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https://github.com/juliapsychometricsbazaar/computeradaptivetesting.jl

Modular/extensible Julia library for Computer Adaptive Tests
https://github.com/juliapsychometricsbazaar/computeradaptivetesting.jl

computerized-adaptive-testing educational-technology irt item-response-theory julia

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Modular/extensible Julia library for Computer Adaptive Tests

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README 

        
# ComputerAdaptiveTesting



[![Stable](https://img.shields.io/badge/docs-stable-blue.svg)](https://frankier.github.io/ComputerAdaptiveTesting.jl/stable)

[![Dev](https://img.shields.io/badge/docs-dev-blue.svg)](https://frankier.github.io/ComputerAdaptiveTesting.jl/dev)

[![Build Status](https://github.com/frankier/ComputerAdaptiveTesting.jl/actions/workflows/CI.yml/badge.svg?branch=main)](https://github.com/frankier/ComputerAdaptiveTesting.jl/actions/workflows/CI.yml?query=branch%3Amain)



## What is it?



In Computer Adaptive Testing (CAT), examinees are given tailored tests which at

each stage present questions chosen to estimate their ability accurately, based

on a provisional estimate. For a brief introduction, see [the Wikipedia

page](https://en.wikipedia.org/wiki/Computerized_adaptive_testing).



This package gives implementations of well-known approaches to CAT in Julia,

which are fast enough to support interactive use when scaling to large item

banks. It also provides flexible scaffolding to support new approaches to CAT

as well as non-standard item banks and difficulty-ability scales.



For a more in-depth introduction to CATs, I recommend the following article

(which outlines the basic definitions before introducing and R package mirtCAT)

and book (which contains various topical chapters detailing different aspects

of CATs).



[Chalmers, R. P. (2016).

*Generating adaptive and non-adaptive test interfaces for multidimensional item

response theory applications.*

Journal of Statistical Software, 71, 1-38.](https://doi.org/10.18637/JSS.V071.I05)



[van der Linden, W. J. & Glas C. A. W. (Eds.) (2010).

*Elements of adaptive testing.*

Statistics for Social and Behavioral Sciences.](https://doi.org/10.1007/978-0-387-85461-8)



## Installation



The package is available through `Pkg`. Install like so:



```

julia> using Pkg

julia> Pkg.add("ComputerAdaptiveTesting")

```



For the current development version (e.g. before filing an issue), install like so:



```

julia> using Pkg

julia> Pkg.add(PackageSpec(url = "https://github.com/frankier/ComputerAdaptiveTesting.jl.git"))

```



## How does this package differ from the alternatives?



The main (open source software library) alternatives are

[catR](https://cran.r-project.org/web/packages/catR/index.html) and

[mirtCAT](https://cran.r-project.org/web/packages/mirtCAT/index.html) in R and

[catsim](https://github.com/douglasrizzo/catsim) in Python.



Of these, mirtCAT is the most complete. At the moment, mirtCAT is more complete

than ComputerAdaptiveTesting.jl. However, this package is already beginning to

have some advantages in terms of flexibility:



 * Flexibility in allowing the lowest level "building blocks" of the algorithm,

   namely optimization and integration algorithms to be freely configured and

   replaced, allowing various levels of speed/accuracy trade off to be reached.

 * Flexibility in allowing a wide variety of item banks to be used. The

   architecture supports (TODO: implement) for example, item banks with

   parameter estimation uncertainties (e.g. from MCMC estimation) and item

   banks with item banks based on hierarchical modelling.



There are two long term goals for the project. The first is to allow and

provide fast implementations of otherwise computationally heavy scenarios and

techniques such as those with large item banks, high dimensionality or many-ply

lookaheads. The second is composability with complimentary Julia packages to

allow for item banks based a wide variety of ways of mixing and matching

model-based and machine learning -based techniques to utilised for CATs and

evaluated in a CAT setting.



## What next?



You can [read the

documentation](https://juliapsychometricsbazaar.github.io/ComputerAdaptiveTesting.jl/dev/) which

also contains a number of examples.



## Repo organisation



 * `/docs/examples`, Example code

 * `/docs`, Documentation source code, build with Documenter.jl

 * `/src`, Source code for the main ComputerAdaptiveTesting.jl package