Building an inter-disciplinary research community to protype computationally-intensive analysis of large scale educational datasets

建立跨学科研究社区来原型大规模教育数据集的计算密集型分析

• 批准号: 1243785
• 负责人: Paul Attewell
• 金额: $ 36.33万
• 依托单位: CUNY Graduate School University Center
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-10-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1243785&HistoricalAwards=false
• 关键词: Building; inter; disciplinary; research; community

This proposal develops a community of researchers that crosses domains of education, social science, statistics and computer science to expand the research agenda that utilizes large scale, longitudinal educational databases. This research collaborative is engaging in the design and implementation of computationally-intense data analytic methodologies to address limitations experienced by researchers who use these techniques to analyze multiple types of large scale data in education including data from a national transcript study, local education agency longitudinal student data and an administrative dataset collected about students across a large university system. The researchers are bringing together scholars to identify a research agenda for the effective engagement of such methodologies and their use in addressing educational practice and policy questions. The researchers are developing and implementing a seminar on data-intense research methodologies that includes practical analysis of three different educational databases, conducting iterative bi-weekly workshops for researchers, and developing, implementing and documenting a graduate level course for new researchers. Researchers from the City University of New York Graduate Center are collaborating with the Inter-University Doctoral Consortium that includes eight additional institutions in the region. Scholars from across the country are recruited from across the country to participate in a virtual community.The project is operating through three distinct mechanisms. The first is the faculty seminar that brings together researchers from different domains to study the challenges and strategies involved in applying computationally-intensive techniques to the analysis of large longitudinal educational datasets, repeated across the two years of the project. The second is a biweekly workshop in which a working group of researchers from computer science, applied statistics and education design and pilot approaches to data analysis from specific datasets. The third mechanism is a semester-long course for doctoral students that connects them with the researchers who are developing and testing new methodologies for data analysis.The growing number of large scale longitudinal datasets in education present multiple analytic challenges. The first is that many techniques that have been developed for such analyses encounter problems when having to address the complexity of many educational datasets. The second is that the understanding of the fit between different computationally-intensive statistical methodology and the types of questions that need to be answered through these methodologies is not always straightforward. The third challenge is the growing need for researchers who have the background and training to use these multiple analytical techniques.

这项建议建立了一个研究人员社区，跨越教育、社会科学、统计学和计算机科学等领域，以扩大利用大规模、纵向教育数据库的研究议程。这项研究合作致力于设计和实施计算密集型数据分析方法，以解决研究人员在使用这些技术分析教育中多种类型的大规模数据时遇到的限制，这些数据包括来自国家成绩单研究的数据、当地教育机构的纵向学生数据以及从大型大学系统收集的关于学生的行政数据集。研究人员正在召集学者，以确定有效参与这类方法及其在解决教育实践和政策问题中的使用的研究议程。研究人员正在制定和实施一项关于数据密集型研究方法的研讨会，其中包括对三个不同的教育数据库进行实际分析，为研究人员举办每两周一次的迭代讲习班，以及为新研究人员开发、实施和记录研究生水平的课程。纽约市立大学研究生中心的研究人员正在与包括该地区另外八所机构在内的大学间博士联盟合作。来自全国各地的学者被招募参加一个虚拟社区。该项目通过三个不同的机制运作。第一个是教师研讨会，来自不同领域的研究人员聚集在一起，研究将计算密集型技术应用于大型纵向教育数据集分析所涉及的挑战和策略，该项目在两年的时间里重复举行。第二个是每两周举办一次的讲习班，来自计算机科学、应用统计学和教育设计的研究人员组成了一个工作组，从特定的数据集进行数据分析的试验方法。第三种机制是为博士生开设的为期一学期的课程，将他们与正在开发和测试数据分析新方法的研究人员联系起来。教育中越来越多的大规模纵向数据集提出了多重分析挑战。首先，为这类分析开发的许多技术在处理许多教育数据集的复杂性时遇到了问题。第二，对不同计算密集型统计方法与需要通过这些方法回答的问题类型之间的契合性的理解并不总是直截了当的。第三个挑战是，对有背景和受过培训的研究人员使用这些多重分析技术的需求日益增长。