# A Cognitive Diagnosis Model Using Convolutional Neural Networks to Predict Student Scores
## 4 Experiments and Analysis of Results
### Ablation Experiment  
Since the BOIT dataset is most similar to the FrCub dataset in terms of data sample size, FrCub is the most commonly used classical cognitive diagnostic dataset, and the BOIT dataset is a self-made dataset, so these two datasets are representative. Therefore, the FrCub dataset and BOIT dataset were selected to test the influence of different modules on the prediction effect of the model in this paper. The experimental results are shown in Figure 5.  
In Figure 5, CDM represents the removal of the attention module and knowledge evaluation module of model CNNCD; CDM + odCNN means removing the attention module of model CNNCD; CDM + att means removing the knowledge evaluation module of model CNNCD; and CDM + odCNN + att represents the complete CNNCD model. After the knowledge evaluation module is removed, matrix multiplication is used to connect the results of students' usual quizzes and the relevant matrix of the usual quizzes and knowledge points. As can be seen from Figure 5a,b, when the model CNNCD removes the attention module and knowledge evaluation module, the prediction index is the lowest; when the attention module and knowledge evaluation module are added, each index has a different degree of improvement, indicating the effectiveness of the attention module and knowledge evaluation module. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**Accuracy**} & \multicolumn{4}{c}{**RMSE**} & \multicolumn{4}{c}{**MAE**} \\ \cline{2-13}  & **0.3** & **0.4** & **0.5** & **0.6** & **0.7** & **0.3** & **0.4** & **0.5** & **0.6** & **0.7** & **0.3** & **0.4** & **0.5** & **0.6** & **0.7** \\ \hline FuzzyCDF & 0.552 & 0.560 & 0.574 & 0.591 & 0.613 & 0.516 & 0.513 & 0.509 & 0.497 & 0.491 & 0.473 & 0.468 & 0.464 & 0.452 & 0.438 \\ DIINA & 0.552 & 0.577 & 0.561 & 0.605 & 0.672 & 0.562 & 0.551 & 0.557 & 0.514 & 0.490 & 0.482 & 0.460 & 0.455 & 0.435 & 0.386 \\ IRT & 0.558 & 0.518 & 0.547 & 0.445 & 0.564 & 0.596 & 0.643 & 0.544 & 0.681 & 0.616 & 0.462 & 0.497 & 0.459 & 0.581 & 0.498 \\ MRT & 0.528 & 0.545 & 0.520 & 0.550 & 0.549 & 0.642 & 0.613 & 0.634 & 0.611 & 0.608 & 0.473 & 0.459 & 0.482 & 0.450 & 0.457 \\ NCDM & 0.582 & 0.593 & 0.573 & 0.651 & 0.710 & 0.646 & 0.638 & 0.653 & 0.591 & 0.507 & 0.418 & 0.407 & 0.427 & 0.349 & 0.345 \\ MCD & 0.529 & 0.542 & 0.578 & 0.566 & 0.684 & 0.565 & 0.555 & 0.529 & 0.528 & 0.465 & 0.475 & 0.473 & 0.449 & 0.463 & 0.448 \\ CNNCD & 0.589 & 0.610 & 0.604 & 0.687 & 0.778 & 0.490 & 0.496 & 0.492 & 0.464 & 0.407 & 0.477 & 0.496 & 0.467 & 0.439 & 0.321 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Experimental comparison results of Math2 dataset. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**Accuracy**} & \multicolumn{4}{c}{**RMSE**} & \multicolumn{4}{c}{**MAE**} \\ \cline{2-13}  & **0.3** & **0.4** & **0.5** & **0.6** & **0.7** & **0.3** & **0.4** & **0.5** & **0.6** & **0.7** & **0.3** & **0.4** & **0.5** & **0.6** & **0.7** \\ \hline FuzzyCDF & 0.517 & 0.543 & 0.541 & 0.563 & 0.590 & 0.531 & 0.521 & 0.520 & 0.512 & 0.501 & 0.492 & 0.480 & 0.481  
Considering the influence of one-dimensional convolutional neural networks with different depths on the prediction results, this paper attempts to use one-dimensional convolutional neural networks with different depths to model the knowledge mastery module in order to verify the influence of network depth on the cognitive diagnosis results. The experimental results are shown in Figure 6.  
Figure 5: Influence of modules on the prediction effect of the CNNCD model. Experimental results of the (**a**) FrCSub and (**b**) BOIT datasets. Figure 6: The effect of the number of convolutional layers on the Accuracy (**a**), RMSE (**b**) and MAE (**c**) of CNNCD models in data sets FrCSub and BOIT.