Information Modeling of the Students’ Residual Knowledge Level

  • S. I. Makarov
  • S. A. SevastyanovaEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 908)


The training of specialists for the economy of the new format will be more effective if all components of the education system are brought in line with the new paradigms. Digitization of economy changes the goals, principles, forms, means and methods of the learning process, including its control measures. Fixing the level of residual knowledge is one of the procedures used in the Russian education system to monitor the quality of education at various levels. Typically, this procedure is implemented in the form of computer testing on the previously learned disciplines. The analysis of test results gives grounds for making decisions on the implementation of corrective measures. This determines the importance of the task to get reliable, accessible and informative monitoring results. The article suggests a method of visualization and interpretation of monitoring results of retained knowledge, and substantiates the possibility of its use in order to analyze the problems of training at the individual and group levels. It is offered to use a vector form of representation of the data set to process statistical information. In this case, the visualization of monitoring results has the form of a spatial vector or a cut-out shape of a multidimensional vector onto a plane. The possibilities of the visual assessment to reach threshold values of knowledge are discussed. Examples of using a vector model for monitoring the level of retained knowledge in three or more disciplines are given. An approach to solve the problem of comparability of monitoring results carried out in various assessment systems is proposed. The main conclusions and results can be used directly in the educational process, in the field of education management, in psychological and pedagogical work.


Transition to digital economy Mathematical methods in pedagogics Monitoring results Monitoring of retained knowledge Processing of monitoring data 


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Samara State University of EconomicsSamaraRussia

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