A new person-fit method based on machine learning in CDM in education

IF 1.5 3区 心理学 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS British Journal of Mathematical & Statistical Psychology Pub Date : 2022-03-27 DOI:10.1111/bmsp.12270
Zhemin Zhu, David Arthur, Hua-Hua Chang
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引用次数: 1

Abstract

Cognitive diagnosis models have become popular in educational assessment and are used to provide more individualized feedback about a student's specific strengths and weaknesses than traditional total scores. However, if the testing data are contaminated by certain biases or aberrant response patterns, such predictions may not be accurate. The current research objective is to develop a new person-fit method that is based on machine learning and improves the functionality of existing person-fit methods. Various simulations were designed under three aberrant conditions: cheating, sleeping and random guessing. Simulation results showed that the new method was more powerful and effective than previous methods, especially for short-length tests.

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教育CDM中基于机器学习的人-拟合新方法
认知诊断模型在教育评估中已经变得很流行,与传统的总分相比,它可以提供关于学生具体优缺点的更个性化的反馈。然而,如果测试数据受到某些偏差或异常反应模式的污染,这样的预测可能不准确。目前的研究目标是开发一种新的基于机器学习的人-拟合方法,并改进现有的人-拟合方法的功能。在三种异常情况下设计了各种模拟:作弊、睡觉和随机猜测。仿真结果表明,该方法比以往的方法更有效,特别是对于短长度的测试。
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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.
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