Article · International Journal of Engineering Trends and Technology · April 30, 2026

Recommendation Algorithms for Educational Systems

Akbayan Bekarystankyzy, Vyacheslav Khan, Aray M. Kassenkhan

Abstract

This study offers an in-depth review of techniques and tools used in supporting personalized learning in e-learning environments. The techniques and tools discussed in this study include user-centric and technology-centric techniques, such as several popular models of learning styles, including the Myers-Briggs Type Indicator, Kolb’s theory of experiential learning, Dunn and Dunn’s learning style model, and Felder-Silverman’s learning style model. The technological aspects discussed in this study include an overview of the Protus system, which is used as an example of how adaptive hypermedia and semantic technologies can be used in supporting personalized learning. The combination of user modeling and recent advances in technology highlights the limitations of both techniques when used in isolation. The limitations discussed in this study include ethical concerns, data privacy, and the lack of standardization in recommendations. The analysis ends with an overview of future research possibilities, such as using natural language processing in combination with adaptive personalization techniques.

Recommender Systems and TechniquesIntelligent Tutoring Systems and Adaptive LearningLearning Styles and Cognitive Differencesrecommendation algorithmspersonalized learningonline learning

Recommendation Algorithms for Educational Systems

Abstract

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