Customized adversarial training enhances the performance of knowledge tracing tasks

Huitao Zhang, Xiankun Zhang, Yuhu Shang, Xin Luo, Yiying Zhang

Abstract: Knowledge Tracing (KT) involves using deep neural networks (DNNs) to track students’ learning progress, but over-fitting can be an issue with small datasets. Adversarial examples have been introduced to improve generalization, but they may overlook individual student differences. To address this issue, we propose a new model called Customized Adversarial Training Knowledge Tracing (CATKT). The model generates unique adversarial perturbations for each sample based on the characteristics of knowledge tracking tasks, thereby better adapting to students’ learning traits and enhancing the effectiveness and accuracy of knowledge tracking tasks. Specifically, CATKT can dynamically adjust the level of perturbations according to the difficulty of the knowledge, adding non-uniform and effective perturbations to each interaction embedding, and replacing the original labels with adaptively smoothed labels to improve task accuracy. Experimental results show that CATKT outperforms previous knowledge tracking methods in terms of performance and provides new ideas and methods for teaching assessment and personalized learning in the education field.