Modeling knowledge proficiency using multi-hierarchical capsule graph neural network

摘要

Knowledge Tracking (KT) predicts student performance by modeling the mastery of knowledge components in past interactions. Although the latest research on KT is excellent to model behavior changes, the increasingly complex knowledge components and semantic relationships of new exercises have made courses sparse and difficult to adequately diagnose knowledge mastery. Hence, it is not enough to satisfy only the personalized learning. This paper proposes a graph-based method with multi-hierarchical network (Caps-HAGKT) for tracking capsule knowledge, which integrates rich structural information of the knowledge space, including multiple skills, prerequisites, attribution, and conceptual meta-paths. Firstly, we use the multi-hierarchical knowledge capsule network to track a variety of high-level knowledge concepts, and then extract the latent knowledge structure between levels. In addition, we adopt a new graph network with an external memory matrix to model the relationship between concept capsules at the same level and update concept states. In previous works, it was difficult to use complex graph information without any expert pre-definition. In Caps-HAGKT, the semantic attention of knowledge capsules is used to merge different semantics in knowledge according to their importance. Extensive experiments on some real-world datasets show that the prediction performance of Caps-HAGKT is better than that of the existing KT method (AUC is increased by 3.05% on an average). The visualization of both latent graphs and case studies indicates the excellent interpretability of predictions.