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B1: Competency Catalyst Phase II

B1: Competency Catalyst Phase II
B1:能力催化剂第二阶段
批准号:
2033578
负责人:
Robert Robson
金额:
$500.0万
依托单位:
依托单位国家:
美国
项目类别:
Cooperative Agreement
财政年份:
2020
资助国家:
美国
项目状态:
已结题
起止时间:
2020-09-01 至 2024-02-29

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中文摘要
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英文摘要
The NSF Convergence Accelerator supports use-inspired, team-based, multidisciplinary efforts that address challenges of national importance and will produce deliverables of value to society in the near future.This Convergence Accelerator Phase II project aims to increase the efficiency and effectiveness of reskilling America’s workforce. To accomplish this, the Competency Catalyst has two major deliverables. The first is an application called "Skillsync" that enables companies to communicate specific reskilling needs and helps colleges respond with accelerated programs that target them. The second is a “C2 platform” that describes and aligns job requirements and reskilling opportunities in terms of knowledge, skills, and abilities (KSAs). Skillsync is built on the C2 platform and replaces existing manual processes with direct, real-time communication between human resource departments and college programs. Skillsync will immediately improve the ability of companies to reskill their workforce, while the C2 platform will catalyze and support a larger ecosystem of workforce-related applications, including other Convergence Accelerator projects and national efforts focused on improving the talent pipeline. Skillsync and the C2 platform will be designed and developed by a multidisciplinary team of learning technologists, AI researchers, education and workforce development professionals, open data architects, and experts in competency-based training. Skillsync will be piloted with multiple company-college pairs to upskill a diverse population of workers. After completing the pilots, the Competency Catalyst team plans to maintain the C2 platform as a public good, and to offer Skillsync as a subscription service paid for by companies and free to colleges. The Competency Catalyst will build on multiple enabling technologies, including (a) the Jill Watson AI-based conversational assistant from the Georgia Institute of Technology; (b) the Credential Engine’s open data infrastructure; and (c) digital competency extraction tools and the open-source Competency and Skills System (CaSS) developed by Eduworks. The development approach will include participatory research, human-centered design, rapid prototyping, and pilot testing. The C2 platform will programmatically ingest data from national sources and apply recent natural language understanding and machine learning methods to represent these data as structured sets of KSAs with relations and links to external resources. In Skillsync, these can be edited and ordered by importance to represent company needs. The C2 platform will offer an open alignment score service that Skillsync will use to analyze matches between desired KSAs and available educational and training resources. In addition, Skillsync users will be able to engage with Jill Watson to better understand how colleges view the skills that learners acquire. This will assist companies in defining their needs in terms that are meaningful to college partners. Equity and bias issues will be considered throughout the design and development of the Skillsync and the C2 platform, including issues such as bias and fairness in AI and machine-learned models. The project will also research the relative contributions of AI, enhanced digital communication, and socio-technological behavioral change to improving company-college reskilling partnerships. Anticipated contributions include new techniques for evaluating and aligning jobs and training based on KSAs and a new public open data resource consisting of the KSAs associated with national repositories of jobs, course syllabi, credentials, and educational resources.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(4)
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会议论文
Extended Abstract: Making AI work for skills-based training: A case study.
扩展摘要:让人工智能用于基于技能的培训:案例研究。
DOI: --
发表时间: 2022
期刊: International Training Technology Exhibition & Conference (IT²EC
影响因子: --
作者: [Kelsey, Elaine, Goel, Ashok, Egerton, Lauren, Nasir, Sazzad, Lisle, Mathew., LaFleur, Alan, Robson, Elliot]
通讯作者: Robson, Elliot
Intelligent links: AI‐supported connections between employers and colleges
智能链接:人工智能支持雇主和大学之间的连接
DOI: 10.1002/aaai.12040
发表时间: 2022
期刊: AI Magazine
影响因子: 0.9
作者: [Robson, Robby, Kelsey, Elaine, Goel, Ashok, Nasir, Sazzad M., Robson, Elliot, Garn, Myk, Lisle, Matt, Kitchens, Jeanne, Rugaber, Spencer, Ray, Fritz]
通讯作者: Ray, Fritz
DOI: 10.48550/arxiv.2206.05030
发表时间: 2022-06
期刊: ArXiv
影响因子: --
作者: [Ashok K. Goel;Harsh Sikka;Vrinda Nandan;Jeonghyun Lee;Matt Lisle;S. Rugaber]
通讯作者: Ashok K. Goel;Harsh Sikka;Vrinda Nandan;Jeonghyun Lee;Matt Lisle;S. Rugaber
DOI: 10.1007/978-3-031-11647-6_17
发表时间: 2022
期刊: Practitioners’ and Doctoral Consortium. AIED 2022
影响因子: --
作者: [Molnar, L., Mehta, R.K., Robson, R.]
通讯作者: Robson, R.
RAPID: Bridging the Health Care Skill Gap
  • 批准号:
    2029590
  • 项目类别:
    Standard Grant
  • 资助金额:
    $19.91万
  • 财政年份:
    2020
  • 负责人:
    Robert Robson
  • 依托单位:
SBIR Phase II: Applying Semantic Paradata to Outcomes-aligned Assessment
  • 批准号:
    1353200
  • 项目类别:
    Standard Grant
  • 资助金额:
    $75.0万
  • 财政年份:
    2014
  • 负责人:
    Robert Robson
  • 依托单位:
SBIR Phase I: ASPOA: Applying Semantic Paradata to Outcomes-aligned Assessment
  • 批准号:
    1214187
  • 项目类别:
    Standard Grant
  • 资助金额:
    $15.0万
  • 财政年份:
    2012
  • 负责人:
    Robert Robson
  • 依托单位:
Ubiquitous Contextual Access to STEM Educational Resources (UCASTER)
  • 批准号:
    1044161
  • 项目类别:
    Standard Grant
  • 资助金额:
    $52.17万
  • 财政年份:
    2010
  • 负责人:
    Robert Robson
  • 依托单位:
海外基金