## 4 Baselines comparison
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Datasets** & **Models** & **RMSE** & **AUC** & **ACC** & \(r^{2}\) \\ \hline \multirow{8}{*}{ASSISTment2009} & **DKT-STDRL** & **0.2826** & **0.9591** & **0.8904** & **0.6440** \\  & DKT-TDRL & 0.2845 & 0.9574 & 0.8896 & 0.6394 \\  & CKT & 0.3953 & 0.8154 & 0.7687 & 0.3039 \\  & DKT-SDRL1 & 0.4265 & 0.7567 & 0.7370 & 0.1894 \\  & DKT & 0.4293 & 0.7509 & 0.7338 & 0.1788 \\  & DKT-STDRRP & 0.2878 & 0.9557 & 0.8861 & 0.6310 \\  & DKT-STDRRJ & 0.4531 & 0.6736 & 0.6890 & 0.0853 \\ \hline \multirow{8}{*}{ASSISTment2015} & **DKT-STDRL** & **0.0766** & **0.9996** & **0.9948** & **0.9698** \\  & DKT-TDRL & 0.0799 & 0.9995 & 0.9942 & 0.9672 \\  & CKT & 0.4099 & 0.7343 & 0.7546 & 0.1370 \\  & DKT-SDRL1 & 0.4176 & 0.7070 & 0.7475 & 0.1043 \\  & DKT & 0.4183 & 0.7042 & 0.7468 & 0.1015 \\  & DKT-STDRRP & 0.0801 & 0.9995 & 0.9943 & 0.9671 \\  & DKT-STDRRJ & 0.4053 & 0.7581 & 0.7511 & 0.1563 \\ \hline \multirow{8}{*}{Synthetic-5} & **DKT-STDRL** & **0.3167** & **0.9482** & **0.8790** & **0.5766** \\  & DKT-TDRL & 0.3186 & 0.9469 & 0.8776 & 0.5714 \\  & CKT & 0.4081 & 0.8241 & 0.7513 & 0.2972 \\  & DKT-SDRL1 & 0.4496 & 0.7283 & 0.6754 & 0.1469 \\  & DKT & 0.4505 & 0.7269 & 0.6752 & 0.1432 \\  & DKT-STDRRP & 0.3194 & 0.9461 & 0.8768 & 0.5693 \\  & DKT-STDRRJ & 0.4716 & 0.6485 & 0.6417 & 0.0612 \\ \hline \multirow{8}{*}{ASSISTChall} & **DKT-STDRL** & **0.2716** & **0.9710** & **0.9078** & **0.6847** \\  & DKT-TDRL & 0.2790 & 0.9675 & 0.9009 & 0.6671 \\  & CKT & 0.4503 & 0.7119 & 0.6846 & 0.1330 \\  & DKT-SDRL1 & 0.4683 & 0.6431 & 0.6642 & 0.0627 \\  & DKT & 0.4682 & 0.6430 & 0.6648 & 0.0628 \\  & DKT-STDRRP & 0.2854 & 0.9633 & 0.8942 & 0.6518 \\  & DKT-STDRRJ & 0.4493 & 0.7170 & 0.6935 & 0.1371 \\ \hline \multirow{8}{*}{Statics2011} & **DKT-STDRL** & **0.2772** & **0.9499** & **0.9010** & **0.5621** \\  & DKT-TDRL & 0.2891 & 0.9395 & 0.8956 & 0.5220 \\ \cline{1-1}  & CKT & 0.3630 & 0.8241 & 0.8099 & 0.2496 \\ \cline{1-1}  & DKT-SDRL1 & 0.3724 & 0.7942 & 0.8042 & 0.2103 \\ \cline{1-1}  & DKT & 0.3739 & 0.7891 & 0.8023 & 0.2036 \\ \cline{1-1}  & DKT-STDRRP & 0.2974 & 0.9275 & 0.8827 & 0.4940 \\ \cline{1-1}  & DKT-STDRRJ & 0.3756 & 0.7873 & 0.7995 & 0.1967 \\ \hline \end{tabular}
\end{table} 
Table 6: Comparison of experimentation results among the DKT-STDRL, CKT, DKT, and variants of DKT-STDRL. Figure 3: Prediction results of DKT-STDRL, CKT, DKT, and the variants of DKT-STDRL on the ASSISTments2009 dataset. From the previous two types of comparison experiments, it can be found that our DKT-STDRL has better prediction accuracy than CKT [14] and DKT [12]. Analyzing the functions of the various parts of DKT-STDRL, the spatial features, temporal features, prior features, and joint features of DKT-STDRL all contribute to improving the prediction accuracy to varying degrees. In short, the success of DKT-STDRL in improving the prediction accuracy is inseparable from the comprehensive analysis of the spatial and temporal characteristics of students' practice sequences. In other words, for KT, a comprehensive consideration of students' personalized learning efficiency and characteristics of students' knowledge state change process is helpful to obtain more accurate prediction results. Figure 4: Prediction results of DKT-STDRL, CKT, DKT, and the variants of DKT-STDRL on the ASSISTments2015 dataset. Figure 5: Prediction results of DKT-STDRL, CKT, DKT, and the variants of DKT-STDRL on the Synthetic-5 dataset. Figure 6: Prediction results of DKT-STDRL, CKT, DKT, and the variants of DKT-STDRL on the ASSISTchall dataset. Our model significantly improved the prediction accuracy of KT, and it had practical significance for different education stakeholders, such as students, LMS administrators, teachers, and instructional designers [2]. First, LMSs which use our method can analyze students' potential knowledge state based on the students' practice logs and make an accurate prediction of students' performance in the next stage.