Ongoing NSF Projects
1) NSF/IUSE Project: An Interdisciplinary Approach to Prepare Undergraduates for Data Science Using Real-World Data from High-Frequency Monitoring Systems
Project Summary
Overview: Motivated by a National Academy of Sciences report on Data Science for Undergraduates, this is the first time interdisciplinary faculty from Virginia Tech (VT), North Carolina Agricultural and Technical University (NC A&T, an HBCU), and Vanderbilt University (VU) proposed a large-scale collaborative effort to promote innovation and change in undergraduate STEM+C curricula. This unique effort is informed by investigators' prior successful NSF grants and will generate new knowledge about data science-integrated STEM learning.
Project Goals: Develop and implement an interdisciplinary collaborative approach to support undergraduate students in developing Data Science expertise through various STEM+C disciplines including engineering, computer science, environmental science, and biology. Specific objectives are to:
- integrate real-world data from two high-frequency monitoring systems (one on water monitoring at VT and another on traffic monitoring at VU) into 8 relevant STEM+C courses at VT, VU, and NC A&T
- conduct research on student and instructor perspectives of data science learning using data across various disciplines, institutions, gender, and ethnicity
- and extend the impact of this project beyond the partnering universities through the dissemination of learning materials created with key considerations for integrating data science into different STEM+C courses.
Data Sources: Two unique, high-frequency data monitoring systems – Learning Enhanced Watershed Assessment System (LEWAS) at VT and Smart Cities at VU – facilitated the proposed teaching, learning, and research activities.
Project Impact: 8 interdisciplinary courses in computer science, engineering, biology, and environmental science, from sophomore to senior levels, reached about 1300 students over the 3-year project period
This project is supported by NSF grants #1029711, #1915487, and #1915268.
Project's Resources
The project's main website where more information about the project as well as its 11 of the 12 data science modules can be found:
The 12th module (named Frequency Analysis in Hydrology) can be found:
Project's Workshops
Workshop 1: NCA&T Workshop
On June 18th, 2025, we hosted a workshop at NCA&T State University featuring six concurrent sessions led by members from our three collaborative universities. The sessions covered civil engineering, environmental science, and engineering statistics. The event drew 12 in-person and 12 virtual attendees from 15 institutions, including eight U.S. states and one foreign country. Participating universities included NCA&T State University, Michigan State University, Purdue University, Wake Technical Community College, Durham Technical Community College, Danville Community College, East Carolina University, Bennett College, the U.S. Coast Guard Academy, California State Polytechnic Humboldt, National University, the Air Force Institute of Technology, Stony Brook University, Morgan State University, and the Lebanese American University.
During the workshop, presenters demonstrated their modules and guided participants through hands-on exercises. Attendees were preassigned to seven groups (three in-person, four virtual), each supported by a team mentor. An afternoon session allowed groups to collaborate and develop implementation plans. Seventeen attendees from 10 institutions—including NCA&T, Michigan State, Purdue, Wake Tech, Durham Tech, Danville Community College, ECU, Bennett College, the U.S. Coast Guard Academy, and the Lebanese American University—later presented plans to integrate our modules into their courses. These courses span diverse disciplines such as Crop Ecology, Microbiology, Soil Chemistry, Statistics/Econometrics, Economics, Environmental Science/Engineering, Hydrology, Probability and Statistics, Biostatistics, Data Science, Transportation Engineering, Engineering Design, Systems Engineering, and more. The broad adoption across these courses highlights the project's significant impact.
We conducted a post-workshop survey to gather attendee feedback, receiving responses from 13 participants. The table below summarizes their ratings on three key questions, measured on a 5-point Likert scale (1: Strongly Disagree to 5: Strongly Agree):
| Question | Mean | Standard Deviation |
|---|---|---|
| I plan to incorporate one or more modules (fully or partly) into courses at my institution. | 4.79 | 0.43 |
| The workshop helped me develop ideas for integrating data science into undergraduate courses. | 4.57 | 0.65 |
| Overall, the workshop met my expectations. | 4.64 | 0.63 |
Feedback was overwhelmingly positive, with attendees praising the workshop's organization and impact. Representative comments included: "This should be a 2-3 day workshop," "I thought the workshop was well organized," and "Keep holding these workshops."
Project's Relevant Publications
Dissertations
C. Vatral, "Design and Development of Educational Technology to Support Collaborative Debriefing for Nurse Training," Ph.D. dissertation, Dept. Comput. Sci., Vanderbilt University, Nashville, TN, USA, 2024.
C. Snyder, "Understanding Students' Collaborative Problem-Solving during STEM+C Learning Using Multimodal Analysis," Ph.D. dissertation, Dept. Comput. Sci., Vanderbilt University, Nashville, TN, USA, 2024.
Md. Y. Naseri, "Data science for multisectoral water use management and STEM Education: A Synergistic Approach," Ph.D. dissertation, Dept. Civil Eng., Virginia Polytechnic Institute and State Univ., Blacksburg, VA, USA, 2025. (Expected defense: Aug. 25, 2025)
H. Workneh, "Physics-guided machine learning for streamflow prediction," Ph.D. dissertation, Dept. Comput. Data Sci. Eng., North Carolina A&T State Univ., Greensboro, NC, USA, 2025.
M. A. Mollah, '[Title of dissertation should be added]," Ph.D. dissertation, Dept. Agricult. Environ. Syst., North Carolina A&T State Univ., Greensboro, NC, USA, 2028. [Partially funded, one semester]
Theses
D. Asamen, "Stream stability analysis medium size streambed material D50," M.S. thesis, Dept. Civil, Archit., Environ. Eng., North Carolina A&T State Univ., Greensboro, NC, USA, 2021.
S. Bhandari, "Land suitability analysis for crop production using geospatial technology: a case study in the three diverse regions of North Carolina," M.S. thesis, Dept. Civil, Archit., Environ. Eng., North Carolina A&T State Univ., Greensboro, NC, USA, 2023.
S. Roy, "Fate of Amoxycillin in hybrid constructed wetlands," M.S. thesis, Dept. Agricult. Environ. Syst., North Carolina A&T State Univ., Greensboro, NC, USA, 2025.
F. H. Pernaleta, "Evaluating stress and attention in response to university food pantry outreach using EEG & eye-tracking," M.S. thesis, Dept. Ind. Syst. Eng., North Carolina A&T State Univ., Greensboro, NC, USA, 2026. [Partially funded, one semester]
Conference presentations
Md. Y. Naseri, V. K. Lohani, M. K. Jha, C. Snyder, and G. Biswas, "Integration of data science modules across interdisciplinary courses at multiple institutions: Analysis of students' and faculty perspectives," in Proc. 2025 Amer. Soc. Eng. Educ. Annu. Conf., Montreal, Canada, Jun. 22-25, 2025.
C. Sear, and L. Marston, "Using GPT personas in the classroom to develop holistic solutions to complex environmental challenges," presented at the Univ. Council Water Resources (UCOWR) Conf., Minneapolis, MN, USA, Jun. 4, 2025.
D. Varshney, Md. Y. Naseri, A. Kothyari, J. Smith, and V. K. Lohani, "Smart environmental monitoring: An AI-driven approach to watershed data integration and access," presented at the Research Creative Scholarship Conf., Virginia Tech, Blacksburg, VA, USA, Apr. 25, 2025
C. Sear and L. Marston, "Using GPT personas in the classroom to develop holistic solutions to complex environmental challenges," presented at the Amer. Geophys. Union Fall Meeting, Washington, DC, USA, Dec. 12, 2024.
Md. Y. Naseri et al., "Integrating data science into multi-university undergraduate STEM courses: Lessons learned," presented at the 2024 Frontiers Educ. Conf., Washington, DC, USA, Oct. 13-16, 2024.
D. Varshney, Md. Y. Naseri, A. Kothyari, and V. K. Lohani, "AI-enhanced teaching assistant: Bridging instructor knowledge and web intelligence," presented at the Summer Conf. Office Undergraduate Research, Virginia Tech, Blacksburg, VA, USA, Jul. 25, 2024.
D. Varshney, Md. Y. Naseri, A. Kothyari, and V. K. Lohani, "Development of an AI-assisted chatbot for a water monitoring environment lab," presented at the Summer Conf. Office Undergraduate Research, Virginia Tech, Blacksburg, VA, USA, Jul. 25, 2024.
Md. Y. Naseri et al., "Integrating data science into multi-university undergraduate STEM courses: Lessons learned," presented at the 2024 IUSE Summit, Washington, DC, USA, Jun. 16-18, 2024.
Md. Y. Naseri et al., "A modular approach for integrating data science concepts into multiple undergraduate STEM+C courses," presented at the 2022 Amer. Soc. Eng. Educ. Annu. Conf., Minneapolis, MN, USA, Jun. 26-29, 2022.
C. Snyder et al., "Understanding data science instruction in multiple STEM disciplines," presented at the 2021 Amer. Soc. Eng. Educ. Annu. Conf., Virtual, Jul. 26-29, 2021.
Conference papers
Md. Y. Naseri, C. Snyder, M. K. Jha, and V. K. Lohani, "Integration of data science modules across interdisciplinary courses at multiple institutions: Analysis of students' and faculty perspectives," in Proc. 2025 Amer. Soc. Eng. Educ. Annu. Conf., Montreal, Canada, Jun. 22-25, 2025.
Md. Y. Naseri, C. Snyder, B. Mcloughlin, S. Bhandari, N. Aryal, G. Biswas, E. Henrick, E. R. Hotchkiss, M. K. Jha, S. X. Jiang, E. C. Kern, V. K. Lohani, L. T. Marston, C. P. Vanags, and K. Xia, "A modular approach for integrating data science concepts into multiple undergraduate STEM+C courses," in Proc. 2022 Amer. Soc. Eng. Educ. Annu. Conf., Minneapolis, MN, USA, Jun. 26-29, 2022. [Online]. Available: 🔗
C. Snyder, D. M. Asamen, Md. Y. Naseri, N. Aryal, G. Biswas, A. Dubey, E. Henrick, E. R. Hotchkiss, M. K. Jha, S. X. Jiang, E. C. Kern, V. K. Lohani, L. T. Marston, C. P. Vanags, and K. Xia, "Understanding data science instruction in multiple STEM disciplines," in Proc. 2021 Amer. Soc. Eng. Educ. Annu. Conf., Virtual, Jul. 26-29, 2021. [Online]. Available: 🔗
Workshops
"Hybrid NSF/IUSE Workshop 2025: Integrating Data Science into Undergraduate Science and Engineering Courses," presented at the NSF IUSE Workshop, Martin Eng. Res. Innovation Complex, North Carolina A&T State Univ., Greensboro, NC, USA, Apr. 18, 2025.
"An Approach to Integrating Data Science into multiple STEM+C undergraduate courses," presented at the ASEE Workshop 2023, Baltimore Convention Center, Baltimore, MD, USA, Jun. 25, 2023.
Journal articles
Md. Y. Naseri et al., "Integrating data science into undergraduate science and engineering courses through discipline-specific modules: The student perspective," Eur. J. Eng. Educ., submitted for publication.
Md. Y. Naseri et al., "Integrating data science into undergraduate science and engineering courses: Lessons learned by instructors in a multiuniversity research-practice partnership," IEEE Trans. Educ., 2024, doi: 🔗
Other Resources
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