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DOI: 10.15862/10FAOR425 (https://doi.org/10.15862/10FAOR425)

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Semenova E.N., Goncharov A.V. Algorithms for intelligent analysis and structuring of educational program content in university management systems. Russian journal of resources, conservation and recycling. 2025; 12(s4). Available at: https://resources.today/PDF/10FAOR425.pdf (in Russian). DOI: 10.15862/10FAOR425

Algorithms for intelligent analysis and structuring of educational program content in university management systems

Semenova Ekaterina Nikolaevna
K.G. Razumovsky Moscow State University of Technologies and Management (the First Cossack University),
Moscow, Russia
E-mail: Katya-Sem00@mail.ru

Goncharov Andrey Vitalievich
K.G. Razumovsky Moscow State University of Technologies and Management (the First Cossack University),
Moscow, Russia
E-mail: a.goncharov@mgutm.ru

Abstract. The modern higher education system is undergoing a fundamental transformation driven by the exponential growth of educational information volume, increasing complexity of interdisciplinary connections, and the need for personalization of educational trajectories in the digital economy. The research is devoted to a comprehensive analysis of algorithms for intelligent processing and structuring of educational content within automated management systems of higher educational institutions. The subject of the study includes machine learning methods, natural language processing technologies, and graph models of knowledge representation applied for automatic analysis, classification, and structuring of curriculum content. Contemporary approaches to building educational knowledge graphs, semantic analysis of educational content, automatic identification of interdisciplinary connections, and competency mapping are examined. The evolution of educational data mining methods from statistical approaches to neural network architectures, including transformer models for processing educational texts, is analyzed. Key technological components of next-generation intelligent university management systems are identified, including modules for automatic knowledge extraction from unstructured educational materials, semantic competency mapping systems, algorithms for identifying prerequisite relationships between educational elements, and mechanisms for personalizing educational trajectories based on academic performance analysis. A conceptual model for integrating intelligent algorithms into university management system architecture is developed, demonstrating multilevel interaction of knowledge extraction, semantic analysis, graph representation, and adaptive recommendation components for educational content. The scientific novelty of the research lies in systematizing modern methods of intelligent analysis of educational programs, identifying the specifics of applying machine learning algorithms for structuring academic content, and developing a comprehensive approach to automating educational program management processes in the context of digital transformation in higher education. The practical significance of the work is determined by the possibility of applying the developed approaches for modernizing existing university management systems, improving the quality of educational program design, optimizing content update processes, and creating intelligent decision support systems for educational process management.

Keywords: intelligent data analysis; educational programs; university management system; machine learning; knowledge graphs; natural language processing; semantic analysis; competency-based approach; education automation; content structuring; digital transformation

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