УДК 378:004.94

DOI: http://doi.org/10.15350/2409-7616.2023.2.38

Bocharova T.A.¹ (Khabarovsk, Russian Federation) – kitaal@yandex.ru

¹Pacific State University

Abstract. The introduction of specialized education at the senior level of school has led to the fact that already at the end of the eighth grade, before choosing the specialized OGE, which determine the further track of education in the final grades, students must decide on the choice of their future profession. The factors that determine this choice are often the social status of the family, abilities, remoteness of the university, the presence of additional pre-university training, which is quite enough to determine the direction of study and not enough to form motivation for further professional implementation. This state of affairs leads to the fact that by the time of graduation from a higher educational institution, almost a third of graduates lose interest in their chosen specialty and are not employed in the field of study. An analysis of the motivational factors for choosing a future profession can help determine the level of interest among students in working in their specialty or in related fields. The article is devoted to solving the problem of classifying the social types of students within the framework of professional orientation. The cluster modeling procedure was applied to identify the factors that have the greatest impact on the professional self-determination of students of the Institute of Socio-Political Technologies and Communications of the Pacific State University. The solution is presented as a model of three clusters, built on the basis of a formalized model that includes seven features of the input elements. As a data processing tool, an artificial neural network of the Kohonen self-organizing map type was chosen, which has the maximum clarity of data presentation. The proposed description of the model can serve as an additional decision support tool in the organization of the educational process, as well as career guidance work at the university. The use of the resulting cluster model as an effective tool for assessing the professional motivation of the student community will improve the employment rates of graduates in their specialty or in related fields.

Keywords: career guidance, professional self-determination, modeling, cluster analysis, Kohonen self-organizing maps.

References:

1. Abilkhanova Zh.T. Problems of choosing a future profession by older students and ways to solve them. Science and Reality, 2022, no. 2 (10), pp. 24–25. (In Russian). URL: https://elibrary.ru/item.asp?id=48453144
2. Bocharova T.A. Solving the problem of classifying social behavior using the apparatus of neural networks. Humanitarian, socio-economic and social science, 2019, no. 11, pp. 25–28. (In Russian). URL: https://elibrary.ru/item.asp?id=41494764
3. Vasilyeva L.V., Tolstoukhova I.V. Main factors influencing the choice of future profession. Bulletin of the Surgut State Pedagogical University, 2020, no. 2 (65), pp. 50–55. (In Russian). URL: https://elibrary.ru/item.asp?id=43949791
4. Grigoryeva E.V., Koval S.V., Malkov D.V. Problems of employment of university graduates in priority areas of the state. Transport business of Russia, 2019, no. 4, pp. 83–85. (In Russian). URL: https://elibrary.ru/item.asp?id=41151687
5. Minets D.V., Puchkov M.Yu., Radeev A.E. Humanities Education in the Age of Technology: Transformation or Crisis? Materials of the discussion round table. Magister, 2022, no. 2, pp. 10–19. (In Russian). URL: https://elibrary.ru/item.asp?id=49525700
6. Zarubina N.K., Ovchinkin O.V., Emelyanov S.G. The use of cluster analysis tools in planning the contingent of students. Bulletin of the South-Western State University. Series: Management, computer technology, informatics. Medical instrumentation, 2016, no. 1 (18), pp. 22–28. (In Russian). URL: https://elibrary.ru/item.asp?id=25942199
7. Kovalenko S.O. Subjective factors influencing the process of professional formation of specialists in the humanities. Bulletin of the South Ural State University. Series: Social Sciences and Humanities, 2019, vol. 19, no. 1, pp. 101–104. (In Russian). URL: https://elibrary.ru/item.asp?id=36735650
8. Novgorodtseva T.Yu., Burdukovskaya A.V., Ivanova E.N. Modeling of the structural elements of the contingent of students based on cluster analysis. Modern pedagogical education, 2021, no. 5, pp. 75–80. (In Russian). URL: https://elibrary.ru/item.asp?id=46206796
9. Panteleeva O.O. Factors influencing the self-determination of applicants. Bulletin of the Leningrad State University named after A.S. Pushkin, 2017, no. 2, pp. 201–213. (In Russian). URL: https://elibrary.ru/item.asp?id=29800510
10. Sitnikova I.V. Professional plans and strategies for the employment of modern students. Bulletin of the Perm National Research Polytechnic University. Socio-economic sciences, 2019, no. 4, pp. 61–77. (In Russian). URL: https://elibrary.ru/item.asp?id=41852924
11. Turchina L.A., Titova V.V. Factors influencing the choice of profession. Armyansk, Institute of Pedagogical Education and Management (branch) of the Federal State Autonomous Educational Institution of Higher Education “Crimean Federal University named after V.I. Vernadsky” in Armyansk Publ., 2019, pp. 168–172. (In Russian). URL: https://elibrary.ru/item.asp?id=42354426
12. Cherednichenko G.A. Graduates of Russian universities in the labor market (data from a Rosstat survey). Sociological science and social practice, 2020, vol. 8, no. 3 (31), pp. 108–124. (In Russian). URL: https://elibrary.ru/item.asp?id=44003227
13. Shirinkina E.V. Statistical study of factors influencing the choice of profession of school graduates. Humanitarian and Pedagogical Education, 2019, vol. 5, no. 3, pp. 139–145. (In Russian). URL: https://www.elibrary.ru/item.asp?id=42988479
14. Hameed A.A., Karlik B., Salman M.S. Robust adaptive learning approach to self-organizing maps. Knowledge-Based Systems, 2019, vol. 171, pp. 25–36. DOI: https://doi.org/10.1016/j.knosys.2019.01.011
15. Vij A., Carrel A., Walker J.L. Incorporating the influence of latent modal preferences on travel mode choice behavior. Transportation Research Part A: Policy and Practice, 2013, vol. 54. pp. 164–178. DOI: https://doi.org/10.1016/j.trpro.2019.09.057

For citation: Bocharova T.A. Modeling student clusters on the basis of self-organizing Kohonen charts. CITISE, 2023, no. 2, pp. 431-441. DOI: http://doi.org/10.15350/2409-7616.2023.2.38