[1]叶子玉,秦春影,杨建芹,等.结合先验信息的多属性诊断测验分类研究[J].江西师范大学学报(自然科学版),2023,(02):111-123.[doi:10.16357/j.cnki.issn1000-5862.2023.02.01]
　YE Ziyu,QIN Chunying,YANG Jianqin,et al.The Classification for High-Dimensional Cognitively Diagnostic Assessment Based on Prior Information[J].Journal of Jiangxi Normal University:Natural Science Edition,2023,(02):111-123.[doi:10.16357/j.cnki.issn1000-5862.2023.02.01]
点击复制

结合先验信息的多属性诊断测验分类研究()
分享到：

《江西师范大学学报》（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2023年02期
页码:
111-123
栏目:
出版日期:
2023-03-25

文章信息/Info

Title:
The Classification for High-Dimensional Cognitively Diagnostic Assessment Based on Prior Information
文章编号:
1000-5862(2023)02-0111-13
作者:
叶子玉1; 秦春影1; 2; 杨建芹2; 喻晓锋1; 3*; 付道轩1
(1.江西师范大学心理学院,江西 南昌 330022; 2.南昌师范学院数学与信息科学学院, 江西 南昌 330032; 3.江西师范大学江西省心理与认知科学重点实验室, 江西 南昌 330022)
Author(s):
YE Ziyu1; QIN Chunying1; 2; YANG Jianqin2; YU Xiaofeng1; 3*; FU Daoxuan1
(1.School of Psychology,Jiangxi Normal University,Nanchang Jiangxi 330022,China; 2.School of Mathematics and Information Science,Nanchang Normal University,Nanchang Jiangxi 330022,China; 3.Key Laboratory of Psychology and Cognition Science of Jiangxi Province,Jiangxi Normal University,Nanchang Jiangxi 330022,China)
关键词:
先验信息;  诊断测验;  多属性;  纵向诊断测评
Keywords:
prior information;  diagnostic assessment;  high-dimensional attributes;  longitudinal diagnostic assessment
分类号:
B 841
DOI:
10.16357/j.cnki.issn1000-5862.2023.02.01
文献标志码:
A
摘要:
在动态的学习过程中,随着学习的深入和知识属性的个数逐渐增加,学生的知识状态也会发生动态变化.在这样的应用场景下如何结合先验信息提高诊断测验的判准率具有较大的挑战.该文提出基于学生对已学习属性的掌握概率来预测包含新属性后的属性向量的先验信息.考虑2种实际的应用情境,并通过模拟和实证研究来评价该方法的表现.结果表明:在2种实际学习情境中,结合先验信息在多属性诊断测验中能起到提高判准率的作用,其中在基于个体先验信息时的表现更好.基于预测先验信息的方法也摆脱了在以往研究中不同学习阶段测验属性个数一致或数量的限制,使得分类精度有较大改进.实证数据分析进一步表明该方法具有较高的应用价值.
Abstract:
In a dynamic learning process, the student’s knowledge state changes dynamically as learning progresses and the number of knowledge attributes increases.In such a scenario,it is challenging to combine a priori information to improve the accuracy of diagnostic assessments.The probability of student mastery of learned attributes is used to predict the prior information of the attribute vector after the inclusion of new attributes.Two realistic application contexts are considered and the performance of the method is evaluated through simulations and empirical studies.The results show that combining a priori information in two real-world learning contexts can be useful in improving accuracy in high-dimensional attribute diagnostic tests, with better performance when based on individual a priori information.The method based on predictive a priori information also breaks away from the limitations of previous studies in terms of the number of attributes that are consistent across different learning stages of the test, resulting in a greater improvement in classification accuracy.The empirical data analysis further demonstrates the applied value of the methods used in the methodology.

参考文献/References:

[1] 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.
[2] HENSON R A, TEMPLIN J L, WILLSE J T. Defining a family of cognitive diagnosis models using log-linear models with latent variables [J].Psychometrika,2009,74(2):191-210.
[3] VON DAVIER M, LEE Y S. Handbook of diagnostic classification Models [M].New York:Springer,2019.
[4] KAYA Y, LEITE W L. Assessing change in latent skills across time with longitudinal cognitive diagnosis modeling: An evaluation of model performance [J].Educational and Psychological Measurement,2017,77(3):369388.
[5] 张颖, 边玉芳. 探索认知诊断研究的新思路:追踪研究中的诊断分析 [J]. 考试研究,2017,13(5):72-77.
[6] 詹沛达, 潘艳方,李菲茗. 面向“为学习而测评”的纵向认知诊断模型 [J].心理科学,2021,44(1):214-222.
[7] LEE S Y. Growth curve cognitive diagnosis models for longitudinal assessment [D].Berkeley:University of California,2017.
[8] KINGSBURY G, FREEMAN E, NESTERAK M. The potential of adaptive assessment [J].Educational Leadership,2014,71(6):18-19.
[9] HANNA G S. Effects of total and partial feedback in multiple-choice testing upon learning [J].The Journal of Educational Research,1976,69(5):202-205.
[10] KLUGER A N,DENISI A.The effects of feedback interventions on performance:a historical review,a meta-analysis,and a preliminary feedback intervention theory [J].Psychological Bulletin,1996,119(2):254-284.
[11] LI Feiming, COHEN A, BOTTGE B, et al. A latent transition analysis model for assessing change in cognitive skills [J].Educational and Psychological Measurement,2016,76(2):181-204.
[12] CHEN Yinghan,CULPEPPER S A,WANG Shiyu,et al.A hidden Markov model for learning trajectories in cognitive diagnosis with application to spatial rotation skills [J].Applied Psychological Measurement,2017,42(1):5-23.
[13] WANG Shiyu,YAN Yang,CULPEPPER S A,et al. Tracking skill acquisition with cognitive diagnosis models:a higher-order,hidden Markov model with covariates [J].Journal of Educational and Behavioral Statistics,2018,43(1):57-87.
[14] ZHANG Susu,WANG Shiyu. Modeling learner heterogeneity:a mixture learning model with responses and response times [J].Frontiers in Psychology,2018,9:2339.
[15] HUANG Hungyu.Multilevel cognitive diagnosis models for assessing changes in latent attributes [J].Journal of Educational Measurement,2017,54(4):440-480.
[16] ZHAN Peida, HONG Jiao,LIAO Dandan,et al.A longitudinal higher-order diagnostic classification model [J].Journal of Educational and Behavioral Statistics,2019,44(3):251-281.
[17] DE LA Torre J,DOUGLAS J A.Higher-order latent trait models for cognitive diagnosis [J].Psychometrika,2004,69(3):333-353.
[18] WU Hueymin. Online individualised tutor for improving mathematics learning:a cognitive diagnostic model approach [J].Educational Psychology,2018,39(10):1218-1232.
[19] GU Yuqi, XU Gong Jun.Learning attribute oatterns in high-dimensional structured latent attribute models [J].Journal of Machine Learning Research,2019,20(1):1-58.
[20] SUN Yan.Strategies for addressing high-dimensional cognitively diagnostic assessment problems [D].New Jersey: Rutgers University,2019.
[21] WANG Juntao,SHI Ningzhong,ZHANG Xue,et al.Sequential gibbs sampling algorithm for cognitive diagnosis models with many attributes [J].Multivariate Behavioral Research, 2021,57(5):1-37.
[22] HUEBNER A,Wang Chun.A note on comparing examinee classification methods for cognitive diagnosis models [J].Educational and Psychological Measurement,2011,71(2):407-419.
[23] HUEBNER A.An overview of recent developments in cognitive diagnostic computer adaptive assessments [J].Practical Assessment,2010,15(3):1-7.
[24] VON DAVIER M.A general diagnostic model applied to language testing data [J].British Journal Mathematical and Statistical Psychology,2008,61(2):287-307.
[25] WANG Chun.On interim cognitive diagnostic computerized adaptive testing in learning context [J].Applied Psychological Measurement,2021,45(4):235-252.
[26] HARTZ S M.A Bayesian framework for the unified model for assessing cognitive abilities:blending theory with practicality [D].Urbana Champign: University of Illinois, 2002.
[27] 戴步云,张敏强,焦璨,等.基于CD-CAT的多策略RRUM模型其选题方法开发 [J].心理学报,2015,47(12):1511-1519.
[28] CULPEPPER S A,Hudson A. An improved strategy for Bayesian estimation of the reduced reparameterized unified model [J].Applied psychological measurement,2018,42(2):99-115.
[29] KIM Y H.Diagnosing EAP writing ability using the reduced reparameterized unified model [J].Language Testing,2011,28(4):509-541.
[30] FENG Yuling,HABING B T,HUEBNER A.Parameter estimation of the reduced RUM using the EM Algorithm [J].Applied Psychological Measurement,2014,38(2):137-150.
[31] SUN Xiaojian,LIU Yanlou,XIN Tao,et al.The impact of item calibration error on variable-length cognitive diagnostic computerized adaptive testing [J].Frontiers in Psychology,2020,11:575141.
[32] HENSON R, DOUGLAS J.Test construction for cognitive diagnosis [J].Applied Psychological Measurement,2005,29(4):262-277.
[33] SINHARAY S.How often do subscores have added value? Results from operational and simulated data [J].Journal of Educational Measurement,2010,47(2):150-174.
[34] SUN Yan,DE LA TORRE J.Innovative psychometric modeling and methods:Improving attribute classification accuracy in high dimensional data:A four-step latent regression approach [M].Charlotte:Information Age,2020.
[35] MARIANT S,FOX J P,AVETISYAN M,et al.Testing for aberrant behavior in response time modeling [J].Journal of Educational and Behavioral Statistics,2014,39(6):426-451.

备注/Memo

备注/Memo:
收稿日期:2022-11-19
基金项目:教育部教育考试院“十四五”规划支撑专项课题(NEEA2021050),江西省社会科学基金(22JY16,21JY06),南昌市教育大数据智能技术重点实验室课题(2020-NCZDSY-012),江西省教育厅科技课题(GJJ2202013,GJJ2202018,GJJ212602,GJJ191691)和江西省教育科学“十四五”规划课题(21YB257,21YB027)资助项目.
通信作者:喻晓锋(1980—),男,安徽太湖人,副教授,博士,主要从事心理统计与测量方面的研究.E-mail:xyu6@jxnu.edu.cn
更新日期/Last Update: 2023-03-25