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This content will become publicly available on July 1, 2023

Title: NeTra: A Neuro-Symbolic System to Discover Strategies in Math Learning
Understanding 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.
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Proceedings of The Third Workshop of the Learner Data Institute , The 15th International Conference on Educational Data Mining (EDM 2022)
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National Science Foundation
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