Abstract
According to the ideas of the Bologna process, the formation of a single European higher education space is currently ongoing. The common space of higher education implies both the presence of foreign students in higher educational institutions and the obtaining of employment opportunities for graduates of higher educational institutions in the European and world labor market. In connection with the situation, the Russian educational system is faced with the task of developing and supporting the competitiveness of its system of higher professional education. A key element in the system of higher education is a higher educational institution. It is important to determine the position of a higher educational institution in the market of educational services. The purpose of this work is the development of approaches to assess the competitiveness of a higher educational institution. The competitiveness of a higher educational institution is characterized primarily by the quality of educational services. To assess the quality of educational services, the quality of knowledge received by students, it is necessary to solve the problem of determining significant indicators. Assessing the quality of educational services is complicated in that there is an influence of indicators of various nature with varying degrees of influence. In addition, indicators that have an impact can be either numerical or non-numerical in nature. The presence of non-numeric indicators complicates the task. To accomplish the task of assessing the quality of educational services, a study was conducted to identify indicators that have the greatest impact on the quality of the educational process. It was found that the needs of the labor market should be taken into account, the quality of the training technologies used, the quality of the educational and methodological complex used in the educational process, the equipment of classrooms and laboratories, and the quality of the teaching staff. In this paper, an approach is proposed for assessing the quality of students’ knowledge, based on the use of a neuro-fuzzy inference system. A set-theoretical model has been formed to describe indicators that affect the quality of students' knowledge. The content of each indicator is disclosed. The proposed approach for constructing a model for assessing the quality of student knowledge allows us to obtain a numerical characteristic.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Sazonov, B.A.: The Bologna Process: Current Issues of Modernization of Russian Higher Education. FIRO, Moscow (2006)
Ooms, A.G., Joan, A.: A model to evaluate online educational resources in statistics. Technol. Innov. Stat. Educ. 2(1), 1–17 (2008)
Ottoboni, L.: The development and evaluation of a mindfulness-education intervention to reduce symptoms and improve quality of life among patients with paroxysmal atrial fibrillation. Ph.D. Thesis. University of California, San Francisco (2017)
Miller, G.A.: The magical number seven, plus or minus two: some limits on our capacity for processing information. Psychol. Rev. 63(2), 81–97 (1956). https://doi.org/10.1037/h0043158
Averkin, A.N.: Fuzzy Sets in Models of Control and Artificial Intelligence. Science, SSSR, Moscow (1986)
Ryzhov, A.P.: Elements of the Theory of Fuzzy Sets and Fuzziness Measurements. Dialogue-MSU, Moscow (1998)
Orlovsky, S.A.: Decision Making Problems with Fuzzy Source Information. Radio and Communication, SSSR, Moscow (1981)
Stativko, R.U.: Some approaches to the analysis of learning trajectory correction using the theory of fuzzy sets. In: Proceedings of the International Conference Communicative Strategies of Information Society, vol. 289, pp. 474–479 (2019)
Stativko, R.U.: Use of the apparatus of fuzzy sets in the theoretical information analysis of the Internet portal of the educational organization. XXI Century: Results Past Prob. Present Plus 7(3), 31–35 (2018)
Avanesov, V.S.: The Scientific Basis of the Test of Knowledge Control. Research Center, Moscow (1994)
Kofman, A.: Introduction to the Theory of Fuzzy Sets. Radio and Communication, SSSR, Moscow (1982)
Zade, L.: The Concept of a Linguistic Variable and its Application for Making Approximate Decisions. Mir, SSSR, Moscow (1976)
Zadeh, L.A.: The role of soft computing and fuzzy logic in the understanding, design and development of information / intelligent systems. News Artif. Intell. 2–3, 7–11 (2001)
Borisov, A.N.: Making Decisions Based on Fuzzy Models: Examples of Use. Knowledge, SSSR, Riga (1990)
Lawson, J.: Value-added modeling: challenges for measuring special education teacher quality. UCLA Journal of Education and Information Studies, vol. 10, no. 1 (2014)
Acknowledgment
This work was carried out as part of the Development Program of the Federal State Budgetary Educational Institution of Higher Education BSTU named after V.G. Shukhov for the period 2017–2021.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Stativko, R.U. (2021). Use of Neuro-Fuzzy Approach in Assessing the Quality of Knowledge. In: Karabegović, I. (eds) New Technologies, Development and Application IV. NT 2021. Lecture Notes in Networks and Systems, vol 233. Springer, Cham. https://doi.org/10.1007/978-3-030-75275-0_58
Download citation
DOI: https://doi.org/10.1007/978-3-030-75275-0_58
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-75274-3
Online ISBN: 978-3-030-75275-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)