二元数据的两用模型:估计能力和误解

IF 1.4 4区 心理学 Q3 PSYCHOLOGY, APPLIED Journal of Educational Measurement Pub Date : 2024-01-04 DOI:10.1111/jedm.12383
Wenchao Ma, Miguel A. Sorrel, Xiaoming Zhai, Yuan Ge
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引用次数: 0

摘要

大多数现有的诊断模型都是为了检测学生是否掌握了一套感兴趣的技能而开发的,但很少有诊断模型侧重于识别学生存在哪些科学误解。本文开发了一种通用的两用模型,可同时估计学生的整体能力以及是否存在误解。本文开发了期望最大化算法来估计模型参数。本文进行了一项模拟研究,以评估在不同条件下参数的准确恢复程度。此外,还分析了科学教育中的一组真实数据,以考察所提模型在实践中的可行性。
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A Dual-Purpose Model for Binary Data: Estimating Ability and Misconceptions

Most existing diagnostic models are developed to detect whether students have mastered a set of skills of interest, but few have focused on identifying what scientific misconceptions students possess. This article developed a general dual-purpose model for simultaneously estimating students' overall ability and the presence and absence of misconceptions. The expectation-maximization algorithm was developed to estimate the model parameters. A simulation study was conducted to evaluate to what extent the parameters can be accurately recovered under varied conditions. A set of real data in science education was also analyzed to examine the viability of the proposed model in practice.

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来源期刊
CiteScore
2.30
自引率
7.70%
发文量
46
期刊介绍: The Journal of Educational Measurement (JEM) publishes original measurement research, provides reviews of measurement publications, and reports on innovative measurement applications. The topics addressed will interest those concerned with the practice of measurement in field settings, as well as be of interest to measurement theorists. In addition to presenting new contributions to measurement theory and practice, JEM also serves as a vehicle for improving educational measurement applications in a variety of settings.
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