British Journal of Mathematical and Statistical Psychology

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

Early View

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.

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