Grant Insights through Research & Development (GIRD): Using Big Data Centered Mixed Methods to Explain Variances in Grant Funding and Outcomes at Two-Year Colleges

通过研究授予见解

• 批准号: 2202169
• 负责人: Benjamin Reid
• 金额: $ 79.7万
• 依托单位: IMPACT ALLIES INC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2202169&HistoricalAwards=false
• 关键词: Grant; Insights; through; Research; &amp

Recognizing disparities in external funding across two-year institutions in advanced technological education programs and the need to build institutional capacity to address these disparities, this research is designed to surface factors and characteristics of institutions that are associated with successful efforts to secure external funds. Descriptive information derived from this investigation will form the basis for a set of empirically derived best practices associated with success in securing funding. The central research question is, "What characteristics and factors differentiate colleges with varying levels of external funding?" The research team will conduct a mixed methods research study that combines a rich set of data: (1) algorithm-derived meta-data on two-year college characteristics and performance; (2) public and campus institutional data; (3) surveys of college and program faculty and administrators; and (4) in-depth interviews with college and program faculty and administrators. The team will adapt quantitative research methods, such as big data algorithms, cluster analyses, and decisions support systems, commonly employed by the financial and health care sectors, and apply them to higher education. In an effort to support skilled technical workforce development in advanced-technology fields through fostering institutional capacity, the goal of the investigation is to establish viable pathways and impactful practices by which less grant-active, two-year colleges can utilize external funding resources to better meet the needs of diverse student populations, faculty, and institutions in advanced technological programs. Additionally, the project will apply and test an innovative use of quantitative research approaches to answer questions that now can be examined using large data sets and data science methods in combination with more traditional data collection methodologies. This project is funded by the Advanced Technological Education program that focuses on the education of technicians for the advanced technology fields that drive the Nation’s economy.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.

认识到两年制高等技术教育项目外部资金的差距，以及需要建设机构能力来解决这些差距，这项研究旨在揭示与成功争取外部资金相关的机构因素和特征。从这项调查中获得的描述性信息将构成一套与成功获得资金有关的经验性最佳做法的基础。研究的核心问题是，“外部资助水平不同的大学有哪些特点和因素？”研究团队将进行一项混合方法的研究，其中结合了一组丰富的数据：(1)基于算法的关于两年制学院特点和表现的元数据；(2)公共和校园机构数据；(3)对学院和项目教职员工和管理人员的调查；以及(4)对学院和项目教职员工和管理人员的深入访谈。该团队将采用金融和医疗保健部门常用的量化研究方法，如大数据算法、聚类分析和决策支持系统，并将其应用于高等教育。为了通过培养机构能力来支持先进技术领域的熟练技术劳动力发展，调查的目标是建立可行的途径和有效的做法，通过这些途径和有效的做法，不太活跃的两年制大学可以利用外部资金资源，更好地满足不同学生群体、教师和机构对先进技术课程的需求。此外，该项目将应用和测试定量研究方法的创新使用，以回答现在可以使用大型数据集和数据科学方法与更传统的数据收集方法相结合进行审查的问题。该项目由高级技术教育计划资助，该计划专注于为推动国家经济的先进技术领域培养技术人员。该奖项反映了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。