Self-supervised heterogeneous hypergraph network for knowledge tracing

Recently online intelligent education has caught more and more attention, especially due to the global influence of Covid-19. A major task of intelligent education is Knowledge Tracing (KT) which aims to capture students’ dynamic status based on their historical interaction records and predict their responses to new questions. However, most existing KT methods suffer from the record data sparsity problem. In reality, there are a huge number of questions in the online database and students can only interact with a very small set of these questions. The records of some questions could be extremely sparse, which may significantly degrade the performance of traditional KT methods. Although recent graph neural network (GNN) based KT methods can fuse graph-structured information and improve the representation of questions to some extent, the pairwise structure of GNN neglects the complex high-order and heterogeneous relations among questions. To resolve the above issues, we develop a novel KT model with the heterogeneous hypergraph network (HHN) and propose an attentive mechanism, including intra- and inter-graph attentions, to aggregate neighbors’ information upon HHN. To further enhance the question representation, we supplement the supervised prediction task of KT with an auxiliary self-supervised task, i.e., we additionally generate an augmented view with adaptive data augmentation to implement contrastive learning and exploit the unobserved relations among questions. We conduct extensive experiments on several real-world datasets. Experimental results demonstrate that our proposed method achieves significant performance improvement compared to some state-of-the-art KT methods.