Journal of East China Normal University(Natural Science) ›› 2022, Vol. 2022 ›› Issue (6): 68-78.doi: 10.3969/j.issn.1000-5641.2022.06.008

• Computer Science • Previous Articles     Next Articles

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

Caidie HUANG1, Xinping WANG1, Liangyu CHEN1,*(), Yong LIU2   

  1. 1. Software Engineering Institute, East China Normal University, Shanghai 200062, China
    2. Basic Education and Lifelong Education Development Department, East China Normal University, Shanghai 200062, China
  • Received:2021-08-10 Online:2022-11-25 Published:2022-11-22
  • Contact: Liangyu CHEN


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

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