# Bridging the Vocabulary Gap: Using Side Information for Deep Knowledge Tracing
## 3 Side Information of Knowledge Tracing
### Side Information Extraction  
In normal circumstances, student interaction records are generally stored in learning systems, and every interaction record includes six fields, as shown in Table 1. Considering that the original data only contain six dimensions and that the features input into the model cannot include all the ID information, we propose 14 features as side information about SKQ for each interaction record (Table 2). The selection of these side information is not accidental. These are the features related to SKQ that can be extracted from the known data. They determine the right and wrong answers of students to a certain extent. For example, at time \(t+1\), we have dataset \(X=\{x_{1},x_{2},x_{3},\ldots,x_{t}\}\), and the interaction record \(x_{t+1}\) is used to predict; if its \(stu\_id=S_{1},q\_id=Q_{1}\), and \(k\_id=K_{1}\), its side information is obtained as follow:  
(1) Obtain all the records of student \(S_{1}\) about knowledge \(K_{1}\) from \(X\), and calculate the mean value of \(is\_right\) (\(sk\_acc\)), the mean value of \(use\_time\) (\(sk\_mt\)), the length of the records (\(sk\_n\)), and the elapsed time since the last record (\(sk\_lt\)). These four features are about the mastery of knowledge \(K_{1}\) for student \(S_{1}\). If \(sk\_acc\) is higher, \(sk\_mt\) is less, \(sk\_n\) is more, and \(sk\_lt\) is shorter, this means that \(S_{1}\) may have mastered \(K_{1}\) at the current moment. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Field** & **Description** & **Example** \\ \hline _stu\_id_ & Student ID. & 23 \\ _q\_id_ & Question ID. & 141 \\ _k\_id_ & Knowledge ID. & 212 \\ _use\_time_ & Answer time. & 60 s \\ _is\_right_ & Correct or incorrect. & 0 or 1 \\ _ts_ & Timestamp. & 2009-08-13 14:30:12 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Fields of interactive records in the dataset. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**SKQ** & **Dimension** & **Description** & **Name** & **Index** \\ \hline \multirow{6}{*}{Student} & \multirow{4}{*}{Mastery} & \(S_{1}\)’s correct rate for \(K_{1}\). & \(sk\_acc\) & \(1\) \\  & & Average time spent by \(S_{1}\) on \(K_{1}\). & \(sk\_mt\) & \(2\) \\  & & The number questions answered by \(S_{1}\) on \(K_{1}\). & \(sk\_n\) & \(3\) \\  & & The elapsed time since the last record of \(K_{1}\). & \(sk\_lt\) & \(4\) \\  & & \(S_{1}\)’s correct rate for all questions. & \(gl\_acc\) & \(5\) \\  & & The average time spent by \(S_{1}\) on all questions. & \(sq\_mt\) & \(6\) \\  & & The number questions answered by \(S_{1}\). & \(sq\_n\) & \(7\) \\  & & The elapsed time since the last record. & \(sq\_lt\) & \(8\) \\ \hline \multirow{2}{*}{Knowledge} & \multirow{2}{*}{Complexity} & The correct rate of \(K_{1}\). & \(k\_acc\) & \(9\) \\  & & The average time of \(K_{1}\). & \(k\_mt\) & \(10\) \\  & & The number of \(K_{1}\) records. & \(k\_n\) & \(11\) \\ \hline \multirow{2}{*}{Question} & \multirow{2}{*}{Difficulty} & The correct rate of \(Q_{1}\). & \(q\_acc\) & \(12\) \\  & & The average time of \(Q_{1}\). & \(q\_mt\) & \(13\) \\  & & The number of \(Q_{1}\) records. & \(q\_n\) & \(14\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: All side information about students, questions, and knowledge components can be derived from six known fields. (2) Obtain all the records of student \(S_{1}\) from \(X\), and calculate the mean value of _is_right_ (_sq_acc_), the mean value of _use_time_ (_sq_mt_), the length of the records (_sq_nt_), and the elapsed time since the last record (_sq_lt_). These four features are about the overall ability of \(S_{1}\). If _sq_acc_ is high, _sq_mt_ is less, _sq_n_ is more, and _sq_lt_ is shorter, it means that the student has a strong learning ability and has a good grasp of all the knowledge components learned. (3) Obtain all the records of knowledge \(K_{1}\) from \(X\), and calculate the mean value of _is_right_ (_k_acc_), the mean value of _use_time_ (_k_mt_), and the length of the records (_k_n_). These three features are about the complexity of \(K_{1}\).