CAREER: Advancing Latent Variable Statistical Modeling for the Analysis of Big and Complex Longitudinal Data to Promote Personalized Learning

职业：推进潜变量统计模型分析大而复杂的纵向数据以促进个性化学习

• 批准号: 1848451
• 负责人: Xiaojing Wang
• 金额: $ 42万
• 依托单位: University of Connecticut
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1848451&HistoricalAwards=false
• 关键词: CAREER; Advancing; Latent; Variable; Statistical

This research project will advance statistical modeling for latent variables in big and complex longitudinal data, with a focus on personalized education. Given the increasingly complex structures of computerized tests and online surveys, education is getting ever closer to a time when personalization will become possible. Work in this area is hampered, however, by a lack of state-of-the-art statistical techniques to analyze the data. This project will contribute a set of statistical models and inference methods to stimulate more longitudinal studies and applications in latent trait analysis. The research will be integrated within K-12 education through the application of the developed methods to a personalized learning platform. This platform is widely used by students from Grade 2 to 12 throughout the United States. The investigator also will apply the new methods to data sets from other disciplines, including psychology, ecology, and engineering. Graduate students will be actively engaged in this research project. Results from this research will be incorporated into an undergraduate seminar, an open online course, and professional development and training courses. The results also will be disseminated through conference presentations and journal publications. All computational algorithms with scalability for big data will be distributed as user-friendly and open-source software packages to the public.This research project will focus on learning the dynamic changes of latent trajectories from big and complex longitudinal testing data. The investigator will develop both parametric and nonparametric statistical models and inference methods, especially for dichotomous and categorical data. First, the project will build up a new class of hierarchical dynamic models. These models will provide more accurate estimates for latent ability (traits), including for real time data. Model criteria will be proposed to assess this improvement. The results will allow educators to design better educational strategies and computerized tests according to students' respective abilities. Second, the project will develop nonparametric modeling to flexibly capture the varying dependence and nonlinear effects of the latent trajectories. The developed methods will be useful for explaining, predicting, and grouping changes in latent ability, which is vital in personalized learning. Scalability strategies will be developed to make computation feasible for the developed statistical models. This last step is critical to ensure wide impact of the project results.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.

该研究项目将推进大型复杂纵向数据中潜在变量的统计建模，重点是个性化教育。鉴于计算机化测试和在线调查的结构越来越复杂，教育越来越接近个性化成为可能的时代。然而，由于缺乏分析数据的最新统计技术，这一领域的工作受到阻碍。本计画将提供一套统计模式与推论方法，以促进更多的潜在特质分析之纵向研究与应用。该研究将通过将开发的方法应用于个性化学习平台，整合到K-12教育中。这个平台被美国2年级到12年级的学生广泛使用。研究人员还将把新方法应用于其他学科的数据集，包括心理学、生态学和工程学。 研究生将积极参与这一研究项目。这项研究的结果将纳入本科研讨会、开放在线课程以及专业发展和培训课程中。研究结果还将通过会议报告和期刊出版物传播。所有具有大数据可扩展性的计算算法将作为用户友好的开源软件包向公众发布。本研究项目将专注于从大型复杂的纵向测试数据中学习潜在轨迹的动态变化。研究者将开发参数和非参数统计模型和推断方法，特别是对于二分和分类数据。 首先，该项目将建立一类新的层次动态模型。这些模型将提供对潜在能力（性状）的更准确的估计，包括对真实的时间数据的估计。将提出模型标准来评估这一改进。研究结果将使教育工作者能够根据学生各自的能力设计更好的教育策略和计算机化测试。 其次，该项目将开发非参数建模，以灵活地捕捉潜在轨迹的变化依赖性和非线性效应。所开发的方法将有助于解释，预测和分组潜在能力的变化，这是至关重要的个性化学习。将制定可扩展性战略，使计算可行的统计模型。这最后一步对于确保项目成果的广泛影响至关重要。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。

项目成果

Bayesian Nonparametric Monotone Regression of Dynamic Latent Traits in Item Response Theory Models

项目反应理论模型中动态潜在特征的贝叶斯非参数单调回归

• DOI: 10.3102/1076998619887913
• 发表时间: 2020
• 期刊: Journal of Educational and Behavioral Statistics
• 影响因子: 2.4
• 作者: Liu, Yang;Wang, Xiaojing
• 通讯作者: Wang, Xiaojing

A classification model for continuous responses: Identifying risk perception groups on health‐related activities

• DOI: 10.1002/bimj.202100222
• 发表时间: 2023-02
• 期刊: Biometrical Journal
• 影响因子: 1.7
• 作者: Eduardo S B de Oliveira;Xiaojing Wang;Jorge L. Bazán

Bayesian Model Assessment for Jointly Modeling Multidimensional Response Data with Application to Computerized Testing

• DOI: 10.1007/s11336-022-09845-x
• 发表时间: 2022-03
• 期刊: Psychometrika
• 影响因子: 3
• 作者: F. Liu;Xiaojing Wang;R. Hancock;Ming-Hui Chen