DIAGNOSING COGNITIVE ERRORS: STATISTICAL PATTERN CLASSIFICATION BASED ON ITEM RESPONSE THEORY
DIAGNOSING COGNITIVE ERRORS: STATISTICAL PATTERN CLASSIFICATION BASED ON ITEM RESPONSE THEORY
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诊断认知错误：基于项目反应理论的统计模式分类
DOI：
10.2333/bhmk.13.19_73
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发表时间：
1986
期刊：
影响因子：
--
通讯作者：
K. Tatsuoka
中科院分区：
文献类型：
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作者：
K. Tatsuoka
本文介绍了一个概率模型，该模型能够对一般问题解决领域中的认知错误进行诊断和分类。该模型不同于人工智能领域中常见的确定性策略，因为该模型利用项目反应理论来处理回答错误的变异性。为了说明模型，使用了38项分数加法测试的数据集，学生的回答被归类为34组错误概念。这些组由先前进行的错误分析的结果预先确定，并利用由典型形式逻辑方法编写的错误诊断程序进行验证。
This paper introduces a probabilistic model that is capable of diagnosing and classifying cognitive errors in a general problem-solving domain. The model is different from the usual deterministic strategies common in the area of artificial intelligence because item response theory is utilized to handle the variability of response errors. As for illustrating the model, the dataset obtained from a 38-item fraction addition test is used, and the students’ responses are classified into 34 groups of misconceptions. These groups are predetermined by the result of an error analysis previously done, and validated with the error diagnostic program written by a typical formal logic approach.