基于CAT的在线标定:设计与方法

doi:10.3724/SP.J.1042.2020.01970

摘要/Abstract

摘要：

项目增补是题库建设和维护的重要手段, 而标定新题参数是项目增补的重要内容。在线标定设计和在线标定方法分别研究新题的施测方式和参数估计方法, 是计算机化自适应测验(computerized adaptive testing, CAT)情景下项目增补的核心技术。重点厘清在线标定设计与在线标定方法的发展思路和脉络, 并对它们的特点、联系和表现进行介绍和评价。未来应基于其他信息指标进一步研究在线标定设计, 可基于联合估计和误差校正的思路探究在线标定方法, 应加强研究认知诊断CAT和多维CAT的在线标定技术, 深入开展项目增补方法的实证研究。

关键词: 计算机化自适应测验, 认知诊断理论, 项目增补, 在线标定设计, 在线标定方法

Abstract:

Item replenishment is essential for item bank development and maintenance, where new items’ parameter calibration plays a significant role. Two core techniques of item replenishment under the circumstances of computerized adaptive testing (CAT) are: 1) online calibration design; 2) online calibration method. The former investigates the administration way of new items, while the later explores parameter estimation methods. This paper aims to clarify the development ideas and contexts of online calibration design and online calibration method. Additionally, their characteristics, relations and performance were illustrated and evaluated in details. At the end, several future research directions were pointed out. It is important to further study online calibration design based on different information indicators and online calibration methods based on joint estimations and error corrections. Moreover, future study could explore the online calibration technique in cognitive diagnostic CAT (CD-CAT) and multidimensional CAT (MCAT), as well as the empirical applications of item replenishment.

Key words: computerized adaptive testing, cognitive diagnostic theory, item replenishment, online calibration design, online calibration method

中图分类号:

B841

张雪琴, 毛秀珍, 李佳. (2020). 基于CAT的在线标定:设计与方法. 心理科学进展 , 28(11), 1970-1978.

ZHANG Xueqin, MAO Xiuzhen, LI Jia. (2020). Online calibration based on computerized adaptive testing: Design and method. Advances in Psychological Science, 28(11), 1970-1978.

图/表 2

参考文献 38

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分类标准	方法	特点
项目视角:参数信息量	D-优化、序贯D-优化	自适应选取被试
项目视角:参数信息量	D-TP、D-VR、ED 和D-c方法	自适应选取项目
考生视角:能力与样本量	OIRPI、SI指标	自适应选取项目

分类标准	方法	特点
项目视角:参数信息量	D-优化、序贯D-优化	自适应选取被试
项目视角:参数信息量	D-TP、D-VR、ED 和D-c方法	自适应选取项目
考生视角:能力与样本量	OIRPI、SI指标	自适应选取项目

分类标准	方法	特点	适用情景
条件极大似然估计	MethodA、MethodB、FFMLE-A和ECSE-A	简单、易操作, 需要大样本	传统CAT/MCAT
	MLE-LBCI-A		传统CAT
	CD-MethodA、MLE		CD-CAT
MMLE/EM算法	OEM、MEM	计算复杂, 耗时, 不易收敛	传统CAT中二级和多级评分项目/MCAT
MMLE/EM算法	CD-OEM、CD-MEM、MMLE	计算复杂, 耗时, 不易收敛	CD-CAT
贝叶斯算法	贝叶斯版本:方法A, OEM和MEM	精度高、计算复杂, 耗时	传统CAT/MCAT
联合极大似然估计	JEA、SIE、SimIE、SIE-R、JEA-R、SIE-R-BIC、JEA-R-BIC RMSEA-N	联合估计Q矩阵和项目参数	CD-CAT

分类标准	方法	特点	适用情景
条件极大似然估计	MethodA、MethodB、FFMLE-A和ECSE-A	简单、易操作, 需要大样本	传统CAT/MCAT
	MLE-LBCI-A		传统CAT
	CD-MethodA、MLE		CD-CAT
MMLE/EM算法	OEM、MEM	计算复杂, 耗时, 不易收敛	传统CAT中二级和多级评分项目/MCAT
MMLE/EM算法	CD-OEM、CD-MEM、MMLE	计算复杂, 耗时, 不易收敛	CD-CAT
贝叶斯算法	贝叶斯版本:方法A, OEM和MEM	精度高、计算复杂, 耗时	传统CAT/MCAT
联合极大似然估计	JEA、SIE、SimIE、SIE-R、JEA-R、SIE-R-BIC、JEA-R-BIC RMSEA-N	联合估计Q矩阵和项目参数	CD-CAT