HDR DSC: Collaborative Research: Transforming Data Science Education through a Portable and Sustainable Anthropocentric Data Analytics for Community Enrichment Program
HDR DSC: Collaborative Research: Transforming Data Science Education through a Portable and Sustainable Anthropocentric Data Analytics for Community Enrichment Program
批准号:
1924278
负责人:
Yu Liang
金额:
$72.36万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2019
资助国家:
美国
项目状态:
已结题
起止时间:
2019-10-01 至 2024-09-30
中文摘要
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英文摘要
This project will focus on human-centric analytics, or anthropocentric data analytics, to provide a common ground for beginning studies in data science and to prepare students to address interdisciplinary problems. As one of the most promising subareas of data science, human-centric data analytics either considers humans as the research object or involves humans as the executors over all stages of data analytics. The proposed Anthropocentric Data Analytics for Community Enrichment (ADACE) project will form a three-institution partnership, with University of Tennessee at Chattanooga (UTC) as the coordinating organization, and UTC, Chattanooga State Community College and Howard University as implementing organizations. Together they will address the challenge of developing a large, high-quality workforce skilled in data-related disciplines. This is essential for the United States to maintain its competitiveness in the 21st century. The project aims to establish an interdisciplinary platform with a curriculum that integrates real-world community research projects. In today's increasingly globalized world, data science occupations are essential for sustained social and economic development. Most U.S. colleges and universities are witnessing an increased interest from students in these programs offered by computer science and other related departments. However, a lack of training programs and community engagement through real-life projects makes it difficult to keep students engaged and interested. Limited access to internships can also reduce students' success in data science and may even direct them away from these fields. This project addresses the critical need to attract higher numbers of talented students, to better stimulate students' interest, to increase the retention rates, and to eventually meet the rising workforce demand. The project endeavors to promote undergraduate training in data science. An interdisciplinary and multi-institutional collaboration will strive to establish an infrastructure that accommodates 41 students and spans the entire four years of their college career. The project aspires to enhance current data science and related curricula of the participating institutions, as two of them have data science programs for undergraduate and graduate students. To attract students with a broad range of interests, ADACE will consist of four core modules - mathematics foundation, computational foundation, data science, and data science applications - and integrate multiple interdisciplinary and human-centric community projects. Those projects will feature six areas: (1) human-in-the-loop data integration, (2) visible neural network architecture, (3) human-in-the-loop machine learning, (4) seamless interaction between users and machines, (5) human-oriented topics investigating the behavior of individuals or society, and (6) studies of ethics in terms of both AI scientists/engineers and artificial agents. Participating institutions will have the opportunity to leverage existing relationships with local for- and non-profit organizations to expose students to real-world problems of data science and provide opportunities for networking. Each concentration will be led by a co-principal investigator with expertise in that area, building on their research findings and experience with interdisciplinary collaboration to shape innovative curricula and research projects. Students will have the opportunity to gain knowledge in data science, problem-solving skills, hands-on experience, and rigorous research training through this proposed program. All curricular materials will be designed to be portable, sustainable, and easily disseminated to ensure their expanded impact. They will also be evaluated for measurable outcomes and tailored to include non-traditional students, who form a large portion of the potential data science workforce in the regions surrounding the participating institutions. NSF's Harnessing the Data Revolution Data Science Corps program focuses on building capacity for harnessing the data revolution at the local, state, national, and international levels to help unleash the power of data in the service of science and society. Projects in this program are being jointly funded by the NSF's Harnessing the Data Revolution Big Idea; the Directorate for Computer and Information Science and Engineering, Division of Information and Intelligent Systems; the Directorate for Education and Human Resources, Division of Undergraduate Education; the Directorate for Mathematical and Physical Sciences, Division of Mathematical Sciences; and the Directorate for Social, Behavioral and Economic Sciences, Office of Multidisciplinary Activities and Division of Behavioral and Cognitive Sciences.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(24)
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DOI:
10.1109/access.2018.2851392
发表时间:
2018-06
期刊:
IEEE Access
影响因子:
3.9
作者:
[Shuaishuai Guo;Dalei Wu;Haixia Zhang;D. Yuan]
通讯作者:
Shuaishuai Guo;Dalei Wu;Haixia Zhang;D. Yuan
Assessing the Presence of Intentional Waveform Structure In Preamble-based SEI
评估基于前导码的 SEI 中是否存在有意波形结构
DOI:
10.1109/globecom48099.2022.10001025
发表时间:
2022
期刊:
IEEE
影响因子:
--
作者:
[Tyler, Joshua H., Fadul, Mohamed K.M., Reising, Donald R., Liang, Yu]
通讯作者:
Liang, Yu
DOI:
10.1080/08839514.2022.2055989
发表时间:
2022-04
期刊:
Applied Artificial Intelligence
影响因子:
2.8
作者:
[Connie Sun;Vijayalakshmi K. Kumarasamy;Yu Liang;Dalei Wu;Yingfeng Wang]
通讯作者:
Connie Sun;Vijayalakshmi K. Kumarasamy;Yu Liang;Dalei Wu;Yingfeng Wang
Mining High School Data to Predict and Increase Student Success in College
挖掘高中数据以预测和提高学生在大学的成功
DOI:
--
发表时间:
2020
期刊:
SAIS 2020 Proceedings
影响因子:
--
作者:
[Barczak, Timothy, Jain, Hemant]
通讯作者:
Jain, Hemant
DOI:
10.1177/1460458221989392
发表时间:
2021-01-01
期刊:
HEALTH INFORMATICS JOURNAL
影响因子:
3
作者:
[Jung, Euisung, Jain, Hemant, Gaudioso, Carmelo]
通讯作者:
Gaudioso, Carmelo
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