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BIGDATA: Collaborative Research: IA: F: Latent and Graphical Models for Complex Dependent Data in Education

BIGDATA：协作研究：IA：F：教育中复杂相关数据的潜在模型和图形模型

基本信息

批准号：
1633353
负责人：
Qiwei He
金额：
$ 31.96万
依托单位：
Educational Testing Service
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-01-01 至 2021-12-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1633353&HistoricalAwards=false
关键词：
BIGDATA Collaborative Research IA Latent

项目摘要

This is a comprehensive research proposal on the statistical modeling and analysis for educational assessment. This research addresses issues concerning fundamental statistical problems that arise in the analysis of Big Data in education. The research focus is on modeling and inference for large-scale data with complex dependence and structures (such as high-dimensional response and process data). These data arise from the introduction of new methods of testing student knowledge that rely on scenarios presented to the students and on simulation-based environments where student responses to a simulated environment are tested. This research is collaborative between Columbia University and the Educational Testing Service.The topics studied include latent graphical modeling for high-dimensional item response data, modeling and segmentation of process data via dictionary models, estimation of item-attribute relationship, dimension reduction, theoretical analysis and computational methods for the proposed models. The analysis combines techniques and concepts from mathematics and probability and applies them to nonlinear statistical models and data analysis. The proposed model combines latent variable and graphical approaches for high-dimensional data; for modeling process data, recent advances in modeling and segmenting techniques for natural language processing will be investigated. In the theoretical development, several algebraic concepts to formulate model identifiability and perform combinatorial analysis on high-dimensional discrete spaces will be studied. In addition, optimization algorithms will be developed using recent advances in numerical methods.

这是一份关于教育评价统计建模与分析的综合性研究方案。本研究解决了在教育大数据分析中出现的基本统计问题。研究重点是具有复杂依赖关系和结构的大规模数据（如高维响应和过程数据）的建模和推理。这些数据来源于测试学生知识的新方法的引入，这些方法依赖于向学生展示的场景和基于模拟的环境，在模拟环境中测试学生对模拟环境的反应。这项研究是哥伦比亚大学和美国教育考试服务中心合作进行的。研究课题包括高维项目响应数据的潜在图形化建模、基于字典模型的过程数据建模和分割、项目属性关系估计、降维、模型的理论分析和计算方法。该分析结合了数学和概率论的技术和概念，并将其应用于非线性统计模型和数据分析。该模型结合了高维数据的潜变量和图形方法；对于过程数据建模，将研究自然语言处理的建模和分割技术的最新进展。在理论发展中，几个代数概念，以制定模型可识别性和执行高维离散空间的组合分析将被研究。此外，优化算法将开发利用数值方法的最新进展。