SBIR Phase II: A K-12 Goal-Setting and Reflection Platform that Builds Student Learning Skills and Mindset

Sown To Grow, Inc., Oakland CA

Investigators

Abstract

The broader impact/commercial potential of the Small Business Innovation Research (SBIR) Phase II project may be a fundamental shift in academic and life outcomes for students, especially those from low-income and vulnerable populations, by building metacognition and Social Emotional Learning (SEL) skills. This project will advance the company's technology that builds critical SEL and metacognition skills in a way that is student-led and integrated with academic content and routines. The effort expands teacher capacity in a measurable way. The project will apply Natural Language Processing (NLP) techniques to the company’s proprietary data of student reflections and teacher feedback, will train Machine Learning (ML) driven predictive algorithms to measure reflection quality and will build a learning strategies recommendation engine. This innovation will allow teachers and students to expand their toolkits of effective pedagogically-sound learning strategies. The deeper application of the innovation unlocks commercialization potential coupled with transformational outcomes for student learning. The Small Business Innovation Research Phase II project will build on the company's NSF-funded research and Phase I results that support the feasibility and commercialization of applying NLP techniques and ML algorithms based on the company’s proprietary data to “read” student reflections, rate them on a quality rubric, and help teachers provide feedback. Stage I of this project will be a lower effort, fast value objective to apply data science and analytics to roll-up real-time quality assessments to measure growth at the student. The project will provide reports on the reflection quality to school administrators and feedback tips to teachers based on student reflection quality and classroom context. Stage II would be a higher effort. One objective will be to build a Strategy Recommendation Engine driven by NLP and ML techniques and based on the latest research in pedagogy and learning sciences. This technology will analyze past reflections to identify which learning strategies a student has tried, then provide teachers with suggestions on pedagogy-driven strategies they can be recommended to improve the students' learning. 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.