[1]章沪超,丁树良,戴勰,等.基于抽样原理的计算机化自适应测验选题策略[J].江西师范大学学报(自然科学版),2014,(02):119-123.
 ZHANG Hu-chao,DING Shu-liang,DAI Xie,et al.The New Item Selection Strategy for Computerized Adaptive Testing Based on Sampling Principle[J].,2014,(02):119-123.
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基于抽样原理的计算机化自适应测验选题策略()
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《江西师范大学学报》(自然科学版)[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2014年02期
页码:
119-123
栏目:
出版日期:
2014-04-30

文章信息/Info

Title:
The New Item Selection Strategy for Computerized Adaptive Testing Based on Sampling Principle
作者:
章沪超;丁树良;戴勰;关潮辉
江西师范大学计算机信息工程学院,江西南昌,330022
Author(s):
ZHANG Hu-chao;DING Shu-liang;DAI Xie;GUAN Chao-hui
关键词:
抽样原理计算机化自适应测验分布因子
Keywords:
sampling principlecomputerized adaptive testingdistribution factor
分类号:
B841.7;TP301.6
文献标志码:
A
摘要:
沿用曝光控制因子的同时,基于抽样原理,引入区分度分布因子,按区分度的分布情况来选取测验中的项目.以lna~N(0,1)为例,Monte Carlo模拟结果表明:该方法在估计精度、效率和安全性等指标上表现得比较优异.
Abstract:
The exposure-control factors has been followed,at the same time,a discrimination-distribution factor has been introduced.According to the distribution of discrimination,the new item selectisn srategy to balance item usage exposure rate has been proposed.Suppose that ln1)and according to Monte Carlo simulation,the results show that the new approach has more preferably performance comparing with other approaches on several assessment indexes.

参考文献/References:

[1] Weiss D J.New horizons in testing-latent trait test theory and computerized adaptive testing [M].New York:Academic Press,1983:237-254.
[2] 漆书青,戴海琦,丁树良.现代教育与心理测量学原理 [M].北京:高等教育出版社,2002.
[3] 汪文义,丁树良.2PLM下CAT选题策略比较 [J].考试研究,2009,5(3):60-70.
[4] 张华华,程莹.计算机化自适应测验(CAT)的发展和前景展望 [J].考试研究,2005,1(1):12-24.
[5] Chang Huahua,Ying Zhiliang.A global information approach to computerized adaptive testing [J].Applied psychological Measurement,1996,20(2):213-219.
[6] Chang Huahua,Ying Zhiliang.A-stratified multistage computerized adaptive testing [J].Applied Psychological Measurement,1999,23(3):211-222.
[7] 程小扬,丁树良,严深海,等.引入曝光因子的计算机化自适应测验选题策略 [J].心理学报,2011,43(2):203-212.
[8] 李萍,甘登文,丁树良.自动控制区分度作用的选题策略研究 [J].江西师范大学学报:自然科学版,2013,37(1):101-105.
[9] 程小扬,丁树良.子题库题量不平衡的按a分层选题策略 [J].江西师范大学学报:自然科学版,2011,35(1):5-9.
[10] 陈平,丁树良,林海菁,等.等级反应模型下计算机化自适应测验选题策略 [J].心理学报,2006,38(3):461-467
[11] 刘珍,丁树良,林海菁.基于GPCM的CAT选题策略比较 [J].心理学报,2008,40(5):618-625.
[12] 罗照盛,欧阳雪莲,漆书青,等.项目反应理论等级反应模型项目信息量 [J].心理学报,,2008,40(11):1212-1220.
[13] 游晓锋,丁树良,刘红云.计算机化自适应测验中原始题项目参数的估计 [J].心理学报,2010,42(7):813-820.

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备注/Memo

备注/Memo:
国家自然科学基金(30860084,31160203,31100756,31360237,31300876);国家社会科学基金(12BYY055,13BYY087);江西省教育厅科技计划(GJJ3207,GJJ13226,GJJ13227,GJJ13208,GJJ13209)
更新日期/Last Update: 1900-01-01