Cover Page

Cite item


The possibility of statement and solution of the problem of searching of theoretical justification and development of efficient didactic mechanisms of the organization of process of pedagogical monitoring and assessment of level of knowledge of trainees can be based on convergence of the leading psychological and pedagogical, mathematical, and informational technologies with accounting of the modern achievements in science. In the article, the pedagogical expediency of realization of opportunities of means of informational technologies in monitoring and assessment of the composite mathematical knowledge, in the management of cognitive activity of students is proved. The ability to integrate fractal methods and neural network technologies in perfecting of a system of pedagogical monitoring of mathematical knowledge of trainees as a part of the automated training systems (ATS) is investigated and realized in practice. It is proved that fractal methods increase the accuracy and depth of estimation of the level of proficiency of students and also complexes of intellectual operations of the integrative qualities allowing to master and apply cross-disciplinary knowledge and abilities in professional activity. Neural network technologies solve a problem of realization of the personal focused tutoring from positions of optimum individualization of mathematical education and self-realization of the person. The technology of projection of integrative system of pedagogical monitoring of knowledge of students includes the following stages: establishment of the required tutoring parameters; definition and preparation of input data for realization of integration of fractal and neural network technologies; development of the diagnostic module as a part of the block of an artificial intelligence of ATS, filling of the databases structured by system; start of system for obtaining the forecast. In development of the integrative automated system of pedagogical monitoring of knowledge the fact that individual evaluation test of tutoring of students is carried out on the basis of two parameters depths of assimilation of a concept, its interrelation with other concepts and assessment of size of the synergetic effect of integration of knowledge and activity of trainees is new. Experience of introduction and operation of the automated system of pedagogical monitoring and assessment of the level of knowledge on the basis of integration of fractal model operation and neural network technologies allowed to increase the level of objectivity of estimation of trainees, quality of management of the educational process, its effectiveness in general.

About the authors

Svetlana N Dvoryatkina

Bunin Yelets State University

Author for correspondence.

Svetlana N. Dvoryatkina - Doctor of Pedagogical Sciences, Professor, Department of Mathematics and Teaching Methods, Bunin Yelets State University (Yelets, Russia).

Kommunarov str., 28, Yelets, Russia, 399770


  1. Avanesov, V.S. (2015). Аpplication of Educational Technologies and Pedagogical Measurements to Modernization of Education. Pedagogicheskie izmerenia, (1), 3—28. (In Russ.).
  2. Bolotov, V., Valdman, I., Kovaleva, G., & Pinskaya, M. (2013). Russian Quality Assessment System in Education: Key Lessons. Education Quality in Eurasia, (1), 85—122.
  3. Dvoryatkina, S.N., & Smirnov, E.I. (2016). Assessment of the Synergetic Effects of Integration of Knowledge and Activity on the Basis of Computer Model Operation. The Modern Informational Technologies and IT Education (pp. 35—42). Moscow: MSU Publ. (In Russ.).
  4. Dvoryatkina, S., Smirnov, E., & Lopukhin, A. (2017). New Opportunities of Computer Assessment of Knowledge Based on Fractal Modeling. Proceedings of the 3rd international conference on higher education advances, HEAd 17 (pp. 854—864). Valensia: Universitat Politecnica de Valencia. doi: 10.22363/2313-1683-2017-14-4: 10.4995/HEAD17.2017.6713.
  5. Grushevsky, S.P, Dobrovolskaya, N.Yu., & Koltsov Yu.V. (2008). Organizatsiya uchebnogo protsessa na osnove neyrosetevoy komp’yuternoy obuchayushchey sistemy. The Bulletin of Adyghe State University: Internet Scientific Journal, (7), 142—148. (In Russ.).
  6. Hebb, D.O. (1949). The Organization of Behavior. New York: Wiley & Sons.
  7. Hopfield, J.J. (1982). Neural Networks and Physical Systems with Emergent Collective Computational Abilities. Proceedings of National Academy of Sciences, 79(8), 2554—2558.
  8. Kibzun, A.I., & Inozemtsev, A.O. (2014). Using the Maximum Likelihood Method to Estimate Test Complexity Levels. Automation and Remote Control, (4), 20—37. doi: 10.22363/2313-1683-2017-14-4: 10.1134/S000511791404002X. (In Russ.).
  9. Kozlov, O.A., Mikhailov, Yu.F., & Vershinina S.V. (2017). Management of Formation of Individual Educational Trajectories with Use of Information Technologies. Scientific notes of the IME RAE, (1—2), 62—64. (In Russ.)
  10. Kruglov, V.V., & Borisov, V.V. (2002). Iskusstvennye neyronnye seti. Teoriya i praktika. Moscow. 382 p. (In Russ.).
  11. Latyshev, V.L. (2009). Criteria of Estimation of Quality of Educational Component of Intellectual Teaching Systems. Informatization of Education and Science, (3), 89—96. (In Russ.).
  12. Monakhov, V.M. (2014). IT-obrazovanie i nekotorye voprosy evolyutsii otechestvennoy metodicheskoy sistemy obucheniya matematike, obespechivayushchie tekhnologizatsiyu uchebnogo protsessa. Modern Information Technologies and IT-education, (10), 100—106. (In Russ.).
  13. McCalloch, W.S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. V. 5, 115—133.
  14. Robert, I.V. (2016). Perspective Fundamental Researches in the Field of Informatization of Education. Scientific Notes of the IME RAE, (59), 78—85. (In Russ.).
  15. Rosenblatt, F. (1962). Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms. Washington, DC: Spartan Books.
  16. Rudinskiy, I.D., & Davydova N.A. (2014). Perspectives for Automation of Knowledge Control Tests Item Preparation. The Tidings of the Baltic State Fishing Fleet Academy: Psychological and pedagogical sciences, (1), 43—47. (In Russ.).
  17. Shadrikov, V.D., & Kuznetsova, M.D. (2011). Metodika otsenki urovnya kvalifikatsii pedagogicheskikh kadrov. Metodicheskaya rabota v shkole, (1), 3—33. (In Russ.).
  18. Uglev, V.A. (2010). On the Specificity of Individualization of Training in Automated Training Systems. Philosophy of Education, (2), 68—74. (In Russ.).
  19. Usova, A.V. (2007) Proverka i puti povysheniya kachestva znaniy. Chelyabinsk. (In Russ.).

Copyright (c) 2017 Dvoryatkina S.N.

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

This website uses cookies

You consent to our cookies if you continue to use our website.

About Cookies