Neural network information management model for a large educational complex

Effective management of the national educational system is a strategically important direction for further development of the entire state [1]. Management of educational processes is the most difficult direction in the theory and practice of management because of their multiplicity and formalization [2]. In connection with the reorganization of the Russian educational system, large educational complexes (LEC) have appeared [3], which are a new structural unit in the national education system. The object of this article is the system connections and processes of functioning of a large educational complex. The subject of the research is algorithms, methods and models of rating evaluation of LEC activities for managing its effectiveness. The practical significance of the work is as follows: - new criteria for evaluating the quality of education in LEC ' individual educational programs of students are proposed; - an information model of rating evaluation of the LEC activity based on the method of integral assessment of the quality of education is proposed, which allows for reliable ranking of the main indicators not by expert means, but by calculating the value of its entropy; - the project of an information system for forming the LEC rating is proposed, which allows the management bodies to monitor the effectiveness of the educational system, carry out operational, tactical and strategic planning, and apply control actions in a timely manner to improve the efficiency of the main activity; - a neural network model is proposed for selecting the control effect on the LEC. Practical use. The obtained scientific results are of applied nature, and their application is confirmed by the results of work in Management of education of administration of Sosnovsky municipal district of the Chelyabinsk region in public-professional Association ' on the Board of Directors of the educational organizations of the city of Moscow No. 10 and GBOU School No. 1389. © 2020 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (CEUR-WS.org)