Cognitive diagnosis models for estimation of misconceptions analyzing multiple-choice data.
Cognitive diagnosis models for estimation of misconceptions analyzing multiple-choice data.
复制标题
用于评估分析多项选择数据的误解的认知诊断模型。
DOI：
10.1007/s41237-019-00100-9
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
N.
中科院分区：
文献类型：
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作者：
Ozaki;K.;Sugawara;S. & Arai;N.
多项选择题中的错误选项往往是故意包括在内的，以便有误解的考生选择它们。确定一个被测试者是否具有误解对于教育目的是有用的。本文提出了两个统计模型，通过对未评分的多项选择题数据进行分析，来估计被试的错误概念。通过将多项选择数据转换为二进制数据，即得分数据（正确，不正确），Bug-DINO模型可以估计考生拥有的错误概念。然而，将多项选择数据转换为二进制数据会导致信息损失，因为考生选择了哪个错误选项对于考生的知识状态来说是重要的信息。这三个模型（两个开发的模型和Bug-DINO模型）进行了比较，在模拟研究中，开发的模型被应用到阅读技能测试数据。
Incorrect options for multiple-choice questions are often intentionally included so that they may be selected by an examinee who possesses a misconception. Determining whether an examinee possess a misconception is useful for educational purposes. In the present paper, two statistical models that can estimate examinees’ possession of misconceptions by analyzing multiple-choice data, which are unscored data were developed. By converting multiple-choice data to binary data, which are scored data (correct,incorrect), the Bug-DINO model can estimate examinees’ possession of misconceptions. However, converting multiple-choice data to binary data causes a loss in information, because which incorrect option an examinee chooses is important information for an examinee’s knowledge state. The three models (two developed models and the Bug-DINO model) are compared in a simulation study, and the developed models are applied to the Reading Skill Test data.