摘要
多级计分认知诊断模型的开发对认知诊断的发展具有重要作用,但对于多级计分模型下的Q矩阵修正还有待研究。本研究尝试对多级计分认知诊断Q矩阵修正进行研究,并聚焦更具诊断价值的基于项目类别水平的Q矩阵修正。将相对拟合统计量应用于多级计分认知诊断Q矩阵修正,并与已有方法Stepwise方法(Ma&de la Torre,2019)进行比较。研究表明:BIC方法对多级计分认知诊断模型的Q矩阵修正具有较高的模式判准率和属性判准率,其对Q矩阵的恢复率也高于Stepwise方法,BIC方法修正后的Q矩阵与数据更加拟合;在复杂模型中,相对拟合指标BIC比AIC和-2LL表现更好,在实践中,使用者可以选择BIC法进行测验Q矩阵修正;Q矩阵修正效果受到被试人数的影响,增加被试人数可以提高Q矩阵修正的正确率。总之,本研究为多级计分认知诊断Q矩阵修正提供了重要的方法支持。
Cognitive diagnostic assessments(CDAs)can provide fine-grained diagnostic information about students’knowledge states,so as to help to teach in accordance with the students’aptitude.The development of cognitive diagnosis model for polytomous response data expands the application scope of cognitive diagnostic assessment.As the basis of CDAs,Q-matrix has aroused more and more attention for the subjective tendency in Q-matrix construction that is typically performed by domain experts.Due to the subjective process of Q-matrix construction,there inevitably have some misspecifications in the Q-matrix,if left unchecked,can result in a serious negative impact on CDAs.To avoid the subjective tendency from experts and to improve the correctness of the Q-matrix,several objective Q-matrix validation methods have been proposed.Many Q-matrix validation methods have been proposed in dichotomous CDMs,however,the research of the Q-matrix validation method under polytomous CDMs is stalling lacking.To address this concern,several relative fit statistics(i.e.,-2 LL,AIC,BIC)were applied to the Q-matrix validation for polytomous cognitive diagnosis model in this research.The process of Q-matrix validation is as follows:First,the reduced Q-matrix is represented byQr,which represents a set of potential q-vectors and contains2 K?1 possible q-vectors when attributes are independent.When validating the q-vector of the first category of item j,all possible q-vectors inQr can be used as the q-vector of the first category of item j,and the Q-matrix of remaining items remains intact.From this,the item parameters and the attribute patterns of students can be estimated,and the-2 LL,AIC,and BIC can be calculated accordingly.The q-vector with the largest likelihood(or smallest AIC/BIC)is regarded as the q-vector of the first category of item j.The q-vector of the next category of the item j can also be obtained in the same way.The algorithm stops when the validated Q-matrix is same as the previous Q-matrix,or every item has been reached.In ord
作者
汪大勋
高旭亮
蔡艳
涂冬波
WANG Daxun;GAO Xuliang;CAI Yan;TU Dongbo(School of Psychology,Jiangxi Normal University,Nanchang 330022,China;School of Psychology,Guizhou Normal University,Guiyang 550000,China)
出处
《心理学报》
CSSCI
CSCD
北大核心
2020年第1期93-106,共14页
Acta Psychologica Sinica
基金
国家自然科学基金(31660278,31760288,31960186)
江西省教育厅研究生创新基金(YC2018-B025)
江西师范大学研究生境内外访学项目的资助
