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