Exposing Students to High-Frequency Data Science

Dr. Manoj Jha, an associate professor in A&T’s Department of Civil, Architectural and Environmental Engineering in the College of Engineering, has teamed with researchers from Virginia Tech and Vanderbilt University to expose students to real world high-frequency data. The data, primarily environmental in nature, will be collected in real time at labs on the Virginia Tech and Vanderbilt campuses and shared with students in their coursework.

“Data is a dominant feature in any curriculum, but exposure to real-world, real time data, such as is collected during a weather event, is another matter altogether,” explains Jha. “Our students will gain exposure to immediate real-time information which will help them develop urgency in their decision making processes which is so useful in today’s world.”

This NSF’s Improving Undergraduate STEM Education research project, inspired by a recent National Academy of Sciences report on Data Science for Undergraduates, is a collaborative effort among investigators at the three universities. This unique effort aims to improve data science-related learning outcomes of students representing a variety of majors including engineering, computer science, environmental science and biology.

Two unique, high-frequency data monitoring labs – Virginia Tech’s Learning Enhanced Watershed Assessment System Lab and Vanderbilt’s Smart City Lab, are the key entities that will support the proposed data science-related intervention in various courses and the related STEM learning research activities.

An interdisciplinary project team including faculty and graduate students focused in STEM education research and curriculum design, hydrology and water resources, computer science, ecology, and environmental science will work together closely to accomplish the project’s three goals:

  1. Integrating real-world data into eight relevant STEM courses at all three institutions
  2. Conducting research on student learning across various disciplines, institutions, gender, ethnicity, and academic settings
  3. Developing and implementing a learning module portability plan to broaden the breadth of the impact of this project beyond the partnering universities