sglnbgrader 0.1.0

LLM-powered Jupyter notebook grading tool with nbgrader compatibility

sglnbgrader: LLM-Assisted Jupyter Notebook Grader

This project provides an automated grading system for Jupyter notebooks that uses Large Language Models (LLMs) to assess student answers against instructor-provided reference solutions. It's compatible with the nbgrader metadata format.

Features

• Grade notebooks with nbgrader metadata
• Compare student answers to reference solutions using LLMs
• Generate detailed feedback and add it directly to notebook cells
• Provide comprehensive scoring and analysis
• Support for both single notebook and batch grading
• Analyze consistency and fairness across multiple submissions
• Export grading results to JSON or HTML-enhanced notebooks
• Customizable LLM prompts and grading criteria

Installation

Prerequisites

• Python 3.12 or higher
• An OpenAI API key for access to GPT-4 models (or other models via LiteLLM)

Installation Options

Option 1: Install as a standalone tool with uv (recommended)

The fastest and easiest way to install is using uv tool install:

# Install directly as a standalone tool
uv tool install sglnbgrader

# This makes the command available globally without activating any environment

Option 2: Install from PyPI

# Using pip
pip install sglnbgrader

# Using uv pip
uv pip install sglnbgrader

Option 3: Install from source

1. Clone the repository:

   git clone https://github.com/yourusername/sglnbgrader.git
   cd sglnbgrader
   

2. Install the package:

   # Using pip
   pip install -e .
   
   # Using uv
   uv pip install -e .
   

API Key Setup

Set up your OpenAI API key as an environment variable:

# Linux/macOS
export OPENAI_API_KEY=your_api_key_here

# Windows
set OPENAI_API_KEY=your_api_key_here

# Or add to your .bashrc or .zshrc for persistence
echo 'export OPENAI_API_KEY=your_api_key_here' >> ~/.bashrc

Usage

Command-line Interface

The grader provides a command-line interface with two main commands:

Grade a Single Notebook

sglnbgrader single --answer path/to/answer_notebook.ipynb --student path/to/student_notebook.ipynb --output results.json --verbose

Or using the Python module:

python -m sglnbgrader single --answer path/to/answer_notebook.ipynb --student path/to/student_notebook.ipynb --output results.json --verbose

Options:

• --answer: Path to instructor's answer notebook (required)
• --student: Path to student notebook (required)
• --model: LLM model to use for grading (default: gpt-4.1-nano)
• --output: Path to save grading results as JSON (optional)
• --verbose, -v: Show detailed grading information

Grade Multiple Notebooks

sglnbgrader batch --answer path/to/answer_notebook.ipynb --submissions path/to/submissions_dir --output path/to/results_dir --verbose

Or using the Python module:

python -m sglnbgrader batch --answer path/to/answer_notebook.ipynb --submissions path/to/submissions_dir --output path/to/results_dir --verbose

Options:

• --answer: Path to instructor's answer notebook (required)
• --submissions: Directory containing student submissions (required)
• --model: LLM model to use for grading (default: gpt-4.1-nano)
• --output: Directory to save grading results (optional)
• --verbose, -v: Show detailed grading information

API Usage

from sglnbgrader import Grader

# Initialize the grader with the instructor's answer notebook
grader = Grader("path/to/answer_notebook.ipynb", model="gpt-4.1-nano")

# Grade a single student notebook
results = grader.grade_user_notebook("path/to/student_notebook.ipynb")

# Print the results
print(f"Total score: {results['total_score']}/{results['max_score']} ({results['percentage']}%)")

# Access individual question results
for result in results["results"]:
    print(f"Question {result['grade_id']}: {result['score']}/{result['max_score']}")
    print(f"Feedback: {result['feedback']}")
    
# Generate feedback in the notebook
feedback_notebook_path = grader.write_feedback_to_notebook(
    "path/to/student_notebook.ipynb", results
)
print(f"Feedback notebook created at: {feedback_notebook_path}")

# Compare multiple submissions
submission_paths = [
    "path/to/student1_notebook.ipynb",
    "path/to/student2_notebook.ipynb",
    "path/to/student3_notebook.ipynb",
]
comparison_results = grader.compare_student_submissions(submission_paths)

# Run benchmarks on the grading system
benchmark_results = grader.run_benchmarks(results, submission_paths)

Notebook Format Requirements

This grader works with notebooks that use the nbgrader metadata format:

• Cells that should be graded must have nbgrader metadata
• Required metadata fields: grade_id, grade: true, points

Example cell metadata:

{
  "metadata": {
    "nbgrader": {
      "grade": true,
      "grade_id": "question-1",
      "points": 10,
      "solution": true
    }
  }
}

Advanced Features

Writing Feedback to Notebooks

The write_feedback_to_notebook method adds HTML-formatted feedback directly into the notebook cell outputs:

feedback_notebook_path = grader.write_feedback_to_notebook(
    "path/to/student_notebook.ipynb", results
)

This creates a new notebook with:

• HTML feedback boxes in each graded cell
• A summary cell at the end with total score and breakdown
• Preserves all original content

Comparing Student Submissions

The compare_student_submissions method analyzes results across multiple submissions:

comparison_results = grader.compare_student_submissions([
    "path/to/student1_notebook.ipynb",
    "path/to/student2_notebook.ipynb",
])

This provides:

• Statistics for each question (mean, median, standard deviation)
• Overall class performance metrics
• Consistency measures between different submissions

Benchmarking Grading Quality

The run_benchmarks method validates the grading system's consistency and fairness:

benchmark_results = grader.run_benchmarks(reference_results, submission_paths)

This analyzes:

• Consistency relative to reference results
• Fairness of scoring across different questions
• Performance metrics for the grading system

Configuration

You can customize the LLM model and prompt by extending the Grader class or modifying the prompt property:

class CustomGrader(Grader):
    @property
    def prompt(self):
        return """
        Your custom prompt template here.
        Question: {question}
        Reference Answer: {reference_answer}
        Student Answer: {student_answer}
        Points: {points}
        """

Development

Testing

Run the tests using pytest:

# Using pytest directly
pytest

# Using uv
uv run pytest

Project Structure

• sglnbgrader/ - Main package
  • __init__.py - Package exports
  • grader.py - Core Grader class implementation
  • cli.py - Command-line interface
  • __main__.py - Entry point for running as module
• tests/ - Test suite

License

MIT

Acknowledgements

• Uses nbgrader metadata format for identifying graded cells
• Powered by OpenAI and other LLM providers through LiteLLM
• CLI built with Typer and Rich for beautiful console output