# A Multi-Layer Attention Knowledge Tracking Method with Self-Supervised Noise Tolerance
## 6 Experiment
### Comparison Experiment
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Algebra75**} & \multicolumn{2}{c}{**ASSIST79**} & \multicolumn{2}{c}{**ASSIST12**} & \multicolumn{2}{c}{**ASSIST15**} & \multicolumn{2}{c}{**EdNet**} \\ \cline{2-10}  & \multirow{2}{*}{**Mean \(\pm\) Std**} & \(t\) & \multirow{2}{*}{**Mean \(\pm\) Std**} & \(t\) & \multirow{2}{*}{**Mean \(\pm\) Std**} & \(t\) & \multirow{2}{*}{**Mean \(\pm\) Std**} & \(t\) \\  & & & & & & & & \(p\) & & \(p\) \\ \hline DKT & \(0.7638\pm 0.0167\) & 7.38 & \(0.7631\pm 0.0214\) & 1.54 & \(0.7504\pm 0.0189\) & 1.23 & \(0.7271\pm 0.0246\) & 1.50 & \(0.7012\pm 0.0221\) & 6.18 \\  & & \(0.0020\) & & 19.168 & & & & \(0.2077\) & 0.02079 & 0.0035 \\ DKT+ & \(0.7394\pm 0.0193\) & 7.41 & \(0.7599\pm 0.0174\) & 2.17 & \(0.7541\pm 0.0188\) & 0.90 & \(0.7371\pm 0.0205\) & 0.84 & \(0.7254\pm 0.0234\) & 3.90 \\  & & \(0.0017\) & & 0.0962 & & & \(0.4203\) & & 0.4475 & 0.0176 \\ DKVMN & \(0.7653\pm 0.0205\) & 5.23 & \(0.7621\pm 0.0187\) & 1.82 & \(0.7437\pm 0.0206\) & 1.47 & \(0.7268\pm 0.0212\) & 1.69 & \(0.6857\pm 0.0253\) & 6.65 \\  & & \(0.0063\) & & 0.1446 & & & \(0.2149\) & & 0.1666 & \(0.0027\) \\ SAKT & \(0.7645\pm 0.0196\) & 5.06 & \(0.7571\pm 0.0192\) & 2.21 & \(0.7491\pm 0.0233\) & 1.16 & \(0.7240\pm 0.0207\) & 1.99 & \(0.6976\pm 0.0276\) & 5.49 \\  & & \(0.0072\) & & 0.0918 & & & \(0.3105\) & & 0.1176 & 0.0176 & 0.0054 \\ SKVMN & \(0.7521\pm 0.0231\) & 5.25 & \(0.7432\pm 0.0245\) & 2.90 & \(0.7594\pm 0.0291\) & 0.19 & \(0.7384\pm 0.0198\) & 0.72 & \(0.7164\pm 0.0258\) & 4.37 \\  & & \(0.0063\) & & 0.0441 & & & \(0.8591\) & & 0.5125 & 0.0120 \\ SAINT & \(0.7751\pm 0.0157\) & 4.47 & \(0.7653\pm 0.0179\) & 1.59 & \(0.7584\pm 0.0266\) & 0.28 & \(0.7421\pm 0.0304\) & 0.22 & \(0.7411\pm 0.0229\) & 2.69 \\  & & \(0.0111\) & & 0.1879 & & & \(0.7951\) & & 0.8377 & & 0.0545 \\ IRT & \(0.7032\pm 0.0267\) & 8.91 & \(0.7254\pm 0.0325\) & 3.44 & \(0.7051\pm 0.0291\) & 3.58 & \(0.7079\pm 0.0273\) & 2.68 & \(0.6940\pm 0.0316\) & 5.06 \\  & & \(0.0009\) & & 0.0260 & & & \(0.0183\) & & 0.0551 & & 0.0072 \\  & & \(0.0034\) & & 0.1233 & & & \(0.4015\) & & 0.4995 & & 0.7649 \(\pm 0.0189\) & 0.70 \\  & & \(0.0036\) & & & & \(0.0415\) & & & 0.4995 & & 0.5230 \\ MSKT & \(0.8024\pm 0.0135\) & 1.25 & \(0.7548\pm 0.0147\) & 3.05 & \(0.7546\pm 0.0175\) & 0.87 & \(0.7269\pm 0.0191\) & 1.84 & \(0.7603\pm 0.0186\) & 1.17 \\  & & \(0.2796\) & & & \(0.0380\) & & & \(0.4332\) & & 0.1399 & & 0.3071 \\ \hline MASKT & \(0.8103\pm 0.00098\) & & \(0.7794\pm 0.0124\) & & & \(0.7620\pm 0.0168\) & & \(0.7453\pm 0.0159\) & & \(0.7714\pm 0.0152\) & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of AUC values \(\pm\) confidence intervals and paired \(t\)-tests under different datasets. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Algebra75**} & \multicolumn{2}{c}{**ASSIST709**} & \multicolumn{2}{c}{**ASSIST12**} & \multicolumn{2}{c}{**ASSIST15**} & \multicolumn{2}{c}{**EdNet**} \\ \cline{2-10}  & \multirow{2}{*}{**Mean \(\pm\) Std**} & \(t\) & \multirow{2}{*}{**Mean \(\pm\) Std**} & \(t\) & \multirow{2}{*}{**Mean \(\pm\) Std**} & \(t\) & \multirow{2}{*}{**Mean \(\pm\) Std**} & \(t\) & \multirow{2}{*}{**Mean \(\pm\) Std**} & \(t\) \\  & & \(p\) & & & & \(p\) & & & \(p\) & & \(p\) \\ \hline DKT & \(0.4113\pm 0.0218\) & 2.51 & \(0.4372\pm 0.0226\) & 0.32 & \(0.4236\pm 0.0207\) & 2.41 & \(0.4275\pm 0.0233\) & 0  
method is slightly shorter compared to the other two methods, possibly due to the loss of features due to sequence deletion affecting its performance. As shown in Table 7, based on the comparative ablation experiments of the MASKT method, a dynamic masking mechanism is added to the MASKT-D single-noise-processing method, which is denoted as MD-Dynamic. Compared with the static masking method, the dynamic masking method enlarges each epoch by 10 times on the basis of the original one.