Intercultural environment as a competitive advantage of higher education system

Main Article Content

Abstract

The article discusses the development of a multicultural environment as a factor of improving the well-being context and competitiveness of the higher education system. The authors believe that the scale of what is happening today in the world at different levels leads to a substantial change in national structure of Russia. An extensive review of the Russian and international literature on multiculturalism is presented in the article. The concepts of interculturalism, polyculturalism and multiculturalism were reviewed. The authors also argue that the formation of the elite and the content of the state national policy are interrelated. We believe that it is the level of higher education institutions, the results of their research and technology development activities, determines not only their place in the international rankings, but also the dynamics of economic and innovative development of individual territories and entire countries. In the process of forming new paradigm to develop the role of higher education system, there are many factors which effect on this process especially in some countries like Russia and Belarus.

 

 Keywords: intercultural environment, higher education system, international relations, institutionalization, human capital, polyculturalism, national policy

Downloads

Download data is not yet available.

Article Details

How to Cite
Intercultural environment as a competitive advantage of higher education system. (2016). Contemporary Educational Researches Journal, 5(2), 55–61. https://doi.org/10.18844/cerj.v5i2.235
Section
Articles

References

Nguyen, T., Phung, D., Adams, B., & Venkatesh, S., (2014). Mood sensing from social media texts and its applications, Knowl Inf Syst, 39(3), 667-702.

Turney, P.D., (2002). Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews, Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, 417-424

O'Connor, B., Balasubramanyan, R., Routledge, B.R., & Smith, N.A., (2010). From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series. Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media, 122-129

Tumasjan, A., Sprenger, T.O., Sandner, P.G., & Welpe, I.M., (2010). Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment. Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media, 178-185

Vural, A.G., Cambazoglu, B.B., Senkul, P., & Tokgoz, Z.O., (2013). A Framework for Sentiment Analysis in Turkish: Application to Polarity Detection of Movie Reviews in Turkish, in Computer and Information Sciences III, Springer London, 437-445

Gezici, G., Yanikoglu, B., Tapucu, D., & Saygın, Y., (2012). New Features for Sentiment Analysis: Do Sentences Matter? SDAD 2012 The 1st International Workshop on Sentiment Discovery from Affective Data,. 5-15

Pang, B., Lee, L., & Vaithyanathan, S., (2002). Thumbs up?: sentiment classification using machine learning techniques, Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10, 2002, pp. 79-86

Go, A., Bhayani, R., & Huang, L., (2009). Twitter sentiment classification using distant supervision, CS224N Project Report, Stanford, 1, 1-12.

Bifet, A., & Frank, E., (2010). Sentiment Knowledge Discovery in Twitter Streaming Data, in Discovery Science, Springer Berlin Heidelberg, 1-15

Jiang, L., Yu, M., Zhou, M., Liu, X., and Zhao, T., (2011). Target-dependent Twitter sentiment classification, Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, 1, 151-160

Erogul, U., (2009). Sentiment Analysis in Turkish, METU, Ankara.

Tantug, A.C., (2010). Document Categorization with Modified Statistical Language Models for Agglutinative Languages, International Journal of Computational Intelligence Systems, 3(5), 632-645.

Received November 02, 2015 from: https://dev.twitter.com/

Read, J., (2005). Using emoticons to reduce dependency in machine learning techniques for sentiment classification, Proceedings of the ACL Student Research Workshop, 43-48

Sparck Jones, K., (1972). A statistical in terpretation of term specificity and its application in retrieval, Journal of Documentation, 28(1), 11-21.

Boser, B.E., Guyon, I.M., & Vapnik, V.N., (1992). A training algorithm for optimal margin classifiers, Proceedings of the fifth annual workshop on Computational learning theory, 144-152

Most read articles by the same author(s)