不同认知结构被试的测验设计模式

Optimization of test design for examinees with different cognitive structures

正如不同的病症需要使用不同的医疗技术方法来诊断一样,不同的认知结构也需要设计对应的测验模式来进行诊断,从而保证测验具有高质量的诊断评估效果。但传统测验形式未考虑不同认知结构的针对性诊断测验需求,导致"千人一卷"在测验效率上有所不足;认知诊断计算机化自适应测验虽可针对不同认知结构的被试施测不同的项目,然而支持自适应过程的题库却没有针对不同认知结构被试设计对应的项目,导致题库使用效率较低。要解决上述问题的关键在于,探索如何针对不同认知结构设计相对应的测验模式。本研究采用Monte Carlo模拟,对六种属性层级关系下,不同认知结构的测验设计模式进行探讨。实验结果表明(1)同一属性层级关系下,不同认知结构的最佳测验设计模式不同;(2)依据不同认知结构的最佳测验设计模式构建的题库具有更高的使用效率。测验编制者可以根据实验结果针对不同认知结构优化对应的测验设计模式,并用于指导题库建设。

Doctors have to use different medical technologies to diagnose different kinds of illness effectively. Similarly, teachers have to use well designed tests to provide an accurate evaluation of students with different cognitive structures. To provide such an evaluation, we recommend to adopt the Cognitive Diagnostic Assessment （CDA）. CDA could measure specific cognitive structures and processing skills of students so as to provide information about their cognitive strengths and weaknesses. In general, the typical design procedure of a CDA test is as follow： firstly, identify the target attributes and their hierarchical relationships; secondly, design a Q matrix （which characterizes the design of test construct and content）; finally, construct test items. Within that designing framework, two forms of test are available： the traditional test and the computerized adaptive test （CAT）. The former is a kind of test that has a fixed-structure for all participants with different cognitive structures, the latter is tailored to each participant＇s cognitive structure. Researchers have not, however, considered the specific test design for different cognitive structures when using these two test forms. As a result, the traditional test requires more items to gain a precise evaluation of a group of participants with mixed cognitive structures, and a cognitive diagnosis computer adaptive test （CD-CAT） has low efficiency of the item bank usage due to the problems in assembling a particular item bank. The key to overcome these hurdles is to explore the appropriate design tailored for participants with different cognitive structures. As discussed above, a reasonable diagnosis test should be specific for the cognitive structure of target examinees so to perform classification precisely and efficiently. This is in line with CAT. In CAT, an ideal item bank serves as a cornerstone in achieving this purpose. In this regard, Reckase （2003, 2007 ＆ 2010） came up with an approach named p-optimality in designing an optimal item bank. Inspired by the p-optimality and working according to the characteristics of CDA, we proposed a method to design the test for different cognitive structures. We conducted a Monte Carlo simulation study to explore the different test design modes for different cognitive structures under six attribute hierarchical structures （Linear, Convergent, Divergent, Unstructured, Independent and Mixture）. The results show that： （1） the optimal test design modes for different cognitive structures are different under the same hierarchical structure in test length, initial exploration stage （Stage 0）, accurately estimation stage （Stage 1）; （2） the item bank for cognitive diagnosis computer adaptive test （CD-CAT） we built, according to the different cognitive structures＇ optimal test design modes, has a superior performance on item pool usage than other commonly used item banks no matter whether the fixed-length test or the variable-length test is used. We provide suggestions for item bank assembling basing on results from these experiments.