A.Yu. Cherepkov1, D.D. Matorin2, D.S. Zaitsev3

1-3 I.A. Bunin Yelets State University (Yelets, Russia)

1 cherepkov.andrey@mail.ru; 2 dmitr.matorin@yandex.ru; 3 dimanz1997@bk.ru

Problems related to the creation and implementation of tools that allow to effectively process pedagogical data and predict objective assessments of knowledge assimilation belong to the important problems of mathematical modeling of educational processes. In this paper we consider the projects of development of methodological and instrumental support of interactive testing and predictive modeling of students' knowledge level with the use of neural networks. Assessment of students' knowledge of mathematical disciplines requires the development of effective and adaptive methods that can take into account the level of training of each student. The aim of the research is to develop an approach to interactive testing and to modeling of educational processes using artificial intelligence methods, to develop algorithms and programs for assessment and prediction of knowledge in the learning process. The algorithm of interactive testing aimed at obtaining an objective and consistent assessment of students' knowledge on a given ten-point scale on the basis of quantitative data is developed and implemented. The realization of the method of predicting students' knowledge using neural networks and machine learning is proposed. The questions of identification of users in the conditions of distance learning are considered. The developed computer programs are aimed at obtaining predictive data of knowledge assimilation in the process of learning. The obtained results can be used in the problems of improving hybrid intelligent learning environments, in the problems of predicting student performance, in the problems of developing adaptive learning systems taking into account personalization, as well as in the tasks of modeling educational processes.

Cherepkov A.Yu., Matorin D.D., Zaitsev D.S. Instrumental support of interactive testing and predictive modeling of students' knowledge level using neural networks. Nonlinear World. 2023. V. 21. № 4. P. 39-45. DOI: https://doi.org/10.18127/j20700970-202304-05 (In Russian)

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