Understanding Student Perceptions of Flipped Linear Algebra Classrooms via Interpretable Machine Learning

TL;DR

This study uses interpretable machine learning to analyze student perceptions of flipped linear algebra classrooms, revealing gender-based perception differences and key factors influencing engagement.

Contribution

It introduces an interpretable machine learning approach to identify stable perception patterns and factors affecting student engagement in flipped classrooms.

Findings

Gender-based perception differences are consistent and stable.

Key engagement and instructional factors influence perception patterns.

Interpretable models help identify structured perception differences.

Abstract

Flipped classroom pedagogy is widely used in undergraduate mathematics to promote active learning, yet it remains unclear whether students experience it in systematically different ways. In this study, we analyze student perceptions from an introductory linear algebra course using survey data collected across multiple semesters. Using an interpretable machine learning approach, we examine patterns across questionnaire responses and evaluate their consistency under repeated analysis. Our results reveal a clear and stable separation in perception patterns when grouped by gender, suggesting that these differences arise from structured combinations of factors rather than isolated responses. The model also identifies key aspects of engagement and instructional design that contribute most to this separation. These findings highlight opportunities for more inclusive flipped classroom design and demonstrate the value of interpretable methods in educational research.