## 3 Proposed System
### Performance Evaluation and Expected Impact
This performance enables teachers to analyze students' learning progress in real time and provide immediate feedback, thereby enhancing the effectiveness of personalized learning. Despite its simple design, the proposed system exhibits high efficiency and real-time data processing capabilities, making it a practical and effective tool for educational applications. To provide a comprehensive summary of the system's overall performance, the combined evaluation results, including accuracy, F1 Score, and real-time processing speed, are \begin{table}
\begin{tabular}{l c} \hline \hline
**Evaluation Metrics** & **Result** \\ \hline Average Processing Time & 1.3 s \\ Maximum Processing Time & 2.8 s \\ Minimum Processing Time & 1.0 s \\ Number of Records Processed per Minute & 45 cases \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Real-time processing performance of the proposed system. presented in Table 5. These metrics collectively assess how effectively the system achieves its primary educational objectives, offering a holistic evaluation of its performance. Accuracy: The proposed system achieved an average accuracy of 91.75% across all labels in the test dataset, demonstrating its capability to classify most types of learning errors with high reliability. Notably, for key labels such as \"Calculation Errors\" and \"Concept Deficiency\", the model attained an accuracy exceeding 95%, indicating its effectiveness in learning essential patterns in students' responses. F1 Score: To address class imbalance issues, the average F1 Score was calculated as 0.87. This result indicates that the system maintained relatively high classification performance even for low-frequency labels. Specifically, labels such as \"Omitted Conditions\" and \"Others\" also exhibited stable F1 Scores, confirming that the proposed system can comprehensively process various types of learning errors. Real-time processing speed: The average processing time of the system was measured at 1.3 s per instance, with a maximum processing capacity of 45 cases per minute. This performance ensures that the system is well-suited for real-time feedback generation, enabling teachers to quickly analyze students' learning progress and provide timely intervention. The combined performance evaluation results demonstrate that the proposed system offers stable and reliable classification performance with high accuracy and F1 Scores, while also maintaining efficient real-time data processing capabilities. This efficiency allows teachers to rapidly analyze multiple students' data and deliver immediate feedback, making the system an effective tool for real-world educational applications. Additionally, its ability to sustain high performance even under class imbalance conditions and accurately classify low-frequency labels is considered a key strength of the proposed approach. As a result, the system proves to be a robust and practical tool for real-time educational data processing and analysis. The proposed system achieved an average accuracy of 96% for key labels (e.g., Calculation Errors, Concept Deficiency) and demonstrated stable classification performance even for low-frequency labels (e.g., Omitted Conditions, Others). The F1 Score exceeded 0.90 across all labels, further confirming the system's reliability. The real-time processing speed evaluation showed that the system can analyze and generate feedback within 2 s per diagnostic assessment, validating its applicability in actual educational environments. These findings confirm that the system can achieve high accuracy and reliability using simple text data, proving its practical applicability in real-time educational settings.