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Modelling and Data Analysis
2021. Vol. 11, no. 4, 5–20
doi:10.17759/mda.2021110401
ISSN: 2219-3758 / 2311-9454 (online)

The Concept of an Adaptive Trainer and Assessing Its Effectiveness in a Mathematical Application

161

L.S. Kuravsky, D.A. Pominov, G.A. Yuryev, N.E. Yuryeva, M.A. Safronova, Y.D. Kulanin, S.N. Antipova

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Abstract

Presented is a mathematical model of the self-learning adaptive trainer intended for adaptive learning and providing task selection. The approach in question is an alternative to the adaptive technologies based on the Item Response Theory. Possibility to take into account temporal dynamics of solution ability as well as smaller number of tasks that must be performed by a subject to provide the given results are among the features of the methods in use. To assess the effectiveness of the adaptive trainer concept under consideration, its web-implementation intended for training school students to solve mathematical tasks covered by the school curriculum was employed. The analysis performed revealed both high efficiency of the adaptive trainer (the mean test rating has increased 1.54 times owing to its use) and proven statistically significant influences of the adaptive training factor on the observed mathematical test results.

General Information

Keywords: adaptive learning, Markovian random processes, adaptive trainer, self-learning systems

Journal rubric: Data Analysis

Article type: scientific article

DOI: https://doi.org/10.17759/mda.2021110401

Funding. The work was financially supported by the Ministry of Education of the Russian Federation within the framework of State Assignment “Development and practical implementation of an adaptive training model based on the identifiable Markovian processes“ dated 10 December 2021, No. 073–00041–21–10.

For citation: Kuravsky L.S., Pominov D.A., Yuryev G.A., Yuryeva N.E., Safronova M.A., Kulanin Y.D., Antipova S.N. The Concept of an Adaptive Trainer and Assessing Its Effectiveness in a Mathematical Application. Modelirovanie i analiz dannikh = Modelling and Data Analysis, 2021. Vol. 11, no. 4, pp. 5–20. DOI: 10.17759/mda.2021110401.

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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

Denis A. Pominov, Research Scholar, Computer Science Faculty, Moscow State University of Psychology and Education, Moscow, Russia, ORCID: https://orcid.org/0000-0002-1321-3713, e-mail: pominovda@mgppu.ru

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

Maria A. Safronova, PhD in Psychology, Dean of the Faculty of Psychology of Education, Research fellow of the laboratory of Theoretical and experimental issues in cultural-historical psychology, Moscow State University of Psychology and Education, Moscow, Russia, ORCID: https://orcid.org/0000-0002-3597-6375, e-mail: mariasaf@gmail.com

Yevgeny D. Kulanin, PhD in Physics and Matematics, Professor, Moscow State University of Psychology and Education, Moscow, Russia, ORCID: https://orcid.org/0000-0001-6093-7012, e-mail: lucas03@mail.ru

Svetlana N. Antipova, Deputy Dean for Extra-Curricular Work, Computer Science Faculty, Moscow State University of Psychology and Education, Moscow, Russia, ORCID: https://orcid.org/0000-0001-6642-7953, e-mail: antipovasn@mgppu.ru

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