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

这项研究项目将推进对大数据和复杂纵向数据中潜在变量的统计建模，重点是个性化教育。鉴于计算机化考试和在线调查的结构日益复杂，教育正越来越接近个性化成为可能的时代。然而，由于缺乏最先进的统计技术来分析数据，这一领域的工作受到了阻碍。该项目将贡献一套统计模型和推理方法，以促进更多的纵向研究和在潜在特质分析中的应用。这项研究将通过将开发的方法应用于个性化学习平台而整合到K-12教育中。这个平台被全美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。这项研究的结果将被纳入本科生研讨会、公开在线课程以及专业发展和培训课程。会议成果还将通过会议报告和期刊出版物传播。所有具有可扩展性的大数据计算算法都将以用户友好的开源软件包的形式向公众发布，本研究项目将专注于从庞大而复杂的纵向测试数据中了解潜在轨迹的动态变化。研究人员将开发参数和非参数统计模型和推理方法，特别是对二分和分类数据。首先，该项目将建立一类新的分层动态模型。这些模型将为潜在能力(特征)提供更准确的估计，包括实时数据。将提出模型标准来评估这一改进。这一结果将使教育工作者能够根据学生的各自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相关性和非线性影响。开发的方法将有助于解释、预测和分组潜在能力的变化，这在个性化学习中至关重要。将制定可伸缩性战略，使所开发的统计模型的计算可行。这最后一步对于确保项目结果的广泛影响至关重要。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.