The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase I project is to empower individuals to thrive in a world of innovative disruption through the development of an enduring, data-driven, ready-access digital service that will assist learners to identify opportunity pathways throughout a lifetime of learning and employment. Completion of this project and the addition of the proposed analytical capabilities applied to learner narratives may create a powerful and unique set of tools for individuals, secondary and higher educational institutions, employers, and a range of informal education providers. The research completed in this project and tools produced by it seek to inform a new approach to matching the interests, skills and educational backgrounds of learners to jobs and educational opportunities. This approach engages learners of diverse backgrounds early in their education and assists them in succeeding in many fields that currently suffer from lack of diversity, especially in the STEM disciplines. Better matching of skills and interests to learning trajectories and career options may enhance human development broadly, increase job satisfaction, and lead to a more productive workforce. This Small Business Technology Transfer (STTR) Phase I project seeks to develop methods for bringing a wide range of learner interests and achievements into a common framework that will be used to match a learnerâs current state to specific opportunities and general development pathways. The project also seeks to assess the value of opportunities and chance of success for the student. This research centers on machine analysis of written reflections. Findings digitally mined from written reflections and potentially from other kinds of narrative artifacts are combined with data from grades, test scores, and other skills assessments and certifications to guide learners toward attractive educational and employment opportunities. In addition, findings from interactive visualization tools support human exploratory analyses of machine categorizations in order to improve the delivery of educational programs.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
