CUE Ethics: Collaborative Research: Open Collaborative Experiential Learning (OCEL.AI): Bridging Digital Divides in Undergraduate Education of Data Science

CUE 伦理：协作研究：开放式协作体验式学习 (OCEL.AI)：弥合数据科学本科教育中的数字鸿沟

• 批准号: 1935076
• 负责人: Yugyung Lee
• 金额: $ 35万
• 依托单位: University of Missouri-Kansas City
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-01-01 至 2022-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1935076&HistoricalAwards=false
• 关键词: CUE; Ethics; Collaborative; Research; Open

The University of Missouri-Kansas City, in partnership with Eastern Michigan University, Essex County College, and University of Florida, proposes the Open Collaborative Experiential Learning (OCEL.AI) project, an Open Knowledge Network (OKN), that supports postsecondary instructors who teach underserved populations Computer Science (CS)+Journalism and Strategic Communication within their existing institutional structure. Recent advances in artificial intelligence (AI), and specifically deep learning (DL) have improved the state-of-the-art results of the data-driven approaches and tools in a wide range of domains. To build the future talent ecosystem, there is an urgent need to create a qualified data science workforce that can perform critical functions in a variety of domains and aspects of human society, such as journalism, health communication, and advertising. To address this need, the intelligent cloud-based collaborative tool will provide to the AI and data-driven research and education communities what GitHub and code repositories provide to the software engineering and open-source communities. In this OKN, minority students will feel empowered and motivated to study data science since they can share, reuse, reproduce, deploy, discuss, learn, and apply data and AI models in real time. It is expected that students taking these CS+Journalism and Strategic Communication courses will see increased interest, self-efficacy, and motivation in studying data science among both CS majors and non-majors - in particular among black, female, and Hispanic students. Organized as a Networked Improvement Community, the project will focus on three major tasks: 1) launching an online faculty professional development program on OCEL.AI that has been developed by the researcher's lab; 2) launching a student learning interface on OCEL.AI to offer engaging learning experiences to CS+Journalism and Strategic Communication students but also providing infrastructure that can be extended to CS+X programs for a range of other Xs such as business and health care; and 3) sparking critical thinking of data science ethics. To make data science more relevant to minority students, the project will highlight minority experiences in the United States by incorporating critical thinking around digital divides and data science ethics into the curriculum.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.

密苏里大学堪萨斯城分校与东密歇根大学、埃塞克斯郡学院和佛罗里达大学合作，提出了开放式协作体验式学习 (OCEL.AI) 项目，这是一个开放知识网络 (OKN)，为高等教育教师提供支持在现有的机构结构内向服务不足的人群教授计算机科学（CS）+新闻学和战略传播。人工智能 (AI)，特别是深度学习 (DL) 的最新进展提高了多个领域中数据驱动方法和工具的最新成果。为了构建未来的人才生态系统，迫切需要培养一支合格的数据科学劳动力队伍，能够在新闻、健康传播和广告等人类社会的各个领域和方面发挥关键作用。为了满足这一需求，基于云的智能协作工具将为人工智能和数据驱动的研究和教育社区提供 GitHub 和代码存储库为软件工程和开源社区提供的服务。在这个 OKN 中，少数族裔学生将感到有能力并有动力学习数据科学，因为他们可以实时共享、重用、复制、部署、讨论、学习和应用数据和人工智能模型。预计参加这些 CS+新闻和战略传播课程的学生，无论是 CS 专业还是非专业学生，尤其是黑人、女性和西班牙裔学生，学习数据科学的兴趣、自我效能感和动力都会有所提高。该项目以网络改进社区的形式组织，将重点关注三项主要任务：1）在 OCEL.AI 上启动由研究人员实验室开发的在线教师专业发展计划； 2) 在 OCEL.AI 上启动学生学习界面，为 CS+新闻学和战略传播专业的学生提供引人入胜的学习体验，同时还提供可扩展到商业和医疗保健等一系列其他 X 的 CS+X 项目的基础设施； 3）激发数据科学伦理的批判性思维。为了使数据科学与少数族裔学生更相关，该项目将通过将围绕数字鸿沟和数据科学伦理的批判性思维纳入课程来突出美国少数族裔的经历。该奖项反映了 NSF 的法定使命，并通过评估认为值得支持利用基金会的智力优势和更广泛的影响审查标准。

项目成果

Defect prediction using deep learning with Network Portrait Divergence for software evolution

• DOI: 10.1007/s10664-022-10147-0
• 发表时间: 2022-06
• 期刊: Empirical Software Engineering
• 影响因子: 4.1
• 作者: V. Walunj;Gharib Gharibi;Rakan Alanazi;Yugyung Lee

Facilitating program comprehension with call graph multilevel hierarchical abstractions

• DOI: 10.1016/j.jss.2021.110945
• 发表时间: 2021
• 期刊: J. Syst. Softw.
• 作者: Rakan Alanazi;Gharib Gharibi;Yugyung Lee

Collaborative deep learning model for tooth segmentation and identification using panoramic radiographs

• DOI: 10.1016/j.compbiomed.2022.105829
• 发表时间: 2022-07-19
• 期刊: COMPUTERS IN BIOLOGY AND MEDICINE
• 影响因子: 7.7
• 作者: Chandrashekar, Geetha;AlQarni, Saeed;Lee, Yugyung
• 通讯作者: Lee, Yugyung

OpenComm: Open community platform for data integration and privacy preserving for 311 calls

• DOI: 10.1016/j.scs.2022.103858
• 发表时间: 2022-05
• 期刊: Sustainable Cities and Society
• 影响因子: 11.7
• 作者: Duy H. Ho;Yugyung Lee;Srichakradhar Nagireddy;C. Thota;Brent Never;Ye Wang