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

规划：交通运输中的机器学习：增强德克萨斯大学埃尔帕索分校的 STEM 教育和研究能力

• 批准号: 2332774
• 负责人: Natalia Villanueva Rosales
• 金额: $ 10万
• 依托单位: University of Texas at El Paso
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2332774&HistoricalAwards=false
• 关键词: Planning; Machine; Learning; Transportation; Enhancing

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.

在“改善本科 STEM 教育：西班牙裔服务机构”（HSI 计划）的支持下，该规划项目旨在利用机器学习的潜力，促进交通工程领域的跨学科教育。这个问题很重要，因为机器学习有潜力彻底改变交通规划和运营，但缺乏能够充分发挥其潜力的跨学科教育。该项目旨在通过开发跨学科课程来弥补这一差距，该课程强调基于项目的学习和学生反馈，重点关注机器学习在交通领域的应用。该课程将通过现实世界的项目和全校范围的机器学习挑战来丰富，其目标是形成一种超越课堂的研究和学习文化。该项目计划包括八项任务，旨在实现拟议的研究和教育目标，重点是通过综合回顾性评估和研究要素来衡量绩效。该项目更广泛的影响是改变交通领域并为 STEM 的更大多样性做出贡献。它的目标是培养一批熟练的学生，他们能够有效地将机器学习应用于交通问题，最终为交通系统的效率、安全和可持续性做出贡献。该项目还希望提高机器学习及其应用在更广泛的社区中的可见性和理解。该项目的具体目标是通过将机器学习融入德克萨斯大学埃尔帕索分校 (UTEP) 的交通教育和研究来弥补当前工程教育的差距。首要的研究问题是：如何将机器学习技术有效地融入到交通工程教育中，提高学生解决复杂现实问题的能力？我们的假设是，结合理论指导和实际应用的全面的、基于项目的方法可以显着提高学生在这个跨学科领域的学习成果。研究方法以新的交叉列出课程的开发和交付为中心。该课程还将辅以 UTEP 每月举办的研讨会系列和“交通运输中的机器学习挑战赛”，以促进实践学习和跨学科合作。预期结果包括提高学生的自我效能、跨学科思维发展以及交通和机器学习交叉领域的概念发展。这些结果将通过针对这些关键领域的回顾性研究进行评估。这项工作的结果将得到传播，使 UTEP 以外的教育工作者和机构能够从研究结果和方法中受益。通过这些举措，年轻学生可能会受到启发，追求 STEM 学习和职业生涯，并鼓励公众参与技术、交通和社会需求的重要交叉点。 HSI 计划旨在加强本科生 STEM 教育并建设 HSI 的能力。 HSI 计划支持的项目还将产生有关如何实现这些目标的新知识。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。