Development of the set models and a method to form information spaces of scientific activity subjects for the steady development of higher education establishments

Authors

DOI:

https://doi.org/10.15587/1729-4061.2021.233655

Keywords:

information space, scientific activity subject, higher education establishment, multiple model

Abstract

This paper describes the basic conceptual apparatus required to form information spaces for scientific activity subjects. Multiple models have been built to identify collective and individual scientific activity subjects, including information on the subjects' publication citations, their abstracts, as well as their indicators in scientometric databases, etc. A conceptual scheme of interaction between collective and individual scientific activity subjects has been described, taking into consideration the dynamics of their productivity.

A method has been proposed to form the information spaces for the collective and individual scientific activity subjects such as higher education establishments and scientists. The method involves a series of stages to identify and construct citation and scientific cooperation networks, to form subject scientific spaces, and, based on them, to devise methods in order to quantify productivity. The results of methods application form the components of the relevant information spaces of scientific activity subjects. The spaces to be built could be used to solve the task of selecting subjects for the implementation of joint scientific and educational projects. In addition, these spaces could be applied to form the organizational and functional framework of the collective scientific activity subjects, including their structural units, which would contribute to ensuring their stable development.

Creating the information spaces of scientific activity subjects underlies resolving those issues that would stimulate investment in research and innovation, strengthen cooperation between universities, improve the efficiency and productivity of the scientific enterprise. It has been confirmed experimentally that the potential of a collective subject of scientific activity, including individual subjects, the rate of change of identifiers of whom is positive, would have a non-negative potential. A rate of change in the normalized indicators of identifiers of individual and collective scientific activity subjects has been calculated for the period from January 2019 to December 2020 for three higher education establishments

Author Biographies

Andrii Biloshchytskyi, Astana IT University; Systems and Technologies Taras Shevchenko National University of Kyiv

Doctor of Technical Sciences, Professor

Department of Information

Alexander Kuchansky, Taras Shevchenko National University of Kyiv

PhD, Associate Professor

Department of Information Systems and Technologies

Yurii Andrashko, Uzhhorod National University

PhD, Associate Pofessor

Department of System Analysis and Optimization Theory

Serik Omirbayev, Astana IT University

Doctor of Economical Sciences, Professor

Rectorate

Aidos Mukhatayev, Astana IT University

PhD, Associate Professor

Department of Strategy and Corporative Management

Adil Faizullin, Astana IT University

Master of Technical Sciences

Department of Quality Assurance

Sapar Toxanov, D. Serikbayev East Kazakhstan Technical University; Astana IT University

Doctoral Student

Center of Competence and Excellence

References

  1. Communication from the commission to the council, the european parliament, the economic and social committee and the committee of the regions. Towards a European research area (2000). Commission of the European Communities. Available at: https://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=COM:2000:0006:FIN:EN:PDF
  2. European research area (ERA). Available at: https://ec.europa.eu/info/research-and-innovation/strategy/era_en
  3. Glänzel, W. (2012). Bibliometric methods for detecting and analysing emerging research topics. El Profesional de La Informacion, 21 (2), 194–201. doi: https://doi.org/10.3145/epi.2012.mar.11
  4. Egghe, L., Rousseau, R. (1993). Evolution of information production processes and its relation to the Lorenz dominance order. Information Processing & Management, 29 (4), 499–513. doi: https://doi.org/10.1016/0306-4573(93)90045-f
  5. Lizunov, P., Biloshchytskyi, A., Kuchansky, A., Andrashko, Y., Biloshchytska, S. (2020). The use of probabilistic latent semantic analysis to identify scientific subject spaces and to evaluate the completeness of covering the results of dissertation studies. Eastern-European Journal of Enterprise Technologies, 4 (4 (106)), 21–28. doi: https://doi.org/10.15587/1729-4061.2020.209886
  6. Kremen, V., Bykov, V. (2013). Category "space" and "environment": features model submissions and educational application. Teoriya i praktyka upravlinnia sotsialnymy systemamy, 2, 3–16.
  7. Otte, E., Rousseau, R. (2002). Social network analysis: a powerful strategy, also for the information sciences. Journal of Information Science, 28 (6), 441–453. doi: https://doi.org/10.1177/016555150202800601
  8. Barabási, A. L., Jeong, H., Néda, Z., Ravasz, E., Schubert, A., Vicsek, T. (2002). Evolution of the social network of scientific collaborations. Physica A: Statistical Mechanics and Its Applications, 311 (3-4), 590–614. doi: https://doi.org/10.1016/s0378-4371(02)00736-7
  9. Hou, H., Kretschmer, H., Liu, Z. (2008). The structure of scientific collaboration networks in Scientometrics. Scientometrics, 75 (2), 189–202. doi: https://doi.org/10.1007/s11192-007-1771-3
  10. Newman, M. E. J. (2001). The structure of scientific collaboration networks. Proceedings of the National Academy of Sciences, 98 (2), 404–409. doi: https://doi.org/10.1073/pnas.98.2.404
  11. Biloshchytskyi, A., Kuchansky, A., Andrashko, Yu., Biloshchytska, S., (2020). Use of the link ranking method to evaluate scientific activities of scientific space subjects. Scientific Journal of Astana IT University, 1, 12–20. doi: https://doi.org/10.37943/aitu.2020.1.63600
  12. Biloshchytskyi, A., Kuchansky, A., Andrashko, Yu., Biloshchytska, S., Kuzka, O., Shabala, Ye., Lyashchenko, T. (2017). A method for the identification of scientists' research areas based on a cluster analysis of scientific publications. Eastern-European Journal of Enterprise Technologies, 5 (2 (87)), 4–11. doi: https://doi.org/10.15587/1729-4061.2017.112323
  13. Lizunov, P., Biloshchytskyi, A., Kuchansky, A., Andrashko, Y., Biloshchytska, S. (2019). Improvement of the method for scientific publications clustering based on n-gram analysis and fuzzy method for selecting research partners. Eastern-European Journal of Enterprise Technologies, 4 (4 (100)), 6–14. doi: https://doi.org/10.15587/1729-4061.2019.175139
  14. Kuchansky, A., Andrashko, Yu., Biloshchytskyi, A., Danchenko, E., Ilarionov, O., Vatskel, I., Honcharenko, T. (2018). The method for evaluation of educational environment subjects’ performance based on the calculation of volumes of m-simplexes. Eastern-European Journal of Enterprise Technologies, 2 (4 (92)), 15–25. doi: https://doi.org/10.15587/1729-4061.2018.126287
  15. Hnatiienko, H., Snytyuk, V., Tmienova, N., Voloshyn, O. (2020). Determining the effectiveness of scientific research of universities staff. CEUR Workshop Proceedings, 2833, 164–176. Available at: http://ceur-ws.org/Vol-2833/Paper_15.pdf
  16. Bykov, V. Yu., Spirin, O. M., Soroko, N. V. (2015). Elektronni bibliometrychni systemy yak zasib informatsiyno-analitychnoi pidtrymky naukovo-pedahohichnykh doslidzhen. Informatsiyno-komunikatsiyni tekhnolohiyi v suchasniy osviti: dosvid, problemy, perspektyvy, 1, 91–100.
  17. Garcez, M. P., Sbragia, R., Kruglianskas, I. (2014). Factors for selecting partners in innovation projects – qualitative evidences from non-equity bilateral alliances in the Brazilian petrochemical leader. Review of Administration and Innovation - RAI, 11 (2), 241. doi: https://doi.org/10.5773/rai.v11i2.1292
  18. Feng, W. D., Chen, J., Zhao, C. J. (2000). Partners selection process and optimization model for virtual corporations based on genetic algorithms. Journal of Tsinghua University (Science and Technology), 40, 120–124.
  19. Lukianov, D., Bespanskaya-Paulenka, K., Gogunskii, V., Kolesnikov, O., Moskaliuk, A., Dmitrenko, K. (2017). Development of the markov model of a project as a system of role communications in a team. Eastern-European Journal of Enterprise Technologies, 3 (3 (87)), 21–28. doi: https://doi.org/10.15587/1729-4061.2017.103231
  20. Korzh, R., Peleshchyshyn, A., Syerov, Y., Fedushko, S. (2016). University’s Information Image as a Result of University Web Communities’ Activities. Advances in Intelligent Systems and Computing, 115–127. doi: https://doi.org/10.1007/978-3-319-45991-2_8
  21. Kolomiiets, A., Morozov, V. (2021). Investigation of Optimization Models in Decisions Making on Integration of Innovative Projects. Lecture Notes in Computational Intelligence and Decision Making, 51–64. doi: https://doi.org/10.1007/978-3-030-54215-3_4
  22. Morozov V., Kalnichenko O., Mezentseva, O. (2020). The method of interaction modeling on basis of deep learning the neural networks in complex IT-projects. International Journal of Computing, 19 (1), 88–96. doi: https://doi.org/10.47839/ijc.19.1.1697
  23. Fruchterman, T. M. J., Reingold, E. M. (1991). Graph drawing by force-directed placement. Software: Practice and Experience, 21 (11), 1129–1164. doi: https://doi.org/10.1002/spe.4380211102
  24. Hu, Y. (2005). Efficient, high-quality force-directed graph drawing. The Mathematica Journal, 10 (1), 37–71. Available at: http://asus.myds.me:6543/paper/ktall/37%20-%201984%20-%20Efficient,%20High-Quality%20Force-Directed%20Graph%20Drawing.pdf
  25. Huang, Q., Feng, J., Zhang, Y., Fang, Q., Ng, W. (2015). Query-aware locality-sensitive hashing for approximate nearest neighbor search. Proceedings of the VLDB Endowment, 9 (1), 1–12. doi: https://doi.org/10.14778/2850469.2850470
  26. Altszyler, E., Ribeiro, S., Sigman, M., Fernández Slezak, D. (2017). The interpretation of dream meaning: Resolving ambiguity using Latent Semantic Analysis in a small corpus of text. Consciousness and Cognition, 56, 178–187. doi: https://doi.org/10.1016/j.concog.2017.09.004
  27. Tymchenko, D., Korogod, N., Novorodovska, T. (2020). Technology transfer office model. Scientific Journal of Astana IT University, 3, 83–90. doi: https://doi.org/10.37943/aitu.2020.73.19.008
  28. Kropachev, P., Imanov, M., Borisevich, Y., Dhomane, I. (2020). Information technologies and the future of education in the Republic of Kazakhstan. Scientific Journal of Astana IT University, 1, 30–38. doi: https://doi.org/10.37943/aitu.2020.1.63639

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Published

2021-06-30

How to Cite

Biloshchytskyi, A., Kuchansky, A., Andrashko, Y., Omirbayev, S., Mukhatayev, A., Faizullin, A., & Toxanov, S. (2021). Development of the set models and a method to form information spaces of scientific activity subjects for the steady development of higher education establishments. Eastern-European Journal of Enterprise Technologies, 3(2 (111), 6–14. https://doi.org/10.15587/1729-4061.2021.233655