Proceedings of International Conference on Applied Innovation in IT
2021/04/28, Volume 9, Issue 1, pp.77-82

Technology of Computer Monitoring of the Quality of Educational Process

Tatiana Monastyrskaya, Alexey Poletaikin, Julia Shevtsova, Elena Melekhina

Abstract: This research aimed at developing operational assessment tool to minimize the university risk background with the purpose to raise the quality of the educational process. The original mathematical approach is proposed as a means to solve the problem of assuring the quality of education. The method of modified risk thermometer and binary fuzzy relations composition were used as the basic methods of sociological monitoring data analysis to measure the satisfaction of students with educational process. The method of modified risk thermometer identifies the risk background of the educational process, defined by the Key Risk Indicators. The method of fuzzy analysis allows to consider and minimize the existing uncertainty of the educational process and risk background. It is shown on the example that if the university risk background is of high degree, it necessitates taking the complex of management decisions to improve the situation with the risk background. The theoretical significance of the research is in development of the methodology of educational computer monitoring. The application of this methodology raises satisfaction of students and teachers with educational process, objectivity of management decisions and their implementation into educational process in order to normalize the risk temperature, which is the practical significance of the research. The degree of this condition corresponding to the normal one is defined at the next stage and needs taking further management decisions. The described methodology is a universal and efficient tool to revaluate the activity of not only universities but also of any company at risk as well as to organize the process of risk management in social and economic systems.

Keywords: Quality of Education, Sociological Monitoring, Computer Monitoring, Risk Thermometer, Key Risk Indicators, Fuzzy Composition, Management Decision

DOI: 10.25673/36587.2

Download: PDF


  1. M. Bratti, A. McKnight, R. Naylor, and J. Smith, “Higher education outcomes, graduate employment and university performance indicators.” in Journal of Royal Statistical Society A,167, part 3, pp. 475-496, 2004.
  2. V. Scherman and R.J.Bosker, “The Role of Monitoring in Enhancing the Quality of Education,” in: Scherman V., Bosker R.J., Howie S.J. (eds) Monitoring the Quality of Education in Schools. [Online]. Available:, 2017.
  3. I. V. Mitrofanova, “Multilevel monitoring as as social technology for managing the quality of professional education in modern Russia” cand. of sci.dissertation, Moscow, p. 158, 2009.
  4. M. A. Burova, “Social monitoring as a means of managing comprehensive education”, Saratov, p.177, 2009.
  5. B. Williamson, “Policy networks, performance metrics and platform markets: Charting the expanding data infrastructure of higher education,” in British Journal of Educational Technology, vol. 50. N 6, pp. 2794-2809, 2019. doi:10.1111/bjet.12849.
  6. H. Lucas, M. Greely, and K. Roelen, “Real Time Monitoring for the Most Vulnerable: Concepts and Methods,” in IDS Bulletin, vol. 44, N 2, pp. 15-30, March 2013.
  7. T.-E. Chen, et al., “An effective monitoring framework and user interface design,” in Software: practice and experience, vol. 45, pp.549-570, 2015.
  8. Commonwealth Risk Management Policy [Online]. Available: files/2019-11/commonwealth-risk-management- policy_0.pdf.
  9. J. Birkinshaw and H. Jenkins, “Making better risk management decisions,” in Business strategy review vol.4, pp. 41-45, 2010.
  10. M. Adib and X.-Z. Zhang, “The risk-based management control system: A stakeholders’ perspective to design management control systems,” in International Journal of Management and Enterprise Development, vol.18, issue 1-2, pp. 20-40, 2019.
  11. J. Young, “The use of key risk indicators by banks as an operational risk management tool: a south African perspective,” in Corporate Ownership & Control, vol. 9, issue 3, pp.172-185, 2012.
  12. A. A. Beloglazov, L. B. Beloglazova, O. V. Bondareva, and H. E.Ismailova, “Monitoring of the efficiency of teaching under conditions of education modernization and computerization,” in Bulletin of the Russian Peoples’ Friendship University, vol.14, N 2, pp. 220-232, 2017.
  13. E. Razinkina, et al., “Student satisfaction as an element of education quality monitoring in innovative higher education institution” in E3S W eb of Conferences, vol.33 [Online]. Available:, 2018.
  14. Methodology of the procedure of accreditation expertise for university curricula, Moscow : Federal State Budget Organization “Rosaccredagentstvo”, p. 164 [Online]. Available:, 2015.
  15. T. S. Iljina, A. I. Baranova, and V. S. Kanev, “Management of the educational competence risk in tertiary education” in SibSUTIS Bulletin, vol. 1, pp. 3-11, 2017.
  16. O. M. Lopez, S. M. Hurtado, O. Botero, and F. Legendre, “Risk assessment methodology: Implementation of duration gap in corporate portfolios in order to reduce the systemic risk” in Estudios Gerenciales, vol. 34, issue 146, pp. 34-41, 2018.
  17. T. I. Monastyrskays, E. E. Gorjachenko, and N. L. Mikidenko, “Development and testing of the methodology of social monitoring for assessing the quality of education in SibSUTIS,” report, Novosibirsk, p. 367, 2015.
  18. R. N. Ismailova, O. V. Krjukova, N. G. Nikolaeva, and E. V. Rakov, “Monitoring of consumer satisfaction” [Online]. Available: udovletvorennosti-potrebiteley/viewer, 2014.
  19. V. N. Vjatkin and V. A. Gamza, “Risk-managemnet of a firm: the program of integrative risk- management,” Moscow: ‘Financy i Statistica’ publisher, p. 400, 2006.
  20. A. Pegat, “Fuzzy modelling and management”, 3-d edition, BINOM publisher, p. 801, 2015.
  21. M. Jevšček, “Competencies assessment using fuzzy logic” in Journal of Universal Excellence, vol. 5, pp. 187-202, 2016.
  22. M. Laal, “Knowledge management in higher education” in Procedia Computer Science, vol. 3, pp. 544-549, 2011.
  23. A. Varghese, Sh. Kolamban, S. Nayaki, and S. J. Prasad, “Outcome based Assessment using Fuzzy Logic” in International Journal of Advanced Computer Science and Applications, vol. 8, issue 1, pp. 103-106, 2017.
  24. Y. Shevtsova, V. Kanev, A. Poletaikin, and N. Kuleshova, “Optimizing Risk-Free Model of Development of Educational Organization Based on Modified Risk Thermometer” in materials of the 15th International Asian School-Seminar Optimization Problems of Complex Systems, Novosibirsk, Russia, pp. 68-72, 2019.
  25. N. V. Katilova and S. Angel, “The practice of key indicators for operational risks,” in Financial risk management, vol. 2, pp. 190-204, 2006.
  26. X. Shi, Y.D. Wong, M.Z.F. Li, and C. Chai, “Key risk indicators for accident assessment conditioned on pre-crash vehicle trajectory” in Accident analysis and prevention, vol.117, pp.346-356, 2018.



       - Call for Papers
       - Paper Submission
       - Important Dates
       - Committee
       - Guest registration


       - Volume 9, Issue 1 (ICAIIT 2021)
       - Volume 8, Issue 1 (ICAIIT 2020)
       - Volume 7, Issue 1 (ICAIIT 2019)
       - Volume 7, Issue 2 (ICAIIT 2019)
       - Volume 6, Issue 1 (ICAIIT 2018)
       - Volume 5, Issue 1 (ICAIIT 2017)
       - Volume 4, Issue 1 (ICAIIT 2016)
       - Volume 3, Issue 1 (ICAIIT 2015)
       - Volume 2, Issue 1 (ICAIIT 2014)
       - Volume 1, Issue 1 (ICAIIT 2013)


       ICAIIT 2021
         - Photos
         - Reports

       ICAIIT 2020
         - Photos
         - Reports

       ICAIIT 2019
         - Photos
         - Reports

       ICAIIT 2018
         - Photos
         - Reports





           ISSN 2199-8876
           Copyright © 2013-2021 Leonid Mylnikov. All rights reserved.