# SFBKT: A Synthetically Forgetting Behavior Method for Knowledge Tracing
## 3 Proposed Method
### Model Architecture  
The Synthetically Forgetting Behavior Knowledge Tracing model comprises three primary components. The first component extracts forgotten information from a student's learning sequences. This process involves integrating additional information related to forgetting from the student's sequence of interactions with their interaction sequence. The second part involves obtaining the student's knowledge status through a continuous-time long short-term memory network (CTLSTM), which captures changes in the student's knowledge state throughout the learning process. Finally, the third component predicts the student's performance in the next time slice by leveraging neighbor information and using cosine similarity to determine the similarity of different students' knowledge states. The model combines individual and group information to determine the student's knowledge state and predict their performance. The SFBKT model is an improvement over existing models such as DKT-forgetting [22] and HawkesKT [23] as it considers forgetting as a critical factor in student learning and uses continuous-time LSTM networks to capture changes in student knowledge states over time. Figure 2 demonstrates how the SFBKT model enhances the model structures of DKT-forgetting and HawkesKT. DKT-forgetting integrates practice sequences and additional forgetting information in the data processing stage and uses LSTM networks for feature extraction to obtain the student's knowledge status. However, DKT-forgetting assumes that the knowledge state remains unchanged until the next exercise, which contradicts the law of forgetting. SFBKT improves upon DKT-forgetting in two primary ways. First, the SFBKT model enhances the LSTM network to capture the change in the knowledge state over time. This improvement addresses the inadequacy of the DKT-forgetting model by incorporating the forgetting curve into the learning process, where the students' mastery of exercises gradually declines over time [34]. Second, the SFBKT model leverages group information \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**KT Model** & **Year** & **Learning Model** & **Knowledge State** & **Forgetting** \\ \hline IRT & 1993 & LR & Real-valued vector & \(\times\) \\ BKT & 1994 & HMM & Binary scalar & \(\times\) \\ AFM & 2008 & LR & Real-valued vector & \(\times\) \\ DKT & 2015 & RNN/LSTM & Vector & \(\times\) \\ DKVMN & 2017 & MVNN & matrix & \(\times\) \\ KTM & 2019 & FM & Real-valued vector & \(\times\) \\ DKT + Forgetting & 2019 & RNN/LSTM & Vector & \(\surd\) \\ GKT & 2019 & GNN & Vector & \(\times\) \\ AKT & 2020 & FFN + MSA & Vector & \(\surd\) \\ HawkesKT & 2021 & FM & Real-valued vector & \(\surd\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of previous main research in KT. The “Learning Model” column means the main machine learning or deep learning techniques used, the “Knowledge State” column means how the obtained student knowledge level is stored, and the “Forgetting” column indicates whether the mode takes forgetting information into account. to improve prediction accuracy. Since students often learn together in groups rather than independently, combining the knowledge state of the group helps to reduce interference from incorrect information recorded in the dataset. This approach combines information from students with similar knowledge states with the student being predicted, resulting in a more precise assessment of the student's knowledge state. Figure 2: Architecture diagram of SFBKT. HawkesKT represents the first model to incorporate the Hawkes process for simulating temporal cross-effects in knowledge tracing, serving as an inspiration for our work. Nevertheless, HawkesKT employs an exponential function as a kernel function to approximate the forgetting curve, which only crudely simulates the average knowledge state of students and cannot be customized for individual students.