Customizable Bayesian Adaptive Testing with Python – The adaptivetesting Package

Jonas Engicht, R. Maximilian Bee and Tobias Koch

No d2xge_v1, OSF Preprints from Center for Open Science

Abstract: This paper introduces an open-source Python package for simplified, customizable Computerized Adaptive Testing (CAT) using Bayesian methods. It addresses the lack of sophisticated packages for CAT in the Python programming language. Moreover, it bridges the gap between the construction and simulation of adaptive tests and their practical application by providing a dedicated API for integration with experiment software. Thereby, it eliminates the need of major code rewrites when transitioning from simulated to real-world adaptive testing. By leveraging Python’s object-oriented programming approach, such as abstract classes, protocols, and inheritance, the package allows for easy extension and customization of its functionality. For example, Bayesian estimators can be modified to incorporate custom priors. This paper outlines the relevance and practical use of the adaptivetesting package through a walkthrough example. The package is fully documented, and its source code is published on GitHub. It is also available on the Python Package Index (PyPi) thus it can easily be installed using Python’s package manager pip. Leveraging R’s reticulate package, adaptivetesting can also be accessed from within RStudio.

Date: 2025-08-06
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Persistent link: https://EconPapers.repec.org/RePEc:osf:osfxxx:d2xge_v1

DOI: 10.31219/osf.io/d2xge_v1

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