Research and implementation of an adaptive learning system based on knowledge graphs: a deep-learning-driven personalized recommendation model

Adaptive learning systems have gained significant attention in recent years for their ability to cater to diverse learner needs and improve educational outcomes. This paper proposes a novel adaptive learning system that leverages knowledge graphs and deep learning to provide personalized learning recommendations. The system integrates structured domain knowledge into a knowledge graph, enabling the representation of complex relationships among concepts. A deep learning-driven recommendation model is employed to analyze user interaction data, dynamically adapt to learner profiles, and generate personalized learning pathways. The framework is evaluated on a real-world educational dataset, demonstrating its effectiveness in enhancing learner engagement and knowledge retention. Experimental results show that the proposed system outperforms traditional methods in recommendation accuracy, adaptability, and scalability. This research provides a robust, scalable solution for implementing adaptive learning systems in modern educational environments, bridging the gap between theoretical advancements and practical applications.