A Question-centric Multi-experts Contrastive Learning Framework for
  Improving the Accuracy and Interpretability of Deep Sequential Knowledge
  Tracing Models
Hengyuan Zhang Zitao Liu Chenming Shang Dawei Li Yong Jiang
Knowledge tracing (KT) plays a crucial role in predicting students' future
performance by analyzing their historical learning processes. Deep neural
networks (DNNs) have shown great potential in solving the KT problem. However,
there still exist some important challenges when applying deep learning
techniques to model the KT process. The first challenge lies in taking the
individual information of the question into modeling. This is crucial because,
despite questions sharing the same knowledge component (KC), students'
knowledge acquisition on homogeneous questions can vary significantly. The
second challenge lies in interpreting the prediction results from existing deep
learning-based KT models. In real-world applications, while it may not be
necessary to have complete transparency and interpretability of the model
parameters, it is crucial to present the model's prediction results in a manner
that teachers find interpretable. This makes teachers accept the rationale
behind the prediction results and utilize them to design teaching activities
and tailored learning strategies for students. However, the inherent black-box
nature of deep learning techniques often poses a hurdle for teachers to fully
embrace the model's prediction results. To address these challenges, we propose
a Question-centric Multi-experts Contrastive Learning framework for KT called
Q-MCKT.