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Identification and Estimation of Dynamic Restricted Latent Class Models for Cognitive Diagnosis

用于认知诊断的动态受限潜在类别模型的识别和估计

基本信息

批准号：
2150628
负责人：
Steven Culpepper
金额：
$ 31.5万
依托单位：
University of Illinois at Urbana-Champaign
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-07-15 至 2024-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2150628&HistoricalAwards=false
关键词：
Identification Estimation Dynamic Restricted Latent

项目摘要

This research project will advance dynamic, cognitive diagnostic assessments for tracking student skill acquisition as an alternative approach to the static classification of student skill mastery. Existing formative assessment frameworks require extensive prior content knowledge and theory concerning the nature of student skill mastery and the process by which skills are joined to determine cognitive performance. This project will provide researchers with new tools for inferring the learning process from dynamic, longitudinal data on student performance and offer a framework for evaluating substantive theory for how students learn. A central concern in the educational sciences is formulating and evaluating interventions. The project will develop accurate methods for researchers to gain a fine-grained understanding as to the effectiveness of learning interventions. The new methods will leverage student responses to longitudinal assessments to offer educators and policymakers with insights regarding the status and timing of student mastery of educational content. The methods also will shed light on the effectiveness of educational interventions with the goal of establishing strategies for formulating personalized learning interventions to accelerate student learning. To achieve these aims, the project will advance fundamental theory in statistics and psychometrics. In addition to broadening methodological theory, the project will strengthen the educational assessment infrastructure. The research to be conducted will create a robust foundation for the development and administration of real-time formative assessments that adapt to the needs of students and provide educators with timely information to inform instructional decisions. Graduate students will be involved in the discovery process, and theoretical developments will be incorporated into the graduate curriculum. Publicly available software will be created to provide researchers and decision makers with cutting-edge tools.This research project will consider statistical problems at the heart of the social, behavioral, and health sciences, and will highlight the interplay among the fields of psychometrics, latent class modeling, longitudinal data analysis, and Bayesian statistics. The project will involve complex statistical modeling and will addresses issues related to computational complexity. New methods and algorithms for dynamic restricted latent class models (D-RLCMs) will be developed. Novel Bayesian methods will be used to deploy D-RLCMs to classify student mastery over time. The longitudinal skill classifications will provide educators and stakeholders with a fine-grained trajectory of student performance and learning. The project will deploy a hidden Markov model (HMM) framework that enables precise information regarding the probability students transition into states with greater content mastery. The mathematical theory of HMMs will be advanced in the project by establishing new identifiability theory to accurately infer student longitudinal skill profiles and learning trajectories. Methods will be developed to provide a powerful evaluation of the role of contextual factors in learning environments, such as student or school characteristics and pedagogical techniques. The methods to be developed will harvest the wealth of longitudinal student response data to provide decision makers and educators with actionable evidence to improve student learning outcomes.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.

该研究项目将推进动态的认知诊断评估，以跟踪学生技能习得，作为学生技能掌握的静态分类的替代方法。现有的形成性评估框架需要广泛的先验内容知识和理论，这些知识和理论涉及学生技能掌握的性质以及技能结合决定认知表现的过程。该项目将为研究人员提供新的工具，从学生表现的动态、纵向数据中推断学习过程，并为评估学生如何学习的实质性理论提供框架。教育科学的一个中心问题是制定和评价干预措施。该项目将为研究人员开发准确的方法，以获得对学习干预有效性的细致理解。新方法将利用学生对纵向评估的反应，为教育工作者和政策制定者提供有关学生掌握教育内容的状态和时间的见解。这些方法还将阐明教育干预的有效性，目标是建立制定个性化学习干预的策略，以加速学生的学习。为了实现这些目标，该项目将推进统计学和心理测量学的基础理论。除了拓宽方法理论外，该项目还将加强教育评价基础设施。即将进行的研究将为开发和管理实时形成性评估奠定坚实的基础，以适应学生的需求，并为教育工作者提供及时的信息，以便为教学决策提供信息。研究生将参与发现过程，理论发展将纳入研究生课程。将创建公开可用的软件，为研究人员和决策者提供尖端工具。该研究项目将考虑社会、行为和健康科学的核心统计问题，并将强调心理测量学、潜在类别建模、纵向数据分析和贝叶斯统计等领域之间的相互作用。该项目将涉及复杂的统计建模，并将解决与计算复杂性相关的问题。动态受限潜在类模型（d - rlcm）的新方法和算法将得到发展。新的贝叶斯方法将被用于部署d - rlcm来对学生掌握程度进行分类。纵向技能分类将为教育工作者和利益相关者提供学生表现和学习的细粒度轨迹。该项目将部署一个隐马尔可夫模型（HMM）框架，提供关于学生过渡到更精通内容状态的概率的精确信息。通过建立新的可识别性理论来准确地推断学生的纵向技能概况和学习轨迹，本项目将推进hmm的数学理论。将开发各种方法，对学习环境中的背景因素（如学生或学校的特点和教学技术）的作用进行强有力的评估。待开发的方法将收集大量的纵向学生反馈数据，为决策者和教育工作者提供可操作的证据，以改善学生的学习成果。该奖项反映了美国国家科学基金会的法定使命，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。