Contextual Research-Empirical Research:Psychometric and Growth Modeling of Complex Patterns of Learning Resulting From the Interrelationships Between Multiple Learning Progressions

情境研究-实证研究:多种学习进程之间的相互关系所产生的复杂学习模式的心理计量和成长建模

• 批准号: 1313513
• 负责人: Nathaniel Brown
• 金额: $ 70.78万
• 依托单位: Boston College
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-08-31 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1313513&HistoricalAwards=false
• 关键词: Contextual; Research; Empirical; Psychometric; Growth

The project aims is to identify a set of psychometric and growth models that will permit researchers and curriculum developers to describe and test the interrelationships between students' progress along two or more learning progressions. Unlike the current thinking in the field, these psychometric and growth models will not assume linearity, continuity, or unidimensionality of student learning. The project proposes three goals: gathering evidence for the existence of non-linear patterns of learning that demonstrate branching of the learning progressions; gathering evidence for the existence of non-linear patterns of learning that demonstrate parallel learning progressions, and proposing the psychometric and growth models for these types of learning progressions. The psychometric models will be developed to estimate the cognitive models underlying complex learning patterns. The work will progress in four phases: (1) exploring, statistically, existing datasets from NSF-funded projects containing simultaneous measures of learning on more than one learning progression; (2) testing the psychometric and growth models using the existing data; (3) collecting and analyzing new data; and (4) testing the psychometric and growth models using new data.

该项目的目的是确定一套心理测量和成长模型，使研究人员和课程开发人员能够描述和测试学生在沿着两个或两个以上的学习进展之间的相互关系。与该领域目前的思维不同，这些心理测量和成长模型不会假设学生学习的线性，连续性或单维性。该项目提出了三个目标：收集非线性学习模式存在的证据，证明学习进展的分支;收集非线性学习模式存在的证据，证明平行学习进展，并提出心理测量和增长模型这些类型的学习进展。将开发心理测量模型来估计复杂学习模式下的认知模型。这项工作将分四个阶段进行：（1）从统计学上探索NSF资助项目的现有数据集，这些数据集包含对多个学习进展的同时测量;（2）使用现有数据测试心理测量和成长模型;（3）收集和分析新数据;（4）使用新数据测试心理测量和成长模型。