Online Deep Knowledge Tracing

Wenxin Zhang, Yupei Zhang, Shuhui Liu, Xuequn Shang
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引用次数: 1

Abstract

This study focuses on solving the problem of knowledge tracing in a practical situation, where the responses from students come in a stream. Most current works of deep knowledge tracing are pursuing to integrate of more side information or data structure, but they often fail to make self-update in the dynamic learning situation. Towards this end, we here proposed an online deep knowledge tracing model, dubbed ODKT, by utilizing the online gradient descent algorithm to develop the traditional deep knowledge tracing (DKT) into online learning. Rather than learning a perfect model, the ODKT aims to train DKT in its using process step by step. Experiments were conducted on four public datasets for knowledge tracing. The results demonstrate that the ODKT model is effective and more suitable for practical applications.
在线深度知识追踪
本研究的重点是在一个实际情境中解决知识追溯的问题,在这个情境中,学生的反应是源源不断的。目前大多数深度知识跟踪的工作都是追求更多侧信息或数据结构的集成,但往往不能在动态学习的情况下进行自我更新。为此,本文提出了一种在线深度知识跟踪模型ODKT,利用在线梯度下降算法将传统的深度知识跟踪(DKT)发展为在线学习。ODKT的目标不是学习一个完美的模型,而是一步一步地训练DKT的使用过程。在四个公共数据集上进行了知识跟踪实验。结果表明,ODKT模型是有效的,更适合于实际应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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