Modeling the Processes of How to Overcome Students’ Cognitive Overload if There is an Excessive Amount of the Material to Study

Abstract

The article considers an approach to the process of overcoming cognitive overwork analysis when students assimilate an excessive amount of educational information. We chose William Kermack and Anderson McKendrick’s SIR model as a mathematical model that can describe the process of overcoming cognitive overwork by a dynamic system in the form of a closed small study group; and then modified it by introducing new parameters characterizing the dynamics of the situation: coefficients of protection from cognitive overwork, susceptibility to cognitive overwork and overcoming cognitive overwork. The study of the SIR model was carried out in two ways, such as by constructing a time dependence of the replenishment of three SIR subgroups interacting with cognitive overwork by students (subjected, overcoming and overcome) as the situation develops, and using a phase plane that allows one to make a complete picture of the phenomenon and conduct a more detailed study of the general and particular conditions of the system’s movement to a state of equilibrium depending on the ratio between its parameters. The conducted research found out that both methods of analyzing the process of overcoming cognitive overwork based on the SIR model can be used as complementary, since each of them emphasizes individual factors affecting the development of the situation at different initial conditions.
The results obtained can be considered as necessary motivators for the further development of SIR modeling in the study of the influence and overcoming of destructive processes in assimilation of new knowledge.

Author Biography

Olga Maksimovna Korchazhkina, Federal Research Center "Computer Science and Control" of Russian Academy of Sciences

Senior Research Fellow of the A.I. Berg Institute for Cybernetics and Educational Computing, Cand. Sci. (Tech.)

References

1. Kermack W.O., McKendrick A.G. A Contribution to the Mathematical Theory of Epidemics. Proceedings of the Royal Society of London. Series A, Containing Papers of a Mathematical and Physical Character. 1927;115(772):700-721. Available at: https://www.jstor.org/stable/94815 (accessed 07.12.2024).
2. Marathe M., Vullikanti A.K.S. Computational epidemiology. Communications of the ACM. 2013; 56(7):88-96. https://doi.org/10.1145/2483852.2483871
3. Korchazhkina O.M. The Study of Extreme Conditions for Destructive Processes Using the SIR Model. Modern Information Technologies and IT-Education. 2020;16(3):610-622. (In Russ., abstract in Eng.) https://doi.org/10.25559/SITI-TO.16.202003.610-622
4. Albert R. Network Inference, Analysis, and Modeling in Systems Biology. The Plant Cell. 2007;19(11):3327-3338. https://doi.org/10.1105/tpc.107.054700
5. An G. Characterization of fundamental aspects of biology with abstract mathematics: Category theory as a pathway for dynamic computational modeling of biologic systems. Journal of Critical Care. 2011;26(2):e8-e9. https://doi.org/10.1016/j.jcrc.2010.12.033
6. Chen X., Zhu W. Network analysis for exploring systems biology. In: 2010 3rd International Conference on Biomedical Engineering and Informatics. Yantai, China: IEEE Press; 2010. p. 2578-2581. https://doi.org/10.1109/BMEI.2010.5639724
7. Moitra, S., Yanamala N., Tastan O., Singh I., Langmead C.J., Klein-Seetharaman J. Analogies between structural and systems biology and systems-of-systems engineering in dynamic environments. In: 2010 5th International Conference on System of Systems Engineering. Loughborough, UK: IEEE Press, 2010. p. 1-7. https://doi.org/10.1109/SYSOSE.2010.5544104
8. Azam Sh., et al. Numerical modeling and theoretical analysis of a nonlinear advection-reaction epidemic system. Computer Methods and Programs in Biomedicine. 2020;193:105429. https://doi.org/10.1016/j.cmpb.2020.105429
9. Andersson H., Britton T. Stochastic Epidemic Models and Their Statistical Analysis. Lecture Notes in Statistics. Vol. 151. New York, NY: Springer; 2000. 156 p. https://doi.org/10.1007/978-1-4612-1158-7
10. Chao D.L., Halloran M.E., Obenchain V.J., Longini I.M.Jr. FluTE, a Publicly Available Stochastic Influenza Epidemic Simulation Model. PLOS Computational Biology. 2010;6(1):e1000656. https://doi.org/10.1371/journal.pcbi.1000656
11. Pastor-Satorras R., Castellano C., Van Mieghem P., Vespignani A. Epidemic processes in complex networks. Reviews of Modern Physics. 2015;87(3):925-979. https://doi.org/10.1103/RevModPhys.87.925
12. Zinoveev I.V., Manko N.I.-V., Spitsa I.A. Construction of a mathematical model of social group behavior based on the medical and biological SIR model of epidemic spread. News of Zaporizhzhya National University. Physical and Mathematical Sciences. 2013;(2):36-41. (In Russ., abstract in Eng.)
13. Lloyd A.L., May R.M. How Viruses Spread Among Computers and People. Science. 2001;292(5520):1316-1317. https://doi.org/10.1126/science.1061076
14. Barrouillet P., Bernardin S., Portrat S., Vergauwe E., Camos V. Time and cognitive load in working memory. Journal of Experimental Psychology: Learning, Memory, and Cognition. 2007;33(3):570-585. https://doi.org/10.1037/0278-7393.33.3.570
15. de Jong T. Cognitive load theory, educational research, and instructional design: some food for thought. Instructional Science. 2010;38:105-134. https://doi.org/10.1007/s11251-009-9110-0
16. Korchazhkina O.M. SIR model for studying destructive processes in the acquisition of new knowledge. Systems and Means of Informatics. 2021;31(1):168-180. (In Russ., abstract in Eng.) https://doi.org/10.14357/08696527210114
17. Bolbakov R.G. Cognitive entropy as a characteristic of educational processes. Distance and virtual learning. 2016;(2):11-17. (In Russ., abstract in Eng.) EDN: VKAMUV
18. Zhumartova B.O., Ysmagul R.S. Application of the SIR model in modeling epidemics. International Journal of Humanities and Natural Sciences. 2021;(12-2):6-9. (In Russ., abstract in Eng.) https://doi.org/10.24412/2500-1000-2021-12-2-6-9
19. Mansal F., Baldé M.A.M.T., Bah A.O. Optimal Control on a Mathematical Model of SIR and Application to Covid-19. In: Seck D., Kangni K., Sambou M.S., Nang P., Fall M.M. (eds.) Nonlinear Analysis, Geometry and Applications. Trends in Mathematics. Cham: Birkhäuser; 2024. p. 101-128. https://doi.org/10.1007/978-3-031-52681-7_4
20. Shakarian P., Bhatnagar A., Aleali A., Shaabani E., Guo R. The SIR Model and Identification of Spreaders. In: Diffusion in Social Networks. SpringerBriefs in Computer Science. Cham: Springer; 2015. p. 3-18. https://doi.org/10.1007/978-3-319-23105-1_2
21. van Merriënboer J.J.G., Sweller J. Cognitive Load Theory and Complex Learning: Recent Developments and Future Directions. Educational Psychology Review. 2005;17:147-177. https://doi.org/10.1007/s10648-005-3951-0
22. Möller M., Winter M., Reichert M. Cognitive Factors in Process Model Comprehension A Systematic Literature Review. Brain Sciences. 2025;15(5):505. https://doi.org/10.3390/brainsci15050505
23. Cherepanov A.A. PhaPl web application for automatic construction and study of phase portraits on a plane. Bulletin of Samara University. Natural Science Series. 2018;24(3):41-52. (In Russ., abstract in Eng.) https://doi.org/10.18287/2541-7525-2018-24-3-41-52
24. Gkintoni E., Antonopoulou H., Sortwell A., Halkiopoulos C. Challenging Cognitive Load Theory: The Role of Educational Neuroscience and Artificial Intelligence in Redefining Learning Efficacy. Brain Sciences. 2025;15:203. https://doi.org/10.3390/brainsci15020203
25. Chernyavsky A.D. Information model of the "social explosion". Modern studies of social problems. 2012;(1):406-426. (In Russ., abstract in Eng.) EDN: PAFCPR
Published
2025-04-28
How to Cite
KORCHAZHKINA, Olga Maksimovna. Modeling the Processes of How to Overcome Students’ Cognitive Overload if There is an Excessive Amount of the Material to Study. Modern Information Technologies and IT-Education, [S.l.], v. 21, n. 1, p. 127-143, apr. 2025. ISSN 2411-1473. Available at: <http://sitito.cs.msu.ru/index.php/SITITO/article/view/1186>. Date accessed: 15 july 2026. doi: https://doi.org/10.25559/SITITO.021.202501.127-143.
Section
IT education: methodology, methodological support