## 2 Materials and Methods
### Measures
#### 2.2.2 Student Classroom Engagement  
Classroom engagement was measured using the Classroom Engagement Scale developed by [PERSON] and [PERSON] [29; 30] and subsequently revised by [PERSON] et al. [28]. This scale assessed students' affective, cognitive, and behavioral engagement in class and consists of 10 items. A 5-point Likert scale was employed. The overall score was derived by summing all items responses, with higher scores reflecting greater level of classroom engagement. In this study, the [PERSON]'s \(\alpha\) coefficient for this scale was 0.972. Figure 1: Hypothetical model. 2.2.3. Perceived Usefulness and Perceived Ease of Use  
The perceived usefulness (PU) and perceived ease of use (PEU) scales were originally developed by [PERSON] [17] and later adapted by [PERSON] [31]. The PU scale and PEU scale each contain three items. Higher scores indicate a greater degree of perceived usefulness and perceived ease of use. In this study, the [PERSON]'s \(\alpha\) coefficients for the PU and PEU scales were 0.957 and 0.944, respectively. 2.2.4. Academic Self-Efficacy  
Academic self-efficacy was measured using the Academic Self-Efficacy Scale originally developed by [PERSON] and [PERSON] [32] and later revised by [PERSON] [33]. This scale consists of two dimensions: academic ability self-efficacy and academic behavior self-efficacy, comprising a total of 22 items. In this study, the [PERSON]'s \(\alpha\) coefficient for this scale was 0.927. Detailed questionnaire items can be found in Appendix A. 2.3.1. Model Development and Performance Evaluation  
Nine machine learning algorithms, 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), were applied to develop predictive models for student classroom engagement. All models were trained using the training dataset (n = 926) and evaluated on an independent test set (n = 232). The predictive performance and robustness of the model were evaluated using a variety of metrics. The hyperparameter details of the nine machine learning models are provided in Table S1. 2.3.2. Interpretable Methods and Variable Importance Analysis  
In this study, we constructed interpretable machine learning procedures to analyze the impacts of digital technologies on classroom engagement. Initially, feature importance was calculated and ranked to quantify the relative contributions of nine digital technology tools to the predictions [34]. This process was implemented using the Python package ELI5 as illustrated by Equation (1):  
\[I_{j}=\frac{\sum_{K=1}^{K}I_{k,j}}{K}=S-\frac{1}{k}{\sum_{K=1}^{K}s_{k,j}} \tag{1}\]  
where I\({}_{j}\) represents the importance score of feature, j, K denotes the total number of permutation iterations, and I\({}_{k,j}\) refers to the contribution of feature j to the model prediction in the k-th permutation. S indicates the overall performance of the original model, while s\({}_{k,j}\) represents the model performance after permuting feature j in the k-th iteration. Based on the ranked feature importance scores, all digital technology tools were ordered accordingly. The top five features with the highest importance scores were identified and designated as key variables for further analysis. Additionally, partial dependence plots (PDPs) were generated to explore the marginal effects of individual or combined digital technology tools on student engagement. PDP analysis quantifies how changes in specific features affect model predictions by averaging out the effects of all other features [35]. Specifically, the univariate PDP examines the marginal impact of a single feature on the predicted outcome, while the bivariate PDPs illustrates the interaction or joint effects between two features [36].