Automatic Essay Scoring

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Automatic Essay Scoring

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₹4,999.00

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Overview
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Detail Description

1. Abstract

Automatic Essay Scoring is an AI-based system that evaluates essays and assigns scores using Natural Language Processing (NLP) and machine learning techniques. It analyzes grammar, vocabulary, sentence structure, relevance, coherence, and essay length to estimate writing quality. The system helps automate essay evaluation, reduce manual grading effort, and provide consistent feedback to students and educators.

2. Objectives

Automatically evaluate essays and assign scores.
Analyze grammar, vocabulary, and sentence structure.
Assess essay coherence, relevance, and organization.
Reduce teachers' manual grading workload.
Provide feedback to help students improve their writing skills.

3. Existing System

Traditional essay evaluation is performed manually by teachers or examiners. This process can be time-consuming, especially when evaluating large numbers of essays, and scoring may vary between evaluators.

Disadvantages

Requires considerable time and effort.
Difficult to evaluate large volumes of essays quickly.
Scoring may vary between evaluators.
Immediate feedback is not always available.

4. Proposed System

The proposed system uses NLP and machine learning models to analyze submitted essays and predict scores based on predefined evaluation criteria. It processes the text, extracts relevant linguistic features, evaluates writing quality, and displays the estimated score with feedback. The system should be validated against human-graded essays and used as a decision-support tool rather than the sole basis for high-stakes assessment.

5. Implementation Procedure

Collect a dataset of essays with human-assigned scores.
Clean and preprocess the essay text.
Extract features such as word count, vocabulary, grammar, and sentence structure.
Convert text into numerical features using suitable NLP techniques.
Train a machine learning or deep learning model on the scored essays.
Evaluate the model using appropriate metrics.
Develop a user interface for essay submission.
Display the predicted score and writing feedback.
Test the system on essays not used during training.

6. Software Requirements

Programming Language: Python
Frontend: Streamlit or HTML, CSS, and JavaScript
NLP Libraries: NLTK, spaCy, or Transformers
Machine Learning: Scikit-learn
Deep Learning: PyTorch or TensorFlow (optional)
Data Processing: Pandas and NumPy
Development Environment: VS Code or Jupyter Notebook
Operating System: Windows, Linux, or macOS

7. Hardware Requirements

Processor: Intel Core i3 or equivalent and above
RAM: Minimum 4 GB; 8 GB recommended
Storage: At least 5 GB of free space
GPU: Optional, depending on the model used

8. Advantages of the Project

Speeds up essay evaluation.
Provides consistent scoring based on defined criteria.
Reduces repetitive grading tasks.
Offers faster feedback to students.
Supports large-scale educational assessments.
Helps identify areas where writing skills can improve.

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