%0 Journal Article
%T New model of IRT item parameter estimation based on neural networks<br>一种新的基于神经网络的IRT项目参数估计模型
%A WANG Cun-you
%A YU Jia-yuan
%A <br>汪存友
%A 余嘉元
%J 计算机应用
%D 2006
%I 
%X A new modeling method based on general regression neural networks(GRNN) of item parameter estimation within IRT(Item Response Theory) was discussed. The methods about how to construct the output pattern of neural networks, and especially the input pattern by using Monte Carlo method were described. Methods about how to improve the learning efficiency and generalization ability have been proposed. Simulation experiments denote that it is feasible to fit the nonlinear relationship of item parameters between CTT(Classical Test Theory) and IRT given any precision. Comparisons of this method to other methods have been done at last, which suggested it somewhat advantageous.
%K general regression neural networks
%K Item Response Theory(IRT)
%K parameter estimation
%K Monte Carlo method<br>广义回归神经网络
%K 项目反应理论
%K 参数估计
%K Monte
%K Carlo方法
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=831E194C147C78FAAFCC50BC7ADD1732&aid=BEAF09740E8DDA41&yid=37904DC365DD7266&vid=96C778EE049EE47D&iid=E158A972A605785F&sid=E1034A3BCFB43055&eid=5C16CF56EB56D002&journal_id=1001-9081&journal_name=计算机应用&referenced_num=4&reference_num=13