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

NSF融合加速器支持以团队为基础的多学科努力，以应对国家重要性的挑战，并在不久的将来展示可交付成果的潜力。 这个融合加速器第一阶段项目的更广泛的影响/潜在利益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）和自动化所改变。我们国家的经济竞争力和社会结构取决于我们如何通过职业相关技能发展为个人做好准备。 然而，随着工作技能要求的不断变化和在线培训选择的激增，终身学习者面临着越来越大的信息差距。 为了解决这个问题，在第一阶段，我们将开发和试点测试有关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，以帮助学习者做出明智的、与劳动力市场一致的再培训选择。在第二阶段，该项目将开发职业推荐“GPS”平台。该平台将从数千门课程和证书产品以及实时劳动力市场数据中收集学习分析和成果数据，为美国工人在一生的学习和技能发展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。 这个融合加速器第一阶段项目促进了对“工作流中的学习”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者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，学习分析以及对所学技能可转移性的见解如何以新颖的方式结合和可视化，以支持学习者对通过教育和工作相关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，并使课程更好地与不断变化的劳动力市场技能需求保持一致。 提出了三条研究和开发的趋同路线：（1）开发基于设计的研究范式，结合创新的学习分析（DBR+LA）来研究，评估和改进课程;（2）应用DBR+LA来评估有效性，并为教授关键技能的课程提出改进建议。(3)通过对数百万个招聘信息和课程大纲进行数据挖掘、建模和可视化，以识别当前技能供求的差距，并及早识别新兴的高需求技能，从而形成一个“再培训推荐系统”，以指导终身学习的选择。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。

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.