Analysis and Classification of Factors Contributing to Personalization in E-learning

Abstract

The transformation brought about by digital technologies has significantly reshaped the field of education, giving rise to innovative approaches like E-learning that extend beyond the conventional confines of traditional education. One of E-learnings’ defining features is the concept of personalization, wherein learning approaches and content dynamically adapt to cater to the unique needs and preferences of individual learners. This research systematically classifies the myriad possibilities of personalization within the E-learning process. The developed classification encompasses factors of creating and delivering adaptive learning objects, employing learner-centered teaching methods, and personalization grounded in individual learner data. This classification structure enables the development of practical models for personalized learning by leveraging the features of contemporary Learning Management Systems (LMS), offering tangible benefits to both learners and tutors. This research enhances the academic discourse by providing a well-organized structure that elucidates the diverse factors of personalization within the context of e-learning. By classifying and discussing various factors, the research shows the perspective of integrating modern technologies in enhancing the personalized educational experience.