A content-balanced adaptive testing algorithm for computer-based training systems

  • Sherman X. Huang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1086)


There are two main obstacles that make use of adaptive testing in computer-based training difficult. One is the requirement of conducting a large-scale empirical study for item calibration. The other is the difficulty of generating content-balanced tests mat meet the goal of the test administrators. In this research, we have developed a new adaptive testing algorithm, CBAT-2, to provide a solution for these problems and some other practical problems in adaptive testing. CBAT-2 generates questions based on the portion of the course curriculum that meets the goals of a test. It uses a simple machine learning procedure to determine the item parameter values.


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Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • Sherman X. Huang
    • 1
  1. 1.Alberta Research CouncilCalgaryCanada

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