## 4 Survey Exploratory Analysis
### Principal Component Analysis  
To reduce the dimensions of the data, we also applied PCA. Table 9 displays the total variance explained by the obtained principal components, while Table 10 shows the rotated component matrix using varimax with Kaiser normalisation. With four principal components, PCA explained 61.96% of the total variance. The first component was highly correlated with variables E1, E2, E3, C1, C4, D1, G1, G2 and G3, and was therefore related to the user experience with the MaGraDa software. The second component, which was strongly correlated with variables D2, D3, D4 and D6, was mainly related to the educational usefulness and academic benefits of MaGraDa. The third component was strongly correlated with variables C2, C3, D5, D6 and G1. This component was mainly related to the users' perceptions of the quality of the MaGraDa content as a tool for graph learning. The fourth component, which was correlated mainly with variables E4 and E5, was related to the users' perceptions of the user-friendliness of MaGraDa.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Item** & **Component 1** & **Component 2** & **Component 3** & **Component 4** \\ \hline
**E1** & 0.793 & 0.109 & \(-\)0.077 & 0.183 \\
**E2** & 0.765 & 0.017 & \(-\)0.049 & 0.293 \\
**E3** & 0.543 & 0.230 & 0.045 & 0.432 \\
**E4** & 0.307 & 0.266 & 0.175 & 0.653 \\
**E5** & 0.106 & \(-\)0.055 & 0.010 & 0.828 \\
**C1** & 0.507 & 0.132 & 0.237 & 0.203 \\
**C2** & 0.312 & 0.382 & 0.607 & \(-\)0.040 \\
**C3** & 0.379 & 0.088 & 0.548 & 0.387 \\
**C4** & 0.573 & \(-\)0.082 & 0.401 & 0.174 \\
**D1** & 0.489 & 0.357 & 0.142 & \(-\)0.068 \\
**D2** & 0.250 & 0.754 & 0.051 & 0.021 \\
**D3** & 0.093 & 0.788 & 0.141 & 0.126 \\
**D4** & 0.100 & 0.819 & 0.226 & 0.102 \\
**D5** & \(-\)0.066 & 0.198 & 0.780 & 0.121 \\
**D6** & 0.045 & 0.658 & 0.523 & 0.040 \\
**G1** & 0.490 & 0.394 & 0.511 & \(-\)0.163 \\
**G2** & 0.635 & 0.276 & 0.448 & \(-\)0.082 \\
**G3** & 0.609 & 0.380 & 0.351 & 0.086 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Rotated component matrix of PCA, varimax rotation (see Table 3 for a description of each variable).  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Initial**} & \multicolumn{2}{c}{**Extraction Sums**} & \multicolumn{2}{c}{**Rotation Sums**} \\  & & **Eigenvalues** & \multicolumn{2}{c}{**of Squared Loadings**} & \multicolumn{2}{c}{**of Squared Loadings**} \\ \cline{2-10}
**Component** & **Total** & **\begin{tabular}{c} **\% of** \\ **Variance** \\ \end{tabular} & **\begin{tabular}{c} **Cumulative** \\ **\%** \\ \end{tabular} & **Total** & **\begin{tabular}{c} **\% of** \\ **Variance** \\ \end{tabular} & **\begin{tabular}{c} **Cumulative** \\ **\%** \\ \end{tabular} & **Total** & **\begin{tabular}{c} **\% of** \\ **Variance** \\ \end{tabular} & **\begin{tabular}{c} **Cumulative** \\ **\%** \\ \end{tabular} & **Total** & **\begin{tabular}{c} **\% of** \\ **Variance** \\ \end{tabular} & **\
\begin{tabular}{c} **Total** \\ **Variance** \\ \end{tabular} & **
\begin{tabular}{c} **\% of** \\ **\%** \\ \end{tabular} \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 9: Total variance explained by principal component analysis (PCA).