• DocumentCode
    2448899
  • Title

    A brief review on Item Response Theory models-based parameter estimation methods

  • Author

    Wang, Hua ; Ma, Cuiqin ; Chen, Ningning

  • Author_Institution
    Inf. Eng. Inst., Capital Normal Univ., Beijing, China
  • fYear
    2010
  • fDate
    24-27 Aug. 2010
  • Firstpage
    19
  • Lastpage
    22
  • Abstract
    Item Response Theory (IRT) is a psychological and educational measurement theory which breaks the limitations of Classical Test Theory (CTT). The core issue of IRT application is parameter estimation. Taking the Logistic model as an example, this article introduces the basic models and parameter estimation methods of IRT, especially the IRT parameter estimation algorithms based on artificial intelligence. By analysis and comparison of various algorithms that are applied to estimate parameter, the major problems of IRT parameter estimation are formulated, and the future development prospect of IRT models is put forward.
  • Keywords
    artificial intelligence; parameter estimation; CTT; IRT; brief review; classical test theory; educational measurement theory; item response theory models; logistic model; parameter estimation methods; psychological measurement theory; Artificial neural networks; Computational modeling; Data models; Logistics; Mathematical model; Maximum likelihood estimation; Parameter estimation; Item Response Theory (IRT); artificial intelligence; parameter estimation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Science and Education (ICCSE), 2010 5th International Conference on
  • Conference_Location
    Hefei
  • Print_ISBN
    978-1-4244-6002-1
  • Type

    conf

  • DOI
    10.1109/ICCSE.2010.5593443
  • Filename
    5593443