Multi-source Education Knowledge Graph Construction and Fusion for College Curricula
This work addresses the specific problem of improving learning outcomes for university students in a specialized domain, representing an incremental application of existing methods to new data.
The paper tackles the problem of complex university curricula and learning resources leading to poor student outcomes by proposing an automated framework for constructing and fusing knowledge graphs for the Electronic Information major, aiming to enhance learning efficiency and explore AI-enabled educational paradigms.
The field of education has undergone a significant transformation due to the rapid advancements in Artificial Intelligence (AI). Among the various AI technologies, Knowledge Graphs (KGs) using Natural Language Processing (NLP) have emerged as powerful visualization tools for integrating multifaceted information. In the context of university education, the availability of numerous specialized courses and complicated learning resources often leads to inferior learning outcomes for students. In this paper, we propose an automated framework for knowledge extraction, visual KG construction, and graph fusion, tailored for the major of Electronic Information. Furthermore, we perform data analysis to investigate the correlation degree and relationship between courses, rank hot knowledge concepts, and explore the intersection of courses. Our objective is to enhance the learning efficiency of students and to explore new educational paradigms enabled by AI. The proposed framework is expected to enable students to better understand and appreciate the intricacies of their field of study by providing them with a comprehensive understanding of the relationships between the various concepts and courses.