Distributed Learning for Undergraduate Programs in Data Science at Diverse Universities

该项目旨在通过改善数据科学本科教育来服务于国家利益。该项目将通过使用灵活的分布式学习（DL）平台，为来自11所不同大学的学生开发和提供10门数据科学（DS）课程。该联盟将为基础设施和资源有限的机构提供更多的DS教学机会，包括七个少数民族服务机构。这些课程将使美国军方先进的DL技术适应学术环境，以便利用人工智能（AI）的力量为每个学生的特定需求量身定制最佳学习体验。普遍的DL技术有助于克服个别机构由于招生人数少和教师专业知识有限而导致的效率低下。至少两百名本科生将通过参加该联盟的DS课程作业、参加夏季研究研讨会以及获得DS联盟认证来获得研究经验。为了扩大该项目对平等学习机会和社会流动性的整体影响，该项目将招募来自不同背景的学生。该项目旨在通过使用自适应分布式学习（ADL），在不同大学实施数据驱动的创新DL实践教学研究。DL和ADL课程之间的区别在于，后者利用美国国防部的可互操作数据交换标准来利用人工智能，大数据和通信技术的力量。ADL提供可以个性化的学习，并随时随地交付给个别学生。在学术环境中的ADL技术的适应仍然在很大程度上未经测试，将大大受益于其功效的分析。该联盟分为四个组织集群，由安柏瑞德航空大学（佛罗里达州），北德克萨斯大学和佛罗里达A M大学领导。每个集群内的机构包括Bethune-Cookman大学（FL），洛杉矶的加州州立大学，汉普顿-悉尼学院（VA），杰克逊州立大学（MS），贾维斯基督教学院（TX），莱恩学院（TN），摩根州立大学（MD）和西蒙斯大学（MA）。利用DL技术的不同机构联盟的综合物理和智力资源，为这些机构的学生提供了与研究型大学环境中预期的一样进行DS培训的机会，从而消除了这些学生可能存在的障碍，为STEM就业市场的竞争做好准备。NSF IUSE：EHR计划支持研究和开发项目，以提高所有学生STEM教育的有效性。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This project aims to serve the national interest by improving undergraduate education in data science. This project will develop and deliver ten Data Sciences (DS) courses to students from a consortium of eleven diverse universities by using a flexible distributed learning (DL) platform. This consortium will provide increased opportunities for DS instruction at institutions with limited infrastructure and resources, including seven minority-serving institutions. The courses will adapt the United States military's advanced DL technology to an academic setting in order to harness the power of artificial intelligence (AI) in tailoring optimal learning experiences for the specific needs of each individual student. Pervasive DL technologies help to overcome inefficiencies found at individual institutions due to small enrollments and limited faculty expertise. At least two hundred undergraduates will gain research experiences from taking the consortium's DS coursework, participating in a summer research workshop, and obtaining a DS consortium certification. To broaden this project’s overall impact on equal learning opportunities and social mobility this project will recruit students from diverse backgrounds.The project aims to implement data-driven pedagogical research on innovative DL practices across diverse universities through the use of adaptive distributed learning (ADL). The difference between DL and ADL courses is that the latter utilizes the interoperable data exchange standard of the U.S. Department of Defense to leverage the power of AI, big data, and communication technologies. ADL provides learning that can be personalized and delivered anytime and anywhere to an individual student. The adaptation of ADL technologies in an academic setting remains largely untested and would benefit greatly from an analysis of its efficacy. The consortium is organized into four organizational clusters headed by Embry-Riddle Aeronautical University (FL), the University of North Texas, and Florida A&M University. Institutions within each cluster include Bethune-Cookman University (FL), California State University at Los Angeles, Hampden-Sydney College (VA), Jackson State University (MS), Jarvis Christian College (TX), Lane College (TN), Morgan State University (MD), and Simmons University (MA). Leveraging the combined physical and intellectual resources of this alliance of diverse institutions with DL technology provides students at these institutions with the opportunity to pursue DS training on par with what would be expected in a research university setting, thereby removing barriers that may exist for these students to prepare for competition in the STEM job marketplace. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools.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.