INTERNATIONAL EDUCATIONAL ACTIVITIES OF RUSSIAN UNIVERSITIES

He article represents the results of a research into international educational activities of Russian Universities. The aims of the research were to identify university groups with similar international educational Activities indicators in certain areas (based on data cluster analysis) and analyze university characteristics for each cluster (foreign students characteristics, university size, specialization, territorial position, university selectivity, teaching and research staff internationalization, international research). The research included 301 universities from all regions of the Russian Federation. We conducted hierarchical agglomeration cluster analysis on the following indicators: student internationalization, development of incoming academic mobility, teaching programs internationalization, internationalization of university income from educational activities, amount of fees paid by foreign students and commercialization of foreign students training. Cluster analysis identified five groups with similar characteristics that got the following names: «internationalization leaders», «leaders», «recruiters», «newcomers», «outsiders». The research demonstrated that most universities diversify the content of foreign students implementing both basic and supplementary teaching programs, offering different formats of training for the students from different countries. The size of the university does not predetermine the scope of its international activities. Each cluster includes universities with less than 1000 students and large universities with several thousand students. High performance clusters usually include multi - profile universities offering a broad spectrum of educational services for foreign consumers. Moscow and Saint Petersburg universities lead in terms of international educational activities. Universities from high performance clusters are the most selective in choosing students, have a large number of foreign teaching and research staff, high international quotation rating of academic publications by university researchers. Obtained results of the research are of interest to university administration as they form an overall picture of the current state and main trends of international educational activities at the Russian universities. Suggested university classification can serve as the basis for analyzing the position of a university in an international educational market and altering strategic plans on obtaining and strengthening positions at different segments of this market. The classification presented in the article does not reflect all the diverse ways Russian universities take to develop and broaden their international educational activities. At the same time research results allow for identifying several general models of the Russian universities’ behavior at the international educational market. Further research in the field of international educational activities of the Russian universities is required that would allow for a more detailed analysis of its current state and its main development trends for the recent years.

international activities of higher education institutions, foreign students, international higher education market, internationalization of educational activities, university income

1. Абанкина И. В., Алескеров Ф. Т., Белоусова В. Ю., Гохберг Л. М., Зиньковский К. В., Кисельгоф С. Г., Швыдун С. В. Типология и анализ научно-образовательной результативности российских вузов // Форсайт. 2013. Т. 7. № 3. C. 48-63.

2. Блаженков В. В., Емелин Н. М., Голодкова О. В. Кластерный анализ инновационной деятельности российских вузов // Известия института инженерной физики. 2014. № 34. С. 94-98.

3. Angulo F., Pergelova A., Rialp J. A market segmentation approach for higher education based on rational and emotional factors, Journal of Marketing for Higher Education, 2010, vol. 20, no. 1, pp. 1-17.

4. Erdogmus N., Esen M. Classifying Universities in Turkey by Hierarchical Cluster Analysis, Education and Science, 2016, vol. 41, no. 184, pp. 363-382.

5. Howells J., Ramlogan R., Cheng S.-L. The Role, Context and Typology of Universities and Higher Education Institutions in Innovation Systems: A UK Perspective. MIoIR Discussion Paper, University of Manchester, 2008, available at: https://ewds.strath.ac.uk/Portals/8/typology.doc (accessed 20.10.2016).

6. Huberty C. J., Jordan E. M., & Brandt W. C. Cluster Analysis in Higher Education Research. In: J. C. Smart (ed.), Higher Education: Handbook of Theory and Research. [S. l.], Springer, 2005, vol. 20, pp. 437-457.

7. Ibanez A., Larranaga P., Bielza C. Cluster Methods for Assessing Research Performance: Exploring Spanish Computer Science, Scientometrics, 2013, vol. 97, pp. 571-600.

8. Shin J. C. Classifying Higher Education Institutions in Korea: a Performance-Based Approach, Higher Education, 2009, vol. 57, pp. 247-266.

9. Valadkhani A., Worthington A. Ranking and Clustering Australian University Research Performance, 1998-2002, Journal of Higher Education Policy and Management, 2006, vol. 28, no. 2, pp. 189-210.

10. Арефьев А. Л., Шереги Ф. Э. Экспорт российских образовательных услуг: Статистический сборник. Выпуск 6. М.: Социоцентр, 2016. 408 с.

11. Информационно-аналитические материалы по результатам анализа показателей эффективности образовательных организаций высшего образования // Официальный сайт Главного информационно-вычислительного центра федерального агентства по образованию РФ [Электронный ресурс]. URL: http://miccedu.ru/monitoring/ (дата обращения: 20.10.2016).

12. Ward J. H., Jr. Hierarchical Grouping to Optimize an Objective Function, Journal of the American Statistical Association, 1963, vol. 58, pp. 236-244.

13. Calinski R., Harabasz J. A Dendrite Method for Cluster Analysis, Communications in Statistics, 1974, no. 3, pp. 1-27.