## 3 Results
### Model Performance Evaluation  
The predictive performance of the nine machine learning models, including Random Forest (RF), Decision Tree (DT), Gradient Boosting Decision Tree (GBDT), AdaBoost (AB), Support Vector Machine (SVM), Multilayer Perceptron (MLP), Ridge Regression (RR), Voting Classifier (VC), and K-Nearest Neighbors (KNN), was evaluated using Receiver Operating Characteristic (ROC) curve analysis (Figure 2). The results indicated that the RF model exhibited the highest area under the ROC curve (AUC) value on the test set (0.80), outperforming the other algorithms. Specifically, the AUC values for RF, DT, GBDT, AB, SVM, MLP, RR, VC, and KNN were 0.80, 0.75, 0.79, 0.79, 0.74, 0.78, 0.71, 0.72, and 0.75, respectively. To further assess model performance, additional metrics were computed and are presented in Table 2. Among these nine machine learning models, the RF algorithm demonstrated the best predictive performance, achieving an accuracy of 79.74% and an F1-score of 0.72, surpassing traditional regression-based approaches. Therefore, the RF model was selected for further interpretive analysis. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & **Option** & **Number** & **\%** \\ \hline \multirow{2}{*}{Gender} & Female & 441 & 38.08 \\  & Male & 717 & 61.92 \\ \hline \multirow{4}{*}{Age} & \(\leq\)18 years old & 37 & 3.20 \\  & 18–25 years old & 1115 & 96.29 \\  & 26–30 years old & 4 & 0.35 \\  & \(\geq\)30 years old & 2 & 0.17 \\ \hline \multirow{2}{*}{Nation} & Han nationality & 1043 & 90.07 \\  & Minority & 115 & 9.93 \\ \hline \multirow{4}{*}{Grade} & First-year undergraduate & 653 & 56.39 \\  & Second-year undergraduate & 402 & 34.72 \\  & Third-year undergraduate & 91 & 7.86 \\  & Fourth-year undergraduate & 12 & 1.04 \\ \hline \multirow{4}{*}{Address} & East China & 393 & 33.94 \\  & South China & 183 & 15.80 \\  & Central China & 329 & 28.41 \\ \cline{1-1}  & North China & 10 & 0.86 \\ \cline{1-1}  & Northwest China & 1 & 0.09 \\ \cline{1-1}  & Southwest China & 84 & 7.25 \\ \cline{1-1}  & Northeast China & 158 & 13.64 \\ \hline \hline \end{tabular} Note: East China: Shandong, Jiangsu, Anhui, Zhejiang, Fujian, Shanghai, and Jiangxi; South China: Guangdong, Guangxi, Hainan; Central China: Hubei, Hunan, and Henan; North China: Beijing, Tianjin, Hebei, Shanxi, and Inner Mongolia; Northwest China: Ningxia, Xinjiang, Qinghai, Shaanxi, and Gansu; Southwest China: Sichuan, Yunnan, Guizhou, Tibet, and Chongqing; Northeast China: Liaoning, Jilin, and Heilongjiang.  
\end{table} 
Table 1: Descriptive statistics for all variables (total n = 1158). Figure 2: The ROC curves of nine machine learning models in predicting student engagement: (**a**) Random Forest, (**b**) Decision Tree, (**c**) Gradient Boosting Decision Tree, (**d**) AdaBoost, (**e**) Support Vector Machine, (**f**) Multilayer Perceptron, (**g**) Ridge Regression, (**h**) Voting Classifier, and (**i**) K-Nearest Neighbor. The diagonal line in the figure represents the baseline for random guessing. Along this line, the true positive rate (TPR) is equal to the false positive rate (FPR), indicating that the classifier possesses no discriminatory power to differentiate between positive and negative samples.