# Graph Neural Network for Senior High Student's Grade Prediction
## 3 Experiments  
The proposed method is conducted in the dataset of senior high students in Ningbo Xiaoshi High School. In order to illustrate the student grade prediction process, we randomly select a student (the student ID is 14295) and build the corresponding personalized grade prediction model. There are nine examination records of the student in the dataset. Each of the examinations contains several subject grades and ranks (in class), seen in Table 2. Due to the limited space, only the math grades and ranks are presented. In fact, the subject grades and ranks contain Chinese, maths, English, physics, chemistry, biology, politics, history, geography, and technology. These subject grades and ranks are shown in Figure 8.  
It can be seen that some subject grades and ranks in some examinations are not recorded. That cannot be used to train or test the proposed grade prediction model. Therefore, we utilize the linear interpolation method [32] to complement the \"null\" data. The result can be seen in Table 3. Tables 2 and 3 are visualized and compared, as shown in Figure 8. From Figure 8a,b, it can be seen that some subject grades and ranks of the examination (i.e., the first semester final exam in the 2017 school year) are missing. In Figure 8c,d, the missing grades and ranks have been complemented. The dashed line of the grades and ranks of maths in Figure 8c,d illustrates the nonlinearity of the student grades and ranks. After the examination data has been complemented, the corresponding graph structure data can be represented. The corresponding graph structure data of the subject grades in the second semester mid-term exam in the 2016 school year is illustrated as an example. The node attribute of maths is \([79,24]\), which is the tensor of maths grade and rank (in class). The edge attribute \(\mathbf{e}\) is the tensor of the ratio of the receiver node attribute to the sender node attribute. The edge attribute is \([79/74,24/33]\), if the receiver node is maths and the sender node is physics (the node attribute of physics is \([74,33]\)). The global attribute \(\mathbf{u}\) is the tensor of the total grade and average rank (in class), which is \([690,36]\). \begin{table}
\begin{tabular}{l c c} \hline \hline
**Examination** & **Date** & **Maths Grade \& Rank** \\ \hline second semester mid-term exam in 2016 SY & 2017/04/20 & 79 \& 24 \\ second semester final exam in 2016 SY & 2017/06/28 & 77 \& 33 \\ first semester mid-term exam in 2017 SY & 2017/11/15 & 93 \& 02 \\ first semester final exam in 2017 SY & 2018/01/26 & null \\ second semester mid-term exam in 2017 SY & 2018/04/23 & null \\ second semester final exam in 2017 SY & 2018/06/24 & 87 \& 10 \\ first semester joint exam with 10 schools in 2018 SY & 2018/09/20 & null \\ first semester joint exam with 5 schools in 2018 SY & 2018/10/11 & null \\ first semester mid-term exam in 2018 SY & 2018/11/05 & 86 \& 13 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Examination Records of Student 14295. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Examination** & **Date** & **Maths Grade \& Rank** \\ \hline second semester mid-term exam in 2016 SY & 2017/04/20 & 79 \& 24 \\ second semester final exam in 2016 SY & 2017/06/28 & 77 \& 33 \\ first semester mid-term exam in 2017 SY & 2017/11/15 & 93 \& 02 \\ first semester final exam in 2017 SY & 2018/01/26 & **91 \& 04** \\ second semester mid-term exam in 2017 SY & 2018/04/23 & **89 \& 07** \\ second semester final exam in 2017 SY & 2018/06/24 & 87 \& 10 \\ first semester joint exam with 10 schools in 2018 SY & 2018/09/20 & **87 \& 11 \\ first semester joint exam with 5 schools in 2018 SY & 2018/10/11 & **86 \& 12** \\ first semester mid-term exam in 2018 SY & 2018/11/05 & 86 \& 13 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Examination Data of Student 14295 Complemented by Linear Interpolation Method. With the subject grades and ranks represented in the graph structure, MLP is employed to learn (or fit) the inner relation in the graph structure data.