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.

在不断改进的本科STEM教育的支持下：西班牙裔服务机构（HSI计划），该计划项目旨在促进运输工程中的跨学科教育，利用机器学习的潜力。这个问题很重要，因为机器学习有可能彻底改变运输计划和运营，但是缺乏跨学科的教育可以充分利用其潜力。该项目旨在通过开发跨学科课程来解决这一差距，该课程强调基于项目的学习和学生反馈，重点是运输机器学习。该课程将充满现实世界的项目和校园范围内的机器学习挑战，其目的是产生一种超越课堂的研究和学习文化。项目计划包括八项旨在实现拟议的研究和教育目标的任务，重点是通过集成的回顾性评估和研究元素来衡量绩效。该项目的更广泛的影响是改变运输领域，并促进STEM的更大多样性。它的目的是生产熟练的学生，他们可以有效地将机器学习应用于运输问题，最终有助于运输系统的效率，安全性和可持续性。该项目还希望增强对机器学习及其在更广泛社区中的应用的可见性和理解。该项目的具体目的是通过将机器学习纳入德克萨斯大学埃尔帕索分校（UTEP）的运输教育和研究中来弥合当前工程教育的差距。主要的研究问题是：如何将机器学习技术有效整合到运输工程教育中，以增强学生解决复杂的现实世界问题的能力？假设是一种结合理论指导和实际应用的全面，基于项目的方法可以显着增强学生在这个跨学科领域的学习成果。研究方法围绕开发和交付新的交叉上市课程。该课程将通过每月一次的研讨会系列和UTEP的“运输挑战中的机器学习”来补充，以培养动手学习和跨学科的合作。预期的结果包括提高学生的自我效能感，跨学科的心态发展以及运输和机器学习交集的概念发展。这些结果将通过重点关注这些关键领域的回顾性研究来评估。这项工作的结果将被传播，使UTEP以外的教育者和机构从发现和方法论中受益。通过这些举措，可能会启发年轻的学生从事STEM研究和职业，并鼓励公众参与技术，运输和社会需求的重要交叉点。 HSI计划旨在增强本科STEM教育并在HSIS的建立能力。 HSI计划支持的项目还将为如何实现这些目标提供新的知识。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响评估标准，被视为值得通过评估来获得支持。