The Effects of Flipped Classrooms in Higher Education: A Causal Machine Learning Analysis

• URL: http://arxiv.org/abs/2507.10140v2
• Date: Mon, 27 Oct 2025 14:59:40 GMT
• Title: The Effects of Flipped Classrooms in Higher Education: A Causal Machine Learning Analysis
• Authors: Daniel Czarnowske, Florian Heiss, Theresa M. A. Schmitz, Amrei Stammann,
• Abstract summary: This study uses double/debiased machine learning (DML) to evaluate the impact of transitioning from lecture-based blended teaching to a flipped classroom concept.<n>Our findings indicate effects on students' self-conception, procrastination, and enjoyment.<n>We do not find significant positive effects on exam scores, passing rates, or knowledge retention.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This study uses double/debiased machine learning (DML) to evaluate the impact of transitioning from lecture-based blended teaching to a flipped classroom concept. Our findings indicate effects on students' self-conception, procrastination, and enjoyment. We do not find significant positive effects on exam scores, passing rates, or knowledge retention. This can be explained by the insufficient use of the instructional approach that we can identify with uniquely detailed usage data and highlights the need for additional teaching strategies. Methodologically, we propose a powerful DML approach that acknowledges the latent structure inherent in Likert scale variables and, hence, aligns with psychometric principles.

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