Research on a knowledge tracking model based on the stacked gated recurrent unit residual network

doi:10.3969/j.issn.1000-5641.2022.06.008

Abstract

Abstract:

The concept of knowledge tracking involves tracking changes in a student’s knowledge level based on historical question records and other auxiliary information, and predicting the result of a student’s subsequent answer to a question. Since the performance of existing neural network knowledge tracking models needs to be improved, this paper proposes a deep residual network based on a stacked gated recurrent unit (GRU) network named the stacked-gated recurrent unit-residual (S-GRU-R) network. The proposed solution aims to address over-fitting caused by too many parameters in a long short-term memory (LSTM) network; hence, the solution uses a GRU instead of LSTM to learn information on the sequence of questions. The use of stacked GRU can expand sequence learning capacity, and the use of residual connections can reduce the difficulty of model training. Experiments on the Statics2011 data set were completed using S-GRU-R, and AUC (area under the curve) and F₁-score were used as evaluation functions. The results showed that S-GRU-R surpassed other similar recurrent neural network models in these two indicators.

Key words: deep learning, knowledge tracking, recurrent neural network, gated recurrent unit, residual network

CLC Number:

TP183

Caidie HUANG, Xinping WANG, Liangyu CHEN, Yong LIU. Research on a knowledge tracking model based on the stacked gated recurrent unit residual network[J]. Journal of East China Normal University(Natural Science), 2022, 2022(6): 68-78.

Figures/Tables 8

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参数	设置	参数	设置
初始学习率	0.001	迭代轮次	100次
最大时间步	50	GRU隐层维度	200
最小批次数量	128	GRU层数	2

模型	AUC	F₁
DKT	0.857	0.872
NKT	0.853	0.872
S-GRU	0.858	0.873
S-GRU-R	0.862	0.875

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