Traditional machine learning for adaptive management of education in crisis contexts

Abstract

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.