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Distributed Learning for Undergraduate Programs in Data Science at Diverse Universities

Distributed Learning for Undergraduate Programs in Data Science at Diverse Universities
不同大学数据科学本科课程的分布式学习
批准号:
2142514
负责人:
Hong Liu
金额:
$97.08万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2022
资助国家:
美国
项目状态:
未结题
起止时间:
2022-10-01 至 2025-09-30

项目摘要

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中文摘要
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英文摘要
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.
期刊论文(2)
专著(0)
科研奖励(0)
会议论文
Using GIFT to Develop an Adaptive Distributed Learning Environment Supporting Data Science Competencies.
使用 GIFT 开发支持数据科学能力的自适应分布式学习环境。
DOI: --
发表时间: 2023
期刊: The U.S. Army Combat Capabilities Development Command – Soldier Center.
影响因子: --
作者: [Anaroua, Fadjimata, Liu, Hong, Malone, Naomi]
通讯作者: Malone, Naomi
DOI: --
发表时间: 2023
期刊: IEEE Computer Society Conference Publishing Services (CPS
影响因子: --
作者: [Feng, Ke, Liu, Dahai, Yongxin Liu, Liu, Hong, Song, Houbin]
通讯作者: Song, Houbin
IUCRC Planning Grant Embry-Riddle Aeronautical University: Center for Aviation Big Data Analytics [ABDA]
Collaborative Research: IGE: Graduate Education in Cyber-Physical Systems Engineering
Embeddings in Sparse Graphs
  • 批准号:
    MR/S016325/1
  • 项目类别:
    Fellowship
  • 资助金额:
    $93.26万
  • 财政年份:
    2019
  • 负责人:
    Hong Liu
  • 依托单位:
I-Corps: Machine Learning Approach for Microbial Process Control and Management
  • 批准号:
    1824119
  • 项目类别:
    Standard Grant
  • 资助金额:
    $5.0万
  • 财政年份:
    2018
  • 负责人:
    Hong Liu
  • 依托单位:
国内基金
海外基金
Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis
Understanding structural evolution of galaxies with machine learning
  • 批准号:
  • 项目类别:
    省市级项目
  • 资助金额:
    10.0万元
  • 批准年份:
    2022
  • 负责人:
    Nicola Rosario Napolitano
  • 依托单位:
煤矿安全人机混合群智感知任务的约束动态多目标Q-learning进化分配
  • 批准号:
    --
  • 项目类别:
    青年科学基金项目
  • 资助金额:
    30万元
  • 批准年份:
    2022
  • 负责人:
    吉建娇
  • 依托单位:
基于领弹失效考量的智能弹药编队短时在线Q-learning协同控制机理
  • 批准号:
    62003314
  • 项目类别:
    青年科学基金项目
  • 资助金额:
    24.0万元
  • 批准年份:
    2020
  • 负责人:
    沈剑
  • 依托单位: