[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119557-en":3,"doc-seo-119557-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119557,1374391974585,"Genevieve","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Traditional machine learning for adaptive management of education in crisis contexts","Modern management systems face constant instability and recurring crisis phenomena, often making traditional approaches less effective. Adaptive education governance requires intelligent technologies capable of processing large datasets and supporting timely, evidence-informed decisions. Classical machine learning can strengthen crisis-oriented educational management by enabling comprehensive data analysis, transparent results, strategic planning, and rapid response to changing conditions, ultimately improving educational processes. It supports greater stability and competitiveness for education institutions amid ongoing transformation.","12. Naumenko, M. (2024) . Models of business knowledge in artificial intelligence systems for an effective competitive enterprise. International scientific journal\"Internauka\". Series: \"Economic Sciences\". № 6. DOI: [https://doi.org/10.25313/2520-](https://doi.org/10.25313/2520-)[ ](https://doi.org/10.25313/2520-)[2294-2024-6-10010](2294-2024-6-10010) [In Ukrainian] .  \n13. Naumenko, М ., & Hrashchenko, І . (2024) . Modern artificial intelligence in anti-crisis management of competitive enterprises and companies. Grail of Science,(42), 120–137. DOI: [https://doi.org/10.36074/grail-of-science.02.08.2024.015](https://doi.org/10.36074/grail-of-science.02.08.2024.015) [In Ukrainian].  \n14. Derbentsev, V. D., V. M. Soloviov, and O. V. Serdiuk (2005) Precursors of critical phenomena in complex economic systems. Modeling of nonlinear dynamics of economic systems. -Donetsk: DonNU, 1 (2005) . pp. 5-13 [in Ukrainian] .  \n15. Derbentsev, V. D., B. O. Tishkov, O. D. Sharapov (2013) . Systematic methodology for studying the dynamics of the current information economy in the minds of increasing instability. Modeling and information systems in economics.–  \n2013.– Vol. 89.–pp. 47-62 [In Ukrainian] .  \n16. Krasnyuk, M. (2014) . Hybridization of intelligent methods of business data analysis (anomaly detection mode) as a standard tool of corporate audit. The state and prospects of the development Education and science of today: materials of the III International science and practice conf. [m. Ternopil, October 10-11. 2014] . TNEU, 2014. pp. 211-212 [in Ukrainian] .  \nUDC 004.94::37 .09  \nTraditional machine learning for adaptive  \nmanagement of education in crisis contexts  \nSvitlana Krasniuk  \nKyiv National University of Technologies and Design, Kyiv  \n[http://orcid.org/0000-0002-5987-8681](http://orcid.org/0000-0002-5987-8681)  \nAbstract. Modern management systems operate in an environment of constant instability and crisis phenomena, where traditional approaches often lose their effectiveness. To ensure flexibility and strategic adaptability, intelligent technologies are needed that can process large amounts of data and make informed management decisions. The integration of classical machine learning into educational management is an effective tool in conditions of instability and crisis situations. This creates opportunities for comprehensive analysis, strategic planning and implementation of innovative solutions that increase the competitiveness of educational organizations. In general, the use of classical machine learning in adaptive education management ensures effective information processing, transparency of results, prompt response to changes and improvement of educational processes. Thus, machine learning becomes a key means of increasing the stability and competitiveness of educational institutions in conditions of constant transformations.  \nKeywords: innovative management, educational management, classical machine learning, crisis.  \nIntroduction. Modern socio-economic systems function under the conditions of constant uncertainty [1, 2], instability [3, 4] and crisis processes. Global economic fluctuations, political confrontations, environmental challenges and technological breakthroughs form an environment where traditional management approaches lose their effectiveness [5-7]. In other words, in conditions of modern instability and crisis phenomena [8, 9], organizations face the need for quick and effective decision-making that can minimize risks and ensure stable work [10] . That is, traditional management approaches are often not flexible enough to work in a fast-changing environment, which necessitates the use of intellectual (KNOWLEDGE-BASED AND DATA DRIVEN) technology [11] capable of adaptation to new circumstances.  \nTo ensure stability, flexibility and strategic adaptability, appropriate innovative tools (information systems [12]) are needed, which are able to deeply analyze data and form rational management decisions.  ","cbCairkxqSXbCIlk","https://ap.wps.com/l/cbCairkxqSXbCIlk","pdf",441777,1,5,"English","en",105,"# Introduction\n## Instability and crisis in socio-economic systems\n## Need for knowledge-based and data-driven intelligent technology\n# Main Part\n## Challenges in the modern education system\n## Role of classical machine learning in adaptive governance","[{\"question\":\"Why do traditional management approaches often fail in education during crises?\",\"answer\":\"Because education systems operate under uncertainty, rapid change, and crisis dynamics, traditional approaches are often slow and not flexible enough for timely response.\"},{\"question\":\"How does classical machine learning support adaptive management in education?\",\"answer\":\"It processes large volumes of data, discovers hidden patterns, forecasts event development, and helps optimize management decisions for crisis response.\"},{\"question\":\"Which classical machine learning methods are highlighted for structured educational data?\",\"answer\":\"The text names linear and logistic regression, decision trees, nearest neighbors, naïve Bayes classifier, and mentions cluster analysis as an additional direction.\"}]","Traditional machine learning for adaptive management of education in crisis contexts | 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