扩展探索性诊断分类模型:推断协变量的影响

IF 1.5 3区 心理学 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS British Journal of Mathematical & Statistical Psychology Pub Date : 2023-01-05 DOI:10.1111/bmsp.12298
Hulya Duygu Yigit, Steven Andrew Culpepper
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引用次数: 0

摘要

诊断模型通过根据细粒度属性集合对学生知识概况进行分类,为设计形成性评估提供了一个统计框架。学生学习的环境和生态系统可能在技能掌握中发挥重要作用,因此开发将学生协变量纳入诊断模型的方法非常重要。包括协变量可以为研究人员和从业者提供评估新干预措施或理解背景知识在属性掌握中的作用的能力。现有的研究旨在将协变量包括在验证性诊断模型中,这也被称为限制潜在类别模型。我们提出了在探索性rlcm中加入协变量的新方法,共同推断潜在结构并评估协变量对绩效和技能掌握的作用。我们提出了一种新的贝叶斯公式,并报道了一种使用metropolis - in- gibbs算法近似模型参数后验分布的马尔可夫链蒙特卡罗算法。我们报告了关于我们新方法准确性的蒙特卡罗模拟证据,并从一个应用程序中提出了结果,该应用程序检查了学生背景知识在掌握概率数据集方面的作用。
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Extending exploratory diagnostic classification models: Inferring the effect of covariates

Diagnostic models provide a statistical framework for designing formative assessments by classifying student knowledge profiles according to a collection of fine-grained attributes. The context and ecosystem in which students learn may play an important role in skill mastery, and it is therefore important to develop methods for incorporating student covariates into diagnostic models. Including covariates may provide researchers and practitioners with the ability to evaluate novel interventions or understand the role of background knowledge in attribute mastery. Existing research is designed to include covariates in confirmatory diagnostic models, which are also known as restricted latent class models. We propose new methods for including covariates in exploratory RLCMs that jointly infer the latent structure and evaluate the role of covariates on performance and skill mastery. We present a novel Bayesian formulation and report a Markov chain Monte Carlo algorithm using a Metropolis-within-Gibbs algorithm for approximating the model parameter posterior distribution. We report Monte Carlo simulation evidence regarding the accuracy of our new methods and present results from an application that examines the role of student background knowledge on the mastery of a probability data set.

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来源期刊
CiteScore
5.00
自引率
3.80%
发文量
34
审稿时长
>12 weeks
期刊介绍: The British Journal of Mathematical and Statistical Psychology publishes articles relating to areas of psychology which have a greater mathematical or statistical aspect of their argument than is usually acceptable to other journals including: • mathematical psychology • statistics • psychometrics • decision making • psychophysics • classification • relevant areas of mathematics, computing and computer software These include articles that address substantitive psychological issues or that develop and extend techniques useful to psychologists. New models for psychological processes, new approaches to existing data, critiques of existing models and improved algorithms for estimating the parameters of a model are examples of articles which may be favoured.
期刊最新文献
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