置信标记建模的有序划分模型。

Journal of applied measurement Pub Date : 2017-01-01
Oliver Prosperi
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引用次数: 0

摘要

置信标记越来越多地应用于选择题测试中,但是当Rasch测量模型应用于数据时,只使用二进制数据,而丢弃了置信标记给出的信息。本研究展示了Wilson的有序划分模型(OPM)是Rasch模型族中的一员,它可以用来建模置信信息。结果是一个与二进制Rasch模型严格相关的模型,因为Rasch ICC被“分割”成一组曲线,每个曲线代表一个置信水平。新模型提供了一组项目参数,这些参数映射了与考生能力相关的每个置信度的概率。这项研究提供了一个强大的诊断工具来评估题目的难度、过度自信或滥用自信水平,以及一个问题特别棘手或产生了很多疑问的事实。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Ordered Partition Model for Confidence Marking Modeling.

Confidence marking is increasingly used in multiple choice testing situations, but when the Rasch measurement model is applied to the data, only the binary data is used, discarding the information given by the confidence marking. This study shows how Wilson's ordered partition model (OPM), a member of the Rasch family of models, can be used to model the confidence information. The result is a model which is in strict relation to the binary Rasch model, since the Rasch ICC's are "split" into a set of curves each representing a confidence level. The new model provides a set of item parameters that map the probability of being in each confidence level in relation to the test-taker's ability. The study provides a powerful diagnostic tool to assess item difficulty, overconfidence or misuse of confidence levels but also the fact that a question is particularly tricky or creates a lot of doubt.

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