Skip to main content

Computerized Adaptive Testing Simulator

Project description

Logo


Unit tests Test Coverage Latest Version Requirements Status Digital Object Identifier

catsim is a Python package for computerized adaptive testing (CAT) simulations. It provides multiple methods for:

These methods can either be used in a standalone fashion [1] to power other software or be used with catsim to simulate the application of computerized adaptive tests [2], given a sample of examinees, represented by their proficiency levels, and an item bank, represented by their parameters according to some logistic Item Response Theory model.

What's a CAT

Computerized adaptive tests are educational evaluations, usually taken by examinees in a computer or some other digital means, in which the examinee's proficiency is evaluated after the response of each item. The new proficiency is then used to select a new item, closer to the examinee's real proficiency. This method of test application has several advantages compared to the traditional paper-and-pencil method or even linear tests applied electronically, since high-proficiency examinees are not required to answer all the easy items in a test, answering only the items that actually give some information regarding his or hers true knowledge of the subject at matter. A similar, but inverse effect happens for those examinees of low proficiency level.

More information is available in the docs and over at Wikipedia.

Installation

Install it using pip install catsim.

Basic Usage

NEW: there is now a Colab Notebook teaching the basics of catsim!

  1. Have an item matrix;
  2. Have a sample of examinee proficiencies, or a number of examinees to be generated;
  3. Create an initializer, an item selector, a proficiency estimator and a stopping criterion;
  4. Pass them to a simulator and start the simulation.
  5. Access the simulator's properties to get specifics of the results;
  6. Plot your results.
from catsim.initialization import RandomInitializer
from catsim.selection import MaxInfoSelector
from catsim.estimation import NumericalSearchEstimator
from catsim.stopping import MaxItemStopper
from catsim.simulation import Simulator
from catsim.cat import generate_item_bank
initializer = RandomInitializer()
selector = MaxInfoSelector()
estimator = NumericalSearchEstimator()
stopper = MaxItemStopper(20)
Simulator(generate_item_bank(100), 10).simulate(initializer, selector, estimator, stopper)

Dependencies

All dependencies are listed on setup.py and should be installed automatically.

To run the tests, you'll need to install the testing requirements pip install catsim[testing].

To generate the documentation, Sphinx and its dependencies are needed.

Compatibility

Since the beginning, catsim has only been compatible with Python 3.4 upwards.

Important links

Citing catsim

You can cite the package using the following bibtex entry:

@article{catsim,
    author = {{De Rizzo Meneghetti}, Douglas and Aquino Junior, Plinio Thomaz},
        title = "{Application and Simulation of Computerized Adaptive Tests Through the Package catsim}",
    journal = {arXiv e-prints},
    keywords = {Statistics - Applications},
        year = 2017,
        month = jul,
        eid = {arXiv:1707.03012},
        pages = {arXiv:1707.03012},
archivePrefix = {arXiv},
    eprint = {1707.03012},
primaryClass = {stat.AP}
}

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

catsim-0.17.0.tar.gz (46.0 kB view hashes)

Uploaded Source

Built Distribution

catsim-0.17.0-py3-none-any.whl (48.8 kB view hashes)

Uploaded Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page