Dubovyk
    • 3
  • Olena Orliyk
    • 4
  • Olga Siryk
    • 5
  • Vasyl Kyzenko
    • 6
  1. 1.Institute of Information Technologies and Learning Tools of National Academy of Educational Sciences of UkraineKievUkraine
  2. 2.Sumy State UniversitySumyUkraine
  3. 3.Sumy National Agrarian UniversitySumyUkraine
  4. 4.Scientific Research Institute of Intellectual PropertyKievUkraine
  5. 5.Kyiv National University named Taras ShevchenkoKievUkraine
  6. 6.Institute of Pedagogy of the National Academy of Educational Sciences of UkraineKievUkraine

Personalised recommendations