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题库结构对原始题在线属性标定准确性之影响研究

ISSN号：1671-6981
期刊名称：心理科学
时间：2012.3.20
页码：452-456
分类：TP311.13[自动化与计算机技术—计算机软件与理论;自动化与计算机技术—计算机科学与技术]
作者机构：[1]江西师范大学心理学院,南昌330022, [2]江西师范大学计算机信息工程学院,南昌330022
相关基金：国家自然科学基金（30860084,31160203,31100756）、国家教育科学规划项目（CCA110109）、教育部人文社科项目（09JJCXLX012,10YJCXLX049,11YJC190002）,江西省教育厅科技计划项目（GJJ11385,GJJ10238）、全国教育考试科研规划课题（2009JKS2009）、江西省研究生创新专项资金项目（YC10A039）和博士点基金（20113604110001）的资助.
相关项目：基于现代测量理论的认知诊断CAT研究

关键词：可达阵, 计算机化自适应诊断测验, 属性向量标定, MMLE, DINA, the reaehability matrix, CD-CAT, On-Line identification, MMLE, DINA

中文摘要：

目前已有研究证明可达阵在认知诊断测验编制中起重要作用，但迄今为止并没有引起普遍注意。本文主要讨论当题库缺少可达阵对应的某些项目类，对原始题的属性向量在线标定的准确性的影响。本文对含6个属性的独立型结构进行了模拟试验，结果显示：如果题库不充要，原始题的属性标定准确性受到影响，题库中非可达阵中项目对标定有一定的弥补作用。间接印证了可达阵在认知诊断题库起到非常重要的作用。

英文摘要：

Cognitive Diagnostic Assessment is based on the incidence Q-matrix （Tatsuoka, 2009）. The entries of Q-matrix indicate which skills and knowledge are involved in the solution of each item. In real situations, no matter whether the items have or have not been identified attributes before its construction, it will cost a lot of money, require more efforts to identify attributes through specialists according to the special procedure and yet can＇ t completely assume the correctness due to the subjectivity. On-line item attributes iden- tification as a new field and study of the impact of item bank hasn＇ t been found in the literature. So this study is concerned with the impact of item bank on on-line item attributes identification in cognitive diagnostic computerized adaptive testing （ CD-CAT）, especially when the item bank doesn＇ t include the whole reachability matrix. The study describes the impact of knowledge states＇ equivalent classes on the item attributes vectors ＇ equivalent classes. Some of those are called the discriminating item attribute vector when the item attribute vectors＇ equivalent classes only include one item attribute vector; the others are called indiscriminate item attribute vector. Moreover, the study introduces the Marginal Maximum Likelihood Estimation （MMLE） for on-line item attribute identification, which integrates the uncertainty of estimate knowledge states in the procedure of identification, to explore whether the accuracy of discriminate item attribute vectors is better than that of indiscriminating item attribute vectors, and whether the columns of reduced Q matrix except the columns of reachability matrix can provide a reasonable accuracy of attribute identification. In terms of six attributes under the unstructured condition, two simulation experiments were conducted using deterministic inputs, noisy ＂and＂ gate model （DINA）. The simulation results show that log odds ratios are almost all above zero. It indicates that the correct classification rate

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