Learning with Dual-graph for Concept Prerequisite Discovering
[Objective]This paper fully utilizes fine-grained information,such as the mention of concepts in learning resources,to more effectively identify prerequisite relationships.[Methods]First,we explored prerequisite relationships using a dual-graph neural network.Then,we constructed a concept semantic graph and a concept prerequisite graph based on the connections between learning resources and concepts.Third,we obtained the representations of concepts with a graph neural network and predicted the unknown prerequisite relationships.[Results]We extensively examined our model on four classic prerequisite relationship mining datasets.Our method achieved promising results,surpassing existing methods.It outperformed the second-best method by 0.059,0.037,0.073,and 0.042 regarding the F1 score on each dataset.[Limitations]This method shows weak predictive ability for concepts not appearing in the learning resources.[Conclusions]The proposed dual-graph neural network method can effectively leverage semantic information in learning resources to enhance prerequisite relationship mining.
NETL
NSTL
万方数据
