CAJ | 학술논문

认知诊断是新一代测量理论的核心,对形成性教学评估具有重要意义。项目认知属性标定是认知诊断中一项基础而重要的工作,现有的项目认知属性辅助标定方法的研究工作很少,并且在应用上存在诸多局限。课堂评估是认知诊断应用的理想场所,但课堂评估中项目的选取具有随意性,教师难以在短时间内准确标识项目认知属性。本研究首次提出采用粗糙集方法对项目认知属性进行标定,该方法无需太多被试和项目,亦无需已知项目参数,且能当场诊断出结果,适于采用纸笔测验的课堂评估。通过Monte Carlo模拟研究表明：采用粗糙集方法能迅速地对项目认知属性进行标定,并具有较高的标定准确率;而且,项目认知属性越少、或被试估计判准率越高、或失误率越小则项目认知属性标定的准确率越高。粗糙集方法的引入,对拓展认知诊断的应用范围,真正实现其辅助性教学功能,具有重要作用。
인지진단시신일대측량이론적핵심,대형성성교학평고구유중요의의。항목인지속성표정시인지진단중일항기출이중요적공작,현유적항목인지속성보조표정방법적연구공작흔소,병차재응용상존재제다국한。과당평고시인지진단응용적이상장소,단과당평고중항목적선취구유수의성,교사난이재단시간내준학표식항목인지속성。본연구수차제출채용조조집방법대항목인지속성진행표정,해방법무수태다피시화항목,역무수이지항목삼수,차능당장진단출결과,괄우채용지필측험적과당평고。통과Monte Carlo모의연구표명：채용조조집방법능신속지대항목인지속성진행표정,병구유교고적표정준학솔;이차,항목인지속성월소、혹피시고계판준솔월고、혹실오솔월소칙항목인지속성표정적준학솔월고。조조집방법적인입,대탁전인지진단적응용범위,진정실현기보조성교학공능,구유중요작용。
Item Cognitive Attribute Identification（ICAI） is the basis of Cognitive Diagnosis（CD）, which is designed to measure specific knowledge structures and processing skills in students. According to the published documents, there are two methods used in ICAI.The one is to indentify item attributes by some experts of relative domains. When there are many items, it will be a huge burgen for experts to identify their attributes in the items. Especially, for some items, it＇s difficult for experts to get a unified opinion about items＇ attributes. As an assistant to this one, the other method is to identify items＇ attributes by CD-CAT（Cognitive Diagnostic Computerized Adaptive Testing）. Using CD-CAT in ICAI is an obvious breakthrough, for that it is not necessary totally depentant on manual labour. But using CD-CAT in ICAI has some heavy limitation. For example, if the items＇ parameters such as difficulty, are unknown, big samples of subjects and items are necessary for CD-CAT to identify item attributes. The second limit of CD-CAT is that it is based on item pool, and the development of item pool is very expensive that the cost of one item is about $1000.Cognitive diagnosis is designed to provide information about students＇ cognitive strengths and weaknesses and to assist the teaching. So, the best place to use it is in classrooms. But cognitive diagnosis is just used in lager–scale examinations now for two reasons： First, most cognitive diagnosis models are based on probability models which need a large sample in estimating item parameters, and the using of these cognitive diagnosis models are also based on a large sample of subjects even the items＇ parameters have been estimated. Secondary, even though the method of CD-CAT can be used in a small–scale examination once the item parameters are known, CAT has been prohibited in many kinds of examinations for other reasons. So, it is very necessary to find a new method to indentify item attributes when item parameters are unknown, examinees are less and feedbacks are timely. In the current studies, we apply a new method – Rough Set Theory（RST） to ICAI. RST can solve the uncertainty in CD caused by the size of knowledge granularity. It doesn＇t require any priori knowledge. Through the knowledge reduction, RST induces decision or classification rules, and then classifies the object. At first, we verificate the application of RST in ICAI. Then, in Study One, we explore how the match ratio of subjects＇ knowledge states and the slippage in subjects＇ responses to items impact the match ratio of item attributes. The number of item attributes is a variable which impacts the accuracy of CD, so, we also examine how the number of cognitive contributes impact the match ratio of item attributes. The results show that：（1） In the absence of item parameters, the rough set theory of ICAI has fast diagnostic speed and good results even though the sample size is small. So RST can be applied to classroom assessment.（2） The lower examinee＇s PMR, the lower PMR of item attribute identification is. And the higher slippage in examinee＇s response, the lower item attribute identification＇s PMR is.（3） The more the number of item attributes, the lower item attribute identification＇s PMR is.（4） Both results are estimated by rough set software, and regardless of sample size and item number, the estimated speed is very fast（about 10 seconds）. It shows the advantage of RST in ICAI.