## 2 Related Work and Motivation
supplemented other relevant features, (such as time, reminder utilization, and attempt counts) collected by education information platforms to the DKT model and acquired more accurate prediction outputs [21]. Although the model could predict better by adding the exercise performance information in other aspects, these features were difficult to fully express the student's personalized learning features. [PERSON] et al. proposed convolutional knowledge tracing (CKT) [14], which used convolutional neural networks (CNNs) to extract students' personalized prior knowledge and learning rates from their answer histories. Although extracting and utilizing personalized learning features could improve prediction accuracy, characterizing temporal changes was not sufficient because only a gate linear unit (GLU) [22] was used to control the forward temporal dependence of personalized prior knowledge. GritNet [17] bidirectionally analyzed students' exercise records by BiLSTM to predict students' future performance, which fully extracted the temporal features of the learning process, namely, the knowledge state for each knowledge point during each practice interaction. In the whole learning process, temporal factors such as remebering and forgetting could affect the students' mastery of knowledge at set intervals. Neural networks could learn representation features of the potential knowledge mastery state under the influence of these temporal factors. However, it did not take into account other learning features, such as personalized prior knowledge features or individualized learning rate features. Moreover, the experimental dataset, Udacity data, was not commonly used. Moreover, GritNet was only proved to be superior to the standard logistic-regression-based method. The dynamic key-value memory network (DKVMN) [13] employed the idea of memory-augmented neural networks (MANNs), which recorded and updated the knowledge points and students' mastery of knowledge with two matrices, respectively. DKVMN had an advantage in explanation. GKT [15] built a graph neural network (GNN) [23] on the basis of the association between knowledge points, which could help improve prediction and interpretation performance [15]. Sequential key-value memory networks (SKVMN) [24] employed the triangular membership function to diagnose students' mastery of knowledge concepts and used the improved LSTM to capture the features of the learning process, which showed good performance in prediction accuracy and explanation. [PERSON] et al. attempted to combine the students' exercise performance with the features extracted from the test content, and proposed an exercise-enhanced recurrent neural network (EERNN) and EKT [25], which exploited BiLSTM, a Markov property, or an attention mechanism. The experiment proved that supplementing the feature information of the problem content could help advance the prediction effect. The self-attention mechanism was utilized in self-attentive knowledge tracing (SAKT) [26] to model learners' performance on related questions, and then to predict learners' future responses. It solved the sparse dataset problem and achieved good prediction results. Although previous research has made up for the deficiencies in different aspects of KT models and promoted the development of KT based on deep learning, most models had relatively simple feature extraction methods, leading to the inadequate use of information on students' practice records. A comparison of previous main research in KT is shown in Table 1.  
As far as we knew, there were spatial features and temporal features in students learning sequences, which had location correlations and time correlations. The spatial features can be extracted by CNN. Typical CNN [27] structures were mainly composed of convolution layers and pooling layers alternately, among which the core was the convolution layer. The convolution layer contained multiple convolution kernels. The convolution kernel contained multiple parameters.