The introduction of intellectual system for evaluating professional abilities of applicants into the activities of educational institutions

Authors

DOI:

https://doi.org/10.15587/2312-8372.2018.149680

Keywords:

identification of abilities and achievements of a person, intellectual system, organization of processing fuzzy information

Abstract

The object of research is the methods and means of professional identification of applicants. The research aims to provide applicants with scientifically based decision support for choosing a field of study. The introduction of intelligent decision support systems in the process of self-actualization of applicants will significantly improve the effectiveness of vocational guidance activities of educational institutions.

One of the biggest problems of the intellectualization of systems for assessing abilities and achievements is that the test results of applicants represent a lot of fuzzy data. At the same time, the correctness of data separation significantly depends on the construction of a fuzzy set of features for the conclusion of a diagnostic solution. In addition, the most common tests do not take into account the requirements for specialists of the construction industry.

The basis of the developed system is tests to determine the personality structure of the Integrated Professional Orientation Diagnostics “Applicant”. This system contains reference information about the vocational category of training and tests for determining the structure of the individual. Conclusions are based on techniques that allow to predict the success of activities in various industries. The ability of a person to a certain professional activity reflects the ability to acquire special knowledge and skills in the learning process. That is why in the course of the study the "Applicant" tests were used. To improve the reliability of assessing the professional abilities of the applicant, it is proposed to use an intelligent system based on the Takagi-Sugeno-Kang fuzzy neural network. This choice is due to the fact that the Takagi-Sugeno-Kang network has a number of features that provide it with advantages in solving the problem of matching the abilities of an applicant to the possibility of acquiring knowledge and skills in a particular specialty. In particular, the ability of fuzzy neural networks to separate linearly inseparable data. This ensures the ability of the system to isolate the natural abilities of applicants from a mixture of data.

Compared to other means, the Takagi-Sugeno-Kang network makes it possible to solve the problem of classifying a very large amount of data by a network of smaller dimension.

Author Biographies

Bohdan Yeremenko, Kyiv National University of Construction and Architecture, 31, Povіtroflotsky ave., Kyiv, Ukraine, 03037

PhD

Department of Information Technology Design and Applied Mathematics

Yulia Ryabchun, Kyiv National University of Construction and Architecture, 31, Povіtroflotsky ave., Kyiv, Ukraine, 03037

Postgraduate Student

Department of Information Technology Design and Applied Mathematics

Ganna Ploska, Limited Liability Company «Building Quarter», 13/2-B, Yaroslaviv Val str., Kyiv, Ukraine, 01054

Researcher

References

  1. SAT Program. Available at: http://www.collegeboard.com/student/testing/sat
  2. Subbotin, S. O. (2008). Podannia y obrobka znan u systemakh shtuchnoho intelektu ta pidtrymky pryiniattia rishen. Zaporizhzhia: ZNTU, 341.
  3. Ewing, M., Huff, K., Andrews, M., King, K. (2005). Assessing the Reliability of Skills Measured by the SAT. Research Notes. Office of Research and Analysis. New York: The College Board.
  4. Delaso (2006). Principles and Practice of Language Testing: Materials of training in the Principles and Practice of Language Testing. UK Ltd.
  5. Fischer, F., Kollar, I., Stegmann, K., Wecker, C. (2013). Toward a Script Theory of Guidance in Computer-Supported Collaborative Learning. Educational Psychologist, 48 (1), 56–66. doi: http://doi.org/10.1080/00461520.2012.748005
  6. Anastazi, A., Urbina, S. (2006). Psikhologicheskoe testirovanie. Saint Petersburg: Piter, 688.
  7. Kompleksna proforiientatsiina diahnostyka «Abituriient». Avialable at: http://cleverdia.com/index.php?lang=uk
  8. Bassina, E. (1990). Identification: reality or a theoretic construct? Dynamische Psychiatrie. Dynamic Psychiatry. West Berlin, 31–48.
  9. Coleman, M. R. (2003). The Identification of Students Who Are Gifted. ERIC EC Digest No. E644, 4.
  10. Identification. National Association for gifted children. Available at: http://www.nagc.org/resources-publications/gifted-education-practices/identification
  11. Shandruk, S. K. (2015). Theoretical-methodological foundtions of organization of training-productive activity of students-psychologists. Naukovyi ohliad, 7 (17), 134–144.
  12. Zaitseva, E. N., Levashenko, V. G. (2013). Importance analysis by logical differential calculus. Automation and Remote Control, 74 (2), 171–182. doi: http://doi.org/10.1134/s000511791302001x
  13. Lytvyn, V. V. (2011). Bazy znan intelektualnykh system pidtrymky pryiniattia rishen. Lviv: Vydavnytstvo Lvivskoi politekhniky, 240.
  14. Blum, C., Puchinger, J., Raidl, G. R., Roli, A. (2011). Hybrid metaheuristics in combinatorial optimization: A survey. Applied Soft Computing, 11 (6), 4135–4151. doi: http://doi.org/10.1016/j.asoc.2011.02.032
  15. Osowski, S. (2000). Sieci neuronowe do przetwarzania informacji. Warszawa, 342.
  16. Tanaka, K., Yoshida, H., Ohtake, H., Wang, H. O. (2009). A Sum-of-Squares Approach to Modeling and Control of Nonlinear Dynamical Systems With Polynomial Fuzzy Systems. IEEE Transactions on Fuzzy Systems, 17 (4), 911–922. doi: http://doi.org/10.1109/tfuzz.2008.924341
  17. Terenchuk, S., Pashko, A., Yeremenko, B., Kartavykh, S., Ershovа, N. (2018). Modeling an intelligent system for the estimation of technical state of construction structures. Eastern-European Journal of Enterprise Technologies, 3 (2 (93)), 47–53. doi: http://doi.org/10.15587/1729-4061.2018.132587

Published

2018-05-31

How to Cite

Yeremenko, B., Ryabchun, Y., & Ploska, G. (2018). The introduction of intellectual system for evaluating professional abilities of applicants into the activities of educational institutions. Technology Audit and Production Reserves, 6(2(44), 22–26. https://doi.org/10.15587/2312-8372.2018.149680

Issue

Section

Information Technologies: Original Research