I-Corps: Assessing Capabilities of Adaptive Learning for Predicting and Optimizing Local School-to-Workforce Talent Management Pipelines

这个i-Corps项目的更广泛的影响/商业潜力是为当地经济更好地为不确定的工作未来做好准备。研究表明，当年轻人通过工作和实习及早接触劳动力市场时，他们更有可能在学校取得成功，获得稳定的工作，成年后赚取更高的工资。然而，在过去的40年里，美国的年轻人就业率大幅下降，目前有超过1350万18-24岁的年轻人失学和失业。在这个关键的年龄被切断联系有长期失业和终身收入下降的重大风险，并导致当地经济和税收损失，估计每个年轻人为94万美元。这项拟议的技术将通过整合基于项目的学习和自适应学习算法来提供、跟踪、激励和个性化当地人才发展，从而加强美国城市从学校到劳动力的管道。我们的干预措施有可能通过当地劳动力管道更好地精简和留住年轻人，从而为学校、雇主和当地经济带来积极回报。这个i-Corps项目涉及自适应学习和基于游戏的机制的整合，以个性化和改编基于项目的学习21世纪劳动力准备干预措施，专为14-24岁的年轻人设计。在课堂和以工作为基础的体验学习环境中的早期试点测试中，这种干预措施显示出改善参与者可证明的技能和能力以及对自我指导目标的支持的潜力。根据过去的研究，假设将自适应学习算法和基于游戏的机制相结合，以最大限度地增加小组对话和集体故障排除的面对面培训时间，为人才发展提供个性化监测和建议，并提高最有风险和表现不佳的学习者的满意度、留存率和表现。随着时间的推移，收集的数据应该具有强大的预测能力，以更好地为人才管道提供信息和加强。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。

The broader impact/commercial potential of this I-Corps project is to better prepare local economies for the uncertain future of work. Research has shown that when young adults have early exposure to the labor market through jobs and internships they are more likely to succeed in school, attain stable employment and earn higher wages as adults. However, youth employment in the U.S. has dropped significantly over the past four decades, and there are currently over 13.5 million young adults ages 18-24 who are out of school and out of work. Being disconnected at this critical age has major risks for chronic unemployment and lifetime lower earnings and costs local economies and tax dollars an estimated $940 k per youth. The proposed technology will strengthen school to workforce pipelines across U.S. cities by integrating project-based learning and adaptive learning algorithms to deliver, track, incentivize and personalize local talent development. Our intervention has the potential to better streamline and retain young adults through local workforce pipelines and thus have positive returns for schools, employers and local economies. This I-Corps project involves the integration of adaptive learning and game based-mechanics to personalize and adapt a project-based learning 21st century workforce readiness intervention designed for young adults ranging from ages 14-24. In early pilot tests in classrooms and experience work-based learning settings, this intervention has shown promising potential to improve participants’ demonstrable skills and competencies and support of self-directed goals. Based on past research, the integration of adaptive learning algorithms and game-based mechanics is hypothesized to maximize in-person training hours for group dialogue and collective troubleshooting, provide personalized monitoring and recommendations for talent development, and promote improved satisfaction, retention and performance of most at-risk and underperforming learners. Over time, the data collected should have strong predictive capabilities to better inform and strengthen the talent pipeline.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.