Deep Trustworthy Knowledge Tracing
  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.
