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The concept of a self-learning adaptive trainer

Keywords:

L.S. Kuravsky - Dr.Sc. (Eng.), Professor, Dean of Computer Science Faculty, Moscow State University of Psychology and Education
E-mail: l.s.kuravsky@gmail.com
A.A. Margolis - Ph.D. (Psych.), Provost, Moscow State University of Psychology and Education
E-mail: margolisaa@mgppu.ru
G.A. Yuryev - Ph.D. (Phys.-Math.), Associate Professor, Deputy Dean, Computer Science Faculty, Moscow State University of Psychology and Education
E-mail: g.a.yuryev@gmail.com
D.A. Pominov - Research Scientist, Computer Science Faculty, Moscow State University of Psychology and Education
E-mail: pominovda@mgppu.ru


Presented is a concept of the self-learning adaptive trainer intended for adaptive learning and providing task selection with the aid of parametric mathematical models represented by the discrete-state continuous-time Markov random processes. Free parameters of the Markov processes used to describe the adaptive trainer are identified with the aid of observed and expected histograms representing frequencies of being in the model states. This identification is carried out separately for each of the attainment levels under consideration. Affiliation with different levels of training is determined with the aid of the Bayesian estimates. The approach in question is an alternative to the adaptive technologies based on the Item Response Theory. Possibility to take into account both temporal dynamics of solution ability and time spent for carrying out the operations as well as smaller number of tasks that must be completed by a subject to provide estimates with the given  accuracy are among the features of diagnostic methods in use.

References:
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