Incorporating Student Covariates in Cognitive Diagnosis Models
Incorporating Student Covariates in Cognitive Diagnosis Models
复制标题
将学生协变量纳入认知诊断模型
DOI：
10.1007/s00357-013-9130-y
复制
发表时间：
2013
影响因子：
2
通讯作者：
Rebecca Nugent
中科院分区：
文献类型：
--
作者：
E. Ayers;S. Rabe;Rebecca Nugent
在教育测量方面，开发了认知诊断模型，以评估执行任务所需的特定技能。技能知识的特征是存在或不存在，并由二进制指示符的矢量或技能集简档表示。在确定每个评估项目需要哪些技能之后，为项目响应和技能集配置文件之间的关系指定了一个模型。认知诊断模型通常用于诊断，即将学生分类为不同的技能集。一般来说，认知诊断模型不会利用学生的协变量信息。然而，在教育研究中，调查学生协变量，如性别、社会经济地位或教育干预对技能知识掌握的影响是重要的，协变量信息可能会改善学生对技能集概况的分类。通过对潜在技能知识指标与协变量之间关系的建模，扩展了一种常见的认知诊断模型DINA模型。技能掌握的概率被建模为Logistic回归模型，可能带有学生水平的随机截获，给出了具有潜在回归的高阶DINA模型。仿真表明，参数恢复对这些模型是有利的，包含协变量可以提高技能诊断。当将我们的方法应用于在线教师的数据时，我们获得了合理和可解释的参数估计，从而可以更详细地描述预测技能集概况不同的学生群体。
In educational measurement, cognitive diagnosis models have been developed to allow assessment of specific skills that are needed to perform tasks. Skill knowledge is characterized as present or absent and represented by a vector of binary indicators, or the skill set profile. After determining which skills are needed for each assessment item, a model is specified for the relationship between item responses and skill set profiles. Cognitive diagnosis models are often used for diagnosis, that is, for classifying students into the different skill set profiles. Generally, cognitive diagnosis models do not exploit student covariate information. However, investigating the effects of student covariates, such as gender, SES, or educational interventions, on skill knowledge mastery is important in education research, and covariate information may improve classification of students to skill set profiles. We extend a common cognitive diagnosis model, the DINA model, by modeling the relationship between the latent skill knowledge indicators and covariates. The probability of skill mastery is modeled as a logistic regression model, possibly with a student-level random intercept, giving a higher-order DINA model with a latent regression. Simulations show that parameter recovery is good for these models and that inclusion of covariates can improve skill diagnosis. When applying our methods to data from an online tutor, we obtain reasonable and interpretable parameter estimates that allow more detailed characterization of groups of students who differ in their predicted skill set profiles.