Adaptive Technology of Psychological Diagnostics Based on the Markovian and Quantum Representations of the Task Performing Process

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Abstract

A method for constructing adaptive diagnostic assessments using identifiable probabilistic Markovian models is presented, which provides individual test trajectories of subjects by reasonably choosing the optimal sequence of presenting tasks. The features of the applied adaptive approach are: identification and use in the construction of calculated estimates of the time dynamics of changes in the ability to cope with tasks; the possibility of taking into account the time spent on completing tasks; the number of tasks that should be submitted is smaller in comparison with other approaches, which provides the presented approach with advantages over analogues. A new approach to solving diagnostic problems by convolving applied Markovian models into quantum representations is considered, which makes it possible to identify the structure of the task execution process using quantum spectral analysis and use only essential information when forming a diagnostic solution, increasing the reliability of the results.

General Information

Journal rubric: Mathematical Modelling

Article type: scientific article

DOI: https://doi.org/10.17759/mda.2022120403

Funding. The work is carried out within the framework of the state task of the Ministry of Education of the Russian Federation No. 073-00110-22-06 dated 12.12.2022.

Received: 15.12.2022

Accepted:

For citation: Kuravsky L.S., Yuryev G.A., Yuryeva N.E., Isakov S.S., Nesimova A.O., Nikolaev I.A. Adaptive Technology of Psychological Diagnostics Based on the Markovian and Quantum Representations of the Task Performing Process. Modelirovanie i analiz dannikh = Modelling and Data Analysis, 2022. Vol. 12, no. 4, pp. 36–55. DOI: 10.17759/mda.2022120403. (In Russ., аbstr. in Engl.)

References

  1. Kuravskii L.S., Yur'ev G.A. Adaptivnoe testirovanie kak markovskii protsess: modeli i ikh identifikatsiya. - Neirokomp'yutery: razrabotka i primenenie, №2, 2011, pp. 21-29.
  2. Kuravskii L.S., Yur'ev G.A. Veroyatnostnyi metod fil'tratsii artefaktov pri adaptivnom testirovanii. - Eksperimental'naya psikhologiya, t.5, No.1, 2012, pp. 119-131.
  3. Kuravskii L.S., Yur'ev G.A. Ob odnom podkhode k adaptivnomu testirovaniyu i ustraneniyu ego artefaktov. // Neirokomp'yutery: razrabotka i primenenie, №1, 2012.
  4. Kuravskii L.S., Artemenkov S.L., Yur'ev G.A., Grigorenko E.L. Novyi podkhod k komp'yuterizirovannomu adaptivnomu testirovaniyu. Eksperimental'naya psikhologiya= Experimental Psychology (Russia). 2017. T. 10. №. 3. pp. 33—45. doi:10.17759/exppsy.2017100303
  5. G. Amosov. On Markovian Cocycle Perturbations in Classical and Quantum Probability. Int. J. Math. & Math. Sci., 2003 (54), 3443-3467 (2003).
  6. G. Amosov. On Markovian Perturbations of the Group of Unitary Operators Associated with a Stochastic Process with Stationary Increments. Theory Prob. & its Applications, 49 (1), 123-132 (2005).
  7. S. Kuravsky et al. Markovian Models in Diagnostics and Forecasting Problems: Textbook (Moscow State Univ. Psych. Educ., Moscow, 2017) [in Russian].
  8. S. Kuravsky, A.A. Margolis, P.A. Marmalyuk, A.S. Panfilova, G.A. Yuryev, P.N. Dumin. A Probabilistic Model of Adaptive Training. Applied Math. Sciences, 10 (48), 2369-2380 (2016).
  9. S. Kuravsky, S.L. Artemenkov, G.A. Yuryev, E.L. Grigorenko. A New Approach to Computerized Adaptive Testing. Exp. Psychology, 10 (3), 33-45 (2017).
  10. S. Kuravsky, S.N. Baranov and G.A. Yuryev. Synthesis and Identification of Hidden Markov Models Based on a Novel Statistical Technique in Condition Monitoring. In: Proc. 7th Int. Conf. on Condition Monitoring & Machinery Failure Prevention Technologies (Stratford-upon-Avon, England, 2010).
  11. S. Kuravsky. Discriminant analysis based on the approaches of quantum computing. Lobachevskii J. Math. 41 (12), 2338–2344 (2020).
  12. S. Kuravsky. Modeling Dynamical Behavior of Stochastic Systems: Spectral Analysis of Qubit Representations vs the Mutual Markovian Model Likelihood Estimations. Lobachevskii J. Math., 42 (10), 2364–2376 (2021).
  13. Марковские модели в задачах диагностики и прогнозирования: Учеб. пособие /Под ред. Л.С. Куравского. – 2-е изд., доп. – М.: Изд-во МГППУ, 2017. – 197 с.
  14. Овчаров Л. А. Прикладные задачи теории массового обслуживания. – М.: Машиностроение, 1969. – 324 с.
  15. Kuravsky L.S., Dumin P.N. and Yuryev G.A. Adaptive Aircraft Crew Training Based on Accumulated Empirical Experience. International Journal of Advanced Research in Engineering and Technology, 12(1), 2021, pp. 256-264. http://www.iaeme.com/IJARET/issues.asp?JType=IJARET&VType=12&IType=1.
  16. Kuravsky L.S., Greshnikov I.I. Optimizing the mutual arrangement of pilot indicators on an aircraft dashboard and analysis of this procedure from the viewpoint of quantum representations. - Journal of Applied Engineering Science, doi:10.5937/jaes0-31855.
  17. Kuravsky L.S., Marmalyuk P.A., Yuryev G.A. and Dumin P.N. A Numerical Technique for the Identification of Discrete-State Continuous-Time Markov Models - Applied Mathematical Sciences. Vol. 9, 2015, No. 8, pp. 379–391. URL: http://dx.doi.org/10.12988/ams. 2015.410882.
  18. Kuravsky L.S., Marmalyuk P.A., Yuryev G.A., Belyaeva O.B. and Prokopieva O.Yu. Mathematical Foundations of Flight Crew Diagnostics Based on Videooculography Data. - Applied Mathematical Sciences, Vol. 10, 2016, no. 30, 1449–1466, http://dx.doi.org/10.12988/ams.2016.6122.
  19. Kuravsky L.S., Yuriev G.A., Dumin P.N. Estimating the Influence of Human Factor on the Activity of Operators of Complex Technical Systems in Civil Engineering with the Aid of Adaptive Diagnostics, International Journal of Civil Engineering and Technology, 10(2), 2019, pp. 1930-1941. http://www.iaeme.com/IJCIET/issues.asp?JType= IJCIET&VType=10&IType=02.
  20. Kuravsky L.S., Yuryev G.A. Detecting Abnormal Activities of Operators of Complex Technical Systems and their Causes Basing on Wavelet Representations, International Journal of Civil Engineering and Technology (IJCIET) 10(2), 2019, pp. 724–742. http://www.iaeme.com/IJCIET/asp?JType=IJCIET&VType=10&IType=2.
  21. Kuravsky L.S., Yuryev G.A., Zlatomrezhev V.I. New approaches for assessing the activities of operators of complex technical systems. Eksperimental’naya psikhologiya = Experimental psychology (Russia), 2019, vol. 12, no. 4, pp. 27—49. doi:10.17759/exppsy.2019120403.
  22. Kuravsky L.S., Yuryev G.A., Zlatomrezhev V.I., Greshnikov I.I., Polyakov B.Y. Assessing the Aircraft Crew Activity Basing on Video Oculography Data. Eksperimental’naya psikhologiya = Experimental Psychology (Russia), 2021. Vol. 14, no. 1, pp. 204—222. DOI: https://doi.org/10.17759/exppsy.2021140110
  23. Kuravsky L.S., Yuryev G.A., Zlatomrezhev V.I., Yuryeva N.E. Assessing the Aircraft Crew Actions with the Aid of a Human Factor Risk Model. Eksperimental’naya psikhologiya = Experimental Psychology (Russia), 2020. Vol. 13, no. 2, pp. 153—181. DOI: https://doi.org/10.17759/exppsy.2020130211.
  24. Kuravsky L.S., Yuryev G.A., Zlatomrezhev V.I., Yuryeva N.E., Mikhaylov A.Y. Evaluating the Contribution of Human Factor to Performance Characteristics of Complex Technical Systems. - Modelirovanie i analiz dannykh = Modelling and Data Analysis, 2020. Vol. 10, no. 1, pp. 7–34. DOI: 10.17759/mda.2020100101.
  25. S. Kuravsky. Modeling Dynamical Behavior of Stochastic Systems: Spectral Analysis of Qubit Representations vs the Mutual Markovian Model Likelihood Estimations. Lobachevskii J. Math., 42 (10), 2364–2376 (2021). 
  26. S. Kuravsky. Simplification of Solving Diagnostics Problems by Convolution of Applied Markovian Models into the Quantum Representations. Lobachevskii J. Math., 43 (7), 1669–1682 (2022).
  27. Borg and P.J.F. Groenen. Modern Multidimensional Scaling Theory and Applications (Springer, New York, 2005).
  28. F. Morrison. Multivariate Statistical Methods, 2nd ed. (McGraw-Hill, New York, 1976).
  29. Lloyd. Handbook of Applicable Mathematics, Vol. 6: Statistics, Ed. by W. Ledermann (Wiley, Hoboken, 1984).
  30. F. Cox and M.A.A. Cox. Multidimensional Scaling, 2nd ed. (Chapman and Hall/CRC, Boca Raton, 2001).
  31. von Neumann. Mathematical Foundations of Quantum Mechanics (Princeton Univ. Press, Princeton, 1955).
  32. Holevo. Probabilistic and Statistical Aspects of Quantum Theory, 2nd ed. (Edizioni della Normale, Pisa, 2011).
  33. S. Kuravsky. Discriminant analysis based on the approaches of quantum computing. Lobachevskii J. Math. 41 (12), 2338–2344 (2020).

Information About the Authors

Lev S. Kuravsky, Doctor of Engineering, professor, Dean of the Computer Science Faculty, Moscow State University of Psychology and Education, Moscow, Russia, ORCID: https://orcid.org/0000-0002-3375-8446, e-mail: l.s.kuravsky@gmail.com

Grigory A. Yuryev, PhD in Physics and Matematics, Associate Professor, Head of Department of the Computer Science Faculty, Leading Researcher, Youth Laboratory Information Technologies for Psychological Diagnostics, Moscow State University of Psychology and Education, Moscow, Russia, ORCID: https://orcid.org/0000-0002-2960-6562, e-mail: g.a.yuryev@gmail.com

Nataliya E. Yuryeva, PhD in Engineering, Head of Laboratory, Youth Laboratory Information Technologies for Psychological Diagnostics, Research Fellow, Information Technology Center for Psychological Studies of the Computer Science Faculty, Moscow State University of Psychology and Education, Moscow, Russia, ORCID: https://orcid.org/0000-0003-1419-876X, e-mail: yurieva.ne@gmail.com

Sergey S. Isakov, Lecturer, Postgraduate Student of the Computer Science Faculty, Moscow State University of Psychology & Education, Moscow, Russia, ORCID: https://orcid.org/0000-0003-1719-2355, e-mail: isakovss@mgppu.ru

Alexandra O. Nesimova, Research Assistant, Youth Laboratory Information Technologies for Psychological Diagnostics, Moscow State University of Psychology and Education, Moscow, Russia, ORCID: https://orcid.org/0000-0002-8394-7376, e-mail: sasha.n2230@gmail.com

Ivan A. Nikolaev, Research Assistant, Youth Laboratory Information Technologies for Psychological Diagnostics, Moscow State University of Psychology and Education, Moscow, Russia, ORCID: https://orcid.org/0000-0002-7715-5575, e-mail: stripeddog@yandex.ru

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