Collaborative Research: An Interdisciplinary Approach to Prepare Undergraduates for Data Science Using Real-World Data from High Frequency Monitoring Systems

在NSF改善本科生STEM教育计划：教育和人力资源(IUSE：EHR)的支持下，该项目旨在通过提高本科生对数据科学的理解来服务于国家利益。它将通过将数据科学的概念和技能发展纳入生物、计算机科学、工程和环境科学的本科课程来实现这一目标。通过弗吉尼亚理工大学、范德比尔特大学和北卡罗来纳农业与技术州立大学的合作，该项目将基于来自水和交通监控系统的高频实时数据开发跨学科学习模块。该项目旨在开发一种在STEM学科课程中引入数据科学概念的共同方法。通过将数据科学纳入各种本科生STEM课程，并建立包括历史上的黑人学院/大学在内的合作伙伴关系，该项目有可能扩大对数据科学的参与，包括来自数据科学和/或STEM领域代表性不足的人群的学生的参与。该项目将开发数据科学学习模块，以便在合作机构现有的八个STEM课程中实施。学习模块的动机将来自真实世界的问题和高频数据集，包括弗吉尼亚理工大学的水监测数据集，以及Vanderbilt的交通和建筑监测数据集。学习单元的主题将包括：跨学科学习、数据分析和行业伙伴关系。这些主题将有助于纳入真实世界的数据集，以增强学生的学习体验，并且它们足够广泛，可以在未来纳入其他数据集。该项目旨在开发和实施一种跨学科协作方法，以支持本科生通过他们的学科课程工作发展数据科学专业知识。这些专业知识将为学生进入STEM劳动力市场做好更好的准备，特别是那些专注于智能和互联计算的STEM专业。该项目将调查这些模块如何以及以什么方式支持学生学习数据科学。该项目还将调查各合作机构对模块的执行情况有何不同。预计该项目将确定将数据科学概念纳入STEM课程的关键考虑因素，并将主办研讨会，向教师介绍这些考虑和战略，以便他们能够将学习模块纳入他们教授的STEM课程。项目合作者将为推广学习单元并将其转移到其他STEM教育界提供框架，从而扩大该项目的范围和影响，使之超越三个合作机构。NSF IUSE：EHR计划支持研究和开发项目，以提高所有学生的STEM教育的有效性。通过参与的学生学习路径，该计划支持有前景的实践和工具的创建、探索和实施。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。

With support from the NSF Improving Undergraduate STEM Education Program: Education and Human Resources (IUSE: EHR), this project aims to serve the national interest by improving undergraduate understanding of data science. It will accomplish this goal by incorporating data science concepts and skill development in undergraduate courses in biology, computer science, engineering, and environmental science. Through a collaboration between Virginia Tech, Vanderbilt University, and North Carolina Agricultural and Technical State University, the project will develop interdisciplinary learning modules based on high frequency, real-time data from water and traffic monitoring systems. The project intends to develop a common approach for introducing data science concepts in STEM disciplinary courses. By embedding data science into a variety of undergraduate STEM courses and creating a partnership that includes a Historically Black College/University, this project has the potential to broaden participation in data science, including participation of students from populations that are underrepresented in data science and/or STEM fields. This project will develop data science learning modules to implement in eight existing STEM courses at the collaborating institutions. The learning modules will be motivated by real-world problems and high-frequency datasets, including a water monitoring dataset from Virginia Tech, and transportation and building monitoring datasets from Vanderbilt. The learning module topics will include: Interdisciplinary Learning, Data Analytics, and Industry Partnerships. These topics will facilitate incorporation of real-world data sets to enhance the student learning experience and they are broad enough that they can incorporate other data sets in the future. The project aims to develop and implement an interdisciplinary collaborative approach to support undergraduate students in developing data science expertise through their disciplinary course work. Such expertise will better prepare students to enter the STEM workforce, especially those STEM professions that focus on smart and connected computing. The project will investigate how and in what ways the modules support student learning of data science. The project will also investigate how implementation of the modules varies across the collaborating institutions. It is expected that the project will define key considerations for integrating data science concepts into STEM courses and will host workshops to introduce faculty to these considerations and strategies so they can incorporate the learning modules into the STEM courses that they teach. The project collaborators will provide the framework for generalizing and transferring the learning modules to other STEM education communities, thus broadening the scope and the impact of this project beyond the three collaborating institutions. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools.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.