## 4 Experiment and Analysis
### Comparison and Analysis of Experimental Results  
#### 4.5.1 Display of Experimental Results (RQ1)  
The experimental results are shown in Table 6.  
From the table, we concluded that:  
1. Compared with the six comparison models, the experimental results of the model in this paper on the three data sets were optimal, and the AUC values of the 09, 12, and 17 data sets were 0.7703, 0.7780, and 0.7860, respectively. In the experiment, the performance of the model on the 17 data set was better than other data sets because the interaction records of the Assistant17 data set were relatively dense, which was more conducive to model learning and training. 2. The model in this paper improved 7.68% in AUC and 7.08% in ACC (in the Assistments12 data set) compared to the classical DKT model. It was also observed in Figure 3 that the attention-based knowledge-tracking models SAKT and SAINT outperformed the recurrent-neural-network-based DKT model in terms of results because the recurrent neural network suffered from information loss when the interaction sequence was too long, which affected the prediction results. The SAINT model outperformed SAKT because it used a deeper level of attention network, which was better for the model to capture the complex relationship between questions and responses. The model outperformed the SAINT model on all three data sets because we considered both question difficulty and student ability and designed an embedding vector containing both factors into the prediction layer so that the model could learn more effective features for prediction, thus alleviating the problem of model underfitting due to sparse data sets and, ultimately, improving the accuracy of prediction. We also improved the calculation of attention weights based on student answer performance, which made the calculation of attention weights more reasonable. 3. As shown in Figure 4, the results of this model also outperformed the CKT model based on convolutional neural networks, with increases in the AUC values of 3.63%, 6%, and 4.57% for the three data sets, as well as of 5.04%, 3.92%, and 6.49% for ACC, respectively. The CKT attempts to consider students' prior knowledge and the effect of learning rate on students' answers in the model. However, in considering these factors, only the answer result is used as an evaluation indicator, i.e., the author believed that the student could fully master the questions answered correctly. This approach, which considers only the effect of answer results on student ability, is not accurate and objective because it ignores the difficulty levels of different questions. Therefore, the model in this paper, which fully considered the information on the difficulty of the questions, worked better. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Data Sets** & \multicolumn{2}{c}{**Assistments09**} & \multicolumn{2}{c}{**Assistments12**} & \multicolumn{2}{c}{**Assistments17**} \\ \cline{2-7}  & **AUC** & **ACC** & **AUC** & **ACC** & **AUC** & **ACC** \\ \hline DKT-Q & 0.7141 & 0.6985 & 0.7012 & 0.6834 & 0.7314 & 0.7210 \\ DKVMN-Q & 0.6961 & 0.6543 & 0.7000 & 0.6710 & 0.7211 & 0.7059 \\ CKT & 0.7340 & 0.7155 & 0.7180 & 0.7150 & 0.7403 & 0.7004 \\ SAKT-Q & 0.7322 & 0.6953 & 0.7472 & 0.7083 & 0.7507 & 0.7322 \\ SAINT-Q & 0.7432 & 0.7231 & 0.7501 & 0.7138 & 0.7533 & 0.7026 \\ PEBG & 0.7649 & 0.7360 & 0.7710 & 0.7423 & 0.7789 & 0.7521 \\ MTLKT & 0.7703 & 0.7659 & 0.7780 & 0.7542 & 0.7860 & 0.7653 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance of each model on different data sets. (4) As shown in Figure 5, Compared with the pre-trained PEBG model, MTLKT performed better when using the same prediction layer, increasing by 0.54%, 0.7%, and 0.71%, respectively, for AUC and 2.99%, 1.19%, and 1.32%, respectively, for ACC. A PEBG is dedicated to discovering advanced information between questions and skills, and it considers the cross-relations between questions and skills, where a question contains multiple skills and a skill is related to multiple questions.