## 4 Baselines comparison
Since DKT-STDRL adopts BiLSTM to the express bidirectional feature of the sequence, two schemes can be obtained to study the influence of unidirectional and bidirectional temporal feature extraction, respectively. The first scheme is to remove the bidirectional temporal feature extraction structure of the DKT-STDRL model. Following this idea, to obtain the prediction results of the next time step, the model is transformed into the CKT model. The second scheme is to change the BiLSTM structure of the DKT-STDRL model into the LSTM structure, which can show the influence of one-way temporal output on the prediction effect of the model. Then, we can compare the difference between two-way time characteristics and one-way time characteristics in solving the prediction problem of students' learning performance. We abbreviate the second scheme as DKT-SDRL1. Specifically, DKT-SDRL1 first extracts and uses the prior learning features, and then a multi-layer convolution structure is used to extract students' spatial learning features. Then, after a simple intermediate process, it is input to the one-way temporal feature extraction layer based on LSTM. Finally, the prediction results are output;
* _DKT-STDRRP._ In order to study the influence of the prior learning features of the DKT-STDRL model on the prediction effect, we removed the part of extracting the prior learning features from the DKT-STDRL model. This variant model is called DKT-STDRRP. DKT-STDRRP transforms the input students' learning history data \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Datasets** & **Models** & **RMSE** & **AUC** & **ACC** & \(r^{2}\) \\ \hline \multirow{4}{*}{ASSISTment2009} & **Ours** & **0.2826** & **0.9591** & **0.8904** & **0.6440** \\  & DKT & 0.4006 & 0.8049 & 0.7663 & 0.2835 \\  & CKT & 0.3912 & 0.8237 & 0.7748 & 0.3181 \\ \hline \multirow{4}{*}{ASSISTment2015} & **Ours** & **0.0766** & **0.9996** & **0.9948** & **0.9698** \\  & DKT & 0.4131 & 0.7235 & 0.7504 & 0.1235 \\  & CKT & 0.4107 & 0.7322 & 0.7542 & 0.1338 \\ \hline \multirow{4}{*}{Synthetic-5} & **Ours** & **0.3167** & **0.9482** & **0.8790** & **0.5766** \\  & DKT & 0.4109 & 0.8167 & 0.7475 & 0.2868 \\  & CKT & 0.4051 & 0.8279 & 0.7553 & 0.3075 \\ \hline \multirow{4}{*}{ASSISTchall} & **Ours** & **0.2716** & **0.9710** & **0.9078** & **0.6847** \\  & DKT & 0.4538 & 0.7022 & 0.6791 & 0.1198 \\ \cline{1-1}  & CKT & 0.4500 & 0.7127 & 0.6857 & 0.1342 \\ \hline \multirow{4}{*}{Statics2011} & **Ours** & **0.2772** & **0.9499** & **0.9010** & **0.5621** \\  & DKT & 0.3697 & 0.8012 & 0.8054 & 0.2216 \\ \cline{1-1}  & CKT & 0.3630 & 0.8232 & 0.8101 & 0.2496 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of experimentation results among DKT-STDRL, DKT, and CKT models. into an embedded matrix, and then extracts spatial features through CNN layers and extracts bidirectional temporal features through the BiLSTM layer, so as to predict students' learning performance. By comparing the prediction results before and after removing the prior learning features, it is helpful to intuitively understand the role of the prior features in the task of predicting learning performance;
* _DKT-STDRRJ_. For studying the impact of the joint features in the DKT-STDRL model on the prediction, the process of merging the one-hot coded exercise history data is removed from the DKT-STDRL model in the intermediate data processing. In other words, the variant scheme keeps the structure of the first half of the DKT-STDRL model unchanged, inputs the one-hot coded spatial features directly into the temporal feature extraction part, and then obtains the final prediction result. This scheme is called DKT-STDRRJ. Table 6 shows the prediction performance of DKT-STDRL, CKT, DKT, and the variants of DKT-STDRL (DKT-TDRL, DKT-SDRL1, DKT-STDRRP, and DKT-STDRRJ) on the ASSISTment2009, ASSISTment2015, Synthetic-5, ASSISTChall, and Statics2011 datasets. It should be noted that different models are based on the same hyperparameters in the experiments on the same datasets.