关键词: 知识追踪, 时间卷积网络, 注意力机制, 序列建模, 教育数据挖掘

Abstract:

To address the problems of insufficient interpretability and long sequence dependency in the deep knowledge tracing model based on Recurrent Neural Network （RNN）， a model named Temporal Convolutional Knowledge Tracing with Attention mechanism （ATCKT） was proposed. Firstly， the student historical interactions embedded representations were learned in the training process. Then， the exercise problem-based attention mechanism was used to learn a specific weight matrix to identify and strengthen the influences of student historical interactions on the knowledge state at each moment. Finally， the student knowledge states were extracted by Temporal Convolutional Network （TCN）， in which dilated convolution and deep neural network were used to expand the scope of sequence learning， and alleviate the problem of long sequence dependency. Experimental results show that compared with four models such as Deep Knowledge Tracing （DKT） and Convolutional Knowledge Tracing （CKT） on four datasets （ASSISTments2009、ASSISTments2015、Statics2011 and Synthetic-5）， ATCKT model has the Area Under the Curve （AUC） and Accuracy （ACC） significantly improved， especially on ASSISTments2015 dataset， with an increase of 6.83 to 20.14 percentage points and 7.52 to 11.22 percentage points respectively， at the same time， the training time of the proposed model is decreased by 26% compared with that of DKT model. In summary， this model can accurately capture the student knowledge states and efficiently predict student future performance.

Key words: knowledge tracing, Temporal Convolutional Network (TCN), attention mechanism, sequence modeling, educational data mining