## 3 Proposed System
### Dataset  
The dataset used in the proposed system was constructed by collecting 15,000 elementary school mathematics diagnostic assessment records from students in grades 4 to 6 between 2020 and 2024. This dataset was designed to analyze students' learning status and error patterns and to provide personalized feedback. Each diagnostic assessment record was categorized into nine major labels, including\" Concept Deficiency\", \"Calculation Errors\", and \" Problem Comprehension Deficiency\", and stored in a text format. To enhance the accuracy of system training, the collected data were stored in CSV (Comma-Separated Values) format. The CSV format facilitates efficient data management and ensures compatibility across different systems, enabling effective utilization in both training and analysis processes. For model training and evaluation, the dataset was split into an 80:20 ratio for training and testing, respectively. Additionally, data augmentation techniques such as text transformation and synonym replacement were applied to expand the dataset to 30,000 records while maintaining semantic integrity. Text transformation involves restructuring common mathematical expressions while preserving their original meaning. For example, \"Calculation error in subtraction\" was rephrased as \"Mistake in subtracting numbers\". Synonym replacement substituted equivalent words to increase linguistic diversity. For instance, \"incorrect\" was replaced with \"wrong\", and \"missing\" with \"omitted\". These augmentation techniques helped balance the dataset by increasing the representation of underrepresented error categories. However, text-based data augmentation pose several challenges. Unlike image-based augmentation, textual data must maintain semantic accuracy to prevent misinterpretation. For large-scale datasets exceeding 500,000 records, the following computational optimizations can be applied:  
* Parallel processing and distributed computing: leveraging frameworks such as Apache Spark or Hadoop to efficiently process large-scale text augmentation tasks in parallel. * Transformer-based contextual augmentation: utilizing pre-trained language models (e.g., BERT, GPT) to generate semantically coherent augmented text instead of simple synonym replacement. * Lexicon-based augmentation: implementing domain-specific vocabularies and rule-based transformations to maintain mathematical accuracy while increasing data diversity. * Memory optimization techniques: using batch processing and on-the-fly augmentation during training instead of permanently storing expanded datasets, reducing storage overhead. By applying these scalable augmentation techniques, researchers working with massive datasets can mitigate computational inefficiencies and enhance the robustness of text-based models for educational diagnostics. The preprocessing stage involved the following steps:  
1. Correction of typographical errors: ensured the accuracy of textual data. 2. Removal of special characters and unnecessary spaces: cleaned and standardized text data. 3. Stopword removal: eliminated irrelevant words to enhance learning efficiency. The composition and distribution of the dataset's key labels are summarized in Table 1.  
Among the labels, \"Calculation Errors\" accounted for the highest proportion (33.34%), indicating that calculation accuracy is a critical issue in mathematics learning. This was followed by \"Concept Deficiency\" (26.65%) and \" Problem Comprehension Deficiency\" (16.67%), which reflect students' struggles in understanding mathematical concepts or interpreting problem conditions. Other error patterns, including \"Error in the solution process\" (7.96%), \"Unit Conversion Errors\" (5.32%), \"Inequality Usage Error\" (2.67%), and \"Graph Interpretation Errors\" (2.00%), were categorized separately, providing valuable insights for improving learning strategies. Meanwhile, \"Condition Omission\" (1.34%) and \"Others\" (0.71%) represented rare yet distinctive learning errors, which could contribute to specialized interventions.