TRIPODS+X:EDU: Collaborative Education: Data-driven Discovery and Alliance

TRIPODS X:EDU：协作教育：数据驱动的发现和联盟

• 批准号: 1839339
• 负责人: Rachel Kuske
• 金额: $ 10万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1839339&HistoricalAwards=false
• 关键词: TRIPODS+X; EDU; Collaborative; Education; Data

The overall goal of this project is to devise targeted exposure to data science in the context of science and engineering fields through the establishment of a multi-institutional cooperative alliance. The complexity of challenges surrounding data science necessitates collaboration, as no one organization can identify and resolve all relevant issues. The current massive demand for data scientists urgently requires training in analyzing real-world datasets and a solid understanding of the mathematics and statistics behind the methods and computational tools used in data science. Both curricula and co-curricular activities are needed to prepare undergraduates and graduate students to enter the data-driven workforce. As an initial step in this educational program, the proposed project aims to set forth coordinated initiatives that intentionally develop and administer communal data science curricula for application in the sciences and engineering. These curricula and co-curricular initiatives also broaden the participation of women and underrepresented minorities (who have been traditionally underrepresented across all STEM disciplines) in data science. Any research findings will be disseminated to scholarly venues. The proposed activity devises communal curricula units comprising two parts: concept overview and context application. The concept overview provides standardized material and contributes to data science pedagogy and practice. The context application builds upon the concept overview to bring situational awareness and domain expertise to faculty and student learners. These communal curricula units identify synergies of data science to (specific) science and engineering disciplines. Additionally, these units could be the impetus for a data science pedagogical platform available to science and engineering faculty within the alliance. Specifically, the proposed project seeks to engage in curriculum development, fill gaps in student and faculty knowledge in data science and enact a sustainability model by embedding partnerships. We will additionally develop and host one week-long data science boot camp each year for students at the participating institutions, and one half-day data science workshop each year for students and faculty. The shared technologies, tools and resources developed through this grant can be adapted for other research-intensive, women's and HBCU institutions. The overall project will provide courses and sessions to enhance domain-specific data analytical skills to both faculty and students across at least four institutions.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.

该项目的总体目标是通过建立多机构合作联盟，在科学和工程领域的背景下，在科学和工程领域的背景下设计有针对性的数据科学。由于没有人可以识别和解决所有相关问题，因此围绕数据科学的挑战的复杂性需要合作。当前对数据科学家的大规模需求急需培训，以分析现实世界数据集，并对数据科学中使用的方法和计算工具背后的数学和统计数据有深入的了解。需要课程和课外活动来准备本科生和研究生进入数据驱动的劳动力。作为该教育计划的第一步，该项目旨在制定有意开发和管理公共数据科学课程以在科学和工程中应用的协调计划。这些课程和课外活动还扩大了数据科学中妇女和代表性不足的少数民族的参与（传统上在所有STEM学科中的代表性不足）。任何研究发现都将被传播到学术场所。 提出的活动设计了包括两个部分的公共课程单元：概念概述和上下文应用程序。该概念概述提供了标准化的材料，并有助于数据科学教学法和实践。上下文应用程序建立在概念概述的基础上，以将情境意识和领域的专业知识带给教师和学生学习者。这些公共课程单元确定了（特定）科学和工程学科的数据科学协同作用。此外，这些单元可能是该联盟内科学和工程学院可用的数据科学教学平台的动力。具体而言，拟议的项目旨在从事课程开发，填补数据科学的学生和教师知识的空白，并通过嵌入合作伙伴关系来制定可持续性模型。我们还将针对参与机构的学生每年开发和主持一个为期一周的数据科学训练营，每年为学生和教职员工开发一个半天的数据科学研讨会。通过这笔赠款开发的共享技术，工具和资源可以适用于其他研究密集型，妇女和HBCU机构。 整个项目将提供课程和会议，以提高至少四个机构的教职员工和学生的特定领域数据分析技能。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的审查标准通过评估来获得支持的。