Knowledge Tracing with Sequential Key-Value Memory Networks

Ghodai M. Abdelrahman, Qing Wang
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引用次数: 96

Abstract

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.
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基于顺序键值记忆网络的知识跟踪
机器能像人类一样追踪人类的知识吗?知识追踪(KT)是大规模在线开放课程(MOOCs)、智能辅导系统、教育游戏、学习管理系统等教育领域广泛应用的基础任务。它通过不同的学习概念与学习活动的相互作用,对学生的知识状态进行动态建模。最近,人们尝试使用深度学习模型来解决KT问题。尽管这些深度学习模型显示出了令人鼓舞的结果,但它们也有局限性:要么缺乏更深入地追踪学生如何掌握知识状态中的特定概念的能力,要么无法捕捉练习序列中的长期依赖关系。在本文中,我们通过提出一种新的知识跟踪深度学习模型,即顺序键值记忆网络(SKVMN)来解决这些限制。该模型结合了现有深度学习KT模型的循环建模能力和记忆能力的优势,对学生学习进行建模。我们在五个基准数据集上广泛评估了我们提出的模型。实验结果表明:(1)SKVMN在所有数据集上都优于最先进的KT模型;(2)SKVMN可以更好地发现潜在概念和问题之间的相关性;(3)SKVMN可以跟踪学生动态的知识状态,并利用练习序列中的顺序依赖关系提高预测精度。
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