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A Dynamic Mixture Model to Detect Student Motivation and Proficiency

This article discusses a machine learning-based approach to assess student motivation and proficiency in a geometry tutoring system tailored for high school learners preparing for assessments like the SAT and MCAS. We present a dynamic mixture model that estimates the state of a student, enabling the system to personalize learning experiences and intervention strategies. Through comprehensive testing, we analyze the model's effectiveness and provide insights into future enhancements that can further support student learning outcomes.

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A Dynamic Mixture Model to Detect Student Motivation and Proficiency

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