Information-Extreme Technology of Intelligent Analysis of Educational Content Quality in Higher Education Institutions

The article is dedicated to the development of an informational analytical-intellectual system for assessing the quality of educational content produced by the graduating department, using information-extreme intellectual technology. The research focuses on improving the accuracy of classifying educational materials according to the evaluations of graduates and employers, considering the dynamics of the labor market. The main attention is on developing a mathematical model of the machine learning process for a system that classifies the quality of educational materials based on the assessments of employers and graduates. A mathematical model for the operation of the analytical intellectual analysis system has been developed, based on the concept of nested hyperspherical containers of quality classes. Algorithms for parallel and sequential optimization of assessment feature tolerance limits have been developed, allowing the improvement of the system's functional efficiency under conditions of limited input data. The optimal geometric parameters of containers for each quality class have been defined in accordance with the Kullback information criterion. Experimental results show a significant improvement in classification accuracy - from 55% to 97%. The obtained results confirm the feasibility of using IEI technology for assessing the quality of educational materials in higher education. The proposed approach allows adapting the educational content to the current market requirements, which contributes to improving the competitiveness of graduates. A promising direction for further research is the integration of hybrid intelligent methods, including neuro-fuzzy models, as well as expanding the system's functional capabilities in the context of automated monitoring of the quality of the educational process.