Cognitive Diagnosis with Multinomial Response Distributions

多项响应分布的认知诊断

• 批准号: 9810202
• 负责人: Curtis Tatsuoka
• 金额: $ 2万
• 依托单位: Tanar Software
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-09-15 至 2000-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9810202&HistoricalAwards=false
• 关键词: Cognitive; Diagnosis; Multinomial; Response; Distributions

The purpose of this study is to extend the capabilities of cognitively diagnostic adaptive testing to incorporate multiple response outcomes. The cognitively diagnostic adaptive testing methodology of interest employs underlying cognitive models that are finite partially ordered sets (posets). Main research objectives include understanding how to incorporate misconception and erroneous response information into cognitive poset models, establishing techniques for identifying erroneous responses that are both cognitively and statistically interesting, and extending a data-analytic framework for model fitting and analysis of items to the case when items have more than two different class conditional response distributions. Having the means to validate implementation of the methodology through data analysis is critically important, as the underlying cognitive processes are complex and latent. Markov Chain Monte Carlo estimation techniques will be employed. Cognitively diagnostic adaptive testing can form the basis of `intelligent` tutoring systems. Incorporating multinomial responses should allow such systems to utilize response information more fully, provide enhanced cognitive information about students, and perhaps even classify students with fewer number of items. Cognitively diagnostic adaptive testing can be viewed as an important statistical tool for cognitive measurement. Implementation involves building focused cognitive models, which gives insight into the processes of problem-solving. The resulting methods of this research will be applied to actual data from a linguistics domain.

本研究的目的是扩展认知诊断适应性测试的能力，以纳入多种反应结果。 感兴趣的认知诊断适应性测试方法采用了有限偏序集（偏序集）的基础认知模型。 主要研究目标包括了解如何将误解和错误反应信息纳入认知偏序集模型中，建立识别认知和统计上都有趣的错误反应的技术，并将用于模型拟合和项目分析的数据分析框架扩展到项目具有两个以上不同类别条件响应分布的情况。 拥有通过数据分析验证方法实施的方法至关重要，因为底层的认知过程是复杂且潜在的。 将采用马尔可夫链蒙特卡罗估计技术。 认知诊断适应性测试可以构成“智能”辅导系统的基础。 纳入多项响应应该允许此类系统更充分地利用响应信息，提供有关学生的增强的认知信息，甚至可能用较少数量的项目对学生进行分类。 认知诊断适应性测试可以被视为认知测量的重要统计工具。 实施涉及建立有针对性的认知模型，从而深入了解问题解决的过程。 这项研究的结果方法将应用于语言学领域的实际数据。