%AShakya, A.%ARus, V.%AFancsali, S.%ARitter, S%AVenugopal, D.%BJournal Name: Proceedings of The Third Workshop of the Learner Data Institute , The 15th International Conference on Educational Data Mining (EDM 2022)
%D2022%I
%JJournal Name: Proceedings of The Third Workshop of the Learner Data Institute , The 15th International Conference on Educational Data Mining (EDM 2022)
%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.
%0Journal Article
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