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.)

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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, Junior Researcher, 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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