Interrelation between the use of adaptive learning platforms and student academic performance in blended learning environments

Abstract

The relevance of the study is determined by the intensive digitalization of higher education and the expansion of blended learning, which actualizes the problem of ensuring stable educational outcomes under conditions of increasing heterogeneity of students’ academic trajectories. The use of adaptive learning platforms is considered a promising tool for improving academic performance; however, their actual effectiveness remains debatable and requires scientific substantiation.

The aim of the article is to determine and scientifically substantiate the nature and strength of the relationship between the use of adaptive learning platforms and students’ academic performance in blended learning environments.

The research methods are based on theoretical generalization, analysis and synthesis of scientific sources, a structural-functional approach, and logical-analytical modeling to identify relationships between the parameters of adaptive learning environments and students’ academic outcomes.

Results. The study analyzes theoretical approaches to defining the essence of adaptive learning platforms and their functional capabilities within blended learning. Key factors influencing students’ academic performance in a digital educational environment have been identified, including the level of personalization, cognitive readiness, motivation, and digital competence. Methodological approaches to evaluating the effectiveness of adaptive platforms have been substantiated based on the analysis of learning dynamics and behavioral indicators. The main scientific and practical problems have been revealed, including limited validity of adaptive algorithms, the gap between learning analytics and pedagogical practice, fragmented integration into the educational process, and organizational constraints of blended learning implementation. It has been proven that the effectiveness of adaptive platforms is ensured through their systemic integration into the educational process and the combination of technological and pedagogical mechanisms.

The conclusions substantiate that improving students’ academic performance is achieved through the implementation of personalized learning trajectories, the use of learning analytics, and the optimization of feedback. Prospects for further research are associated with the development of standardized models for evaluating the effectiveness of adaptive learning, empirical verification of different types of personalization, and the study of their impact on long-term academic performance and learner autonomy.