Preview

Informatics and education

Advanced search

The transformation education concept in the digital ecosystem of a territorial production cluster

https://doi.org/10.32517/0234-0453-2022-37-6-5-11

Abstract

A new approach to the formation of distributed digital educational ecosystems in territorial production clusters is proposed. This can not only eliminate the shortcomings of the traditional education system, but also create a promising platform for a new modern educational technology. The basis for building such an ecosystem are modern achievements in the field of control systems, digitalization and artificial intelligence. Currently, the education market is seriously influenced by scientific results in the field of artificial intelligence and the rapid development of digital technologies. As practice has shown, it is necessary to take into account quite unpredictable natural disasters and pandemics, as well as the complex geopolitical and economic situation in the world. In these conditions, one of the few serious opportunities for universities to maintain and strengthen their positions in the education market, which will change rapidly in the near future, is the transformation of processes within the framework of new technological trends based on digital platforms of industrial cluster ecosystems. The digital twin can be used both for real-time decision-making and for forecasting and planning the process for the entire training period. The university and the enterprises of the territorial cluster that provide part of the educational services under this approach are integrated into a single mechanism for solving the tasks of flexible individual training in accordance with the needs of the cluster in highly qualified personnel. Within the framework of the proposed approach, it is possible to build a hybrid educational university environment integrated with the real objects of the territory’s economy, which in this case becomes the most important component of the digital educational ecosystem. The proposed concept makes it possible to predict and plan the training of the right specialists, since the entire scheme of the educational process in this case is closely connected with the enterprises of the real sector for the entire period of training. This becomes possible due to the fact that training takes place according to flexible, updated programs that reflect the constantly changing requirements of enterprises to the competencies of employees.

About the Authors

R. A. Sabitov
Kazan National Research Technical University named after A. N. Tupolev-KAI
Tajikistan

 Rustem A. Sabitov, Candidate of Sciences (Engineering),
Senior Research Fellow, Associate Professor at the Department of Dynamics of Processes and Control, Institute for Computer Technologies and Information Protection

Kazan, The Republic of Tatarstan



G. S. Smirnova
Kazan National Research Technical University named after A. N. Tupolev-KAI
Russian Federation

 Gulnara S. Smirnova, Candidate of Sciences (Engineering), Docent, Associate Professor at the Department of Dynamics of Processes and Control, Institute for Computer Technologies and Information Protection

Kazan, The Republic of Tatarstan



N. Yu. Elizarova
Kazan National Research Technical University named after A. N. Tupolev-KAI
Russian Federation

 Natalia Yu. Elizarova, Candidate of Sciences (Economic), Docent, Associate Professor at the Dep artment of Dynamics of Processes and Control, Institute for Computer Technologies and Information Protection

Kazan, The Republic of Tatarstan



Sh. R. Sabitov
Kazan Federal University
Russian Federation

 Shamil R. Sabitov, Candidate of Sciences (Engineering),
Docent, Associate Professor at the Department of Data Analysis and Operations Research, Institute of Computational Mathematics and Information Technology

Kazan, The Republic of Tatarstan



A. V. Eponeshnikov
Innopolis University
Russian Federation

 Alexander V. Eponeshnikov, master student of the education program “Informatics and Computer Engineering”, profile “Data analysis and artificial intelligence”

Innopolis (Kazan), The Republic of Tatarstan



I. S. Grigoriev
Private Institution for Digitalization of the Nuclear Industry “Cifrum”
Russian Federation

 Ivan S. Grigoriev, Head of Analytics in the Group of Administrative Support of the Project Management Office

Moscow



References

1. Koliada M. G., Bugayova T. I., Artificial intelligence as a moving force of improvement and innovative development in education and pedagogic. Informatics and Education. 2018;33(10):21–30. (In Russian.) DOI: 10.32517/0234-0453-2018-33-10-21-30]

2. Andryushkova O. V., Grigoriev S. G. Methodology for assessment of the quality of learning based on negentropy. Informatics and Education. 2019;34(10):37–45. (In Russian.) DOI: 10.32517/0234-0453-2019-34-10-37-45]

3. Martin F., Chen Y., Moore R.L., Westine C.D. Systematic review of adaptive learning research designs, context, strategies, and technologies from 2009 to 2018. Educational Technology Research and Development. 2020;68(4):1903–1929. DOI: 10.1007/s11423-020-09793-2

4. Andryushkova O. V., Grigoriev S. G., Emergent learning in the information-educational environment. Moscow, Education and Informatics; 2018. 104 p.(In Russian.) Available at: https://www.elibrary.ru/item.asp?id=41518855]

5. Dolgui A., Bakhtadze N., Smirnova G., Sabitov R., Elpashev D. Identification and simulation models in logistics control systems for production processes and freighting. IFAC-PapersOnLine. 2017;50(1):14638–14643. DOI:10.1016/j.ifacol.2017.08.1903

6. Parkinson B. On the way to intelligent production. Control Engineering Russia. 2017;72(6):58–60. (In Russian.) Available at: https://controlengrussia.com/internetveshhej/intillektualnoe-proizvodstvo/]

7. Larabell J. T. The rise of the robots. The New American. 2016;32(21):10–17. Available at: https://thenewamerican.com/the-rise-of-the-robots/

8. Sadler P. Sustainable growth in a post-scarcity world: consumption, demand, and the poverty penalty. London, Routledge; 2010. 264 p. DOI: 10.4324/9781315611518

9. Frey C. B., Osborne M. A. The future of employment: how susceptible are jobs to computerisation. Technological Forecasting and Social Change. 2017;114:254–280. DOI: 10.1016/j.techfore.2016.08.019

10. Grigoriev S. G., Sabitov R. A., Smirnova G. S., Sabitov Sh. R. The concept of the formation and development of a digital intellectual ecosystem of blended university learning. Informatics and Education. 2020;35(5):15–23. DOI:10.32517/0234-0453-2020-35-5-15-23

11. Sabitov R., Smirnova G., Sabitov S., Elizarova N.,Korobkova E. Planning, building and development distributed integrated blended education ecosystem at different levels.CEUR Workshop Proceedings. 2020:79–86. Available at: https://www.elibrary.ru/item.asp?id=45072043

12. Vostroknutov I., Grigoriev S., Surot L. Modern challenges of humanity and the search for a new paradigm of education. Proceedings of the 4th International Conference on Informatization of Education and E-learning Methodology: Digital Technologies in Education (IEELM-DTE 2020). Krasnoyarsk, Russia. 2020;2770:49–54. Available at: https://ceur-ws.org/Vol-2770/paper7.pdf

13. Smirnova G., Sabitov R., Morozov B., Sabitov Sh.,Sirazetdinov B., Elizarova N. To the problem of dynamic modeling and management in an integrated environment of the industrial cluster. IFAC-PapersOnLine. 2015;48(3):1230–1235. DOI: 10.1016/j.ifacol.2015.06.252

14. Bakhtadze N., Suleykin A. Industrial digital ecosystems: predictive models and architecture development issues. Annual Reviews in Control. 2020;51(1):56–64. DOI:10.1016/j.arcontrol.2020.11.001

15. Bakhtadze N., Elpashev D., Suleykin A., Pyatetsky V. Digital ecosystem situational control based on a predictive model. IFAC-PapersOnLine. 2021;54(1):300–306. DOI:10.1016/j.ifacol.2021.08.035

16. Luksha P., Kubista J., Laszlo A., Popovic M., Ninenko I. Education for a complex society. Report for Global Education Futures 2018. 232 p. (In Russian.) Available at: http://vcht.center/wp-content/uploads/2019/06/Obrazovanie-dlyaslozhnogo-obshhestva.pdf]

17. Sirazetdinov R. T., Brazhkina A. A. Universal structural model of a standard economic cluster. Large-Scale Systems Control. 2010;(29):152–166. (In Russian.) Available at: https://www.elibrary.ru/item.asp?id=15101914]

18. Sabitov R. A., Smirnova G. S., Elizarova N. U., Sabitov Sh. R. Intelligent management of supply chains and outsourcing in mechanical engineering under “Industry 4.0” conditions. IOP Conference Series: Materials Science and Engineering. 2019;570(1). DOI: 10.1088/1757-899X/570/1/012081

19. Kuhn Ch., Luckea D. Supporting the digital transformation: a low-threshold approach for manufacturing related higher education and employee training. Procedia CIRP. 2021;104:647–652. DOI: 10.1016/j.procir.2021.11.109

20. Singh I., Centea D., Elbestawi M. IoT, IIoT and cyberphysical systems integration in the SEPT learning factory. Procedia Manufacturing. 2019;31:116–122. DOI: 10.1016/j.promfg.2019.03.019


Review

For citations:


Sabitov R.A., Smirnova G.S., Elizarova N.Yu., Sabitov Sh.R., Eponeshnikov A.V., Grigoriev I.S. The transformation education concept in the digital ecosystem of a territorial production cluster. Informatics and education. 2022;37(6):5-11. (In Russ.) https://doi.org/10.32517/0234-0453-2022-37-6-5-11

Views: 352


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


ISSN 0234-0453 (Print)
ISSN 2658-7769 (Online)