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

这一建议发展了一个跨越教育、社会科学、统计学和计算机科学领域的研究人员社区，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，以解决研究人员使用这些技术分析教育中多种类型大规模数据的局限性，包括来自国家成绩单研究的数据、地方教育机构的纵向学生数据和收集的关于大型大学系统学生的管理数据集。研究人员正在召集学者们确定一个研究议程，以便有效地利用这些方法，并将它们用于解决教育实践和政策问题。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，其中包括对三种不同的教育数据库进行实际分析，为研究人员举办迭代的双周讲习班，并为新研究人员制定、实施和记录研究生水平的课程。纽约城市大学研究生中心的研究人员正在与包括该地区另外8个机构在内的大学间博士联盟合作。从全国各地招募学者参与虚拟社区。该项目通过三种不同的机制运作。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，研究应用计算密集型技术分析大型纵向教育数据集所涉及的挑战和策略，该项目在两年的时间里重复进行。第二种是每两周一次的研讨会，由来自计算机科学、应用统计学和教育设计的研究人员组成的工作组，以及从特定数据集进行数据分析的试点方法。第三种机制是为博士生开设一个学期的课程，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。随着教育领域大规模纵向数据集数量的不断增加，对数据分析提出了多重挑战。首先，当必须处理许多教育数据集的复杂性时，为这种分析开发的许多技术都会遇到问题。第二，理解不同的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并不总是直截了当的。第三个挑战是越来越需要具有使用这些多种分析技术的背景和训练的研究人员。

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.