Reading
The AEESA project presents a complete automatic English essay scoring system that combines traditional NLP techniques with machine learning models, providing an efficient, consistent, and objective automated solution for the field of educational assessment.
Section 01
The AEESA project presents a complete automatic English essay scoring system that combines traditional NLP techniques with machine learning models, providing an efficient, consistent, and objective automated solution for the field of educational assessment.
Section 02
Section 03
In traditional educational settings, English essay scoring has always been a time-consuming and highly subjective task. Teachers need to read a large number of student essays, which not only involves heavy workload but also leads to difficulties in ensuring the consistency and fairness of scoring results due to potential differences in standards among different raters.
With the rapid development of Natural Language Processing (NLP) technology, using machine learning to implement Automated Essay Scoring (AES) has become a feasible path to solve this problem. The AEESA project emerged in this context; it attempts to simulate the judgment process of human raters through algorithmic models, providing an efficient, objective, and scalable alternative for educational assessment.
Section 04
The AEESA project adopts a complete data processing and modeling workflow, covering the entire chain from raw text to final scoring:
Section 05
The first step of the system is to collect and clean essay data. This includes basic operations such as removing irrelevant characters, unifying formats, and handling missing values, laying a clean data foundation for subsequent feature extraction.
Section 06
AEESA integrates multiple NLP technologies at the feature engineering level:
Traditional NLP Methods:
Modern Embedding Technologies:
Advanced NLP Technologies:
Section 07
One highlight of the AEESA project is the systematic comparison of multiple supervised learning models, including:
| Model | Features | Applicable Scenarios |
|---|---|---|
| Ridge Regression | Introduces L2 regularization to prevent overfitting | Scenarios with high feature dimensions and medium sample sizes |
| Linear Regression | Simple and intuitive, strong interpretability | Scenarios where features and scores have a linear relationship |
| Decision Tree | Non-linear modeling, clear rules | Scenarios requiring explicit decision paths |
By comparing the performance of these models, the project can identify the most suitable algorithm solution for the essay scoring task.
Section 08
To ensure the reliability of the automatic scoring system, AEESA uses multiple evaluation metrics:
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