Deep Trustworthy Knowledge Tracing
This work addresses the reliability of DLKT for intelligent tutoring systems, which is an incremental improvement over existing methods.
The authors tackled the problem of unreliable deep learning-based knowledge tracing (DLKT) in real education environments, proposing a novel regularization method that addresses issues like knowledge state update failure, catastrophic forgetting, and non-interpretability to achieve trustworthy DLKT.
Knowledge tracing (KT), a key component of an intelligent tutoring system, is a machine learning technique that estimates the mastery level of a student based on his/her past performance. The objective of KT is to predict a student's response to the next question. Compared with traditional KT models, deep learning-based KT (DLKT) models show better predictive performance because of the representation power of deep neural networks. Various methods have been proposed to improve the performance of DLKT, but few studies have been conducted on the reliability of DLKT. In this work, we claim that the existing DLKTs are not reliable in real education environments. To substantiate the claim, we show limitations of DLKT from various perspectives such as knowledge state update failure, catastrophic forgetting, and non-interpretability. We then propose a novel regularization to address these problems. The proposed method allows us to achieve trustworthy DLKT. In addition, the proposed model which is trained on scenarios with forgetting can also be easily extended to scenarios without forgetting.