Emerging Research-Empirical: Development and Application of a Multilevel Multiple-Group CDM to Compare Cognitive Attribute Distributions based on Eighth Grade TIMSS Mathematics

新兴研究实证：基于八年级 TIMSS 数学比较认知属性分布的多级多组 CDM 的开发和应用

• 批准号: 1008607
• 负责人: Matthew Johnson
• 金额: $ 100.35万
• 依托单位: Teachers College, Columbia University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-08-15 至 2015-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1008607&HistoricalAwards=false
• 关键词: Emerging; Research; Empirical; Development; Application

The proposed research project will develop new extensions of cognitive diagnostic models (CDM) to understand what math skills U.S. students possess or lack, and compare the distributions of skills across countries and within countries across years. The research team will examine the relationship between these skills and core background variables (e.g., gender). The research focuses on the TIMSS eighth grade mathematics assessments from 1999, 2003, and 2007. To handle the complex sampling design used in TIMSS, the PIs will develop the multi-group CDM to account for the clustering of students within schools. Whereas the TIMSS framework identifies only a single skill for each item (e.g., ability to calculate area) the proposed research will identify multiple skills for each item. This qualitative analysis is required as part of the CDM, but is also important to better understand the TIMSS assessments.The statistical methods developed as part of this research will allow a thorough analysis of the TIMSS assessment data. As part of the proposed research, the principal investigators will work with math education researchers and measurement experts to develop statistical models and codes to analyze assessment data. Although the proposed research focuses on eighth grade TIMSS mathematics data, the statitical methods will have implications for use with other national and international data and assessments (e.g., NAEP and PISA). Policy makers will be able to use this information to make well-informed decisions about curriculum changes. Future studies will be able to use the methods to examine different age groups (e.g., 4th grade), different subject areas (e.g., science), and different assessments (e.g., NAEP and PISA).

拟议的研究项目将开发认知诊断模型（CDM）的新扩展，以了解美国学生拥有或缺乏哪些数学技能，并比较不同国家和国家内部多年来的技能分布。研究小组将研究这些技能与核心背景变量之间的关系（例如，性别）。研究的重点是从1999年，2003年和2007年的TIMSS八年级数学评估。为应付学校学生统计计划所采用的复杂抽样设计，主计长会制订多组学生统计模式，以计及校内学生的聚集情况。而TIMSS框架仅为每个项目识别单个技能（例如，计算面积的能力），拟议的研究将确定每个项目的多种技能。这种定性分析是CDM的一部分，但对于更好地理解TIMSS评估也很重要。作为本研究一部分开发的统计方法将允许对TIMSS评估数据进行全面分析。作为拟议研究的一部分，主要研究人员将与数学教育研究人员和测量专家合作，开发统计模型和代码来分析评估数据。虽然拟议的研究重点是八年级TIMSS数学数据，但统计方法将对其他国家和国际数据和评估（例如，NAEP和比萨）。政策制定者将能够利用这些信息，就课程改革做出明智的决定。未来的研究将能够使用这些方法来检查不同年龄组（例如，4年级），不同的学科领域（例如，科学），以及不同的评估（例如，NAEP和比萨）。