Adaptive Educational Recommendation Systems For Personalized Learning: A Review Of User Modeling And Machine Learning Approaches

The article explores a wide range of tools that are helpful in improving personalized learning, such as models, methods, and slants with an accent to the difficulties and successful moments in their application to the e-learning process. The widely used learning style models of user-centered approach like Myers-Briggs Type Indicator, Kolb’s theory of Empirical learning, Dunn and Dunn Learning Styles Model, Felder-Silverman Learning Style Model were reviewed in detail. The technology-centered approach was discussed on the example "Protus" system. In addition, it was observed that the use of personalization methods in combination with technological advances leads to promising results, because these two approaches can cover each others shortcomings. For example, the Felder-Silverman model is flexible in meeting the diverse needs of students, while systems such as Protus demonstrate the effectiveness of modular and semantic technologies in adaptive learning. In the result were highlighted the role of clustering, data mining, and algorithms for recommendation to success in the delivery of educational content in the mode of real time. However, the paper highlights that ethical reasoning, issues of privacy, and the lack of standardized algorithms for recommendation remain the major problem. Future researches dedicated to the implication of hybrid models, which consider natural language processing and adaptive dynamic algorithms of personalization are very important for the further evolution of recommendation systems in education.