ПЕРСОНАЛІЗАЦІЯ ОСВІТНЬОГО ПРОЦЕСУ НА ОСНОВІ МОДЕЛЮВАННЯ ІНДИВІДУАЛЬНИХ ОСВІТНІХ ТРАЄКТОРІЙ СТУДЕНТІВ В УМОВАХ АДАПТИВНОГО ЗМІШАНОГО НАВЧАННЯ

The relevance of the study is determined by the growing need to personalize the educational process amid the digital transformation of higher education and the expansion of adaptive blended learning. Existing digital platforms generate substantial volumes of learning data, yet their use for constructing coherent individual learning pathways remains insufficiently developed. The purpose of the article is to develop and substantiate a method for modeling students’ individual learning pathways in an adaptive blended learning environment, aimed at personalizing the educational process and improving learning effectiveness. The methodological framework is based on a systemic approach to adaptive blended learning. The synthesis of scholarly sources was used to identify current approaches to learning personalization; structural and functional analyses were used to determine the relationships among the components of the adaptive educational environment; modeling was applied to develop the proposed method for constructing individual learning pathways. The study examines the formation of individual learning pathways in adaptive blended learning, identifies the structural components of this environment, and specifies the adaptive mechanisms used to personalize learning content, sequence, and pace. The main barriers to implementation are identified, including the complexity of formalizing learning routes, the limited completeness of learning data, the integration of digital analytics into pedagogical management, and the need to ensure the validity of adaptive decisions. A method for modeling individual learning pathways, based on the adaptive management of the educational process, is developed. It integrates diagnostic assessment, learning analytics, pedagogical adjustment, and continuous monitoring of learning outcomes within a single decision-making cycle. The proposed method can improve the validity of personalized decisions, support timely adjustment of learning routes, and promote more effective use of digital technologies in higher education institutions. Further research should focus on experimental validation of the method and evaluation of its effectiveness across different educational programs.