UkrainianProfessional Education

Abstract

The article examines a method for forming an individual learning trajectory in adaptive educational systems operating within a digital and Internet-oriented environment. The relevance of the study is determined by the growing need for personalization of the educational process in the context of digitalization of education, the heterogeneous level of learners’ prior knowledge, and the dynamic changes in requirements for learning outcomes. In traditional educational models, learning content is delivered according to a fixed curriculum that does not take into account individual characteristics, the level of preparedness, or the pace of learning of each learner, which reduces the overall effectiveness of the educational process. The paper proposes a method for forming an individual learning trajectory based on the analysis of cognitive, behavioral, and performance indicators of learners’ activities within an adaptive educational system. The method involves the integration of educational content, a learner profile, and adaptation mechanisms that ensure dynamic restructuring of the learning path according to the level of acquired knowledge, skills, and competencies. The formation of an individual learning trajectory is carried out through step-by-step adjustment of the structure and complexity of learning materials, selection of an appropriate learning pace, types of educational resources, and forms of assessment of learning outcomes. Within the framework of the study, a conceptual model of the method for forming an individual learning trajectory has been developed. The model is based on the interaction of three key components: educational content, the learner, and the analytical core of the adaptive system. The analytical core provides collection, processing, and interpretation of data on learners’ educational activity, assessment results, and learning progress dynamics, which serve as the basis for decision-making regarding the adaptation of the learning process. The proposed method enables flexible formation of individual learning paths, taking into account both the current state of learners’ knowledge and the forecasting of their future educational needs. The practical significance of the obtained results lies in the possibility of implementing the proposed method in digital educational platforms and distance and blended learning systems to improve the effectiveness of learning. The use of the method contributes to increased learner motivation, reduced learning overload, and sustained support of individual learning trajectories throughout the entire period of study. 

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