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，解决国家重要性的挑战，并在不久的将来显示出可交付成果的潜力。“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与职业相关的技能发展为个人做好这种转变的准备。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，终身学习者正面临着越来越大的信息鸿沟。为了解决这个问题，在第一阶段，我们将开发和试点测试有意义的数据可视化和分析，以了解现有学习机会和证书的相关性和有效性，帮助学习者做出明智的、符合劳动力市场的再培训选择。二期项目将开发职业推荐“GPS”平台。该平台结合了数千门课程和证书课程的学习分析和成果数据，以及实时劳动力市场数据，将为美国工人在整个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、燃烧玻璃技术公司和O*Net。“融合加速器”第一阶段项目促进了对“工作流程中的学习”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应对快速变化的劳动力市场。它将展示如何将劳动力市场和课程教学大纲数据、学习分析和对所学技能可转移性的见解以新颖的方式组合和可视化，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进行决策、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，并更好地使课程与不断变化的劳动力市场技能需求保持一致。本文提出了三条趋同的研究和开发路线：(1)开发基于设计的研究范式，并结合创新学习分析（DBR+LA）来研究、评估和改进课程；(2)应用DBR+LA对教授关键技能的课程进行有效性评估并提出改进建议。(3)对数以百万计的职位招聘和课程大纲进行数据挖掘、建模和可视化，以识别当前技能供需的差距，并早期识别新兴的高需求技能，从而形成“再技能推荐系统”，指导终身学习选择。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识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.