The content balancing method for item selection in CAT

  • Authors:

  • Peng Lu;Dongdai Zhou;Xiao Cong;Wei Wang;Da Xu

  • Affiliations:

  • Ideal Institute of Information and Technology, Northeast Normal University, China and Engineering & Research Center of E-learning, China;Ideal Institute of Inf. and Techn., Northeast Normal Univ., China and School of Software, Northeast Normal Univ., China and Eng. & Research Center of E-learning, China and E-learning Lab. of J ...;Ideal Institute of Information and Technology, Northeast Normal University, China and Engineering & Research Center of E-learning, China;Ideal Institute of Information and Technology, Northeast Normal University, China and Engineering & Research Center of E-learning, China;Ideal Institute of Information and Technology, Northeast Normal University, China and Engineering & Research Center of E-learning, China

  • Venue:
  • Edutainment'10 Proceedings of the Entertainment for education, and 5th international conference on E-learning and games
  • Year:
  • 2010

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Abstract

Compared with traditional testing, Computerized Adaptive Testing owes incomparable advantages. Such as flexibility, reduce the test length and measurement accuracy. There are some components in CAT, the most one is the item selection algorithm. To perform adaptive test, the most frequently adopted method is based on the maximum information (MI) of items to select the examination questions, with the view to draw the most accurate estimation for tester's capacity. There exists, however, flaws of unbalanced item-exposure as well as unequalled usage of item pool in this method. In this paper, we propose a new item selection algorithm CBIS to solve those problems, and then compare our method with MI method by an experiments. The experiment results are promising.