DINA-BAG: A Bagging Algorithm for DINA Model Parameter Estimation in Small Samples

IF 1.9 3区心理学 Q2 EDUCATION & EDUCATIONAL RESEARCH

Journal of Educational and Behavioral Statistics Pub Date : 2023-08-07 DOI:10.3102/10769986231188442

D. Arthur, Hua-Hua Chang

引用次数: 0

Abstract

Cognitive diagnosis models (CDMs) are the assessment tools that provide valuable formative feedback about skill mastery at both the individual and population level. Recent work has explored the performance of CDMs with small sample sizes but has focused solely on the estimates of individual profiles. The current research focuses on obtaining accurate estimates of skill mastery at the population level. We introduce a novel algorithm (bagging algorithm for deterministic inputs noisy “and” gate) that is inspired by ensemble learning methods in the machine learning literature and produces more stable and accurate estimates of the population skill mastery profile distribution for small sample sizes. Using both simulated data and real data from the Examination for the Certificate of Proficiency in English, we demonstrate that the proposed method outperforms other methods on several metrics in a wide variety of scenarios.

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小样本条件下DINA模型参数估计的Bagging算法

认知诊断模型(CDMs)是一种评估工具，可以在个人和群体水平上提供关于技能掌握的有价值的形成性反馈。最近的工作已经在小样本量的情况下探索了cdm的性能，但是只关注于个体概况的估计。目前的研究重点是在人口水平上获得对技能掌握程度的准确估计。我们引入了一种新的算法(用于确定性输入噪声和门的bagging算法)，该算法受到机器学习文献中的集成学习方法的启发，并对小样本量的总体技能掌握概况分布产生更稳定和准确的估计。使用英语水平证书考试的模拟数据和真实数据，我们证明了所提出的方法在各种场景下的几个指标上优于其他方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Educational and Behavioral Statistics Multiple-

CiteScore

4.40

自引率

4.20%

发文量

期刊介绍： Journal of Educational and Behavioral Statistics, sponsored jointly by the American Educational Research Association and the American Statistical Association, publishes articles that are original and provide methods that are useful to those studying problems and issues in educational or behavioral research. Typical papers introduce new methods of analysis. Critical reviews of current practice, tutorial presentations of less well known methods, and novel applications of already-known methods are also of interest. Papers discussing statistical techniques without specific educational or behavioral interest or focusing on substantive results without developing new statistical methods or models or making novel use of existing methods have lower priority. Simulation studies, either to demonstrate properties of an existing method or to compare several existing methods (without providing a new method), also have low priority. The Journal of Educational and Behavioral Statistics provides an outlet for papers that are original and provide methods that are useful to those studying problems and issues in educational or behavioral research. Typical papers introduce new methods of analysis, provide properties of these methods, and an example of use in education or behavioral research. Critical reviews of current practice, tutorial presentations of less well known methods, and novel applications of already-known methods are also sometimes accepted. Papers discussing statistical techniques without specific educational or behavioral interest or focusing on substantive results without developing new statistical methods or models or making novel use of existing methods have lower priority. Simulation studies, either to demonstrate properties of an existing method or to compare several existing methods (without providing a new method), also have low priority.