2025 ASEE Annual Conference & Exposition

Enhancing Data Science Education for Critical Infrastructures with Project-Based Learning

Presented at DSAI Technical Session 3: Integrating Data Science in Curriculum Design

Enhancing Data Science Education for Critical Infrastructure Security with Project-Based Learning
Abstract:
A workforce equipped with essential data science skills is crucial for maintaining the United States’ competitiveness and strengthening infrastructure security in today’s highly interconnected digital world. By analyzing large volumes of data, data science techniques can identify patterns and anomalies that may indicate potential security threats. Enhancing data science education helps students generate data-driven, robust solutions to solving the security problems in many infrastructures including transportation systems, healthcare systems, power plants, etc. Machine learning algorithms, for example, can predict and detect cyber-attacks before they occur. This paper presents the technical approach that aims to boost data science education using the Project-Based Learning (ProjBL) pedagogy. In this study, the data science projects are designed to employ different machine learning models to analyze various datasets related to Internet of Things (IOTs) attacks, power plant operations, and public health, which exposes the students to real-world security challenges. The experimental results obtained from this pilot study show the effectiveness of the approach in enhancing students’ ability to understand data, analyze data and develop data-driven solutions to various problems. The student survey also indicates favorable/positive feedback toward this intervention as expected. This study intends to share the project team’s experience and lessons learned with the STEM education community.

Authors

Dr. Xiang Zhao Alabama A&M University [biography]

Dr. Xiang (Susie) Zhao, Professor in the Department of Electrical Engineering and Computer Science at the Alabama A&M University, has over 20 years of teaching experience in traditional on-campus settings or online format at several universities in US and aboard. Her teaching and research interests include programming languages, high performance algorithm design, data science, and evidence-based STEM teaching pedagogies. Her recent research work has been funded by DOE, USED, NASA and NSF.
Dr. Mebougna Drabo Alabama A&M University [biography]

Dr. Mebougna L. Drabo is currently a professor of Mechanical Engineering at Alabama A&M University (AAMU). He is the chair of the department of Mechanical & Civil Engineering and Construction Management at AAMU. He is also serving as the director of the Alabama EPSCoR Agency for the Department of Energy. He joined AAMU in 2012, leveraging his expertise in teaching and mentoring STEM students while fostering on-campus research and DOE Lab internships. Currently, he directs the DOE/NNSA's Consortium, SPINS and is working on integrating radiation detection systems into cyber manufacturing environments.

His research interests include STEM education, Additive Manufacturing, Thermoelectric Devices for Energy Harvesting, Digital Twinning Technology, Nuclear Radiation Detectors, Nuclear Security and Safety, Small Nuclear Modular Reactors (SMR), Material Characterization (X-ray Photoelectron Spectroscopy & Infrared Microscopy), Nanotechnology, Data Analytics and Visualization, Biofuels Applications, Computational Fluid Dynamics analysis, Heat Transfer, Energy Conservation in building, and Multi Fuel Optimization.

Note

The full paper will be available to logged in and registered conference attendees once the conference starts on June 22, 2025, and to all visitors after the conference ends on June 25, 2025

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