UDC 519.8
The paper provides a reasoned justification for the importance of the correct selection of a candidate for the position of junior commander for further effective training, service and daily activities in the group. The authors provide a brief overview of the works on the research topic, which most often contain a psychological and pedagogical analysis of the criteria for evaluating candidates, the work experience of senior officers of specific universities, and historical experience. In the proposed work, the authors successfully realize the goal, which consists in mathematically modeling the multi-criteria task of optimal selection of junior commanders in the unit and solving the resulting problem model using the Rush method. The applied approach minimizes the subjectivity and bias of decision makers. The paper contains a general statement of the problem and the theoretical foundations of its solution by the Rasch method. In the practical part, according to the personal files of ten cadets of one of the groups, four most suitable candidates were proposed based on calculations. In addition, the use of the Rasch method made it possible to identify the least and most feasible criteria, which may allow for more flexible work with the selection of criteria.
Rasch's method, latent variables, optimal choice, selection of junior unit commanders, multi-criteria tasks, criteria for evaluating candidates, weighting criteria, leadership qualities, organizational abilities.
1. Valkov A.V. Development of leadership qualities of an officer of the internal troops in the educational process of a military university // Young Scientist. - 2012. – No. 8. – pp. 312-315.
2. Evtikhov O. V. Analysis of leadership qualities of junior commanders of training groups at the University of the Ministry of Internal Affairs of Russia // Psychopedagogy in law enforcement agencies, 2009, № 2(37). – Pp. 27-31.
3. Lysakov N.D. The experience of training command personnel at the Air Force Academy during the Great Patriotic War // Human Capital, 2020, No. 3(135). – Pp. 49-54.
4. Zhelezkin V.N. Problematic issues in the management of cadet teams // Bulletin of the St. Petersburg University of the Ministry of Internal Affairs of Russia No. 2 (98) 2023. – pp. 187-192.
5. Korypaeva Yu.V., Kuznetsova L. D., Sumin V.I. Modeling using the hierarchy analysis method in the assignment of a commander in a group of military university cadets // Bulletin of the Voronezh Institute of the Federal Penitentiary Service of Russia. Scientific Journal, 2022, No. 2, pp. 73-80.
6. Neiman, Yu.M. Introduction to the theory of modeling and parameterization of pedagogical tests / Yu.M. Neiman, V.A. Khlebnikov, Moscow: Prometheus, 2000. 168 p.
7. Chelyshkova, M. B. Theory and practice of designing pedagogical tests: a textbook / M. B. Chelyshkova — M.: Logos, 2002. — 432 p.
8. Mathematical models of preparation and verification of the quality of competence development in the educational process / S.A. Barkalov, S.I. Moiseev, N.S. Kocherga, E.V. Solovyova // Open education. - 2014. – No. 2. – pp. 9-16.
9. Letova L. V., Maslak A. A., Osipov S. A. A family of Rasch models for objective measurement of latent variables. / Informatization of education and science. – No. 4, 2013– pp. 131-141.
10. Moiseev, S.I. Decision-making methods based on the Rasch model for estimating latent variables / S.I. Moiseev, A.Yu. Zenin // Economics and management of management systems. – 2015. – № 2.3 (16). – Pp. 368-375.
11. Moiseev, S.I. Methods of making optimal decisions: textbook. manual / S.I. Moiseev, A.A. Zaitsev. Voronezh: Institute of Management, Marketing and Finance, 2016. 144 p.
12. Rasch G. Probabilistic models for some intelligence and achievement tests // G. Rasch.- Copenhagen, Denmark: Danish Institute for Educational Research, 1960.
13. Mokhov, A. I. Development of a recommendation system based on sessions using a multilevel candidate selection system / A. I. Mokhov, V. G. Kislinsky, A. S. Alekseychuk // Proceedings of MAI. – 2022. – № 126. – DOIhttps://doi.org/10.34759/trd-2022-126-20 . – EDN KXYFUB.
14. Peshevets, S. V. Methodology for substantiating additional requirements for the training of radio-electronic warfare specialists / S. V. Peshevets // Proceedings of MAI. – 2021. – No. 120. – DOIhttps://doi.org/10.34759/trd-2021-120-17 . – EDN WCJVRA.
15. Using artificial intelligence methods to assess competencies during testing / V. N. Gusyatnikov, T. N. Sokolova, A. I. Bezrukov, I. V. Kayukova // Informatics and Education. – 2023. – Vol. 38, No. 6. – pp. 75-85. – DOIhttps://doi.org/10.32517/0234-0453-2023-38-6-75-85. – EDN ABPXYT.
16. Maslak, A. A. Statistical analysis of the aggregated rating of Russian universities as a measuring instrument / A. A. Maslak, A. I. Korobko // Bulletin of Voronezh State University. Series: System analysis and Information Technology. - 2023. – No. 1. – pp. 68-79. – DOIhttps://doi.org/10.17308/sait/1995-5499/2023/1/68-79. – EDN RUOORO.
17. GNSS best integer equivariant estimation using multivar-iant t-distribution: a case study for precise point position-ing / V. Duong, K. Harima, S. Choy, Ch. Rizos // Journal of Geodesy. – 2021. – Vol. 95, No. 1. – P. 1-17. – DOIhttps://doi.org/10.1007/s00190-020-01461-w. – EDN MYIZFH.
18. Van Vo, De. Development of scientific reasoning test measuring control of variables strategy in physics for high school students: evidence of validity and latent pre-dictors of item difficulty / De. Van Vo, B. Csapó // Inter-national Journal of Science Education. – 2021. – Vol. 43, No. 13. – P. 2185-2205. – DOIhttps://doi.org/10.1080/09500693.2021.1957515. – EDN EHFCTL.
19. Rasch analysis to evaluate the psychometric properties of junior metacognitive awareness inventory in the indone-sian context / M. I. Sukarelawan, Jumadi, H. Kuswanto [et al.] // Jurnal Pendidikan IPA Indonesia. – 2021. – Vol. 10, No. 4. – P. 486-495. – DOIhttps://doi.org/10.15294/jpii.v10i4.27114. – EDN XZUFDK.
20. Generalizability, Replicability, and New Insights Derived From Registered Reports Within Understudied Popula-tions / P. A. Edelsbrunner, K. Ruggeri, K. Damnjanović [et al.] // European Journal of Psychological Assessment. – 2022. – Vol. 38, No. 6. – P. 427-431. – DOIhttps://doi.org/10.1027/1015-5759/a000743. – EDN TMHXPR.
21. Exploring consumer perception of entomophagy by ap-plying the Rasch model: data from an online survey / L. Grispoldi, L. Zampogni, E. Costanzi [et al.] // Journal of Insects as Food and Feed. – 2023. – Vol. 10, No. 1. – P. 9-24. – DOIhttps://doi.org/10.1163/23524588-20230045. – EDN BE-QXLE.
22. Research analytics capabilities (RAC) survey: develop-ment, validation and revision using the Rasch model / K. L. Robershaw, M. Xiao, E. Wallett, B. G. Wolf // Journal of Applied Research in Higher Education. – 2024. – DOIhttps://doi.org/10.1108/jarhe-12-2023-0578. – EDN SCQXBA.
23. Prasetyaningrum, P. T. Analyzing Customer Engagement with Gamification Approach in the Banking Sector Using the Rasch Model / P. T. Prasetyaningrum, P. Purwanto, A. F. Rochim // Ingenierie des Systemes d'Information. – 2024. – Vol. 29, No. 3. – P. 1051-1064. – DOIhttps://doi.org/10.18280/isi.290323. – EDN IKUULX.
