NSF Convergence Accelerator Track H: Developing Experiential Accessible Framework for Partnerships and Opportunities in Data Science (for the deaf community)
NSF 融合加速器轨道 H:为数据科学领域的合作伙伴和机会开发可体验的框架(针对聋人社区)
基本信息
- 批准号:2235473
- 负责人:
- 金额:$ 75万
- 依托单位:
- 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
- 起止时间:2022-12-15 至 2024-11-30
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
This project will create university-to-industry use-inspired research partnerships, curriculum in Data Science, and projects geared specifically for deaf learners, to empower deaf data scientists in the workforce. DEAF PODS will collaborate with partners in industry and academia to create strategic initiatives to overcome barriers and biases that deaf individuals face in the workplace. Developing Experiential Accessible Framework for Partnerships and Opportunities in Data Science (for the deaf community) ["DEAF PODS"] will enable 75 deaf and hard of hearing students to work in teams on data-driven projects with mentors. These interdisciplinary partnerships will span several applied domains. These research stipends will be available for deaf undergraduate students from any college in the USA. The materials for data science training will be available to be used in the many use-inspired research projects in the Convergence Accelerator program. DEAF PODS will also provide nationwide coordination of accessible research experiences for deaf students (from any university) in the data sciences. The DEAF PODS model is designed to be reproducible at other universities. DEAF PODS will strengthen and support the experience of students whose universities cannot (or will not) invest sufficient funds for isolated deaf learners. A key goal is to align faculty, students, and companies, to work closely on use-inspired research. This project will foster a welcoming research culture throughout the year. A key expected outcome is a broader pathway for deaf college students to graduate school and/or to data science careers in industry.The project will use culturally responsive pedagogical strategies to teach data science content to deaf populations. Deaf mentors and students will organically develop the fast-developing language of data sciences in American Sign Language and will freely share this data science content online. Because ASL expression is heavily influenced by the subject's context, it is imperative to work directly with deaf business owners and deaf scientists who are domain experts. A key strategy for success is the emphasis on deaf-deaf mentoring: deaf employees and fluent signers mentoring deaf students. While deaf employees share domain expertise with student learners, the mentors will also benefit by picking up new data science skills during their mid-career. Industry culture and pathways are key aspects of the planned project. The investigators will partner with dedicated corporate partners who have committed to cultivating a welcoming environment, with a culture where deaf data scientists can thrive. DEAF PODS and the greater STEM community have a timely opportunity to create career pathways in data-intensive industries, by offering use-inspired research experiences for deaf and hard of hearing students and mentors. DEAF PODS will use flipped classrooms with active learning, in projects (not lectures) as a crucial piece of the student learning environment. One expected outcome of the project is the integration of captioning, transcripts, and bilingual videos, in both ASL and English, as well as front-loading new vocabulary, immersive examples, and vignettes. Gallaudet has also just begun a new Data Science program. The project will start the work toward building a Corporate Partners program at Gallaudet during Phase 1. Purdue will help to integrate 9-month academic year Corporate Partners experiences for deaf students at Gallaudet, providing working data science experience and professional development. RIT/NTID is also building new data science programs; the team of investigators will work closely with RIT during Phase 1, with plans to expand and build on these activities in Phase 2. DEAF PODS has a comprehensive recruiting and supportive mentoring plan for students to participate remotely in all activities.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.
该项目将创建大学到行业的使用启发研究伙伴关系,数据科学课程,以及专门针对聋人学习者的项目,以增强聋人数据科学家的工作能力。 聋人PODS将与工业界和学术界的合作伙伴合作,制定战略举措,以克服聋人在工作场所面临的障碍和偏见。 开发数据科学伙伴关系和机会的经验可理解框架(聋人社区)[“聋人PODS”]将使75名聋人和重听学生与导师一起在数据驱动的项目中进行团队合作。 这些跨学科的伙伴关系将跨越几个应用领域。 这些研究津贴将提供给来自美国任何大学的聋人本科生。 数据科学培训材料将用于Convergence Accelerator计划中的许多使用启发式研究项目。 DEAF PODS还将在全国范围内为聋人学生(来自任何大学)在数据科学方面提供无障碍研究经验的协调。 聋人PODS模型的设计是在其他大学复制。 聋人PODS将加强和支持那些大学不能(或不愿)为孤立的聋人学习者投入足够资金的学生的经验。 一个关键目标是使教师,学生和公司保持一致,在使用启发的研究上密切合作。 该项目将在全年培养一种受欢迎的研究文化。 一个关键的预期成果是为聋人大学生研究生院和/或数据科学行业的职业生涯提供更广阔的途径。该项目将使用文化响应的教学策略向聋人群体教授数据科学内容。 聋人导师和学生将有机地开发美国手语中快速发展的数据科学语言,并将在线免费分享此数据科学内容。 由于美国手语的表达受到主题背景的严重影响,因此必须直接与聋人企业主和聋人科学家领域专家合作。 成功的一个关键策略是强调聋人指导:聋人员工和流利的手语者指导聋人学生。 虽然聋人员工与学生学习者分享领域专业知识,但导师也将在职业生涯中期获得新的数据科学技能。 行业文化和途径是计划项目的关键方面。 调查人员将与致力于营造一个友好环境的企业合作伙伴合作,营造一种聋人数据科学家能够茁壮成长的文化。 聋人PODS和更大的STEM社区有及时的机会在数据密集型行业创造职业道路,为聋人和重听学生和导师提供使用启发的研究经验。 聋人PODS将使用翻转教室与主动学习,在项目(而不是讲座)作为学生学习环境的一个重要组成部分。 该项目的一个预期成果是整合ASL和英语的字幕、文字记录和双语视频,以及前端加载的新词汇、沉浸式示例和小插曲。 Gallaudet也刚刚开始一个新的数据科学计划。 该项目将在第一阶段开始在Gallaudet建立企业合作伙伴计划。 普渡大学将帮助Gallaudet的聋人学生整合9个月的学年企业合作伙伴经验,提供工作数据科学经验和专业发展。 RIT/NTID也在建立新的数据科学项目;研究人员团队将在第一阶段与RIT密切合作,并计划在第二阶段扩展和建立这些活动。 聋人PODS有一个全面的招聘和支持辅导计划,让学生远程参与所有活动。该奖项反映了NSF的法定使命,并通过使用基金会的智力价值和更广泛的影响审查标准进行评估,被认为值得支持。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Mark Ward其他文献
Utility of iliac vein stenting in elderly population older than 80 years
- DOI:
10.1016/j.jvsv.2014.08.001 - 发表时间:
2015-01-01 - 期刊:
- 影响因子:
- 作者:
Seshadri Raju;Mark Ward - 通讯作者:
Mark Ward
52. MICROAGGRESSIONS IN ROTATION EVALUATIONS BY RESIDENTS
- DOI:
10.1016/j.acap.2019.05.066 - 发表时间:
2019-08-01 - 期刊:
- 影响因子:
- 作者:
Alisa A. Acosta;Andria Tatem;Mark Ward;Carla N. Falco - 通讯作者:
Carla N. Falco
Silicon evaluation of longest path avoidance testing for small delay defects
针对小延迟缺陷的最长路径避免测试的芯片评估
- DOI:
10.1109/test.2007.4437564 - 发表时间:
2007 - 期刊:
- 影响因子:0
- 作者:
R. Turakhia;W. R. Daasch;Mark Ward;J. V. Slyke - 通讯作者:
J. V. Slyke
Community partnership lessons learned from the You & Me: Test and Treat study
- DOI:
10.1186/s12889-025-23216-y - 发表时间:
2025-06-06 - 期刊:
- 影响因子:3.600
- 作者:
Emily M. D’Agostino;Isa Granados;Princess Abbott-Grimes;Camille Brown-Lowery;Allyn Damman;Tigidankay Fadika;Mark Ward;Mia Cooper;Jeannine Sato;Janet Kasper;Tatiana Vizcaino;Wes Gray;Allison Swart;Amanda Sparling;Claudia G. Corchado;Christoph P. Hornik - 通讯作者:
Christoph P. Hornik
Comparing the effectiveness of deterministic bridge fault and multiple-detect stuck fault patterns for physical bridge defects: A simulation and silicon study
比较确定性桥梁故障和物理桥梁缺陷的多重检测卡住故障模式的有效性:模拟和硅研究
- DOI:
10.1109/test.2009.5355762 - 发表时间:
2009 - 期刊:
- 影响因子:0
- 作者:
S. Goel;Narendra Devta;Mark Ward - 通讯作者:
Mark Ward
Mark Ward的其他文献
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{{ truncateString('Mark Ward', 18)}}的其他基金
HDR DSC: National Data Mine Network
HDR DSC:国家数据挖掘网络
- 批准号:
2123321 - 财政年份:2021
- 资助金额:
$ 75万 - 项目类别:
Continuing Grant
REU Site: Diverse Undergraduate Research Experiences in Statistics
REU 网站:统计学本科生的多样化研究经验
- 批准号:
1560332 - 财政年份:2016
- 资助金额:
$ 75万 - 项目类别:
Continuing Grant
Lake Michigan Workshop on Combinatorics and Graph Theory
密歇根湖组合学和图论研讨会
- 批准号:
1600382 - 财政年份:2016
- 资助金额:
$ 75万 - 项目类别:
Standard Grant
MCTP: Sophomore Transitions: Bridges into a Statistics Major and Big Data Research Experiences via Learning Communities
MCTP:二年级学生过渡:通过学习社区过渡到统计学专业和大数据研究经验
- 批准号:
1246818 - 财政年份:2013
- 资助金额:
$ 75万 - 项目类别:
Continuing Grant
Collaborative Research: Science of Information: Bringing Many Disciplines Together
合作研究:信息科学:将许多学科结合在一起
- 批准号:
1140489 - 财政年份:2012
- 资助金额:
$ 75万 - 项目类别:
Standard Grant
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