Convergence Accelerator Phase I (RAISE): Analytics-Driven Accessible Pathways To Impacts-Validated Education (ADAPTIVE)

融合加速器第一阶段 (RAISE)：分析驱动的无障碍途径，实现影响力验证的教育 (ADAPTIVE)

• 批准号: 1936656
• 负责人: Katy Borner
• 金额: $ 99.5万
• 依托单位: Indiana University
• 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
• 起止时间: 2019-09-01 至 2021-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1936656&HistoricalAwards=false
• 关键词: Convergence; Accelerator; Phase; RAISE; Analytics

The NSF Convergence Accelerator supports team-based, multidisciplinary efforts that address challenges of national importance and show potential for deliverables in the near future. The broader impact/potential benefit of this Convergence Accelerator Phase I project is the development of powerful, data-driven tools to support the tens of millions of US workers whose jobs are being transformed by Artificial Intelligence (AI) and automation. Our nation's economic competitiveness and social fabric depend on how well we prepare individuals for this transformation via career-relevant skills development. Yet, with ever-changing job skill requirements and the proliferation of online training options, lifelong learners are facing a growing information gap. To address this, in Phase I, we will develop and pilot test meaningful data visualizations and analytics about the relevance and efficacy of available learning opportunities and credentials to help learners make informed, labor market aligned reskilling choices. In Phase II, the project will develop a career recommender "GPS" platform. Incorporating learning analytics and outcomes data from thousands of course and credential offerings and real-time labor market data, the platform will provide equitable access to decision support to US workers across a lifetime of learning and skill development. The team is comprised of data and learning scientists, economists, education researchers, and course designers. Partners include The Boeing Company, Burning Glass Technologies, and O*Net. This Convergence Accelerator Phase I project advances understanding of "learning in the flow of work" and develops new data-rich tools to help learners and education providers navigate the rapidly changing labor market. It will demonstrate how labor market and course syllabi data, learning analytics, and insights on transferability of learned skills can be combined and visualized in novel ways to support a learner's decision-making about, sustained engagement in, and application to their job of professional skills acquired through education and job-related training. Instructors and course developers can also apply these data-driven insights to support faster revision cycles and better alignment of courses with ever-changing labor market skill demands. Three convergent lines of research and development are proposed: (1) Development of a Design-Based Research paradigm coupled with innovative Learning Analytics (DBR+LA) to study, evaluate, and improve courses; (2) Application of DBR+LA to assess the effectiveness and suggest improvements for courses that teach critical skills. (3) Data mining, modeling, and visualization of millions of job postings and course syllabi to identify gaps in current skill supply and demand and early identification of emerging high demand skills resulting in a "reskilling recommender system" to guide lifelong learning choices.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.

NSF融合加速器支持基于团队的多学科努力，以应对国家重要性的挑战，并在不久的将来显示出交付成果的潜力。融合加速器第一阶段项目的更广泛影响/潜在好处是开发强大的数据驱动工具，以支持数千万美国工人，他们的工作正在被人工智能(AI)和自动化转变。我们国家的经济竞争力和社会结构取决于我们通过与职业相关的技能发展为这种转变做好准备的程度。然而，随着工作技能要求的不断变化和在线培训选项的激增，终身学习者正面临着越来越大的信息鸿沟。为了解决这一问题，在第一阶段，我们将开发并试点测试关于可用学习机会和证书的相关性和有效性的有意义的数据可视化和分析，以帮助学习者做出知情的、与劳动力市场一致的再技能选择。在第二阶段，该项目将开发一个职业推荐器“GPS”平台。该平台整合了来自数千门课程和证书提供的学习分析和结果数据以及实时劳动力市场数据，将为美国工人在终身学习和技能发展过程中提供公平的决策支持。该团队由数据和学习科学家、经济学家、教育研究人员和课程设计师组成。合作伙伴包括波音公司、Burning Glass Technologies和O*Net。这一融合加速器第一阶段项目增进了对“在工作流程中学习”的理解，并开发了新的数据丰富的工具，以帮助学习者和教育提供商在快速变化的劳动力市场中导航。它将展示劳动力市场和课程大纲数据、学习分析和对所学技能可转移性的洞察如何以新颖的方式结合和可视化，以支持学习者关于通过教育和与工作相关的培训获得的专业技能的决策、持续参与和应用。教师和课程开发人员还可以应用这些数据驱动的见解来支持更快的修订周期，并根据不断变化的劳动力市场技能需求更好地调整课程。研究和开发的三条趋同路线被提出：(1)发展基于设计的研究范式和创新学习分析(DBR+LA)来研究、评估和改进课程；(2)应用DBR+LA来评估教授关键技能的课程的有效性并提出改进建议。(3)对数以百万计的招聘信息和课程大纲进行数据挖掘、建模和可视化，以找出当前技能供求中的差距，并及早识别新兴的高需求技能，从而产生一个指导终身学习选择的“重新技能推荐系统”。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。