TRUST AS A COGNITIVE BASE OF SOCIAL COHESION IN THE UNIVERSITY COMMUNITIES

Authors

  • Marja Nesterova Faculty of Management of Education and Science, National Pedagogical Dragomanov University, Kyiv, Ukraine https://orcid.org/0000-0001-6703-7797
  • Maryna Dielini Faculty of Agricultural Management, National University of Life and Environmental Sciences of Ukraine, Kyiv, Ukraine https://orcid.org/0000-0003-1016-2305
  • Lidia Shynkaruk Faculty of Agricultural Management, National University of Life and Environmental Sciences of Ukraine. Kyiv, Ukraine https://orcid.org/0000-0003-1763-4432
  • Olena Yatsenko Faculty of Management of Education and Science, National Pedagogical Dragomanov University, Kyiv, Ukraine https://orcid.org/0000-0003-0584-933X

DOI:

https://doi.org/10.5937/IJCRSEE2001015N

Keywords:

cognitive base, education, social cohesion, trust, university community

Abstract

The present article continues the cycle of the cognitive researches of the phenomenon of social cohesion in education, in particular, in the university communities. It contains the cognitive research of trust and its foundation as the central focus of social cohesion. The purpose of the study is to identify the level of trust which is connected with the social cohesion in university communities, to test the author’s questionnaire and to determinate the further steps for the trust enhancement in the educational community. Methods that were used in the study are the author’s questionnaire, math analytics etc. There were 196 people interviewed in both universities, among them 31 employees and 85 students of the National Pedagogical Dragomanov University and 33 employees and 47 students of the National University of Life and Environmental Sciences of Ukraine. According to the research results, the level of trust in each university community (as well as in common) was average, excluding some indicators. Although there were some differences between levels of trust of employees of these universities. We can assume that the quite sufficient average level or trust positively characterizes the attitude of employees and students to each other, reflects their readiness for mutual respect and support, acceptance of differences and tolerance etc. Also, the research highlights weak points of social interactions that form the base for further investigations and actions on the social cohesion development.

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References

Apresyan, J. D., Boguslavsky, I., Iomdin, L., & Sannikov, V. (2010). Theoretical problem of Russian syntax; interaction of grammar and vocabulary. Moscow, Russia: Languages of Slavonic Cultures.

Culicover, P. W., & Jackendoff, R. (2006). The simpler syntax hypothesis. Trends in cognitive sciences, 10(9), 413-418. https://doi.org/10.1016/j.tics.2006.07.007

Evans, V., & Green, M. (2006). Cognitive linguistics: An introduction. Edinburgh, Scotland: Edinburgh University Press.

Jurafsky, D. (1993). A cognitive model of sentence interpretation: The construction grammar approach. International Computer Science Institute. Retrieved from http://http.icsi.berkeley.edu/ftp/pub/techreports/1993/tr-93-077.pdf

JSXGraph Reference. Retrieved from https://jsxgraph.uni-bayreuth.de/docs/index.html

Kenyon-Dean, K., Cheung, J. C. K., & Precup, D. (2016). Verb phrase ellipsis resolution using discriminative and margin-infused algorithms. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (pp. 1734-1743). Retrieved from https://www.aclweb.org/anthology/D16-1179.pdf

Khakhalin, G. K., Kurbatov, S. S., Naidenova, X., & Lobzin, A. P. (2012). Integration of the Image and NL-text Analysis/Synthesis Systems. In Intelligent Data Analysis for Real-Life Applications: Theory and Practice (pp. 160-185). IGI Global. https://doi.org/10.4018/978-1-4666-1806-0.ch009

Kurbatov, S., Naidenova, X., & Ganapol’skii, V. (2019). Resolving ellipses in planimetric tasks as cognitive process. In Masalova, S. I., & Solovyev, V. D. (Eds.). Proceedings of the VIIth International Scientific Conference on Cognitive Modeling in Science, Culture, Education (CMSCE-2019). Part 2. Cognitive Modeling in Linguistics (CML-2019) (pp. 114-121). Rostov-on-Don, Russia: Science and Studies Foundation.

Kurbatov, S., Fominykh, & Vorobyev, A. (2019). Interactive visualization of cognitive structure in an integral system. In Proceedings of the 17th National Conference on Artificial Intelligence with International Participation (CAI-2019). Vol. 2 (pp. 222-230). Ulyanovsk: USTU.

Kurbatov, S., & Vorobyev, A. (2016). Ontological solver of geometry problems in natural language description. In Proceedings of the 15th National Conference on Artificial Intelligence with International Participation (CAI-2016). Vol. 1 (pp. 56-63). Smolensk, Russia: Universum.

Langacker, R. W. (1990). Subjectification. Cognitive Linguistics (includes Cognitive Linguistic Bibliography), 1(1), 5-38. http://dx.doi.org/10.1515/cogl.1990.1.1.5

Liu, Z., Gonzalez, E., & Gillick., D. (2016). Exploring the steps of VPE. In Proceedings of the Workshop on Conference Resolution Beyond OntoNotes (CORBON 2016), co-located with NAACL (pp. 32-40). San Diego, California: Association for Computational Linguistics.

Lobzin, A., Khakhalin, G., Kurbatov, S., & Litvinovich, A. (2016). Integration based on natural language and image ontology in the system Text-To-Picture. In Proceedings of the 8th Scientific-Practical Conference Integrated Models and Soft Computing in Artificial Intelligence. Vol. 1 (pp. 296-305). Moscow, Russia: Physical-Mathematical Literature.

MathJax Documentation, Release 3.0. (2020). Retrieved from https://readthedocs.org/projects/mathjax/downloads/pdf/latest/

McShane, M., & Babkin, P. (2015). Automatic Ellipsis Resolution: Recovering Covert Information from Text. In Proceedings of the Twenty Ninth AAAI Conference on Artificial Intelligence. pp. 572- 578. Palo Alto, California: The AAAI Press. Retrieved from https://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/view/9441/11326

McShane, M., & Babkin, P. (2016). Detection and resolution of verb phrase ellipsis. LiLT (Linguistic Issues in Language Technology), 13, 1-36. Retrieved from http://www.cogsci.rpi.edu/~mcsham2/MargePapers/McShane_Detection_2016.pdf

Mel’čuk, I. A. (2018). Anna Wierzbicka, Semantic Decomposition, and the Meaning-Text Approach. Russian Journal of Linguistics, 22(3), 521-538. http://dx.doi.org/10.22363/2312-9182-2018-22-3-521-538

Naidenova X. A., Kurbatov S. S., & Ganapol’skii, V. P. (2018). An analysis of plane task text ellipticity and the possibility of ellipses reconstructing based on cognitive modelling geometric objects and actions. In A. Elisarov, & N. Loukachevich (Eds.). In Proceedings of Computational Models in Language and Speech Workshop (CMLS 2018) co-located with the 15th TEL International Conference on Computational and Cognitive Linguistics (TEL-2018). Vol. 2 (pp. 70-85). Kazan, Russia: Academy of Sciences of RT. Retrieved from http://ceur-ws.org/Vol-2303/

Sechenov, I. M. (2008). Elements of thoughts. Saint-Petersburg, Russia: “Piter”.

Schuster, S., Nivre, J., & Manning, C. D. (2018). Sentences with gapping: Parsing and reconstructing elided predicates. In Proceedings of the 2018 Conference on the North America Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL 2018) (pp. 1156-1168. Retrieved from https://arxiv.org/abs/1804.06922

Zhao, G. (2016). A cognitive approach to ellipsis. Theory and Practice in Language Studies, 6(2), 372-377. http://dx.doi.org/10.17507/tpls.0602.20

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Published

2020-04-30

How to Cite

Nesterova, M. ., Dielini, M. ., Shynkaruk, L. ., & Yatsenko, O. . (2020). TRUST AS A COGNITIVE BASE OF SOCIAL COHESION IN THE UNIVERSITY COMMUNITIES. International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 8(1), 15–23. https://doi.org/10.5937/IJCRSEE2001015N