Journal of Modern Business Administration

With the deepening integration of information technology in education, personalized learning has emerged as a pivotal trend in educational development. This paper focuses on data-driven optimization of personalized learning pathways, detailing its theoretical foundations, model construction, algorithm design, and practical applications. Through collecting and analyzing multi-source learning data, we establish precise learner models and course knowledge network models. Intelligent algorithms are then employed to recommend customized learning paths for learners, with continuous refinement through practical implementation. The research findings demonstrate that data-driven personalized learning pathways significantly enhance learning efficiency and experience, providing robust support for innovative educational development.

Data-driven; Personalized learning pathway; Learner model