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CLUSTER ANALYSIS OF THE EFFECTIVENESS OF MANAGEMENT OF HIGHER EDUCATION INSTITUTIONS.

Authors :
Mambetkaziyev, Aidar
Baikenov, Zhassulan
Konopyanova, Galina
Source :
Eastern-European Journal of Enterprise Technologies; 2022, Vol. 120 Issue 3, p26-31, 6p
Publication Year :
2022

Abstract

The object of research is the internal structure of management in universities. The research problem is to confirm the causal relationship between management and rating. Higher education is one of the most important indicators of the level of development of the state. That is why many countries of the world attach great importance to the issue of the quality of higher education. Different international and national university ranking systems of universities were created to reflect the quality of education in the corresponding higher educational institutions. Currently, university ranking includes such criteria as quality of education, indicators of employment of university graduates, the demand for the graduates in the labor market, the symbiosis of science, education and business, and mobility of students. These indicators are a direct result of effective management in universities. Based on this hypothesis, the paper makes an assumption about the possibility of clustering universities in the Republic of Kazakhstan in order to determine the effectiveness of management. The authors consider three clustering models: clear and fuzzy clustering based on k-means and agglomerative cluster analysis. It should be noted that the clustering of universities makes it possible to determine some consistency in relation to the organization of university management. The division of universities into clusters according to the degree of deterioration in management makes it possible to create a kind of hierarchical ranking of the organization of management of university activities. This creates prerequisites for analyzing the internal structure of management in leading universities with the purpose of studying and adopting these practices by universities in lower clusters. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
17293774
Volume :
120
Issue :
3
Database :
Complementary Index
Journal :
Eastern-European Journal of Enterprise Technologies
Publication Type :
Academic Journal
Accession number :
161257277
Full Text :
https://doi.org/10.15587/1729-4061.2022.265860