## 3 Proposed System
### System Process  
Figure 2 illustrates the overall process of the proposed system.  
The proposed system consists of four stages: data input, data preprocessing, model training, and result generation. In the data input stage, teachers input the diagnostic assessment data that they have created for the students into the system. The system verifies whether the uploaded data are in text format to ensure validity. If confirmed as a valid text file, it is transmitted from the client server to the system server. The diagnostic assessment data includes students' learning status (e.g., conceptual understanding) and error patterns (e.g., calculation mistakes), which serve as critical inputs for subsequent analysis.  
In the data preprocessing stage, the transmitted diagnostic data undergo data cleaning to ensure optimal quality for model training. First, the system removes whitespace, special characters, and unnecessary stopwords to clean the text data. The refined text is then  
Figure 2: Process of the proposed system.  
tokenized into individual words, converting it into a structured format suitable for further analysis. Next, TF-IDF (Term Frequency-Inverse Document Frequency) is applied to compute the importance of each word, transforming the text data into a vectorized form that serves as input for the model training process.  
During the model training stage, the preprocessed TF-IDF vectorized data are fed into XGBoost (eXtreme Gradient Boosting) for classification. XGBoost is a tree-based boosting algorithm that prevents overfitting and delivers high accuracy even with relatively small datasets. This study selected XGBoost due to its fast training speed and strong performance in multi-class classification tasks.  
The trained model makes predictions based on test data, categorizing student responses into predefined labels such as \"Concept Deficiency\", \"Calculation Mistake\", and \"Misunderstanding of Problem Requirements\". The classification results are stored as labels, which are later utilized in the result generation stage. The predicted results are temporarily saved as CSV files before being uploaded to the database. The database is structured to allow teachers to quickly retrieve and review analysis results. Additionally, it enables efficient data retrieval and storage for future queries, ensuring ease of access to past student performance records. Teachers can view both the analysis results and the automatically generated textual feedback in real time.  
Finally, in the result generation stage, the system generates personalized feedback based on the model's predictions. The classification results stored in the database are used to create individualized feedback for each student. For example, if a student is classified under the \"Concept Deficiency\" label, the system generates feedback such as \"Student A needs to review the concept of fraction addition and practice solving related example problems\".  
Future diagnostic assessment data will undergo the same analysis process, allowing automatic feedback retrieval and generation based on predicted labels. The generated feedback is delivered to teachers' devices in real time, enabling them to review prediction results and utilize personalized feedback to design customized learning plans for each student.