## 2 Related Work and Motivation
By translating the convolution kernel to scan different positions of samples, parameters in the convolution kernel could learn some local features in the sample space, which was conducive to the judgment of classification. The pooling layer played the role of sub-sampling to reduce model parameters and reduce model complexity. CNN was often used for two-dimensional image processing, such as handwriting recognition [27], due to its good spatial feature extraction ability. One-dimensional sequence samples could be regarded as special two-dimensional structures, so CNN was also suitable for processing time-series data, such as natural language processing [28], and only one-dimensional convolution kernels were required. In KT, we used one-dimensional convolution to extract the spatial features of students' practice sequences. In addition, temporal features can be extracted by BiLSTM [16]. BiLSTM is a method based on LSTM [29] for bidirectional parameter learning, which consists of circularly connected memory modules. Each module contains three gate units, which are used to control the input, output, and forgetting operations of transmitted information. Through this gating mechanism, the long-term dependence of the information transfer process was solved, which was conducive to the time series analysis of samples. LSTM and BiLSTM were often used in sequence learning tasks [16], such as word sequence processing. In KT, we used BiLSTM to extract the temporal features of student exercise sequences. Therefore, we tried to integrate CNN and BiLSTM to extract the spatial features and temporal features of interaction sequences, which helped to use feature information fully [30]. In addition, the BiLSTM further improved the adequate utilization of the information by extracting features in both directions. \begin{table}
\begin{tabular}{l l l l l l l l l} \hline
**Category** & **KTs** & **Year** & **Technology** & **AUC** & **Key Article** & **Unity** & **Characteristics** \\ \cline{2-9} \cline{7-9} \multicolumn{1}{c}{} & RXT & 1994 & HMM & 0.670 & [PERSON] et al. [8] & Programming performance prediction & Single knowledge \\ \cline{2-9} \multicolumn{1}{c}{} & KT-IDEM & 2011 & HMM-IRT & 0.690 & [PERSON] et al. [10] & Performance prediction on math services & Ignore long term dependences \\ \hline \multirow{9}{*}{Deep Learning based KT} & DKT & 2015 & RNN-LSTM & 0.865 & [PERSON] et al. [21] & Performance prediction on math courses & Multiple knowledge, single feature \\ \cline{2-9}  & DKT+ & 2017 & RNN-LSTM & 0.863 & [PERSON] et al. [21] & Performance prediction on statistics courses & Multiple knowledge, multiple features \\ \cline{2-9}  & DKVMN & 2017 & MVNN & 0.816 & [PERSON] et al. [21] & Performance prediction on math services & Adaptively update knowledge mastery \\ \cline{2-9}  & SKVMN & 2019 & LSTM-MVNN & 0.836 & [PERSON] et al. [24] & Performance prediction on scientific courses & Multiple knowledge, long-term dependencies \\ \cline{2-9}  & CKT & 2019 & CNN & 0.723 & [PERSON] et al. [15] & Performance prediction on math courses & Model relationship between services and knowledge \\ \cline{2-9}  & RXT & 2019 & LSTM & 0.890 & [PERSON] et al. [25] & Performance prediction on math courses & Apply semantic information of exercises \\ \cline{2-9}  & SAKT & 2019 & FFN-MSA & 0.848 & [PERSON] et al. [25] & Performance prediction on scientific courses & Model relationship among knowledge points \\ \cline{2-9}  & CKT & 2020 & CNN & 0.825 & [PERSON] et al. [14] & Performance prediction on math exercises & Extract spatial features \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison of previous main research in KT.