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Automated Essay Scoring Using Natural Language Processing

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Introduction

Automated Essay Scoring (AES) is a rapidly advancing field that utilizes natural language processing (NLP) and machine learning to evaluate and score essays. This project proposal aims to develop an AES system that can accurately assess essays based on various linguistic features, thereby reducing the time and effort required for manual grading.

Background

Recent research in NLP has significantly improved the capabilities of AES systems. These systems analyze essays based on factors such as coherence, grammar, vocabulary usage, and overall structure. The integration of neural networks, particularly Long Short-Term Memory (LSTM) networks, has enhanced the ability to capture complex patterns in text data.

Project Objective

The primary objective of this project is to create a robust AES system that leverages NLP techniques to provide accurate scores for essays. This system will aim to match or exceed human grading accuracy by incorporating advanced feature extraction methods and utilizing large-scale essay datasets.

Methodology

1. Data Collection and Preprocessing

  • Datasets: Utilize the Automated Essay Scoring Dataset provided by The Hewlett Foundation, available on Kaggle [5].
  • Preprocessing: Clean the dataset by removing unnecessary symbols, stop words, and punctuations. Extract features such as sentence count, word count, average word length, and grammatical structures.

2. Model Architecture

  • Feature Extraction: Implement Word2Vec for embedding essays into numerical vectors that capture semantic meaning.
  • Neural Network Model: Develop a model using LSTM layers to process sequential data from essays. Incorporate a dropout layer to prevent overfitting and a dense layer to output a single score.

3. Training and Evaluation

  • Training: Train the model using backpropagation with a suitable loss function.
  • Evaluation Metrics: Evaluate the model using metrics such as accuracy, precision, recall, and F1-score to ensure reliable performance.

Expected Outcomes

The proposed AES system is expected to provide scores that are consistent with human evaluations. By utilizing NLP techniques and neural networks, the system should effectively handle variations in writing styles and topics across different essays.

Conclusion

This project aims to advance the field of automated essay scoring by developing a state-of-the-art system capable of accurately evaluating student essays. The integration of NLP and machine learning techniques is anticipated to significantly improve grading efficiency and accuracy.

For further details on related research, please refer to the paper "Automated Essay Scoring Using Natural Language Processing" available at https://www.sciencedirect.com/science/article/pii/S1877050920307687.

Dataset link: The Hewlett Foundation Automated Essay Scoring Dataset.

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