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
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;
Conference_Titel :
Computer Science and Education (ICCSE), 2010 5th International Conference on
Conference_Location :
Hefei
Print_ISBN :
978-1-4244-6002-1
DOI :
10.1109/ICCSE.2010.5593443