# SFBKT: A Synthetically Forgetting Behavior Method for Knowledge Tracing
## 4 Experiments
### Results and Discussion  
We conducted 5-fold cross-validation in our experiments. For each fold, we divided the dataset into three parts: the training set, the validation set, and the test set, accounting for 70%, 10%, and 20%, respectively. Area under the curve (AUC) and accuracy (ACC) were used as evaluation indices to assess the model's performance. In order to strike a balance between prediction accuracy and computational resources, we established the hyperparameters, as presented in Table 5. It is worth mentioning that, irrespective of the available computational resources, the SFBKT model can achieve even more accurate predictions by adjusting its hyperparameters. Furthermore, in the collaborative filtering component of our model, the parameter \(\rho\) is utilized to adjust the proportion of influence of individual knowledge levels of students and the common knowledge level of the student group on the recommendation results. The value range of \(\rho\) is \([0,1]\). When \(\rho\) is smaller, the predicted exercise score vector is closer to that of the knowledge state of the student group, similar to the target students. On the other hand, when the value of \(\rho\) is closer to 1, the final vector is more similar to the students' personal knowledge status vector. Figure 3 illustrates the relationship between AUC and \(\rho\) when the SFBKT method predicts varying \(\rho\) across the four datasets. The experimental results indicate that \(\rho\) has a significant impact on prediction effectiveness. For all four datasets, \(\rho\) between 0.4 and 0.5 yielded the highest AUC values, suggesting a strong correlation between individual student states and the group's state. \begin{table}
\begin{tabular}{c c} \hline \hline
**Configuration Environment** & **Configuration Parameters** \\ \hline Operating System & Windows 10 64-bit \\ GPU & RTX 3070 \\ CPU & R7 5800H \\ Memory & 16 GB \\ Programming language & Python3.6 \\ Deep learning framework & Pytorch 1.7.1 \\ Python library & Scikit-learn, Numpy, Pandas \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Description of the experimental environment. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Students** & **Skills** & **Records** \\ \hline ASSISTment09-10 & 3700 & 111 & 110,200 \\ ASSISTment12-13 & 25,300 & 245 & 879,500 \\ ASSISTment17 & 1700 & 102 & 9,786,500 \\ Stepemapycz & 81,700 & 1473 & 2,877,500 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Introduction of the datasets. Baseline comparison. To evaluate the performance of our synthetically forgetting behavior knowledge tracing method, we selected five classic models for comparative experiments, including:  
* DKT. It was the first to employ deep learning methods to trace knowledge progression. By utilizing the long short-term memory model, it has the capability to monitor changes in students' proficiency levels over an extended period. This is a conventional technique that has been refined by numerous models in recent years. * DKT + Forgetting. Although several models have explored the concept of forgetting behaviors in knowledge tracing, DKT-Forgetting stands out as the pioneer in integrating forgetting-related information into deep learning. * KTM. It employs Factorization Machines to model the interplay between features. The characteristics taken into account in this approach comprise question ID, skill ID, previous responses to diverse skills, and temporal features within DKT + Forgetting. * HawkesKT. It represents a pioneering category of knowledge-tracing methodologies that resulted from the integration of temporal point processes and knowledge-tracing studies. This approach builds upon the observed temporal crossover effects in the data, where each historical interaction has a continuously changing effect on the target skill, offering fresh perspectives on the incorporation of forgetting behavior. * AKT. It employs a novel attention mechanism that links a student's forthcoming responses with their previous responses.