NRT-IGE: Data Science for the Built Environment

NRT-IGE：建筑环境的数据科学空间和时间维度的海量高分辨率数据的可用性，加上日益快速的分布式计算资源，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，关于培养数据科学技能的最佳教学机制的研究仍在兴起。对于工程专业的研究生来说尤其如此，他们是一群精挑细选、技术成熟的群体，能够快速掌握材料。在加州大学戴维斯分校的研究生教育创新(IGE)轨道上，这个国家科学基金会研究培训(NRT)奖将试验、测试和比较数据科学教学模式。TestBed项目将提供关键的新信息，为开发新的学习平台提供信息，该平台旨在培养工程学研究生强大的计算、统计和数据推理能力。该项目将实施一种混合的短期课程方法，1)连接现有的代码营和学期长的课程，2)与正式的用户组体验相结合。将进行一次强有力的评估，以确定代码营、短期课程和用户组的单独影响，以及参与体验组合的影响。此外，还将审查学习收获、从事需要数据科学原理的跨学科研究的自我效能以及职业轨迹(包括决定参加额外的数据科学课程，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，解决知识中的一个特别重要的差距，即密集的短期学习经验是否会导致长期保持技能发展和计算推理。关于工程领域不同数据科学教学模式有效性的研究结果将广泛适用于所有数据支持的科学和工程领域。NSF研究培训(NRT)计划旨在鼓励STEM研究生教育培训的大胆、新的、潜在的变革和可扩展的模式的开发和实施。研究生教育创新轨道专门致力于试验、测试和评估研究生教育的新颖、创新和潜在的变革性方法。

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.