SBIR Phase II: A System for Enhancing Metacognitive and Problem-Solving Abilities in Engineers

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project involves both reduced costs for technology companies as well as increased access to cutting-edge engineering jobs for people from all backgrounds. Commercializing this product will provide the market with an innovative, automated solution that reduces the time hiring managers spend on recruiting and growing the best candidates. Until now, metacognitive skill development has been the purview of in-person mentors – a practice that provides fewer on-ramps into engineering careers for individuals from backgrounds historically underrepresented in technology as they often lack access to in-person engineer mentors. A scalable system which delivers metacognitive benefits to users in an automated way may create greater access to technology education by reducing the need for in-person mentorship. The product will also enable job seekers to enhance their engineering potential by strengthening the "hard to teach" practices which distinguish professional engineers from aspiring ones. Overall, the product commercialized during Phase II seeks to increase broader participation in engineering careers and a more equitable set of hiring practices in technology companies. This Small Business Innovation Research (SBIR) Phase II project brings cutting-edge Cognitive Science to bear on the problems of manual candidate screening and ongoing employee upskilling. For the first-time, it marries an Intelligent Tutoring System (ITS) with an Applicant Tracking System (ATS) and opens the door to build talent pipelines for individuals whose metacognitive traits match closely with the needs of tech employers. The proposed project combines key advances in the field of Cognitive Science with best practices in software design in order to create novel user interactions inside a Computer-Based Learning Environment (CBLE). Objectives include: 1) collecting microdata (also called "trace data") from user interactions to make automated inferences about the user's metacognitive traits and 2) creating inputs designed to foster metacognitive growth. Correlating real-time trace data with metacognitive ability is a new area of research enabled only recently by the increased adoption of CBLEs. The proposed project contributes to the fields of Cognitive Science and Human-Computer Interaction by validating novel methods measuring metacognition against existing static look-back techniques. The project also pioneers new modes of delivering personalized metacognitive growth through CBLE interaction. 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.