Bayesian Curriculum Generator: Initial Release for Academic Publication

Release Version 1.0.0

This release of Bayesian Curriculum Generator marks a significant milestone, coinciding with our manuscript currently under peer review. It encapsulates the full suite of code and datasets underpinning the research presented, aimed at fostering transparency, reproducibility, and engagement within the academic community.

Highlights of This Release:

• Complete Source Code: Includes all Python scripts and Jupyter notebooks demonstrating the Bayesian approach to curriculum generation as discussed in our manuscript.
• Datasets: Features sample datasets employed in our experiments, allowing for complete replication of our study's results.
• Documentation: Provides comprehensive documentation on setup, installation, and execution, along with detailed explanations of the methodologies implemented.

Getting Started:

Refer to the README.md file for detailed instructions on configuring and running BayesianCurriculumGenerator. The documentation is designed to assist users in navigating the codebase effortlessly.

Feedback and Contributions:

Your feedback and contributions are highly appreciated. For suggestions, issues, or contributions, please use the GitHub issues and pull requests features, or contact us directly for more substantial contributions or inquiries.