分级数据认知诊断评估的多元体模型

IF 1 Q2 SOCIAL SCIENCES, INTERDISCIPLINARY
Dongbo Tu, Chanjin Zheng, Yan Cai, Xuliang Gao, Daxun Wang
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引用次数: 10

摘要

遵循IRT中的差异模型(Thissen&Steinberg,1986),本文提出了一种新的基于确定性输入、噪声和门的分级/多模数据的认知诊断模型(Haertel,1989;Junker&Sijtsma,2001),称为分级数据的DINA模型(DINA-GD)。我们研究了所提出的模型的完全贝叶斯估计的性能。在模拟中,研究了DINA-GD模型的分类精度和项目回收率。结果表明,该模型具有可接受的考生正确属性分类率和项目参数恢复率。此外,还用一个真实的数据例子说明了这种新模型在分级数据或多面体评分项目中的应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Polytomous Model of Cognitive Diagnostic Assessment for Graded Data
Pursuing the line of the difference models in IRT (Thissen & Steinberg, 1986), this article proposed a new cognitive diagnostic model for graded/polytomous data based on the deterministic input, noisy, and gate (Haertel, 1989; Junker & Sijtsma, 2001), which is named the DINA model for graded data (DINA-GD). We investigated the performance of a full Bayesian estimation of the proposed model. In the simulation, the classification accuracy and item recovery for the DINA-GD model were investigated. The results indicated that the proposed model had acceptable examinees' correct attribute classification rate and item parameter recovery. In addition, a real-data example was used to illustrate the application of this new model with the graded data or polytomously scored items.
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来源期刊
International Journal of Testing
International Journal of Testing SOCIAL SCIENCES, INTERDISCIPLINARY-
CiteScore
3.60
自引率
11.80%
发文量
13
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