## 3 Proposed System
### Performance Evaluation and Expected Impact  
The proposed system is designed to effectively analyze students' learning status and error patterns while providing personalized feedback. It learns from diagnostic assessment data, enabling the identification of individual weaknesses and assisting teachers in developing customized learning plans. The system's performance was validated using both training and test datasets, with accuracy, F1 Score, and real-time processing speed serving as key evaluation metrics. These performance indicators were selected as critical factors to assess the system's practical applicability in real-world educational settings. 1. Accuracy: The performance evaluation was conducted using three key metrics. To assess the effectiveness of the proposed system, accuracy was calculated using the test dataset. Accuracy represents the proportion of correctly classified instances compared with the actual labels, making it one of the primary indicators of system reliability. Table 2 summarizes the number of test data instances and accuracy for each label. The \"Calculation Error\" label recorded an accuracy of 95.0% from a total of 10,001 test samples, demonstrating the highest classification accuracy. This result indicates that \begin{table}
\begin{tabular}{l c c} \hline \hline
**Label** & **Data Count** & **Proportion (\%)** \\ \hline Concept Deficiency & 7996 & 92.5 \\ Calculation Error & 10,001 & 95.0 \\ Problem Comprehension Deficiency & 5002 & 90.0 \\ Error in the Solution Process & 2389 & 88.5 \\ Unit Conversion Error & 1597 & 87.0 \\ Inequality Usage Error & 800 & 86.0 \\ Graph Interpretation Error & 601 & 85.5 \\ Condition Omission & 402 & 83.0 \\ Other & 212 & 80.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Accuracy evaluation of the proposed system across different error categories. calculation errors are frequent in students' learning processes and that the model has effectively learned to identify such errors. For the \"Concept Deficiency\" label, an accuracy of 92.5% was achieved based on 7996 test samples, confirming that the model successfully captured the major error patterns related to students' difficulties in understanding mathematical concepts. The \"Problem Comprehension Deficiency\" label achieved an accuracy of 90.0% from 5002 test samples, demonstrating the model's capability to classify cases where students struggled with understanding problem conditions and contexts. For relatively infrequent error categories such as \"Error in the Solution Process\" (2389 instances), \"Unit Conversion Error\" (1597 instances), \"Inequality Usage Error\" (800 instances), and \"Graph Interpretation Error\" (601 instances), the system maintained an accuracy of 85.5% or higher. These results highlight the model's robustness, even when handling labels with fewer data points. The \"Condition Omission\" and \"Others\" labels recorded accuracies of 83.0% and 80.0%, respectively. Despite their lower data counts and the inherent ambiguity in classification, the model achieved relatively high classification performance. The accuracy evaluation results confirm that the proposed system achieves high classification accuracy for major error labels (e.g., Calculation Error, Concept Deficiency), effectively analyzing and categorizing students' primary learning errors. Additionally, the system demonstrated stable classification performance for lower-frequency labels, indicating its ability to mitigate class imbalance issues to some extent. These findings validate the proposed system as a reliable tool for providing personalized feedback in educational environments. 2. F1 Score: In this study, F1 Score was employed to evaluate the performance of the proposed system more precisely. The F1 Score represents the harmonic mean of Precision and Recall, making it particularly useful for assessing model performance in situations involving data imbalance. Table 3 provides a summary of the Precision, Recall, and F1 Score for each label.