Multifactorial Analysis of Indicators of Assessment of the Institutional Factors of Russian Universities and their Range Influence on the Level of International Competitiveness

In the context of global changes in the world socio-economic system, development of global educational and research areas, modification of the industrial technological platform, development of smart and intelligent networks and environments, there was a great transformation of competition conditions in the world educational services’ markets. The improvement of the competitiveness of Russian education is one of the main strategic objectives of the state policy in the field of education. The one can see the transformation of ratings as a set of methods for assessing the performance of higher educational institutions becoming one of the instruments of influence on the management of universities. One of such methods became public funding mechanism. The following tasks are set and solved in the article: 1) processing of the initial data was carried out, including the graphic image, the elimination of «empty» lines, the addition of logical variables; 2) study of correlations between factors and the resulting indicator for all groups of ratings was carried out; 3) econometric models of the dependence of the total score on various factors were built, the models were verified; 4) clustering was carried out to highlight the relationship between the university and various indicators; 5) recommendations were developed to increase the competitiveness of Russian universities. Materials and methods. The article uses materials from the research work of the Government Task for 2021 on the topic: «Development of the international competitiveness of Russian universities in the context of global transformations and epidemiological threats». When modeling there was used data from the QS World University Rankings and Times Higher Education. There were also studied methods of system analysis, correlation analysis, econometric modeling, regression analysis, clustering, k-means. Results. Models were built and significant factors were selected for each of the variant of the QS rating. The analysis of the correlation dependence made it possible to exclude multicollinearity in the models. According to the simulation results, it turned out that the following indicators have the greatest influence on the overall score: «Academic Reputation»; «Faculty Student»; «Papers per faculty»; «International faculty»; «International students»; «Web Impact». As a result of the analysis of the T.H.E. rating, it turned out that having high indicators and being taught in many universities, physical and engineering sciences prevail and affect the final assessment.

international competitiveness, digital economy, intellectual property, rankings in education, international education