In the face of the dilemma of learners' "learning loss" and "information overload" in information resources, a personalized learning resource recommendation algorithm is proposed by conducting in-depth and extensive research on the knowledge graph. This algorithm relies on the similarity or correlation between learners' characteristics and course knowledge (learning resources) for recommendation. It analyzes learners' characteristics in depth from four aspects: data collection and processing, model construction, resource and path recommendation, and model application, and establishes a multi layered dynamic feature model for learners; Analyze the core elements of the curriculum knowledge graph, decompose the curriculum knowledge into nanoscale knowledge granularity, and construct a curriculum knowledge graph model. The experimental results indicate that this algorithm improves learners' learning efficiency and promotes their personalized development.
1, Bozhi; Wang, SaiNan; and 3, Yuyi
"Research on Personalized Learning Resource Recommendation Based on Knowledge Graph Technology,"
International Journal of Computer and Communication Technology: Vol. 8:
4, Article 5.
Available at: https://www.interscience.in/ijcct/vol8/iss4/5