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On content-balanced adaptive testing

  • Sherman X. Huang
Learning Environments: Modelling and Design
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1108)

Abstract

This paper presents the results of an on-going research on tackling difficulties in using adaptive testing algorithms in computer-based learning environments. Among these difficulties, one is the requirement of conducting a large-scale empirical study for item calibration. Another is the difficulty of generating content-balanced tests that 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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