Development of model of intellectual system of evaluation of professional qualities of abiturients

Authors

  • B. Yeremenko Department of Information Technology Design and Applied Mathematics, Kyiv National University of Civil Engineering and Architecture, Povіtroflotsky Avenue, 31, Kyiv
  • Y. Ryabchun Department of Information Technology Design and Applied Mathematics, Kyiv National University of Civil Engineering and Architecture, Povіtroflotsky Avenue, 31, Kyiv
  • A. Pachko Taras Shevchenko National University of Kyiv, Volodymyrska str., 60, Kyiv
  • H. Ploska Humane Recourses Building Portal , Zahidna street, 4, 03067, Kyiv

DOI:

https://doi.org/10.30838/P.CMM.2415.270818.31.226

Keywords:

identification, professional skills, fuzzy neural network

Abstract

The question of choosing the direction and specialty of study becomes especially relevant at the stage of admission to a higher educational institution. In this paper, a decision support system is developed for entrants who cannot independently determine what they want to do in the future. Goal. The development of a model and algorithm for training an intellectual system for assessing the professional abilities of university entrants, which is designed to assess their ability to acquire knowledge and skills in a particular industry. Method. Increasing the level of automation of the process of assessing the professional abilities of the applicant in the light of natural properties, mental activity and requirements for the profile of a specialist is proposed to be implemented through the introduction of the process of self-actualization of the intellectual system. The basis of the system is the fuzzy neural network Takagi-Sugeno-Kang. The development of input and formalization of output data, as well as the creation of a knowledge base of the system at this stage is left to the experts. Results. The research of modern methods and means of identification of abilities and achievements of university entrants was conducted. The approach to the development of an intellectual system for assessing the professional abilities of applicants in the choice of the direction of training is proposed. The structure of the system is described, which is intended to assess the possibilities of applicants to the acquisition of knowledge, which are necessary for successful training in the chosen specialty. Scientific novelty. The input and output parameters of the Takagi-Sugeno-Kang neural network are investigated. The choice of the algorithm of network learning with the teacher is substantiated. The training algorithm is adapted to the decision of the task of assessing the applicant's ability to study in the specialties of the field of knowledge "Information Technologies". Minimization of error is proposed to use direct method of random search. Practical value. The model of the intellectual system of the estimation of professional abilities that is capable of processing the fuzzy data accumulated as a result of communication with the entrant is developed. Practical value from the introduction of such systems is based on the choice of profession, which provides an opportunity to increase competitiveness and improve the quality of the future life of young people. Further work will be aimed at developing fuzzy rules of withdrawal

Author Biographies

B. Yeremenko, Department of Information Technology Design and Applied Mathematics, Kyiv National University of Civil Engineering and Architecture, Povіtroflotsky Avenue, 31, Kyiv

Ph. D.

Y. Ryabchun, Department of Information Technology Design and Applied Mathematics, Kyiv National University of Civil Engineering and Architecture, Povіtroflotsky Avenue, 31, Kyiv

graduate student

A. Pachko, Taras Shevchenko National University of Kyiv, Volodymyrska str., 60, Kyiv

doctor of physical and mathematical sciences., senior researcher

H. Ploska, Humane Recourses Building Portal , Zahidna street, 4, 03067, Kyiv

executive director

References

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Published

2018-11-27

Issue

Section

Computer systems and information technologies in education, science and management