HTKT: Knowledge Tracing Based on Hypergraph Transformer

Xiaojin Guo, Xilong Chang, Xiankun Zhang, Kun Liang, Yuhu Shang, Yiying Zhang

Abstract: Knowledge tracing(KT) is one of the supporting technologies for adaptive learning. It obtains learners' knowledge level by analyzing their online historical answer records, thereby predicting their future answer performance. Since there are a large number of continuous or repeated exercises in KT datasets, and traditional graph structures can usually only represent one-to-one or one-to-many relationships. Some existing models tend to ignore the potential connections between knowledge concept, and thus fail to accurately obtain high-level representations of students' knowledge state. Hypergraphs can directly represent many-to-many relationships and have advantages in extracting high-level information and processing complex relationships. In this paper, we propose a KT method (HTKT) based on Hypergraph Transformer. This method is based on the Encoder-only architecture and introduces a hypergraph attention mechanism to obtain a high-order feature representation of students' knowledge state. Experiments on multiple public and general datasets show that our proposed HTKT method outperforms existing baseline methods.