Planning: Machine Learning in Transportation: Enhancing STEM Education and Research Capacity at The University of Texas at El Paso

University Of Texas At El Paso, El Paso TX

Investigators

Abstract

With support from the Improving Undergraduate STEM Education: Hispanic-Serving Institutions (HSI Program), this Planning project aims to foster cross-disciplinary education in transportation engineering, leveraging the potential of machine learning. This problem is important because machine learning has the potential to revolutionize transportation planning and operations, but there is a lack of cross-disciplinary education that can fully leverage its potential. This project seeks to address this gap by developing an interdisciplinary course that emphasizes project-based learning and student feedback, with a focus on the application of machine learning in transportation. The course will be enriched with real-world projects and a campus-wide machine learning challenge, with the goal of generating a culture of research and learning that extends beyond the classroom. The project plan includes eight tasks designed to attain the proposed research and education objectives, with a focus on measuring performance through an integrated retrospective evaluation and research element. This project's broader impact is to transform the transportation field and contribute to greater diversity in STEM. It aims to produce a skilled cohort of students who can effectively apply machine learning to transportation problems, ultimately contributing to the efficiency, safety, and sustainability of transportation systems. The project also expects to enhance the visibility and understanding of machine learning and its applications in the broader community. The specific aim of the project is to bridge the gap in current engineering education by integrating machine learning into transportation education and research at the University of Texas at El Paso (UTEP). The primary research question is: How can integrate machine learning techniques be integrated effectively into transportation engineering education to enhance students' capabilities in solving complex real-world problems? The hypothesis is that a comprehensive, project-based approach combining theoretical instruction and practical application can significantly enhance students' learning outcomes in this interdisciplinary area. The research methods center around the development and delivery of a new cross-listed course. The course will be supplemented by a monthly seminar series and a "Machine Learning in Transportation Challenge" at UTEP to foster hands-on learning and interdisciplinary collaboration. The expected results include improved student self-efficacy, interdisciplinary mindset development, and conceptual development in the intersection of transportation and machine learning. These results will be evaluated through a retrospective study focusing on these key areas. The results of this work will be disseminated, allowing educators and institutions beyond UTEP to benefit from the findings and methodologies. Through these initiatives, younger students might be inspired to pursue STEM studies and careers, and encourage public engagement with the important intersections of technology, transportation, and societal needs. The HSI Program aims to enhance undergraduate STEM education and build capacity at HSIs. Projects supported by the HSI Program will also generate new knowledge on how to achieve these aims. 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.