This article considers the problem of finding optimal values of weighting factors for the efficient use of information and communication technologies (ICT) at the stages of information processing by employees of an educational organization. The assessment is carried out by a specialist using a model for determining the basic probability of competent use of ICT as a basis for calculating the risk of using ICT. The linear programming problem is formulated and analyzed in the context of decision-making related to the use of ICT. The article uses a method of modeling computer models that imitate cognitive processes, as well as an analysis of the linear programming problem for optimizing weighting factors. A model for estimating weighting factors has been developed that takes into account the features of each stage of information processing and the time parameters of using ICT. It is shown that the transition to stochastic programming allows one to take into account the uncertainty and variability of model parameters, which is important for achieving an accurate risk assessment when using ICT. The developed approach can be applied in practice to optimize information processing and improve the efficiency of using ICT in various industries. The theoretical significance lies in expanding the methodology for risk assessment and decision-making based on computer modeling of cognitive processes.
Keywords
Information and Communication TechnologiesLinear ProgrammingStochastic ProgrammingRisk of Using TechnologiesProbability of Competent Use of TechnologiesUncertainty.
References
I. L. Akulich, Mathematical Programming in Examples and Problems, 3rd ed. Moscow, Russia: Lan, 2019, p. 348.
M. R. Alijanzadeh, S. A. Shayannia, and M. M. Movahedi, “Optimization of maintenance in supply chain process and risk-based critical failure situations (case study: Iranian oil pipeline and telecommunication company, north district),” Journal of Applied Research on Industrial Engineering, vol. 11, no. 1, pp. 125–142, 2024, doi: 10.22105/jarie.2022.322072.1419.
Yu. M. Ermoliev, Problems and Methods of Stochastic Programming, 3rd ed. Moscow, Russia: Nauka, 2020, p. 236.
G. R. Frederick, H. Schweizer, and R. Lowe, “After the inservice course: Challenges of technology integration,” Computers in the Schools, vol. 23, pp. 73–84, 2020.
Y. Goktas, S. Yildirim, and Z. Yildirim, “Main barriers and possible enablers of ICT integration into pre-service teacher education programs,” Educational Technology and Society, vol. 12, pp. 193–204, 2022.
L. V. Kantorovich, Mathematical-Economic Works. Novosibirsk, Russia: Nauka, 2019, p. 760.
P. Mishra and M. J. Koehler, “Technological pedagogical content knowledge: A framework for teacher knowledge,” Teachers College Record, vol. 108, no. 6, pp. 1017–1054, 2022.
A. Mishra, D. Kumar, M. Shuaib, M. Tyagi, and R. Singh, “Measurement of critical factors: A case of telecommunication industry,” 2021, doi: 10.1007/978-981-15-6017-0_16.
UNESCO, ICT Competency Framework for Teachers. Paris, France: UNESCO, 2023, pp. 96–99.
E. Smith, “How ICT is transforming education: Impact on pedagogical technologies,” Journal of Educational Technology, vol. 45, no. 2, pp. 112–130, 2020.
Y. Shevtsova, V. Kanev, A. Poletaikin, and N. Kuleshova, “Optimizing risk-free model of development of educational organization based on modified risk thermometer,” in Proc. 15th Int. Asian School-Seminar Optimization Problems of Complex Systems (OPCS), Novosibirsk, Russia, 2019, pp. 68–72.
E. V. Shikin and G. E. Shikina, Operations Research. Moscow, Russia: Prospekt, 2022, p. 281.
A. N. Shiryaev, Fundamentals of Stochastic Financial Mathematics, vol. 1. Moscow, Russia: MCCME, 2022, p. 440.
G. R. Sotirov and A. P. Voshchinin, Optimization under Uncertainty Conditions. Moscow, Russia: Sofia, 2020, p. 224.
H. Zhang, Z. R. Wang, X. W. Wang, and F. C. Lin, “Practice and research on FMEA of telecommunication satellite system,” in Signal and Information Processing, Networking and Computers, J. Sun, Y. Wang, M. Huo, and L. Xu, Eds., Lecture Notes in Electrical Engineering, vol. 917. Singapore: Springer, 2023, doi: 10.1007/978-981-19-3387-5_110.
D. B. Yudin, Problems and Methods of Stochastic Programming, 2nd ed. Moscow, Russia: Krasand, 2022, p. 392.