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.

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.人工智能（AI），特别是深度学习（DL）的最新进展改善了广泛领域的数据驱动方法和工具的最新结果。为了建立未来的人才生态系统，迫切需要创建合格的数据科学劳动力，该劳动力可以在人类社会的各个领域和各个方面执行关键职能，例如新闻，健康沟通和广告。为了满足这一需求，基于云的智能协作工具将为AI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东西。在此OKN中，少数族裔学生将有能力和动力研究数据科学，因为他们可以实时共享，重复使用，复制，部署，讨论，学习，学习和应用数据和AI模型。预计参加这些CS+新闻业和战略沟通课程的学生将会发现兴趣，自我效能感和动力增加CS专业和非律师之间的数据科学，尤其是在黑人，女性和西班牙裔学生中。该项目作为一个网络改进社区组织，将重点放在三个主要任务上：1）在研究人员实验室开发的OCEL.AI上启动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+新闻学和战略沟通学生提供吸引人的学习经验，同时还提供了可以扩展到CS+X计划的基础架构，以供其他一系列其他XS（例如商业和卫生保健）； 3）引发对数据科学伦理的批判性思考。为了使数据科学与少数族裔学生更加相关，该项目将通过将数字鸿沟和数据科学伦理围绕批判性思考纳入课程中来强调美国的少数派经验。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的审查标准来通过评估来支持的。

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