NRT-IGE: Data Science for the Built Environment

NRT-IGE：建筑环境的数据科学

• 批准号: 1545193
• 负责人: Debbie Niemeier
• 金额: $ 49.95万
• 依托单位: University of California-Davis
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1545193&HistoricalAwards=false
• 关键词: NRT; IGE; Data; Science; Built

NRT-IGE: Data Science for the Built EnvironmentThe availability of voluminous, high resolution data in both the spatial and temporal dimensions, coupled with increasingly fast, distributed computational resources offers enormous opportunities for tackling complex engineering and science challenges in urban settings. These data can also play an important role in interdisciplinary problem solving and have increasingly high value to multiple communities of scientists and engineers. However, research in the optimal instruction mechanisms to develop data science skills is still emerging. This is particularly true for engineering graduate students, who are a highly selected, technologically sophisticated population with the ability to quickly master material. This National Science Foundation Research Traineeship (NRT) award in the Innovations in Graduate Education (IGE) Track to the University of California-Davis will pilot, test, and compare modes of data science instruction. The testbed project will provide critical new information to inform the development of new learning platforms designed to cultivate robust computational, statistical, and data reasoning skills in engineering graduate students. The project will implement a hybrid short-course approach that 1) bridges existing code camps and semester long classes, and 2) is coupled with a formal user group experience. A robust evaluation will be conducted to identify the individual effects of code camps, short courses, and users groups, as well as the effect of participating in combinations of experiences. In addition, learning gains, self-efficacy to engage in interdisciplinary studies that require data science principles, and career trajectories (including decisions to take additional coursework in data science and decisions to pursue interdisciplinary research and employment involving data science) will be examined. The project will generate new knowledge that addresses a particularly important gap in knowledge in terms of whether intense short-term learning experiences result in longer-term retention of skill development and computational reasoning. Findings on effectiveness of different modes of data science instruction in engineering will be broadly applicable to all data-enabled science and engineering fields.The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new, potentially transformative, and scalable models for STEM graduate education training. The Innovations in Graduate Education Track is dedicated solely to piloting, testing, and evaluating novel, innovative, and potentially transformative approaches to graduate education.

NRT-IGE：在空间和时间维度上提供大量高分辨率数据，加上越来越快的分布式计算资源，为解决城市环境中复杂的工程和科学挑战提供了巨大的机会。这些数据还可以在跨学科问题解决中发挥重要作用，并对多个科学家和工程师社区具有越来越高的价值。然而，开发数据科学技能的最佳教学机制的研究仍在兴起。这对于工程研究生来说尤其如此，他们是一个经过高度挑选的，技术先进的人口，有能力快速掌握材料。这个国家科学基金会研究培训（NRT）奖在研究生教育创新（IGE）轨道加州大学戴维斯分校将试点，测试和比较数据科学教学模式。 该测试平台项目将提供关键的新信息，以告知新的学习平台的开发，旨在培养工程研究生强大的计算，统计和数据推理技能。该项目将实施一种混合的短期课程方法，1）桥接现有的代码营地和学期长的课程，2）与正式的用户组体验相结合。将进行一项强有力的评估，以确定代码营、短期课程和用户组的个体影响，以及参与体验组合的影响。 此外，还将研究学习成果、从事需要数据科学原理的跨学科研究的自我效能感和职业轨迹（包括决定在数据科学中学习额外的课程，以及决定从事涉及数据科学的跨学科研究和就业）。该项目将产生新的知识，解决一个特别重要的知识差距，即密集的短期学习经验是否会导致长期保留技能发展和计算推理。 关于不同模式的数据科学教学在工程领域的有效性的研究结果将广泛适用于所有数据驱动的科学和工程领域。NSF研究培训计划（NRT）旨在鼓励开发和实施大胆的，新的，潜在的变革性和可扩展的STEM研究生教育培训模型。 研究生教育创新轨道专门致力于试点，测试和评估新颖，创新和潜在的变革性研究生教育方法。