# SFBKT: A Synthetically Forgetting Behavior Method for Knowledge Tracing
## 4 Experiments
### Results and Discussion
This attention mechanism calculates attention weights through exponential decay and a context-sensitive relative distance metric, as well as the resemblance between the questions. Table 6 presents a performance comparison of our SFBKT model with five classic models. As the DKT model outperforms traditional KT methods significantly, we only select models that appear after DKT as baseline methods. Figure 4 visualizes the AUC results of all models on the four datasets in the form of histograms, providing an intuitive comparison of their predictive effects. Figure 3: The relationship between AUC and \(\rho\) in the four datasets. Among these methods, the extent of the considered forgetting factors positively correlates with their predictive performance. DKT and KTM do not consider forgetting factors, resulting in lower performance than the other three methods that incorporate forgetting behaviors. HawkesKT performs best on most datasets by deeply considering the time cross-effect in the learning sequence. However, our SFBKT model outperforms other models in both ACC and AUC for all datasets. Specifically, in the ASSISTment09-10 dataset, SFBKT achieves an ACC of 0.7593 and an AUC of 0.7909, which are 1.21% and 2.86% higher than the best results among the five baseline methods, respectively. On the ASSISTment12-13 dataset, SFBKT achieves an ACC of 0.7544 and an AUC of 0.7842, which are 0.31% and 1.79% higher than the best results among the five baseline methods, respectively. On the ASSISTment17 dataset, SFBKT achieves an ACC of 0.7404 and an AUC of 0.8095, which are 2.30% and 5.12% higher than the best results among the five baseline methods, respectively. On the Slepemapy.cz dataset, SFBKT achieves an ACC of 0.8087 and an AUC of 0.7736, which are 0.59% and 2.13% higher than the best results among the five baseline methods, respectively. Therefore, SFBKT can better classify the prediction results of students' correctness. Ablation Study. We conduct a series of ablation experiments to evaluate the performance of individual components in SFBKT, thereby enabling us to assess the effectiveness of each component and determine its impact on the overall system. Since the model consists of three modules, we remove one module in turn to observe its impact on the prediction. \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 & AUC & ACC \\ \hline DKT & 0.7396 & 0.7508 & 0.7275 & 0.7314 & 0.6981 & 0.7273 & 0.7971 & 0.7421 \\ DKT + Forgetting & 0.7402 & 0.7537 & 0.7388 & 0.7457 & 0.6993 & 0.7302 & 0.8009 & 0.7498 \\ KTM & 0.7327 & 0.7415 & 0.7439 & 0.7532 & 0.6935 & 0.7237 & 0.7967 & 0.7415 \\ HawkesKT & 0.7472 & 0.7623 & 0.7513 & 0.7663 & 0.7063 & 0.7487 & 0.8028 & 0.7523 \\ AKT-R & 0.7375 & 0.7462 & 0.7452 & 0.7552 & 0.7174 & 0.7583 & 0.7986 & 0.7462 \\ \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 6: AUC and ACC of all methods on the four datasets (higher is better), the best results are in bold face, and the best baseline is underlined. Figure 4: AUC results of all models for the four datasets. * SFBKT-GF. The SFBKT-Group Forgetting variant model is used to study the influence of personal forgetting information processing in the SFBKT model on prediction. In the input module of SFBKT-GF, the part of extracting students' past forgetting records is removed. Thus, the variant model maintains the network module and output module of the SFBKT model, while reducing the input data of the CTLSTM network to a series of student exercises. SFBKT-GF can effectively reflect the degree of influence of students' personal forgotten information on predictions. * SFBKT-TF. To study the importance of the continuous-time long short-term memory network in the SFBKT model for prediction performance, the SFBKT-TF variant model removes it and compares it with the original model.