Ir para o menu de navegação principal Ir para o conteúdo principal Ir para o rodapé

Desenvolvimento do ensino superior nas regiões da Rússia: número do pessoal pedagógico

Resumo

A pesquisa visou a avaliar os indicadores que caracterizam o número de professores nas universidades e outras instituições de ensino superior (IES) nas regiões da Rússia. Esses indicadores foram: (1) o número de professores por mil habitantes em idade de trabalhar em cada região, (2) o número médio de professores por IES na região, (3) a proporção de professores doutorados em ciências (DSc) e doutorados (PhD) no número de professores de IES na região e (4) o número de alunos por professor de IES. Foram utilizadas informações estatísticas oficiais das 82 regiões da Rússia. Constatou-se que, em média, há pouco mais de dois professores trabalhando em IES para cada mil residentes em idade de trabalhar. O número médio de professores por IES na Rússia é de 158. Nas universidades, o número médio de estudantes por docente ultrapassa 20. A abordagem metodológica proposta e os resultados obtidos são uma novidade científica, já que a avaliação das características regionais do número de professores nas regiões russas não foi realizada antes.

Palavras-chave

instituições de ensino superior, universidades, pessoal docente, estudantes, desenvolvimento regional

PDF (Español) HTML (Español) EPUB (Español) XML (English)

Referências

  1. Abel, J., & Deitz, R. (2011). Do colleges and universities increase their region’s human capital? Journal of Economic Geography, 12(3), 667-691. https://doi.org/10.1093/jeg/lbr020 DOI: https://doi.org/10.1093/jeg/lbr020
  2. Afeez, B., Maxwell, O., Otekunrin, O., & Happiness, O. (2018). Selection and Validation of Comparative Study of Normality Test. American Journal of Mathematics and Statistics, 8(6), 190-201.
  3. Anisimova, E., & Babich, S. (2016). Study of the main trends in the field of higher education. Economics and Management in the XXI Century: Development Trends, (33-2), 154-158.
  4. Cervantes, M. (2017). Higher education institutions in the knowledge triangle. Foresight and STI
  5. Governance, 11(2), 27-42. https://doi.org/10.17323/2500-2597.2017.2.27.42 DOI: https://doi.org/10.17323/2500-2597.2017.2.27.42
  6. Ciriaci, D. (2014). Does university quality influence the interregional mobility of students and graduates? The case of Italy. Regional Studies, 48(10), 1592-1608. https://doi.org/10.1080/00343404.2013.821569 DOI: https://doi.org/10.1080/00343404.2013.821569
  7. Cour des Comptes. (2019). La mobilité internationale des étudiants : S'organiser pour les défis à venir. https://www.vie-publique.fr/sites/default/files/rapport/pdf/194000726.pdf
  8. Douglass, J. (2011). Higher education’s new global order: How and why governments are creating structured opportunity markets. Educational Studies Moscow, (1), 73-98. https://doi.org/10.17323/1814-9545-2011-1-73-98 DOI: https://doi.org/10.17323/1814-9545-2011-1-73-98
  9. Federal State Statistics Service. (2021). https://gks.ru/bgd/regl/B19_16/Main.htm
  10. Guri-Rosenblit, S., Sebkova, H., & Teichler, U. (2007). Massification and diversity of higher education systems: interplay of complex dimensions. Higher Education Policy, 20(4), 373-389. https://doi.org/10.1057/palgrave.hep.8300158 DOI: https://doi.org/10.1057/palgrave.hep.8300158
  11. Jaspers, K. (1960). The Idea of the University. Beacon Press.
  12. Kirillina, Y. (2015). Quality in higher education in quantitative indicators. Problems and Prospects of Education Development in Russia, (33), 132-136.
  13. Kurilova, O. (2020). Features of changes in the number and structure of university teachers. Regional Bulletin, 17(56), 48-49.
  14. Lomonosov, A. (2013). Definition of students’ number standards for one staff position of the teaching staff. Creative Economy, 12(84), 102-111.
  15. Maksimova, I. (2019). Prospects for increasing the competitiveness of higher education in Russia. Scientific Bulletin of the Volgograd Branch of the RANEPA, (2), 81-93.
  16. Melikyan, A. (2021). Statistical analysis of the dynamics of performance indicators of Russian universities. Questions of Statistics, 28(1), 38-49. https://doi.org/10.34023/2313-6383-2021-28-1-38-49 DOI: https://doi.org/10.34023/2313-6383-2021-28-1-38-49
  17. Ministry of Science and Higher Education of the Russian Federation. (2021). Official statistical information on additional professional and higher education. https://minobrnauki.gov.ru/action/stat/highed/
  18. OECD. (2019). Education at a Glance 2019: OECD Indicators. OECD Publishing. https://doi.org/10.1787/f8d7880d-en. DOI: https://doi.org/10.1787/f8d7880d-en
  19. Pinkovetskaia, I., & Slepova, V. (2018). Estimation of Fixed Capital Investment in SMEs: the Existing Differentiation in the Russian Federation. Business Systems Research, 9(1), 65-78. https://doi.org/10.2478/bsrj-2018-0006 DOI: https://doi.org/10.2478/bsrj-2018-0006
  20. Pinkovetskaia, I., Lebedev, A., Slugina, O., Arbeláez, D, & Rojas, M. (2021). Informal Personal Financing of Entrepreneurs: Gender Characteristics. Universal Journal of Accounting and Finance, 9(3), 442-449. https://doi.org/10.13189/ujaf.2021.090319 DOI: https://doi.org/10.13189/ujaf.2021.090319
  21. Popova, S., & Vdovina, E. (2017). Specificity of higher professional education in the Penza region. Bulletin of Penza State University, 4(20), 8-11.
  22. Rahman, M., & Wu, H. (2013). Tests for normality: A comparative study. Far East Journal of Mathematical Sciences, 75(1), 143-164.
  23. Razali, N., & Yap, B. (2011). Power comparisons of Shapiro-Wilk, Kolmogorov-Smirnov, Lilliefors and Anderson-Darling tests. Journal of Statistical Modeling and Analytics, 2(1), 21-33.
  24. Romanov, E. (2018). Threats to the personnel potential of regional universities. Economy of the region, 14(1), 95-108. https://doi.org/10.17059/2018-1-8 DOI: https://doi.org/10.17059/2018-1-8
  25. Seier, E., & Bonett, D. (2002). A test of Normality with high uniform power. Computational Statistics & Data Analysis, (40), 435-445. DOI: https://doi.org/10.1016/S0167-9473(02)00074-9
  26. Stiglitz, J. (2014). Creating a Learning Society: A New Approach to Growth, Development, and Social Progress. Columbia University Press. DOI: https://doi.org/10.7312/stig15214
  27. Unger, M., & Polt, W. (2017). The knowledge triangle between research, education and innovation – A conceptual discussion. Foresight-Russia, (2), 10-26. https://doi.org/10.17323/2500-2597.2017.2.10.26 DOI: https://doi.org/10.17323/2500-2597.2017.2.10.26
  28. Vadimova, I. (2015). Transition to new relations number of teachers and students. Counselor in the field of education, (6), 14-18.
  29. Vardanyan, G., & Keshishyan, G. (2020). Comparative statistical analysis of the dynamics number and structure of the professor-teaching staff of higher educational institutions of the RF and RA for the past decade. In The collection: Technologies in education - 2020. Collection of materials of the International Scientific and Methodological Conference. Novosibirsk (pp. 384-393).
  30. Vlasova, O. (2021). The influence of transformation processes in the higher education system on the number of its staff. Baltic Humanitarian Journal, 1(34), 59-62. https://doi.org/10.26140/bgz3-2021-1001-0013 DOI: https://doi.org/10.26140/bgz3-2021-1001-0013
  31. Yap, B, & Sim, C. (2011). Comparisons of various types of normality tests. Journal of Statistical Computation and Simulation, 81(12), 2141-2155. DOI: https://doi.org/10.1080/00949655.2010.520163
  32. Yazici, B., & Asma, S. (2007). A comparison of various tests of normality. Journal of Statistical Computation and Simulation, 77(2), 175-183. DOI: https://doi.org/10.1080/10629360600678310

Downloads

Não há dados estatísticos.

Artigos Semelhantes

<< < 2 3 4 5 6 7 8 9 10 11 > >> 

Você também pode iniciar uma pesquisa avançada por similaridade para este artigo.