## 5 Conclusions  
This study proposed a system that analyzes learning states and error patterns in elementary school students' mathematics diagnostic assessments and provides personalized feedback. The proposed system effectively processes textual data by integrating TF-IDF (Term Frequency-Inverse Document Frequency) and XGBoost (eXtreme Gradient Boosting), demonstrating superior performance through comparative analysis across various dataset sizes and baseline models. A dataset comprising 30,000 records was constructed using mathematics diagnostic assessments collected from 2020 to 2024 for elementary students in grades 4 to 6. The data underwent preprocessing and augmentation to enhance their suitability for training. The system extracted feature vectors using TF-IDF and classified them using XGBoost. Performance evaluations conducted on training datasets of varying sizes (5000 to 30,000 records) demonstrated high accuracy (98.85%) and an F1 Score of 0.9860. Additionally, the system exhibited an average real-time processing speed of 1.3 s, confirming its capability to efficiently process large volumes of student data. Comparisons with Logistic Regression, SVM, LightGBM, BERT, and DistilBERT further validated that the TF-IDF + XGBoost model outperformed alternative models across key performance metrics. This study makes significant contributions to the field of learning data analysis and automated feedback generation through the following aspects. First, it proposes a simple yet high-performing classification model by integrating TF-IDF and XGBoost, effectively capturing the characteristics of textual data. Second, it conducts a quantitative analysis  
Figure 4: Impact of training data size on model performance. of the relationship between dataset size and model performance, providing insights into efficient data utilization strategies. Lastly, by ensuring real-time processing performance and high classification accuracy, the proposed system serves as a practical tool for educators, enabling them to design personalized learning plans tailored to individual students. However, this study has several limitations. First, the dataset used in this research is limited to mathematics diagnostic assessments for elementary school students in grades 4 to 6, and its applicability to other subjects or age groups has not been validated. Second, the simple vectorization method using TF-IDF does not fully capture sentence structures or contextual information, and more advanced language models such as BERT may provide better performance. Third, the class distribution within the dataset is imbalanced, which may lead to relatively lower classification performance for labels with fewer occurrences. Future research can be extended in the following directions. First, the scope of application can be expanded by incorporating diagnostic assessment data from middle and high school students or by applying the system to other subjects, such as science and English. Second, advanced language models such as BERT and GPT can be employed instead of TF-IDF to enable a more in-depth analysis of textual data. Additionally, future research could explore alternative classification models to further enhance performance. While XGBoost has demonstrated superior results in this study, optimizing other ensemble learning techniques, such as LightGBM with refined hyperparameters, may yield improvements in classification accuracy and efficiency. Furthermore, hybrid models combining TF-IDF with deep learning approaches, such as CNNs or RNNs, could be investigated to leverage both traditional feature extraction and contextual understanding in text classification. These efforts would provide a broader evaluation of potential model enhancements for automated learning assessment systems. Third, data augmentation techniques can be further refined, or weight-based learning methods can be applied to improve classification performance for labels with low-frequency occurrences.