## 4 Results and Analysis
### Data Size and Performance Analysis  
This study analyzes the impact of training data size on the performance of the proposed system. The size of the training dataset directly influences the model's generalization capability and classification accuracy. Insufficient data may lead to underfitting, where the model fails to capture essential patterns, whereas an adequately large dataset enhances model stability and increases the likelihood of maintaining high accuracy. This section evaluates the relationship between dataset size and performance, examining the system's data efficiency and scalability. For performance evaluation, the training dataset size was adjusted to 5000, 10,000, 15,000, and 30,000 instances, while the same test dataset (20% of the total dataset) was used for all experiments. The evaluation was conducted based on key performance metrics, including Accuracy, F1 Score, Precision, and Recall, under identical training and testing conditions described in Section 3.1 (System Architecture). The purpose of this experiment is to measure performance improvements as the dataset size increases and identify the saturation point, assessing the scalability and data efficiency of the proposed system.  
As shown in Table 7, accuracy consistently improved as the dataset size increased. Figure 4 visually represents the impact of dataset size on model performance. As illustrated, both accuracy and F1 Score consistently improve as the training dataset expands. A notable performance boost occurs between 5000 and 15,000 instances, confirming that increasing data volume enhances both the model's learning capability and generalization. However, beyond 15,000 instances, the rate of improvement gradually diminishes, suggesting that the model approaches an optimal performance level at 30,000 instances. These findings underscore the importance of dataset size in achieving reliable and robust classification outcomes.  
When the dataset contained 5000 instances, the accuracy was 94.50%. However, as the dataset expanded to 30,000 instances, accuracy significantly improved to 98.85%, demonstrating that a larger dataset enhances the reliability of predictions.  
Similarly, Precision and Recall followed the same trend, improving with an increase in dataset size. For instance, Precision rose from 0.9423 with 5000 instances to 0.9867 with 30,000 instances. Likewise, the F1 Score increased from 0.9311 to 0.9860, indicating a well-balanced improvement in Precision and Recall.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data Size** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline
5000 & 0.9450 & 0.9423 & 0.9201 & 0.9311 \\
10,000 & 0.9655 & 0.9630 & 0.9456 & 0.9542 \\
15,000 & 0.9770 & 0.9750 & 0.9600 & 0.9674 \\
30,000 & 0.9885 & 0.9867 & 0.9654 & 0.9860 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance evaluation results based on training data size.  
These findings confirm that dataset size plays a crucial role in system performance. Notably, performance exhibited significant improvement when the dataset size increased from 5000 to 15,000 instances, demonstrating that sufficient training data enhance both learning capability and generalization performance. However, after 15,000 instances, the rate of improvement gradually diminished, and at 30,000 instances, the model performance appeared to reach an optimal level.  
The results highlight that dataset size is a critical factor in determining system performance. As the dataset grows, Accuracy and FI Score converge at higher values, demonstrating the system's capability to handle more diverse and complex patterns effectively. These findings confirm that the proposed model maintains high reliability even with large-scale datasets, making it highly applicable to real-world educational environments.