Methodological basis of system analysis in the context of creating national educational information systems

DOI: 10.31673/2412-9070.2026.017411

Abstract

The article provides a comprehensive examination of the methodological foundations of system analysis as a scientific basis for the design, integration, and strategic development of national educational information systems (IS) in Ukraine. The study is driven by the urgent need to transition from fragmented digitization efforts to a unified ecosystem. Based on a thorough analysis of regulatory and legal acts, international best practices, and scientific works by domestic and foreign researchers, the authors have identified key structural deficiencies and bottlenecks within the current national digital educational ecosystem.
To address these challenges, a robust methodological framework was applied, utilizing systemic structural, functional-process, comparative, and expert analysis techniques. This approach enabled the development of a model for the target architecture of educational IS capable of meeting modern demands. A core proposition of the study is the concept of building a national educational data center. This proposed infrastructure is founded on the principles of interoperability, data openness, architectural modularity, and data-driven governance.
A significant practical outcome of the systematic analysis is the formulation of an efficiency for mula for educational IS. This quantitative model synthesizes metrics of data quality, the level of system integration, and the risks associated with functional incompatibility. The article argues that a systematic approach should not merely be an auxiliary method but must become the fundamental tool for implementing the state policy of digital transformation in education. This ensures the sustainability, scalability, and adaptability of the educational environment.

Keywords: systems analysis; educational information systems; digital transformation of education; interoperability; analytical module; open data; data-driven management.