EGANKT: Enhancing Graph-Attention Networks for Knowledge Tracing by Predicting Concepts and Abilities

Qiwen Zheng, Shun Mao, Kai Chen, Yuncheng Jiang

Abstract: Knowledge Tracing (KT) aims to assess students’ mastery of knowledge concepts and predict their performance from their historical response records. However, most existing KT models only consider the correspondence between questions and knowledge concepts given in the dataset when constructing the question-knowledge concept structure. This approach fails to deeply explore the knowledge concepts hidden in the questions and may propagate mislabeled associations. In addition, they overlook the impact of individual differences in students’ learning abilities on the accuracy of KT models. In this paper, we propose EGANKT to solve the above problems by introducing a knowledge concept prediction module and a learning ability prediction module. The knowledge concept prediction module identifies and optimizes potential knowledge concepts within questions, mitigating mislabeling issues. The learning ability prediction module improves the prediction accuracy of the model by calculating students’ learning abilities. To further improve the accurate modeling of knowledge states, we introduce self-supervised tasks to support the KT task through data augmentation and contrastive learning of knowledge states. Experimental results show that the EGANKT model outperforms baseline models on five datasets, demonstrating the effectiveness of our approach.