Comparative analysis of two new concepts of adaptive training



Two new concepts of adaptive learning are presented. The first of them is based on a self-learning probabilistic model, the second one uses multivariate statistical analysis of wavelet representations for task execution trajectories as well as a matrix of recommended transitions. A comparative analysis of various aspects of their practical application has been carried out.

General Information

Keywords: adaptive training, IRT, method of patterns, Markov random processes, wavelet analysis, self-learning systems.

Journal rubric: Mathematical Psychology

Article type: scientific article


Funding. This work has been supported by the Russian Foundation for Basic Research (Project No 17-29-07034).

For citation: Kuravsky L.S., Yuryev G.A., Dumin P.N., Pominov D.A. Comparative analysis of two new concepts of adaptive training. Eksperimental'naâ psihologiâ = Experimental Psychology (Russia), 2019. Vol. 12, no. 2, pp. 177–192. DOI: 10.17759/exppsy.2019120213. (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:, e-mail:

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:, e-mail:

Pavel N. Dumin, PhD in Physics and Matematics, head of laboratory of quantitative psychology, faculty of information technologies, Moscow State University of Psychology and Education (MSUPE), Moscow, Russia, ORCID:, e-mail:

Denis A. Pominov, Research Scholar, Computer Science Faculty, Moscow State University of Psychology and Education, Moscow, Russia, ORCID:, e-mail:



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