## 4 Experiments
### Design  
In the experiment, according to the datasets size and data feature distribution of Party A and Party B, four scenarios were established to simulate the actual situation. They are as follows:  
* Scenario 1: Both Party A and Party B have the same feature distribution and data scales;
* Scenario 2: Party A and Party B have the same characteristics but different data scales;
* Scenario 3: Party A and Party B have different characteristics but the same data scales;
* Scenario 4: Both Party A and Party B have different feature distribution and data scales. To correspond to these four scenarios, we processed the data and selected some data and attributes. In addition, in the scene with different feature distribution, the correlation analysis of attributes is carried out to ensure that the correlation with domain invariant features does not exceed the threshold. We carry out a factor analysis on the features contained in the datasets. The calculated factor scores are shown in Table 2, indicating eight principal components in total. \begin{table}
\begin{tabular}{c c l} \hline
**No.** & **Attribute** & **Description (Domain)** \\ \hline
1 & address & student’s home address type (binary: “U”—urban or “R”—-rural) \\
2 & famsize & family size (binary: “LE3”—less or equal to 3 or “GT3”—greater than 3) \\
3 & Medu & mother’s education (numeric: 0—none, 1—primary education (4 th grade), 2—5 th to 9 th grade, 3—secondary education or 4—higher education) \\
4 & Fedu & father’s education (numeric: 0—none, 1—primary education (4 th grade), 2—5 th to 9 th grade, 3—secondary education or 4—higher education) \\
5 & studytime & weekly study time (numeric: 1—2 h, 2—2 to 5 h, 3—5 to 10 h, or 4—\textgreater{}10 h) \\
6 & paid & extra paid classes within the course subject (Math or Portuguese) (binary: yes or no) \\
7 & activities & extra-curricular activities (binary: yes or no) \\
8 & higher & wants to take higher education (binary: yes or no) \\
9 & internet & Internet access at home (binary: yes or no) \\
10 & romantic & with a romantic relationship (binary: yes or no) \\
11 & famrel & quality of family relationships (numeric: from 1—very bad to 5—excellent) \\
12 & freetime & free time after school (numeric: from 1—very low to 5—very high) \\
13 & goout & going out with friends (numeric: from 1—very low to 5—very high) \\
14 & Dalc & workday alcohol consumption (numeric: from 1—very low to 5—very high) \\
15 & Walc & weekend alcohol consumption (numeric: from 1—very low to 5—very high) \\
16 & absences & number of school absences (numeric: from 0 to 93) \\
17 & G1 & first period grade (numeric: from 0 to 20) \\
18 & G2 & second period grade (numeric: from 0 to 20) \\
19 & G3 & final grade (numeric: from 0 to 20, output target) \\ \hline \end{tabular}
\end{table} 
Table 1: Preprocessed student-related variables and their descriptionsGiven the scenarios with consistent feature distribution, we selected eight variables that contributed the most to the principal components as the input features of the model, namely walc, fedu, paid, address, romantic, famrel, famsize, and activities. The source domain still adopts the eight features selected from previous scenarios for scenarios with inconsistent features, while the target domain adopts the new eight features. They are dalc, absences, medu, goout, higher, freetime, studytime and internet. We visualize these two groups of features, use PCA to reduce the dimension to three-dimensional space, and check the distribution of data points. Figure 3 respectively shows the distribution of the features of the source domain and the target domain in the three-dimensional space. To ensure the domain invariant features in the transfer process, we also conducted a pairwise correlation analysis on a total of 16 features and tested the significance of the correlation results.