Interpretable ML on multi-semester student surveys from a flipped linear algebra course identifies stable gender-based differences in perception patterns driven by combinations of engagement and design factors.
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Understanding Student Perceptions of Flipped Linear Algebra Classrooms via Interpretable Machine Learning
Interpretable ML on multi-semester student surveys from a flipped linear algebra course identifies stable gender-based differences in perception patterns driven by combinations of engagement and design factors.