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

Vanderbilt University, Nashville TN

Investigators

Abstract

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.