## 3 Proposed System  
### System Architecture  
The overall architecture of the proposed system is illustrated in Figure 1.  
The proposed system consists of four main stages: data input, preprocessing, model training, and result generation. The data input stage involves receiving diagnostic assessment data from students for analysis. At this stage, teachers input text-based data that assess students' learning progress, problem-solving patterns, and common errors in mathematics. The user interface (UI) is provided as a web application, allowing teachers to validate the data format and content through a preview feature, ensuring the integrity of the input data. In the data preprocessing stage, the input diagnostic data undergoes a transformation to be suitable for processing by the proposed model. This step involves removing unnecessary special characters and whitespace, handling duplicate words, and correcting incorrect formats to refine the textual data. The cleaned text is then tokenized into individual words, and TF-IDF (Term Frequency-Inverse Document Frequency) is applied to compute the importance of each term, converting the text into vectorized representations. These processed vectors are then passed to the system's learning stage. The model training stage focuses on analyzing the preprocessed data to predict and classify diagnostic assessment results. In this study, XGBoost was selected as the classification model due to its high accuracy, even with relatively small datasets. The model training process is implemented using XGBClassifier, with key hyperparameters optimized to improve classification performance. A detailed explanation of the dataset configuration and hyperparameter settings is provided in Section 3.3. The model takes TF-IDF vectors as input, classifies student learning patterns into categories such as conceptual errors, calculation mistakes, and misunderstanding of problem requirements, and provides an effective diagnosis of individual learning conditions. The result generation stage produces personalized feedback based on the analysis results. The generated feedback includes specific recommendations on students' learning status and areas for improvement, which are delivered to teachers in real time. The feedback is provided in a structured text format, allowing teachers to design customized learning plans for each student. For example, the system may generate feedback such as \"Student A needs to review fraction arithmetic concepts and practice solving related example problems\". The proposed system is developed in the following technical environment:  
* Server deployment: the system is deployed using Amazon Web Services (AWS), a highly scalable and reliable cloud computing platform, ensuring efficient server management and operational stability. Figure 1: System architecture diagram. * Framework: Django (version 5.0), a Python-based web framework, was selected due to its strong support for Natural Language Processing (NLP) tasks and high compatibility with TF-IDF and XGBoost. Django facilitates seamless integration of data processing components and enables efficient management of interactions between the UI and the system's backend server. * User interface (UI): The UI was developed using Python (version 3.12.6), HTML5, CSS, ECMAScript 2023, and Ajax. The use of Ajax allows for real-time data processing without page refreshes, significantly enhancing the user experience (UX). * Database management: PostgreSQL (version 16.4) was chosen as the database system due to its high compatibility with Django and ability to handle large-scale datasets efficiently. PostgreSQL provides fast data retrieval performance and stability, ensuring consistent system operation even as the dataset grows. Given these advantages, PostgreSQL was selected as the primary database solution for this study. * Data storage and management: Diagnostic assessment data and classification labels are stored in PostgreSQL, allowing efficient data management and retrieval.