Look-ahead content balancing method in variable-length computerized classification testing

Research output: Contribution to journalArticlepeer-review


Content balancing is one of the most important issues in computerized classification testing. To adapt to variable-length forms, special treatments are needed to successfully control content constraints without knowledge of test length during the test. To this end, we propose the notions of ‘look-ahead’ and ‘step size’ to adaptively control content constraints in each item selection step. The step size gives a prediction of the number of items to be selected at the current stage, that is, how far we will look ahead. Two look-ahead content balancing (LA-CB) methods, one with a constant step size and another with an adaptive step size, are proposed as feasible solutions to balancing content areas in variable-length computerized classification testing. The proposed LA-CB methods are compared with conventional item selection methods in variable-length tests and are examined with different classification methods. Simulation results show that, integrated with heuristic item selection methods, the proposed LA-CB methods result in fewer constraint violations and can maintain higher classification accuracy. In addition, the LA-CB method with an adaptive step size outperforms that with a constant step size in content management. Furthermore, the LA-CB methods generate higher test efficiency while using the sequential probability ratio test classification method.

Original languageEnglish (US)
Pages (from-to)88-108
Number of pages21
JournalBritish Journal of Mathematical and Statistical Psychology
Issue number1
StatePublished - Feb 1 2020


  • computerized classification testing
  • content balancing
  • item response theory
  • look-ahead
  • variable length

ASJC Scopus subject areas

  • Arts and Humanities (miscellaneous)
  • Statistics and Probability
  • General Psychology


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