%AShakya, A.%ARus, V.%AFancsali, S.%ARitter, S%AVenugopal, D.%D2022%I %K %MOSTI ID: 10353238 %PMedium: X %TNeTra: A Neuro-Symbolic System to Discover Strategies in Math Learning %XUnderstanding how students with varying capabilities think about problem solving can greatly help in improving personalized education which can have significantly better learning outcomes. Here, we present the details of a system we call NeTra that we developed for discovering strategies that students follow in the context of Math learning. Specifically, we developed this system from large-scale data from MATHia that contains millions of student-tutor interactions. The goal of this system is to provide a visual interface for educators to understand the likely strategy the student will follow for problems that students are yet to attempt. This predictive interface can help educators/tutors to develop interventions that are personalized for students. Underlying the system is a powerful AI model based on Neuro-Symbolic learning that has shown promising results in predicting both strategies and the mastery over concepts used in the strategy. Country unknown/Code not availableOSTI-MSA