catsim – Computerized Adaptive Testing Simulator

Quick start

catsim is a computerized adaptive testing simulator written in Python 3.4 (with modification in Python 3.8). It allow for the simulation of computerized adaptive tests, selecting different test initialization rules, item selection rules, proficiency reestimation methods and stopping criteria.

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, 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.

catsim allows users to simulate the application of a computerized adaptive test, given a sample of examinees, represented by their proficiency levels, and an item bank, represented by their parameters according to some Item Response Theory model.

Computerized Adaptive Testing Lifecycle


Important links


  • In the requirements.txt file.

Files structures


  • Randomly initializes the first estimate of an examee’s proficiency with RandomInitializer, the distribution is either normal or uniform.
  • Initializes fixed point with FixedPointInitializer.


  • Select item by Maximum information with MaxInfoSelector.
  • Select item in a linear order (not present item) with LinearSelector.
  • Randomly select item with RandomSelector.
  • Cluster-based Item selection with ClusterSelector.
  • Stratified selection with StratifiedSelector.
  • Alpha Stratified selection method with AStratifiedSelector.
  • Alpha Stratified selection and divide to block with AStratifiedBBlockingSelector.
  • Maximum information stratification (MIS) with MaxInfoStratificationSelector.
  • Maximum information stratification with block with MaxInfoBBlockingSelector.
  • 5-4-3-2-1 Selector with The54321Selector.
  • Randomsque Selection with RandomesqueSelector.
  • Interval integration selection with IntervalIntegrationSelector.


  • Maximum log-likelihood function with HillClimbingEstimator
  • Minimize negative log-likelihood function with DifferentialEvolutionEstimator
  • Ability estimation using BayesianEstimator


  • Maximum length item stop rule with MaxItemStopper.
  • Minimum error for ability estimation with MinErrorStopper.
  • Minimum confidence that ability estimated exceeds a threshold MinConfidenceStopper.


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