Can machines trace human knowledge like humans? Knowledge tracing (KT) is a
fundamental task in a wide range of applications in education, such as massive
open online courses (MOOCs), intelligent tutoring systems, educational games,
and learning management systems. It models dynamics in a student's knowledge
states in relation to different learning concepts through their interactions
with learning activities. Recently, several attempts have been made to use deep
learning models for tackling the KT problem. Although these deep learning
models have shown promising results, they have limitations: either lack the
ability to go deeper to trace how specific concepts in a knowledge state are
mastered by a student, or fail to capture long-term dependencies in an exercise
sequence. In this paper, we address these limitations by proposing a novel deep
learning model for knowledge tracing, namely Sequential Key-Value Memory
Networks (SKVMN). This model unifies the strengths of recurrent modelling
capacity and memory capacity of the existing deep learning KT models for
modelling student learning. We have extensively evaluated our proposed model on
five benchmark datasets. The experimental results show that (1) SKVMN
outperforms the state-of-the-art KT models on all datasets, (2) SKVMN can
better discover the correlation between latent concepts and questions, and (3)
SKVMN can trace the knowledge state of students dynamics, and a leverage
sequential dependencies in an exercise sequence for improved predication
accuracy.

本研究使用深度学习提出了一种新的知识追踪模型，名为 SKVMN，以解决现有深度学习知识追踪模型在如何追踪特定知识点以及在练习序列中捕捉长期依赖性等方面存在的局限性。实验结果表明，SKVMN 在所有数据集上优于现有的 KT 模型，能够更好地发现潜在概念和问题之间的相关性，并能跟踪学生的知识状态动态，借助练习序列中的时序依赖性提高预测准确性。