Combining Knowledge Tracing and Graph Convolution for Knowledge Concept Recommendation
The innovative development of technology has led to the flourishing advancement of online education platforms,which provide a huge amount of educational resources,each type of which contains rich knowledge concepts.The current research mainly focuses on personalized course resource recommendation by knowledge graph,which is vulnerable to the data sparsity problem and difficult to be extended.Difficulty in matching learners'learning status with learning resources,the model KT-GCN(Knowledge Tracing-Graph Convolution Network)is proposed.Firstly,the overall modeling of learners'knowledge level is performed using knowledge tracing,getting the learner's current learning status.Then path encoding is performed using graph convolutional network,accessing to learner-adapted learning paths,path selection is performed using TransE method and multi-hop path.Finally,predictive scoring is performed to obtain a recommended list of the most matching learning resources.To vali-date the performance of the model,comparison experiments are conducted with the baseline model on multiple datasets,and cor-responding ablation experiments are performed to verify the performance of each component of the model.
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