EAGER: Collaborative Research: Changing the Paradigm: Developing a Framework for Secondary Analysis of EER Datasets

EAGER：协作研究：改变范式：开发 EER 数据集二次分析框架

基本信息

批准号：
2039871
负责人：
John Morelock
金额：
$ 4.74万
依托单位：
University of Georgia Research Foundation Inc
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-03-01 至 2024-02-29
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2039871&HistoricalAwards=false
关键词：
EAGER Collaborative Research Changing Paradigm

项目摘要

To help develop the nation’s engineering workforce, the National Science Foundation has invested substantial public funding in engineering education research over the past twenty years. This investment has helped markedly improve courses and programs at many universities by testing and sharing research-based practices that promote active learning, increase student motivation and engagement, diversify the field, and better prepare students for work. At the same time, the investment has typically focused on researchers collecting new data, resulting in hundreds of data sets that remain underexplored. These existing data sets have significant potential to be analyzed and even combined in new ways to further support large-scale changes in how we recruit, teach, and prepare engineering students for the demands and challenges of the 21st century. Currently, however, engineering education researchers do not have productive and effective ways for sharing and analyzing data beyond the original project. Thus the full potential of these data sets remains untapped. This project will address that gap by developing and promoting a viable approach that will enable researchers to leverage the rich data currently available. In doing so, it will simultaneously improve engineering education nationally and increase the return on investment of public funds. The project will bring experienced researchers together with those just beginning their careers to identify the major roadblocks to sharing and re-using data, develop strategies and practices for overcoming those roadblocks, and conduct a series of test cases that demonstrate how to put those strategies and practices into action. The results will help create a paradigm shift that can move both the study and the practice of engineering education in the U.S. to a new level and spur the kind of sea changes needed to keep the nation’s engineering workforce at the forefront of the global marketplace.Changing the paradigm of single-use data collection is a high-risk proposition that requires actionable, proven practices for effective, ethical data sharing, coupled with sufficient incentives to both share and use existing data. To that end, this proposal draws together a team of experts to overcome substantial obstacles in data sharing and build a framework to guide secondary analysis in engineering education research. In particular, we will bring together established and emerging scholars to deliver a tested framework that outlines methodological best practices for formally and informally sharing data, making data sets public, combining data from different studies, performing secondary analyses of both qualitative and quantitative data, publishing and sharing the results, securing the needed funding, and ensuring that the work is valued in the field. To create this framework, the research team will hold a series of six workshops over two years. In the first year, we will bring highly respected, experienced researchers from institutions across the country together with newer researchers to create the initial framework for data sharing and data re-use. In the second year, we will test and refine that framework on two existing data sets. We will solicit data sets from the wider community, and invite teams of scholars to conduct secondary analysis on those data sets, in conversation with the original researchers. Importantly, in selecting both the data sets and the approaches to secondary analysis, we will emphasize methodological diversity to ensure that the framework is widely applicable. The outcome will be a framework document that will comprise a set of tested guidelines for data sharing and secondary analysis in engineering education research, distributed through both journals and workshops to promote widespread adoption.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.

为了帮助发展国家的工程劳动力，国家科学基金会在过去二十年中在工程教育研究方面投入了大量公共资金，通过测试和分享促进积极主动的研究实践，帮助显着改善了许多大学的课程和项目。同时，投资通常集中在研究人员收集新数据上，从而导致数百个数据集仍未得到充分探索。巨大的潜力值得分析，甚至可以结合起来进一步支持我们如何招收、教授和准备工程学生以应对 21 世纪的需求和挑战的大规模变革。然而，目前，工程教育研究人员没有高效且有效的方法来共享和分析超出范围的数据。因此，这些数据集的全部潜力仍未得到开发，该项目将通过开发和推广一种可行的方法来解决这一差距，使研究人员能够利用现有的丰富数据，同时改善工程教育。提高公共资金的投资回报率。该项目将把经验丰富的研究人员和刚刚开始其职业生涯的研究人员聚集在一起，以确定共享和重用数据的主要障碍，制定克服这些障碍的策略和实践，并进行一系列测试用例来演示如何应用这些策略和实践研究结果将有助于创造范式转变，将美国工程教育的研究和实践提升到一个新的水平，并推动保持美国工程劳动力处于全球前沿所需的巨大变革。市场。改变范式一次性数据收集是一个高风险的提议，需要可操作的、经过验证的实践来实现有效、道德的数据共享，再加上共享和使用现有数据的足够激励措施。为此，该提议召集了一个专家团队来克服这一问题。特别是，我们将汇集知名学者和新兴学者，提供一个经过测试的框架，概述正式和非正式共享数据、公开数据集的方法最佳实践。，结合不同研究的数据，进行定性和二次分析为了创建这个框架，定量研究团队将在两年内举办一系列六次研讨会。将汇集来自全国各地机构的备受尊敬、经验丰富的研究人员与新研究人员一起创建数据共享和数据重用的初始框架。第二年，我们将在两个现有数据集上测试和完善该框架。向更广泛的社区征集数据集，并邀请学者团队进行二次分析重要的是，在选择数据集和二次分析方法时，我们将强调方法论的多样性，以确保该框架具有广泛的适用性。包括一套经过测试的工程教育研究数据共享和二次分析指南，通过期刊和研讨会分发，以促进广泛采用。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的评估进行评估，被认为值得支持影响审查标准。