Learning with Dual-graph for Concept Prerequisite Discovering
Xu Guolan1,Bai Rujiang2()
1Shandong University of Technology Library, Zibo 255000, China 2School of Information Management, Shandong University of Technology, Zibo 255000, China
[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.
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