A Question-centric Multi-experts Contrastive Learning Framework for Improving the Accuracy and Interpretability of Deep Sequential Knowledge Tracing Models

arXiv:2403.07322v1 Announce Type: cross
Abstract: 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' …

abstract accuracy arxiv cs.ai cs.cy cs.lg experts framework future however interpretability knowledge networks neural networks performance processes question role students tracing type