蔡艳

发布者：张岚发布时间：2025-03-06浏览次数：55



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  • 基本情况

蔡艳，教授，博士生导师，博士后合作导师，心理学博士，美国伊利诺伊州立大学香槟分校访问学者。入选江西省“双千计划”（哲学社会科学领军人才），江西省青年井冈学者，江西师范大学第二期青年英才培育计划，和第一期正大学者计划。

研究领域为心理统计与测量，主要研究方向为项目反应理论（Item Response Theory, IRT）、认知诊断（Cognitive Diagnosis, CD）和计算机化自适应测验（Computerized Adaptive Testing, CAT）等相关领域的理论与应用。

近年来，主持并承担了国家自然科学基金项目4项，6项省部级基金项目，在外文SSCI/SCI期刊和国内《心理学报》，《心理科学》等杂志发表的论文70篇。主编或参与编写学术专著3部，现在主要承担的本科教学课程为《心理统计学》，《心理学科数学基础I、II》，承担研究生课程《心理统计与测量前沿探究I》和博士生课程《现代测量理论专题研究》。



  • 主持并承担的国家级课题


  • 国家自然科学基金：机器学习与现代教育测量理论结合视角下的学习测评方法研究（编号：62467002； 立项时间：2024）

  • 国家自然科学基金：现代教育测量理论下学习测评的自适应测评关键技术研究（编号：62167004； 立项时间：2021）

  • 国家自然科学基金：基于多级评分的多维计算机化自适应测验技术及其应用研究（31760288；立项时间：2017）

  • 国家自然科学基金：基于IRT的认知诊断计量方法研究（31300876；立项时间：2013）  

  

  • 主要学术发表（SSCI/SCI外文期刊，部分）


  • Liu K., Zheng Y., Wang D. X., , Cai, Y. *, Shi Y. Y., Xi C. Q. & Tu, D. B (2024). A Framework for Detecting Both Main Effect and Interactive DIF in Multidimensional Forced-choice Assessments. Organizational Research Methods.https://doi.org/10.1177/10944281241244760 ( IF=8.9，IF-5y=12.7)

  • Peng, S.W., Man, K.W., Veldkamp, B., Cai, Y. *, & Tu, D. B.* (2023). A Mixture Model for Random Responding Behavior in Forced‐Choice Noncognitive  Assessment: Implication and Application in Organizational Research. Organizational Research Methods. https://doi.org/10.1177/10944281231181642.  

  • Tan, Q.R., Cai, Y.,*, & Tu, D. B. (2024). Online Calibration Method of Q-Matrix and Item Parameters for Polytomous responses in Cognitive Diagnostic Computerized Adaptive Testing. Behavior Research Methods.https://doi.org/10.3758/s13428-024-02392-6(IF-5y=7.7, JCR, Q1，中科院二区Top)

  • Wang, Q., Zheng, Y., Liu, K., Cai, Y.,* Peng, S. W., & Tu, D. B. (2023). Item selection methods in multidimensional computerized adaptive testing for forced choice items using Thurstonian IRT model. Behavior Research Methods. https://doi.org/10.3758/s13428-022-02037-6

  • Jinag, Y.X., Tan, Q.R., Wen, W., Cai, Y.,* & Tu, D. B. (2024). A Family of Cognitive Diagnosis Models for Continuous Bounded Responses. Journal of Educational and Behavioral Statistics. DOI: 10.3102/10769986241255970

  • Xi, C.Q., Guo, W.J., Tan, Q.R., Tu, D. B. & Cai, Y.*. (2024). A Mixture Response Model for Identifying Item Preknowledge. Journal of Educational and Behavioral Statistics. DOI: 10.3102/10769986241268460

  • We, J.F., Wang, Q., Dai, B.Y., Cai, Y.*, & Tu D. B. (2024). An Item Response Tree Model for Items with Multiple-Choice and Constructed-Response Parts. Journal of Educational Measurement, 58 (4),538–563. DOI: 10.1111/jedm.12414.

  • Liang, K. J., Tu, D. B., Cai, Y.,* (2023). Using Process Data to Improve Classification Accuracy of Cognitive Diagnosis Model. Multivariate Behavioral Research. https://doi.org/10.1080/00273171.2022.2157788

  • Wang, D. X., Ma, W. C., Cai, Y.,* & Tu, D. B. (2023). A general nonparametric classification method for multiple strategies in cognitive diagnostic assessment. Behavior Research Methods.https://doi.org/10.3758/s13428-023-02075-8

  • Xi, C. Q., Tu D. B & Cai, Y.* (2022). Dual-Objective Item Selection Methods in Computerized Adaptive Test Using the Higher-Order Cognitive Diagnostic Models. Applied Psychological Measurement, 46 (5), 422–438. https://doi.org/10.1177/01466216221089342

  • Xu, L. L., Wang, S. Y., Cai, Y*, & Tu D. B. (2022). The Automated Test Assembly and Routing Rule for Multistage Adaptive Testing with Multidimensional Item Response Theory. Journal of Educational Measurement, 58 (4),538–563.  https://doi.org/10.1111/jedm.12305.

  • She, M. F., Li, Y. L., Tu, D. B., Cai, Y. * (2021). Computerized Adaptive Testing for Sleep Disorders. European Journal of Health Psychology, 28 (3), 111-119. https://doi.org/10.1027/2512-8442/a000076.

  • Liu, S. Y., Tu, D. B, & Cai, Y. * (2020). Development and Validation of an Item Bank for Drug Dependence Measurement Using Computer Adaptive Testing. Substance Use & Misuse, 55(14), 2291-2304. https://doi.org/10.1080/10826084.2020.1801743.

  • Tu, D. B., Li, Y. L., Cai, Y. (2022). A new Perspective on Detecting Performance Decline: A Change-point Analysis based on Jensen-Shannon Divergence. Behavior Research Methods. https://doi.org/10.3758/s13428-021-01779-z

  • Luo, F., Wang, X. C., Cai, Y., Tu, D. B. (2022). Two Efficient Selection Methods for High-dimensional CD-CAT Utilizing Max-marginals Factor from MAP Query and Ensemble Learning Approach. British Journal of Mathematical and Statistical Psychology. https://doi.org/10.1111/bmsp.12288

  • Tan, Q., R., Cai, Y., Luo, F., Tu, D. B.. (2022). Development of a High-Accuracy and Effective Online Calibration Method in CD-CAT Based on Gini Index. Journal of Educational and Behavioral Statistics.

  • Peng, S. W., Cai, Y., Wang, D. X., Luo, F., & Tu, D. B.. (2021). A Generalized Diagnostic Classification Modeling Framework Integrating Differential Speediness: Advantages and Illustrations in Psychological and Educational Testing. Multivariate Behavioral Research. https://10.1080/00273171.2021.1928474

  • Wang, D. X., Cai, Y., & Tu, D. B.. (2020). Q-matrix Estimation Methods for Cognitive Diagnosis Models: Based on Partial Known Q-matrix. Multivariate Behavioral Research.  https://dor.org/10.1080/00273171.2020.1746901

  • Luo, H., Wang, D. X., Guo, Z. M., Cai, Y., & Tu D. B. (2022). Combining Cognitive Diagnostic Computerized Adaptive Testing with Multidimensional Item Response Theory. Applied Psychological Measurement, 46 (4), 288-302. https://doi.org/10.1177/01466216221084214

  • Jiang, Y. X., Yu, X.F., Cai, Y., & Tu D. B. (2022). A multidimensional IRT model for ability-item-based guessing: the development of a two-parameter logistic extension model. Communications in Statistics - Simulation and Computation. https://www.tandfonline.com/loi/lssp20

  • Tu, D. B., Hang Y. T., & Cai, Y. (2018). Item Selection Methods in Multidimensional Computerized Adaptive Testing with Polytomously-scored Items. Applied Psychological Measurement, 43 (8), 677–694. DOI: 10.1177/0146621618762748.

  • Tu, D. B., Wang S. Y., Cai, Y., Douglas J., & Chang, H. H. (2019). Cognitive Diagnostic Models with Attribute Hierarchies: Model Estimation with a Restricted Q matrix Design. Applied Psychological Measurement, 43 (3), 255-271. DOI: 10.1177/0146621618765721.

  • Zhang. L. F., Lin, J.K., Cai, Y. *, & Tu, D. B. (2021). Factor Structure and Psychometric Properties of the Purpose in Life Test (PIL) in a Sample of Chinese College Students: An Application of Confirmatory Factor Analysis and Item Response Theory. Current Psychology. https://doi.org/10.1007/s12144-021-02356-5

  • 蔡艳,涂冬波.（2015）.属性多级化认知诊断模型拓展及其Q矩阵设计.心理学报, 47 (10)， 1300-1308

  • 蔡艳,涂冬波,丁树良.（2013）.五大认知诊断模型的诊断正确率比较及其影响因素:基于分布形态、属性数及样本容量的比较.心理学报, 45 (11) , 1295-1304

  • 高旭亮, 汪大勋, 王芳,蔡艳, 涂冬波. （2019）. 基于分部评分模型思路的多级评分认知诊断模型开发 .心理学报, 51 (12): 1386-1397.  DOI: 10.3724/SP.J.1041.2019.01386.

  • 汪大勋, 高旭亮, 蔡艳,涂冬波. （2020）. 基于类别水平的多级计分认知诊断Q矩阵修正：相对拟合统计量视角.心理学报,  52 (1): 93-106.

  • 涂冬波,蔡艳.(2015). 基于属性多级化的认知诊断计算机化自适应测验设计与实现.心理学报, 11,1405-1414.

  • 涂冬波,蔡艳,戴海琦.（2013）.几种常用非补偿性认知诊断模型的比较与选用：基于属性层级关系的考量.心理学报, 45 (2) , 243-252.

  • 涂冬波,蔡艳,戴海琦,丁树良.（2012）.一种多策略的认知诊断方法：MSCD方法的开发.心理学报, 44（11）,1547-1533.

  • 涂冬波,蔡艳,戴海琦.（2012）.基于DINA模型的Q矩阵修正方法.心理学报, 44（4）,558-568.

  • 涂冬波,蔡艳,戴海琦,丁树良.（2011）.多维项目反理论：参数估计及其在心理测验中的应用.心理学报,43（11）,1329-1340.

  • 涂冬波,蔡艳,戴海琦,丁树良.（2010）.一种多级评分的认知诊断模型：P-DINA模型的开发. 心理学报,42（10）,1011-1020.

  • 谭青蓉, 蔡艳*,汪大勋,罗芬, 涂冬波.（2024）.CD-CAT中基于SCAD惩罚和EM视角的在线标定方法开发——基于G-DINA模型.心理学报. 56（5）：670-688

  • 罗芬,王晓庆,蔡艳,涂冬波.（2020）. 基于基尼指数的双目标 CD-CAT 选题策略.心理学报,  52 (12),1452-1465. https://dx.doi.org/10.3724/SP.J.1041.2020.001

  • 谭青蓉,汪大勋,罗芬,蔡艳, 涂冬波.（2021）.一种高效的CD-CAT在线标定新方法：基于熵的信息增益与EM视角.心理学报,   53 (11), 1286-1298. https://dx.doi.org/10.3724/SP.J.1041.2021.01286