HGKT: Hypergraph-based Knowledge Tracing for Learner Performance Prediction

Knowledge tracing focuses on modeling learners' past answer sequences to trace the evolving knowledge state and predict their performance in the future. Most of the existing GNN-based knowledge tracing model only considers the static pairwise relationship between concepts and exercises, but ignores the mining of edge features. Also, the dynamic and complex higher-order relationships hidden in the learners' answer sequence have not been fully exploited. In this paper, a novel hypergraph-based knowledge tracing model (HGKT) is proposed to address these limitations. Firstly, we exploit edge feature that indicates the frequency of exercise-concept's occurrence to extend the common bipartite graph. Then we use Node and Edge features based graph Neural Networks (NENN) to obtain the embedding representation of exercises and concepts. Secondly, a hypergraph with different weights on vertices is constructed during the learners' exercise-answering process and then it is transformed to a simple graph based on its similarity between hyperedges. Thereafter, we use the hypergraph neural networks (HGNN) and line hypergraph convolution network (LHCN) to obtain the learners' embedding and discover the higher-order relationships formed during this process. Thirdly, the difficulty of exercises and the average response time are utilized to improve the learning of LSTM's hidden states. Finally, all the embeddings are jointly added to the generalized interaction module of GIKT to draw attention to the useful information for prediction. Experiments demonstrate that the proposed HGKT outperforms previous classical methods in terms of AUC on the three widely used datasets.