adaptivetesting 1.2.1

adaptivetesting is a Python package that can be used to simulate and evaluate custom CAT scenarios as well as implement them in real-world testing scenarios from a single codebase

adaptivetesting

An open-source Python package for simplified, customizable Computerized Adaptive Testing (CAT) using Bayesian methods.

Key Features

• Bayesian Methods: Built-in support for Bayesian ability estimation with customizable priors
• Flexible Architecture: Object-oriented design with abstract classes for easy extension
• Item Response Theory: Full support for 1PL, 2PL, 3PL, and 4PL models, GRM, GPCM
• Multiple Estimators:
  • Maximum Likelihood Estimation (MLE)
  • Bayesian Modal Estimation (BM)
  • Expected A Posteriori (EAP)
• Item Selection Strategies: Maximum information criterion
• Content Balancing: Maximum Priority Index, Weighted Penalty Model
• Exposure Control: Randomesque Item Selection, Maximum Priority Index
• Simulation Framework: Comprehensive tools for CAT simulation and evaluation
• Real-world Application: Direct transition from simulation to production testing
• Stopping Criteria: Support for standard error and test length criteria
• Data Management: Built-in support for CSV and pickle data formats

Installation

Install from PyPI using pip:

pip install adaptivetesting

For the latest development version:

pip install git+https://github.com/condecon/adaptivetesting

Documentation

You can find our documentation in the GitHub wiki.

Contributing

We welcome contributions! Please see our GitHub repository for:

• Issue tracking
• Feature requests
• Pull request guidelines
• Development setup

Research and Applications

This package is designed for researchers and practitioners in:

• Educational assessment
• Psychological testing
• Cognitive ability measurement
• Adaptive learning systems
• Psychometric research

The package facilitates the transition from research simulation to real-world testing applications without requiring major code modifications.

Citation

If you use this package for your academic work, please provide the following reference: Engicht, J., Bee, R. M., & Koch, T. (2025). Customizable Bayesian Adaptive Testing with Python – The adaptivetesting Package. Open Science Framework. https://doi.org/10.31219/osf.io/d2xge_v1

@online{engichtCustomizableBayesianAdaptive2025,
  title = {Customizable {{Bayesian Adaptive Testing}} with {{Python}} – {{The}} Adaptivetesting {{Package}}},
  author = {Engicht, Jonas and Bee, R. Maximilian and Koch, Tobias},
  date = {2025-08-06},
  eprinttype = {Open Science Framework},
  doi = {10.31219/osf.io/d2xge_v1},
  url = {https://osf.io/d2xge_v1},
  pubstate = {prepublished}
}

License

This project is licensed under the terms specified in the LICENSE file.