[1]罗照盛,杭丹丹,秦春影,等.可以处理补偿作用的认知诊断模型:CDINA模型[J].江西师范大学学报(自然科学版),2020,(05):441-453.[doi:10.16357/j.cnki.issn1000-5862.2020.05.01]
 LUO Zhaosheng,HANG Dandan,QIN Chunying,et al.The Cognitive Diagnostic Model Which Can Deal with Compensation and Noncompensation Effects:CDINA[J].Journal of Jiangxi Normal University:Natural Science Edition,2020,(05):441-453.[doi:10.16357/j.cnki.issn1000-5862.2020.05.01]
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可以处理补偿作用的认知诊断模型:CDINA模型()
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《江西师范大学学报》(自然科学版)[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2020年05期
页码:
441-453
栏目:
心理教育与测量
出版日期:
2020-10-20

文章信息/Info

Title:
The Cognitive Diagnostic Model Which Can Deal with Compensation and Noncompensation Effects:CDINA
文章编号:
1000-5862(2020)05-0441-13
作者:
罗照盛1杭丹丹1秦春影2喻晓锋1
1.江西师范大学心理学院,江西 南昌 330027; 2.南昌师范学院数学与计算机系,江西 南昌 330032
Author(s):
LUO Zhaosheng1HANG Dandan1QIN Chunying2YU Xiaofeng1
1.School of Psychology,Jiangxi Normal University,Nanchang Jiangxi 330022,China; 2.Department of Mathematics and Computer Science,Nanchang Normal University,Nanchang Jiangxi 330032,China
关键词:
补偿作用 参数估计 判准率 EM算法
Keywords:
compensate parameter estimation match ratio EM algorithm
分类号:
B 841
DOI:
10.16357/j.cnki.issn1000-5862.2020.05.01
文献标志码:
A
摘要:
对DINA和DINO模型进行改进,构建了一个比DINA和DINO模型更“一般”的模型,称为CDINA模型.除了猜测和失误参数之外,在CDINA模型中每个项目还包含1个补偿参数.构建的模型不仅保留了DINA模型简单和易于解释的特点,而且模拟和实证研究结果表明:(i)CDINA模型可同时处理项目属性之间是连接、部分补偿或完全补偿的情况(可将DINA和DINO看成是CDINA的特例),且CDINA模型具有较高的参数估计精度和被试分类准确率;(ii)通过比较CDINA、DINA、DINO模型的分类判准率和相对拟合指标,发现CDINA不会比DINA和DINO的分类准确率更低,且在存在部分补偿的情况下CDINA模型更优于DINA和DINO模型.
Abstract:
The DINA and DINO models are improved to build a more “general” model that is called CDINA model.In addition to the guessing and slipping parameters,each item in the CDINA model contains a compensation parameter.The model built preserves the parsimonious and easy-to-explain characteristics of the DINA model,and the simulation and empirical results show that the CDINA model can handle the item with its attributes are conjunctive,partially compensated,or fully compensated.That is,CDINA can be regarded as a general case of DINA and DINO,and it has relatively high parameter estimation accuracy and classification accuracy.The classification accuracy of the CDINA model is evaluated by comparing the three models of interest based on the relative fit indicators,CDINA is found to be no less accurate than DINA and DINO classifications.In the case of partial compensation,CDINA outperforms the DINA and DINO models.

参考文献/References:

[1] Yu Xiaofeng,Cheng Ying.Data-driven Q-matrix validation using a residual-based statistic in cognitive diagnostic assessment[EB/OL].[2019-08-17].British Journal of Mathematical and Statistical Psychology,http://dx.doi.org/10.1111/bmsp.12191.
[2] Leighton J P,Gierl M J.Cognitive diagnostic assessment for education:theory and applications[M].Cambridge:Cambridge University Press,2007:19-60.
[3] Rupp A A,Templin J,Henson R.Diagnostic measurement:theory,methods and application[M].Guilford:Guilford Press,2010:10-28.
[4] 罗照盛.项目反应理论基础[M].北京:北京师范大学出版社,2012:134-139.
[5] 涂冬波,蔡艳,丁树良.认知诊断理论、方法与应用[M].北京:北京师范大学出版社,2012:15-47.
[6] Embretson S E,Yang Xiangdong.A multicomponent latent trait model for diagnosis[J].Psychometrika,2013,78(1):14-36.
[7] Tatsuoka K K.Rule space:an approach for dealing with misconceptions based on item response theory[J].Journal of Educational Measurement,1983,20(4):345-354.
[8] Tatsuoka K K.Cognitive assessment:an introduction to the rule space method[M].Abingdon:Taylor and Francis Group,2009:47-79.
[9] de la Torre J.DINA model and parameter estimation:a didactic[J].Journal of Educational and Behavioral Statistics,2009,34(1):115-130.
[10] Junker B W,Sijtsma K.Cognitive assessment models with few assumptions,and connections with nonparametric item response theory[J].Applied Psychological Measurement,2001,25(3):258-272.
[11] Wang Chun,Chang Huahua,Douglas J.Combining CAT with cognitive diagnosis:a weighted item selection approach[J].Behav Res,2012,44(1):95-109.
[12] Wang Chun.Mutual information item selection method in cognitive diagnostic computerized adaptive testing with short test length[J].Educational and Psychological Measurement,2013,73(6):1017-1035.
[13] Leighton J P,Gierl M J,Hunka S M.The attribute hierarchy method for cognitive assessment:a variation on Tatsuoka's rule-space approach[J].Journal of Educational Measurement,2004,41(3):205-237.
[14] Roussos L A,Templin J L,Henson R A.Skills diagnosis using IRT-based latent class models[J].Journal of Educational Measurement,2007,44(4):293-311.
[15] 涂冬波,蔡艳,戴海崎.几种常用非补偿型认知诊断模型的比较与选用:基于属性层级关系的考量[J].心理学报,2013,45(2):243-252.
[16] DiBello L V,Roussos L A,Stout W.Review of cognitively diagnostic assessment and a summary of psychometric models[M].North Holland:Elsevier,2007.
[17] Stout W.Skills diagnosis using IRT-based continuous latent trait models[J].Journal of Educational Measurement,2007,44(4):313-324.
[18] Xiang Rui.Nonlinear penalized estimation of true Q-Matrix in cognitive diagnostic models[D].Columbia:Columbia University,2013.
[19] Liu Hongyun,You Xiaofeng,Wang Wenyi,et al.Large-scale applications of cognitive diagnostic computerized adaptive testing in China[C].the Annual Meeting of National Council on Measurement in Education,Denver,CO,2010.
[20] Liu Hongyun,You Xiaofeng,Wang Wenyi,et al.The development of computerized adaptive testing with cognitive diagnosis for an english achievement test in China[J].Journal of Classification,2013,30(2):152-172.
[21] Templin J L,Henson R A.Measurement of psychological disorders using cognitive diagnosis models[J].Psychological Methods,2006,11(3):287-305.
[22] 罗兴南.DINA与DINO模式于国小自然科电脑化诊断之应用:以五年级“水溶液”单元为例[D].台中:亚州大学,2012.
[23] Stanovich K E.Toward an interactive-compensatory model of individual differences in the development of reading fluency[J].Reading Research Quarterly,1980,16(1):32-71.
[24] 丁树良,毛萌萌,汪文义,等.教育认知诊断测验与认知模型一致性的评估[J].心理学报,2012,44(11):1535-1546.
[25] Liu Jingchen,Xu Gongjun,Ying Zhiliang.Data-driven learning of Q-matrix[J].Applied Psychological Measurement,2012,36(7):548-564.
[26] Cui Ying,Leighton J P,Zheng Yinggan.Simulation studies for evaluating the performance of the two classification methods in the AHM[EB/OL].[2019-09-16].http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.385.8437.
[27] Huo Yan,de la Torre J.Estimating a cognitive diagnostic model for multiple strategies via the EM algorithm[J].Applied Psychological Measurement,2014,38(6):464-485.
[28] Chen Jinsong,de la Torre J,Zhang Zao.Relative and absolute fit evaluation in cognitive diagnosis modeling[J].Journal of Educational Measurement,2013,50(2):123-140.
[29] Tatsuoka K K.Toward an integration of item-response theory and cognitive error diagnosis[M]∥Frederiksen N,Glaser R,Lesgold A,et al.Diagnostic monitoring of skill and knowledge acquisition.Hillsdale,New Jersey:Erlbaum,1990:453-488.
[30] de la Torre J.An empirically based method of Q-matrix validation for the DINA model: development and applications[J].Journal of Educational Measurement,2008,45(4):343-362.
[31] de la Torre J.The generalized DINA model framework[J].Psychometrika,2011,76(2):179-199.
[32] de la Torre J,Douglas J A.Higher-order latent trait models for cognitive diagnosis[J].Psychometrika,2004,69(3):333-353.
[33] DeCarlo L T.On the analysis of fraction subtraction data:the DINA model,classification,latent class sizes,and the Q-matrix[J].Applied Psychological Measurement,2011,35(1):8-26.
[34] de la Torre J,Douglas J A.Model evaluation and multiple strategies in cognitive diagnosis:an analysis of fraction subtraction data[J].Psychometrika,2008,73(4):595-624.
[35] Maris E.Estimating multiple classification latent class models[J].Psychometrika,1999,64(2):187-212.
[36] 丁树良,汪文义,杨淑群.认知诊断测验蓝图的设计[J].心理科学,2011,34(2):258-265.
[37] 丁树良,杨淑群,汪文义.可达矩阵在认知诊断测验编制中的重要作用[J].江西师范大学学报:自然科学版,2010,34(5):490-494.

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备注/Memo

备注/Memo:
收稿日期:2019-09-06
基金项目:国家自然科学基金(31660279,31600909),教育部人文社会科学研究课题(17YJC190029),江西省教育厅科学技术研究课题(GJJ160309,GJJ191128,GJJ191691,GJJ181077,GJJ191129),江西省教育厅人文社科课题(XL1509),江西省社科规划课题(16JY11),江西省科技厅重点研发课题(20192BBEL50040),江西省教育科学“十三五”规划(20YB250),江西省高校人文社会科学青年课题(XL20202)和江西省高等学校教学改革研究课题(JXJG-19-2-13,JXJG-19-23-2)资助项目.
作者简介:罗照盛(1971-),男,江西万安人,教授,博士,博士生导师,主要从事心理统计与测量学的研究.E-mail:luozs@126.com
更新日期/Last Update: 2020-10-20