Learning Behavior Interest Propagation Strategy of MOOCs Based on Multi Entity Knowledge Graph

MOOCs might be an important organization way to realize the online learning process. Online technology and sharing technology enable MOOCs to realize the adaptive scheduling of learning resources, as well as the independent construction of learning sequences. At the same time, it also generates a large number of complex learning behaviors. How to mine and predict the value and laws of MOOCs is a difficult problem in learning analytics applied to MOOCs. This study integrates the context information of the learning process of MOOCs, designs a deep learning model based on multi-entity knowledge graph, in order to predict and track the interest propagation of learning behaviors, and realizes the learning trend guidance mechanism supported by multi features and complex relationships. Through sufficient experiments, the effectiveness and reliability of this model are verified, and based on the analysis results, the best path and implementable scheme for the interest propagation of learning behaviors are constructed. The whole research might provide more references for the study of learning behaviors described by multi-entity knowledge graph of MOOCs.