# SFBKT: A Synthetically Forgetting Behavior Method for Knowledge Tracing
## 4 Experiments
### Results and Discussion
At this time, the second part of the model becomes the traditional LSTM network, which is called the SFBKT-Traditional Forgetting variant model. * SFBKT-PF. To investigate how group forgetting impacts the predictive accuracy of the SFBKT model, the SFBKT-Personal Forgetting variant model removes the part of collaborative filtering by considering the state of group knowledge from the SFBKT model. Specifically, the SFBKT-PF model first integrates the students' forgetting information and the record of doing questions as input. Then, through the CTLSTM network, the time feature is extracted to obtain the student's knowledge status and predict the result based on this output. Table 7 presents the prediction performance of SFBKT and its variants (SFBKT-GF, SFBKT-TF, and SFBKT-PF) on four datasets, namely, ASSISTment09-10, ASSISTment12-13, ASSISTment17, and Slepemapy.cz. Notably, different models based on the same hyper-parameters were used in experiments on the same datasets. The other modules within SFBKT maintained identical configurations, with the only variation being the structure of the module in each individual experiment. To facilitate a more intuitive comparison between the original model and its variants, the histograms in Figure 5 display the AUC outcomes for SFBKT and its variant models on the four datasets. Among the various model variants, the SFBKT-TF model performed the worst, which suggests that the CTLSTM network played a crucial role in improving model performance. Although the AUC of the other two variant models was similar to the original model, subtle differences still existed, reflecting certain underlying rules. Specifically, the SFBKT-GF model performed better than the SFBKT-PF model, which could be attributed to two factors. First, although the former eliminated the artificial integration of personal information, the CTLSTM network in the model still captured and processed personal forgetting information, thereby reducing the impact of deleted modules on prediction. Second, the collaborative filtering module fused similar students' knowledge states with the target students' knowledge states, thereby mitigating the influence of unreasonable data collected in individual practice sequences and enhancing the weight of effective data in the process of knowledge state formation. These two reasons contributed to the excellent performance of the SFBKT-GF model. By conducting ablation experiments, we assessed the importance of each module of the model in enhancing its performance. The most crucial module was found to be the improved network module. When this module was removed and the original RNN \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & **ASSISTment 09-10** & **ASSISTment 12-13** & **ASSISTment17** & **Slepemapy.cz** \\ \hline Metrics & ACC & AUC & ACC & AUC & ACC & ACC & AUC \\ \hline SFBKT-GF & 0.7581 & 0.7883 & 0.7535 & 0.7822 & 0.7361 & 0.8057 & 0.8064 & 0.7721 \\ SFBKT-TF & 0.7423 & 0.7592 & 0.7448 & 0.7544 & 0.7036 & 0.7431 & 0.8001 & 0.7512 \\ SFBKT-PF & 0.7566 & 0.7870 & 0.7518 & 0.7810 & 0.7345 & 0.8046 & 0.8026 & 0.7688 \\ \hline SFBKT & **0.7593** & **0.7909** & **0.7544** & **0.7842** & **0.7404** & **0.8095** & **0.8087** & **0.7736** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of the experimental results among the SFBKT and variants of SFBKT. network was restored, the prediction accuracy of the model was significantly reduced. Specifically, the AUC of the SFBKT-TF variant model on the ASSISTment09-10 dataset decreased by 3.17% when compared with the original model. Similar outcomes were observed in other datasets, which further underscores the importance of this module in improving model performance. Our findings demonstrate the feasibility of integrating the theory of neurally self-modulating multivariate point processes into the field of knowledge tracing and the promising results that can be achieved. Additionally, the other two modules played significant roles and contributed to the improvement of the model's performance.