基于可达阵的补偿模型Q矩阵标定方法
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摘要
Q矩阵标定是认知诊断评估中研究的热点问题,Q矩阵的好坏决定了认知诊断评估的准确性.根据确定性输入噪声"与"门模型(DINA)中可达阵R与简化Q矩阵存在布尔"与"的关系,提出基于确定性输入噪声"或"门模型(DINO)的可达阵R与简化Q矩阵在列向量上存在布尔"或"的关系,并由此推导出基于可达阵的补偿模型Q矩阵标定方法.实验结果表明:当可达阵失误与猜测小于0.20且待标定项目参数小于0.25时,该方法所得Q矩阵元素返真率达到90%以上,且在可达阵失误与猜测参数均小于0.25时真实Q矩阵与估计Q矩阵之间的差异较小.
It is very important that the calibration method of Q-matrix in cognitive diagnosis,which directly determines the correctness of the classification of individual. The augment algorithm provides fact that any column of the reduced Q-matrix can be expressed by the columns of the reachability matrix under the logical OR operation. The purpose of this study is to propose a method for compensatory model' s Q-matrix specification based on the reachability matrix. Simulation results show that the performance of the new method is promising in terms of correct classification rates of examinees' attributes.
It is very important that the calibration method of Q-matrix in cognitive diagnosis,which directly determines the correctness of the classification of individual. The augment algorithm provides fact that any column of the reduced Q-matrix can be expressed by the columns of the reachability matrix under the logical OR operation. The purpose of this study is to propose a method for compensatory model' s Q-matrix specification based on the reachability matrix. Simulation results show that the performance of the new method is promising in terms of correct classification rates of examinees' attributes.
引文
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[7]陈平,辛涛.认知诊断计算机化自适应测验中在线标定方法的开发[J].心理学报,2011,43(6):710-724.
[8]汪文义,丁树良.题库结构对原始题在线属性标定准确性之影响研究[J].心理科学,2012,35(2):452-456.
[9]汪文义,丁树良,游晓锋.计算机化自适应诊断测验中原始题的属性标定[J].心理学报,2011,43(8):964-976.
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[11]Maris E.Estimating multiple classification latent class models[J].Psychometrika,1999,64(2):187-212.
[12]Templin J L,Henson R A.Measurement of psychological disorders using cognitive diagnosis models[J].Psychol Methods,2006,11(3):287-305.
[13]詹沛达,王立君,陈飞鹏.不同因素对认知诊断DINO模型诊断准确率的影响[J].考试研究,2013(4):60-67.
[14]杨淑群,蔡声镇,丁树良,等.求解简化Q矩阵的扩张算法[J].兰州大学学报:自然科学版,2008,44(3):87-91.
[15]丁树良,罗芬,汪文义,等.Q矩阵标定的一种简便方法[J].江西师范大学学报:自然科学版,2018,42(2):130-133.
[16]汪文义,宋丽红,丁树良.基于可达阵的一种Q矩阵标定方法[J].心理科学,2018,41(4):968-975.
[17]Chiu Chia Yi,Douglas J.A nonparametric approach to cognitive diagnosis by proximity to ideal response patterns[J].Journal of Classification,2013,30(2):225-250.
[2]涂冬波,蔡艳,戴海琦.基于DINA模型的Q矩阵修正方法[J].心理学报,2012,44(4):558-568.
[3]Decarlo L T.Recognizing uncertainty in the Q-matrix via a Bayesian extension of the DINA model[J].Applied Psychological Measurement,2012,36(6):447-468.
[4]喻晓锋,罗照盛,秦春影,等.基于作答数据的模型参数和Q矩阵联合估计[J].心理学报,2015,47(2):273-282.
[5]Liu Jingchen,Xu Gongjun,Ying Zhiliang.Data-driven learning of Q-matrix[J].Applied Psychological Measurement,2012,36(7):548.
[6]Chiu Chia Yi.Statistical refinement of the Q-matrix in cognitive diagnosis[J].Applied Psychological Measurement,2013,37(8):598-618.
[7]陈平,辛涛.认知诊断计算机化自适应测验中在线标定方法的开发[J].心理学报,2011,43(6):710-724.
[8]汪文义,丁树良.题库结构对原始题在线属性标定准确性之影响研究[J].心理科学,2012,35(2):452-456.
[9]汪文义,丁树良,游晓锋.计算机化自适应诊断测验中原始题的属性标定[J].心理学报,2011,43(8):964-976.
[10]Chen Ping,Xin Tao,Wang Chun,et al.Online calibration methods for the DINA model with independent attributes in CD-CAT[J].Psychometrika,2012,77(2):201-222.
[11]Maris E.Estimating multiple classification latent class models[J].Psychometrika,1999,64(2):187-212.
[12]Templin J L,Henson R A.Measurement of psychological disorders using cognitive diagnosis models[J].Psychol Methods,2006,11(3):287-305.
[13]詹沛达,王立君,陈飞鹏.不同因素对认知诊断DINO模型诊断准确率的影响[J].考试研究,2013(4):60-67.
[14]杨淑群,蔡声镇,丁树良,等.求解简化Q矩阵的扩张算法[J].兰州大学学报:自然科学版,2008,44(3):87-91.
[15]丁树良,罗芬,汪文义,等.Q矩阵标定的一种简便方法[J].江西师范大学学报:自然科学版,2018,42(2):130-133.
[16]汪文义,宋丽红,丁树良.基于可达阵的一种Q矩阵标定方法[J].心理科学,2018,41(4):968-975.
[17]Chiu Chia Yi,Douglas J.A nonparametric approach to cognitive diagnosis by proximity to ideal response patterns[J].Journal of Classification,2013,30(2):225-250.