Research on information construction of knowledge graph based on literature retrieval in english learning

Abstract

This study aimed to explore the construction of an English language knowledge graph based on literature retrieval to support intelligent education. A questionnaire was administered to collect data on students’ experiences with traditional and technology-enhanced learning approaches. Literature was also retrieved and analyzed to populate the knowledge graph domains. The results showed that implementing a knowledge graph significantly improved learning personalization and fostered greater student engagement compared to conventional teaching methods. Real-time analytics and continuous feedback further optimized the learning process. Post-implementation assessments found notable gains in students’ academic performance and inclination toward English learning. The personalized, adaptive learning environment facilitated by the knowledge graph more effectively sustained interest and promoted achievement. In conclusion, knowledge graphs constructed through literature analysis hold promising potential for advancing English education when incorporated into intelligent tutoring systems. By mapping interconnections within the subject domain visually and computationally, they can power highly customized instruction tailored to individual needs.