ISSN 0439-755X
CN 11-1911/B
主办：中国心理学会
　　　中国科学院心理研究所
出版：科学出版社

心理学报 ›› 2015, Vol. 47 ›› Issue (10): 1300-1308.doi: 10.3724/SP.J.1041.2015.01300

• 论文 • 上一篇    

属性多级化的认知诊断模型拓展及其Q矩阵设计

蔡艳;涂冬波   

  1. (江西师范大学心理学院, 江西省心理与认知科学重点实验室, 南昌 330022)
  • 收稿日期:2014-09-09 发布日期:2015-10-25 出版日期:2015-10-25
  • 通讯作者: 涂冬波, E-mail: tudongbo@aliyun.com
  • 基金资助:

    国家自然科学基金(31100756, 31300876, 31160203, 31360237), 江西省社会科学规划项目重点项目(13JY01), 江西省教育科学规划项目(12YB088, 13YB029), 高等院校博士点基金项目(20123604120001), 江西师范大学青年英才培育资助计划, 东北师范大学应用统计教育部重点实验室开放课题(130026509)资助。。

Extension of Cognitive Diagnosis Models Based on the Polytomous Attributes Framework and Their Q-matrices Designs

CAI Yan; TU Dongbo   

  1. (School of Psychology of Jiangxi Normal University, Lab of Psychology and Cognition Science of JiangXi, Nanchang 330022, China)
  • Received:2014-09-09 Online:2015-10-25 Published:2015-10-25
  • Contact: TU Dongbo, E-mail: tudongbo@aliyun.com

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摘要：

本研究在传统0-1属性的基础上, 拓展出可以处理属性多级化的认知诊断模型——PA-rRUM和PA-DINA模型。Monte Carlo模拟研究表明：拓展模型具有较高的属性诊断正确率和参数估计精度, 且参数估计的稳定性较强, 说明拓展模型基本可行, 可以用于实现多级化属性的认知诊断。这弥补了传统0-1化属性认知诊断模型的不足, 具有较好的发展和应用前景|同时本研究还探讨了拓展模型性能及属性多级化下测验Q矩阵的设计。总之, 本研究对于进一步拓展认知诊断在实践中的应用提供了重要的方法和技术支持。

关键词: 多级化属性, 认知诊断模型, rRUM, DINA

Abstract:

Based on the traditional cognitive diagnosis models (CMDs), this study developed two new cognitive diagnosis models, PA-rRUM and PA-DINA model respectively, to handle the polytomous attributes. Through Monte Carlo simulation, it indicated that:
The parameters in the models could be identified, and robustness of the parameter estimation is relatively strong. Furthermore, the correct match ratios and accuracies of parameter estimation are decent. All these findings verified that the models are feasible for ploytomous attribute cognitive diagnosis.
It also found that the precision of might be influenced by the sample size and the number of replications for the RP*matrix. The larger the sample size is, or the greater the number of replications is, the more precise they might be. The results suggested that the Q matrix should include the RP* matrix while the attribute is polytomous.

In conclusion, the models overcame the shortcomings stemmed from dichotomous attribute models, thus they might provide a richer diagnostic result and more flexible models.

Key words:  polytomous attributes, cognitive diagnosis, rRUM, DINA