## 2 Related Works
### XGBoost (eXtreme Gradient Boosting)  
XGBoost (eXtreme Gradient Boosting) is a widely used tree-based boosting algorithm in machine learning, recognized for its high predictive accuracy and fast training speed. This algorithm incorporates built-in regularization techniques to prevent overfitting, leverages parallel processing to accelerate learning and operates efficiently even with large-scale datasets. Additionally, it provides feature importance analysis, enabling a clearer understanding of key patterns and characteristics in the data [26; 27; 28]. XGBoost has demonstrated exceptional performance in both structured and textual data analysis, achieving notable success in data science competitions such as Kaggle [29]. XGBoost has been widely applied in various domains, including financial data analysis, medical diagnosis, and recommendation systems. It has demonstrated its strength in learning complex nonlinear relationships and addressing data imbalance issues. For instance, a study on cardiovascular risk prediction employed an XGBoost-based model incorporating hospital-level random effects, achieving high accuracy in medical data analysis [30]. Another study utilized XGBoost to analyze the relationship between metabolic syndrome and chronic kidney disease, highlighting its potential for real-time applications in healthcare programs [31]. In porosity and permeability experiment data analysis, XGBoost was optimized by integrating K-means clustering to enhance model performance [32]. Additionally, a method combining PEACE and XGBoost was proposed for handling datasets with missing values, demonstrating stable performance even in incomplete data environments [33]. In credit evaluation for financial data analysis, an XGBoost ensemble model incorporating Boruta feature selection and a GHM loss function achieved superior classification accuracy [34]. Despite its advantages, XGBoost has certain limitations. For example, in deep learning-based image log analysis, XGBoost was found to be insufficient in capturing complex image data patterns, leading to the development of a hybrid model integrating Long Short-Term Memory (LSTM) to address this limitation [35]. Similarly, a study on predicting KOSPI 200 stock index fluctuations highlighted XGBoost's inability to fully capture market volatility, prompting discussions on alternative approaches to enhance its effectiveness [36]. These studies illustrate both the strengths and limitations of XGBoost and suggest various strategies to compensate for its shortcomings. This study applies the advantages of XGBoost to the analysis of elementary school mathematics diagnostic assessment data. Specifically, TF-IDF vectorized textual data were used as input for XGBoost, enabling precise analysis of learning states and error patterns while providing personalized feedback for students. To maintain high accuracy and prevent overfitting, key hyperparameters were optimized, including learning_rate to control training speed, max_depth to limit tree depth, and subsample to adjust the proportion of samples used in each iteration. These configurations ensured an optimal learning environment tailored to the characteristics of the dataset. Furthermore, this study examined the impact of dataset size on model performance, confirming that XGBoost maintains stable performance even with small datasets and scales effectively with larger datasets. When compared with Logistic Regression, SVM, and LightGBM, XGBoost exhibited superior performance in key evaluation metrics, including accuracy and F1 Score, demonstrating its efficiency in educational data analysis. Through this study, XGBoost was validated as an effective algorithm for processing complex educational data and performing detailed learning state analysis. It plays a crucial role in understanding individual students' learning patterns and delivering practical, personalized feedback, highlighting its applicability in educational settings and reinforcing its potential for broader adoption in educational data analysis.