From Fragmentation to Coherence: A Knowledge Graph-Driven Integration Framework for Blended Learning

Blended learning in higher education often struggles with disconnected resources across various platforms, causing cognitive fragmentation for students and heavy workloads for instructors. This study addresses these issues by proposing a knowledge graph-driven integration framework, with its core being a model that organizes key course concepts into a hierarchical knowledge graph and connects them to resources from online courses, digital textbooks, and virtual simulations. The model resolves long-standing resource integration inefficiencies, boosting teaching efficiency and learner engagement. A quasi-experimental study with 180 undergraduates (evenly split into experimental and control groups) showed that the experimental group using the model achieved higher final scores (82.1 vs. 76.2, p<0.001), stronger perceived resource coherence (4.0/5 vs. 3.2/5, p<0.001), and reduced cognitive load (4.9/10 vs. 6.1/10, p<0.001). The instructor also saw a 37.8% weekly workload reduction, particularly in grading and resource coordination. These findings demonstrate that the knowledge graph-based model enhances student learning outcomes and eases instructor burdens by improving resource coherence. Universities using multiple digital platforms are recommended to adopt this approach, especially for courses requiring strong concept linkage. Future research will test the model in other subjects and over longer periods to verify its scalability.