Models for forecasting the need for teaching staff in general education

Introduction. The article is devoted to the problem of forecasting the need for teaching staff in general education in the context of a shortage of pedagogical resources, inaccurate forecast values of the main parameters of general education for the formation of a new strategy for the development of the education system. The purpose of the work is to build models for forecasting the need for teaching staff in general education. Materials and Methods. The methodological basis of the study includes empirical and aspect approaches. The empirical approach is based on the existing experience of researchers in the scholarly practice of teacher education in substantiating the choice of models for forecasting the need for teaching staff in general education and indicators for forecasting the education system. The aspect approach is based on the studying the staffing of general education in all constituent entities of the Russian Federation between 2022 and 2024 and the identification of factors that determine the development of the staff infrastructure. The study used an empirical method of smoothing time series using an exponential window function and multivariate regression analysis in order to establish future patterns (forecast values of the need for teaching staff) based on previous statistics and checking the relationships between the expected result and several independent factors (macroeconomic indicators of the region) that influence it. Results. Based on the theoretical analysis of the research problem, the authors, in accordance with the empirical forecasting of the need for teaching staff in general education, constructed forecast models (mathematical equations) for the number of teachers in the Saratov region: firstly, using the exponential smoothing method with a high approximation reliability value of R2 = 85 %; secondly, using multivariate regression analysis with an established reliability level of 95 % and the obtained single values of the multiple regression result (R) and the multiple determination coefficient (R2). The obtained results confirm the reduction in the number of teachers, taking into account the influence of regional macroeconomic indicators (demographic factors): the population of the region; the coefficient of migration growth per 10.000 people, the crude birth rate. Conclusions. The study concludes that it is necessary to use a multifactorial regression model to conduct an advanced assessment of the need for teaching staff in general education, taking into account the relationship between the expected result and several independent factors (socioeconomic, demographic) that influence it.