Science for Education Today, 2022, vol. 12, no. 2, pp. 74–91

Cognitive modeling the level of university students’ perceptions of distance learning in the COVID-19 pandemic

Tyumentseva E. Y. 1 (Omsk, Russian Federation), Abramchenko N. V. 2 (Omsk, Russian Federation), Shamis V. A. 3 (Omsk, Russian Federation), Mukhametdinova S. K. 4 (Omsk, Russian Federation)
1 Omsk State Technical University
2 Financial University under the Government of the Russian Federation, Omsk branch,
3 Private Educational Organization of Higher Education "Siberian Institute of Business and Information Technologies"
4 Financial University under the Government of the Russian Federation, Omsk branch

Introduction. The research problem is the contradiction between the widespread use of distance learning technologies at universities, not only during the COVID-19 pandemic, and the absence of an appropriate system of effective organizing the educational process, taking into account the peculiarities of students' perceptions of distance learning.
The aim of the study is to identify and predict the features of the mutual influence of various factors on the level of perception of distance learning by university students using cognitive methodology to develop effective forms of organizing the educational process and management methods.
Materials and Methods. The study follows the methodology of cognitive modeling developed by R. Axelrod for the analysis of problems that are difficult to formalize.
The empirical data were collected using a survey based on Google forms and Internet technologies. The sample included 188 university students from Omsk and Norilsk.
Cognitive models are developed as cognitive maps, which, as a rule, are weighted oriented graphs. Thus, cognitive methodology is a specialized approach designed to structure knowledge about complex systems, taking into account their relationships with the external environment, in order to comprehensively study the features of their functioning and predict state changes. In addition, questionnaires, expert evaluation and computer simulation were used.
Results. In the course of the study, the controlling factors influencing the target factor – the level of perception of distance learning by university students in the context of the COVID-19 pandemic, as well as the relationship between these factors and the degree of their mutual influence were identified. Taking into account the data obtained, a cognitive model of the level of university students’ perceptions of distance learning in the context of the COVID-19 pandemic in the form of an oriented graph has been created.
The developed cognitive model served as a basis for a series of simulation experiments using specialized software. The results of the simulation experiments are a system of predictions about the impact on the target factor of impulses which influence the control factors with different intensity. Based on the received predications, recommendations on the management of the educational process in the conditions of distance learning have been formulated.
Conclusions. The results obtained can be used in the design and development of effective methods and specific didactics of distance learning, taking into account the peculiarities of its perception and the level of psychological comfort of university students.
The forecasts of changes in the target factor obtained in the process of modeling under the influence of control factors are the basis for the design and development of effective methods and specific didactics of distance learning.


Distance technologies; Higher education institutions; Target factor; Control factors; Cognitive model; Simulation experiment; Forecasting.

For citation:
Tyumentseva E. Y., Abramchenko N. V., Shamis V. A., Mukhametdinova S. K. Cognitive modeling the level of university students’ perceptions of distance learning in the COVID-19 pandemic. Science for Education Today, 2022, vol. 12, no. 2, pp. 74–91. DOI:

1. Axelrod R. The Structure of Decision: Cognitive Maps of Political Elites. Princeton University Press, 1976, 405 р. URL:

2. de Oliveira Kubrusly Sobral J. B., Lima D. L. F., Lima Rocha H. A., de Brito E. S., Goveia Duarte L. H., Bento L. B. B. B., Kubrusly M. Active methodologies association with online learning fatigue among medical students. BMC Medical Education, 2022, vol. 22 (1), pp. 74. DOI: URL: 

3. Frei-Landau R., Avidov-Ungar O. Educational equity amidst COVID-19: Exploring the online learning challenges of Bedouin and jewish female preservice teachers in Israel. Teaching and Teacher Education, 2022, vol. 111, pp. 103623. URL:

4. Guo Y., Liu H., Hao A., Liu S., Zhang X., Liu H. Blended learning model via small private online course improves active learning and academic performance of embryology. Clinical Anatomy, 2022, vol. 35 (2), pp. 211–221. DOI:  

5. Han S., Lee M. K. FAQ chatbot and inclusive learning in massive open online courses. Computers and Education, 2022, vol. 179, pp. 104395. DOI:  

6. Ji H., Park S., Shin H. W. Investigating the link between engagement, readiness, and satisfaction in a synchronous online second language learning environment. System, 2022, vol. 105 (6), pp. 102720. DOI:  

7. Keiller L., Nyoni C., van Wyk C. Online faculty development in low- and middle-income countries for health professions educators: A rapid realist review. Human Resources for Health, 2022, vol. 20 (1), pp. 12. DOI:  URL:

8. Khodaei S., Hasanvand S., Gholami M., Mokhayeri Y., Amini M. The effect of the online flipped classroom on self-directed learning readiness and metacognitive awareness in nursing students during the COVID-19 pandemic. BMC Nursing, 2022, vol. 21 (1), pp. 22. DOI:  URL:

9. Lee Y.-J., Davis R., Li Y. implementing synchronous online flipped learning for pre-service teachers during COVID-19. European Journal of Educational Research, 2022, vol. 11 (2), pp. 653–661. DOI:  

10. Li S., Singh K., Riedel N., Yu F., Jahnke I. Digital learning experience design and research of a self-paced online course for risk-based inspection of food imports. Food Control, 2022, vol. 135, pp. 108698. DOI: URL:  

11. Moussa N. M., El-Khalil N. S. Psychological health, competencies and readiness for the transition to distance learning among teachers in the UAE. Psychological Science and Education, 2021, vol. 26 (6), pp. 83–95. DOI: URL:

12. Paristiowati M., Rahmawati Y., Fitriani E., Satrio J. A., Hasibuan N. A. P. Developing preservice chemistry teachers’ engagement with sustainability education through an online, project-based learning summer course program. Sustainability, 2022, vol. 14 (3), pp. 1783. DOI: URL:  

13. Pluzhnik I. L.Guiral F. H. А. Modelling a high quality education for international students. Education and Science Journal, 2020, vol. 22 (6), pp. 49–73. DOI: URL:

14. Rotar O. Online student support: A framework for embedding support interventions into the online learning cycle. Research and Practice in Technology Enhanced Learning, 2022, vol. 17 (1), pp. 2. DOI: URL: 

15. Smelkova I. Y., Tuana E. N., Gubareva S. A., Krasnova I. A. Distance learning in the university foreign language environment through the eyes of Chinese students. Perspectives of Science and Education, 2021, no. 5, pp. 125–138. DOI: URL:

16. Tao J., Xu Y. Parental support for young learners’ online learning of English in a Chinese primary school. System, 2022, vol. 105, pp. 102718. URL: 

17. Tchoshanov M. A. Learning sciences perspective on engineering of distance learning. Part 2. Higher Education in Russia, 2021, vol. 30 (3), pp. 43–58. DOI: URL:

18. Turk M.Heddy B. C.Danielson R. W. Teaching and social presences supporting basic needs satisfaction in online learning environments: How can presences and basic needs happily meet online? Computers and Education, 2022, vol. 180, pp. 104432. DOI:  

19. Voloshyna V.Stepanenko I.Zinchenko A.Andriiashyna N.Hohol O. Moderating the neuropsychological impact of online learning on psychology students. European Journal of Educational Research, 2022, vol. 11 (2), pp. 681–695. DOI: URL: 

20. Warshawski S. Academic self-efficacy, resilience and social support among first-year Israeli nursing students learning in online environments during COVID-19 pandemic. Nurse Education Today, 2022, vol. 110, pp. 105267. DOI:   URL:

21. Yau A. H. Y.Yeung M. W. L.Lee C. Y. P. A co-orientation analysis of teachers’ and students’ perceptions of online teaching and learning in Hong Kong higher education during the COVID-19 pandemic. Studies in Educational Evaluation, 2022, vol. 72, pp. 101128. DOI:  

22. Luchko O. N., Mukhametdinova S. Kh., Patlasov O. Yu. Using the tool-kit of cognitive modeling within the analysis of a vector educational migration. Novosibirsk State Pedagogical University Bulletin, 2017, vol. 7 (6), pp. 232–248. (In Russian) DOI:   URL:

23. Tyumentseva E. Yu., Mukhametdinova S. Kh., Abdullayev К. К. Modeling of the ecological culture level of higher educational institutions students employing cognitive methodology. Prospects for Science and Education, 2019, no. 6, pp. 91–103. (In Russian) DOI:  URL:

24. Frolova E. V., Rogach O. V., Ryabova T. M. Benefits and risks of switching to distance learning in a pandemic. Perspectives of Science and Education, 2020, no. 6, pp. 78–88. (In Russian) DOI: URL:

25. Chernyshov S. A. Massive shift of schools towards distance learning in the estimates of a local pedagogical community. Education and Science, 2021, vol. 23 (3), pp. 131–155. (In Russian) DOI: URL:

Date of the publication 30.04.2022