使用一维和多维项目反应模型映射数据建模和统计推理学习进程。

Journal of applied measurement Pub Date : 2017-01-01
Robert Schwartz, Elizabeth Ayers, Mark Wilson
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引用次数: 0

摘要

有不同的方法来构思和衡量学习进度。ADMSR项目使用的方法遵循伯克利评估和评估研究中心(BEAR)和BEAR评估系统概述的“四个构建模块”方法。该方法的最后一个构建块涉及到度量模型的应用。本文主要研究了单维度和多维项目反应理论(IRT)测量模型在ADMSR项目数据中的应用。一维IRT模型用于帮助构建开发和验证,以查看构建图所提出的开发理论是否得到工具管理结果的支持。多维IRT测量模型被用于检验七个构念在ADMSR学习进展中的关系。在应用多维模型时,在应用一种对齐七个维度的技术之后,将跨结构分析结构级别之间的特定链接。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mapping a Data Modeling and Statistical Reasoning Learning Progression using Unidimensional and Multidimensional Item Response Models.

There are different ways to conceive and measure learning progressions. The approach used by the ADMSR project followed the "four building blocks" approach outlined by the Berkeley Evaluation and Assessment Research (BEAR) Center and the BEAR Assessment System. The final building block of this approach involves the application of a measurement model. This paper focuses on the application of unidimensional and multidimensional item response theory (IRT) measurement models to the data from the ADMSR project. Unidimensional IRT models are applied to aid in construct development and validation to see if the proposed theory of development presented by the construct map is supported by the results from an administration of the instrument. Multidimensional IRT measurement models are applied to examine the relationships between the seven constructs in the ADMSR learning progression. When applying the multidimensional model, specific links between levels of the constructs are analyzed across constructs after the application of a technique to align the seven dimensions.

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