A Family of Diagnostic Models for Evaluating Learning Progressions

用于评估学习进度的一系列诊断模型

基本信息

批准号：
2050138
负责人：
Matthew Madison
金额：
$ 19.41万
依托单位：
University of Georgia Research Foundation Inc
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-08-01 至 2024-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2050138&HistoricalAwards=false
关键词：
Family Diagnostic Models Evaluating Learning

项目摘要

This research project will develop psychometric methodology to empirically evaluate developmental progressions. A developmental progression describes a theorized or observed sequence of cognitive, psychological, or behavioral developments in an individual or group. In educational settings, learning progressions describe the increasingly sophisticated ways of reasoning that develop as students learn about specific content domains over time. Despite their prevalence and utility, quantitative methodological developments to evaluate learning progressions have stagnated. This project will advance the fields of psychometrics and learning sciences by providing a modern, multidimensional, and longitudinal framework for modeling developmental progressions. Although the project focuses on educational applications, the developed methods will be widely applicable in disciplines across the social and behavioral sciences. The project will train a graduate student from an underrepresented group. Free and easy-to-use software will be developed for researchers to utilize in their own examinations of learning progressions. The results and products stemming from this project have the potential to change the way researchers design, interpret, and analyze assessments in the empirical evaluation of developmental progressions.The investigator will use a diagnostic classification model (DCM) framework to model learning progressions. DCMs are multivariate psychometric models that classify examinees into specified levels of categorical latent traits (e.g., basic, proficient, advanced). DCMs have become attractive in educational settings because they provide much desired diagnostic and criterion-referenced score interpretations in the form of classifications. Recently, DCMs have been developed for longitudinal contexts that provide criterion-referenced interpretations of student growth. To model learning progressions, the developed model will combine a generalized longitudinal DCM with the hierarchical DCM designed to model attribute hierarchies. This fusion of modeling frameworks allows for the simultaneous examination of attribute hierarchies and student learning over time, which together comprise the basis of a learning progression. Simulation studies will guide and inform the practical application of the developed methods with respect to data requirements (i.e., number of items or sample size), test design, model fit, and factors impacting the accuracy, validity, and reliability of model-based inferences.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该研究项目将开发心理测量方法，以经验评估发育进展。发展进展描述了一个或群体中认知，心理或行为发展的理论或观察到的序列。在教育环境中，学习进步描述了随着学生随着时间的流逝而学习特定内容域的越来越复杂的推理方式。尽管它们流行和效用，但评估学习进程的定量方法论发展仍然停滞不前。该项目将通过为建模发展进步提供现代，多维和纵向的框架来推动精神计和学习科学领域。尽管该项目侧重于教育应用，但开发的方法将广泛适用于社会和行为科学的学科。该项目将培训来自代表性不足的小组的研究生。将开发免费且易于使用的软件，供研究人员在自己的学习进展研究中使用。来自该项目的结果和产品有可能改变研究人员在发展进步的经验评估中设计，解释和分析评估的方式。研究人员将使用诊断分类模型（DCM）框架来建模学习进展。 DCM是多元心理测量模型，将考生分类为特定的分类潜在特征（例如，基本，熟练，高级）。 DCM在教育环境中变得有吸引力，因为它们以分类的形式提供了众多所需的诊断和标准引用的分数解释。最近，已经开发出DCMS针对纵向背景，这些纵向背景提供了对学生成长的标准引用的解释。为了建模学习进度，开发的模型将将广义纵向DCM与旨在建模属性层次结构的层次DCM结合在一起。建模框架的这种融合允许同时检查属性层次结构和学生随着时间的流逝，这共同构成了学习进步的基础。模拟研究将指导并告知开发方法在数据要求（即项目数量或样本量的数量），测试设计，模型拟合以及影响基于模型的推论的准确性，有效性和可靠性的因素上的实际应用。该奖项反映了NSF的法规任务，并认为通过基金会的知识优点和广泛的crietia crietia crietia criteria criteria criteria crietia criteria crietia criteria criteria criteria criteria crietia criteria criteria cristeria cripitia cripitia均值得一提。