# NeuralNCD: A Neural Network Cognitive Diagnosis Model Based on Multi-Dimensional Features
## 4 Performance Analysis
### Experimental Results
#### 4.5.1 Comparison Experiments with Other Cognitive Diagnostic Models  
In this paper, the proposed model and the comparison algorithm are compared on the two datasets and the results are shown in Table 2, which shows that the model outperforms all the comparison algorithms on the ASSIST2009 and FrCsub datasets for all three evaluation metrics.  
Among them, in the ASSIST 2009 dataset, the NeuralNCD model showed a large improvement (5-10%) in individual evaluation metrics compared to the CDMS without neural networks (IRT, MIRT, PMF, DINA), indicating that the neural networks used in this paper can better capture the relationships between students compared to traditional cognitive diagnostic models. There is also a 2.5% improvement in the evaluation metric AUC, compared to the latest NeuralCDM model using neural networks, verifying the effectiveness of the model in modeling guess and slip features and the ability of the data preprocessing mechanism to improve the neural network's ability to capture student and exercise relationships. In addition, the NeuralNCD has a relatively low AUC metric improvement relative to each comparison algorithm on the FrCsub dataset, e.g., only 1.1% improvement compared to DINA, and 13.3% improvement on the ASSIST2009 dataset. This may be due to the following two reasons: (1) the sample size of the FrCsub dataset is small; (2) the variety of exercises on the FrCsub dataset is small, with a fixed number of 20 exercises. In addition, all models performed better on the FrCsub dataset than on the ASSIST2009 dataset, which may be due to the fact that the exercises on the FrCsub dataset have fewer knowledge points, only eight, and students can learn the knowledge points more easily.  
Although the performance of the model proposed in this paper on the ASSIST2009 dataset demonstrated a relatively small improvement compared to that on the FrCsub dataset, the overall results are all better than other models for the following three main reasons: (1) the neural network used in this paper can better capture the relationship between students and exercises compared to traditional cognitive diagnostic models that only use interaction func  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**ASSIST2009**} & \multicolumn{3}{c}{**FrCsub**} \\ \cline{2-7}  & **ACC** & **REMS** & **AUC** & **ACC** & **REMS** & **AUC** \\ \hline IRT & 0.664 & 0.459 & 0.684 & 0.802 & 0.385 & 0.867 \\ MIRT & 0.695 & 0.451 & 0.709 & 0.819 & 0.378 & 0.885 \\ PMF & 0.671 & 0.478 & 0.729 & 0.810 & 0.386 & 0.877 \\ DINA & 0.649 & 0.467 & 0.675 & 0.825 & 0.373 & 0.890 \\ NeuralCDM & 0.720 & 0.436 & 0.749 & 0.824 & 0.375 & 0.888 \\ NeuralNCD & 0.734 & 0.425 & 0.776 & 0.836 & 0.364 & 0.905 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Evaluation results of the model on ASSIST2009 and FrCsub datasets.  
tions (e.g., logistic functions, inner products). (2) Integrating multiple exercise features into the cognitive diagnostic model can obtain more accurate and reasonable diagnostic results. (3) The data preprocessing and monotonicity assumptions added in this paper enhance the performance of the neural network and improve the accuracy and interpretability of the model diagnosis results.