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

• 批准号: 1921373
• 负责人: Matthew Madison
• 金额: $ 22.94万
• 依托单位: Clemson University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-15 至 2020-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1921373&HistoricalAwards=false
• 关键词: Family; Diagnostic; Models; Evaluating; Learning

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.

本研究计画将发展心理测量学的方法，以经验评估发展的进展。 发展进程描述了个体或群体中认知、心理或行为发展的理论化或观察到的序列。在教育环境中，学习进展描述了随着学生随着时间的推移学习特定内容领域而发展的日益复杂的推理方式。尽管它们的流行和实用性，定量方法的发展，以评估学习进展停滞不前。该项目将通过提供一个现代的，多维度的，纵向的框架来模拟发展进程，从而推进心理测量学和学习科学领域。虽然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各个学科。该项目将从代表性不足的群体中培养一名研究生。将开发免费和易于使用的软件，供研究人员在自己的学习进展检查中使用。该项目的成果和产品有可能改变研究人员设计、解释和分析发展进展经验评估的方式。研究人员将使用诊断分类模型（DCM）框架来模拟学习进展。DCM是多变量心理测量模型，其将受试者分类为特定级别的分类潜在特质（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非常理想的诊断和标准参考分数解释。最近，DCMs已经开发了纵向的背景下，提供标准参考的学生成长的解释。为了对学习过程进行建模，所开发的模型将联合收割机与设计用于对属性层次进行建模的分层DCM相结合。这种建模框架的融合允许随着时间的推移同时检查属性层次结构和学生学习，它们共同构成了学习进展的基础。模拟研究将指导和告知所开发方法在数据要求方面的实际应用（即，该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。