Exam Sheets Evaluator

Overview

The Exam Sheets Evaluator is a Python-based system designed to automate the evaluation of student exam answers. It supports two types of questions:

• One-word answers (1 mark each)
• Short-answer questions (2 marks each)

The system uses natural language processing (NLP) techniques to assess student responses, providing both numeric scores and detailed feedback.

Features

Core Functionality

• Automated Grading: Evaluates student answers against correct answers using:
  • Cosine similarity for short answers
  • Levenshtein distance for one-word answers
  • Lemmatization for word normalization
• Question Types:
  • One-word questions (exact matching with spelling tolerance)
  • Short-answer questions (semantic similarity evaluation)

User Modes

1. Professor Mode:
   • Load/create question banks
   • Set evaluation thresholds
   • View question banks
2. Student Mode:
   • Take randomized exams
   • View results with detailed feedback
   • Track performance over time
   • Practice with question bank

Additional Features

• Performance analytics and progress tracking
• Weak area identification
• Practice mode with immediate feedback
• Exam history storage
• Visualization of performance trends

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/exam-sheets-evaluator.git
   cd exam-sheets-evaluator

2. Install required dependencies:

   pip install pandas numpy scikit-learn nltk matplotlib

3. Download NLTK data:

   import nltk
   nltk.download('punkt')
   nltk.download('stopwords')
   nltk.download('wordnet')

Usage

1. Run the system:

   python Exam_Sheets_Evaluator.ipynb

2. Choose between Professor or Student mode at startup.

Professor Mode Options:

• Load question bank from CSV
• Create sample question bank
• Adjust evaluation thresholds
• View current question bank

Student Mode Options:

• Take a new exam (randomized questions)
• View previous results
• Check performance analytics
• Practice with questions
• Review question bank

File Structure

• Exam_Sheets_Evaluator.ipynb: Main Jupyter notebook containing the system
• exam_results/: Directory where student results are stored (created automatically)
• Sample question bank CSV files can be created through the professor interface

Dependencies

• Python 3.9+
• pandas
• numpy
• scikit-learn
• nltk
• matplotlib

Customization

Professors can:

• Adjust the evaluation threshold (short_answer_threshold)
• Modify the scoring scale for short answers
• Create custom question banks in CSV format with columns:
  • Question
  • Type ("one-word" or "short-answer")
  • Correct Answer

License

This project is open-source and available under the MIT License.

Contributing

Contributions are welcome! Please fork the repository and submit a pull request with your improvements.