SBIR Phase I: A novel method to scaling mentoring and career development in Institutes of Higher Education

Award ID: 2232502

The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project is to increase post-secondary student success via academic and career guidance. A large body of research on career navigation has studied how post-secondary education, career readiness (understanding viable career paths at graduation), and its interconnectedness are important for a growing number of first-generation, low-income, and underrepresented students. With increasing undergraduate degree program offerings in response to an evolving future of work and student-to-counselor ratios of 1: 1,800 in public colleges, career guidance, and academic navigation risk being unavailable. As a result, during the pandemic, Institutes of Higher Education (IHEs) that serve students of color and students from low-income backgrounds saw declines in enrollment that far outpaced their predominantly White peer institutions. The proposed platform intends to increase the visibility, accessibility, and discoverability of competencies to potential career and academic paths for students at IHEs. The platform envisions doing this via near-peer role models who are similar in their dimensions of self-efficacy. With more than 85 million jobs that could go unfilled by 2030, the proposed platform may help alleviate part of that shortage by widening the talent aperture. The intellectual merit of this project is in the companys patented technology of a unified, multi-dimensional, data representation model that creates a competency fingerprint for each user. The data representation method enables better machine learning models to infer competency from unstructured data of a students traditional and non-traditional learning experience, rather than degrees, majors, grade point averages (GPAs), or test scores. The platform uses a consistent, scalable, competency nomenclature for hard and soft skills gained via traditional academic and outside-of-the-classroom experiences to discover academic-career paths where the students learning competencies may be in demand. There is a significant technical challenge in adopting this technology for the inter/cross-disciplinary jobs of the future: such a platform requires a robust, larger data set to evaluate the relevance of matching, in a discipline-agnostic context. Reduction of this variability is the key technical risk to be overcome by the proposed research and development. 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.

NSF Program Director: Rajesh Mehta