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

Author

Listed:
  • Daniel Czarnowske
  • Florian Heiss
  • Theresa M. A. Schmitz
  • Amrei Stammann

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.

Suggested Citation

  • Daniel Czarnowske & Florian Heiss & Theresa M. A. Schmitz & Amrei Stammann, 2025. "The Effects of Flipped Classrooms in Higher Education: A Causal Machine Learning Analysis," Papers 2507.10140, arXiv.org, revised Oct 2025.
    • Handle: RePEc:arx:papers:2507.10140
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    References listed on IDEAS

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    2. De Paola, Maria & Gioia, Francesca & Scoppa, Vincenzo, 2023. "Online teaching, procrastination and student achievement," Economics of Education Review, Elsevier, vol. 94(C).
    3. Chamberlain, Gary & Rothschild, Michael, 1983. "Arbitrage, Factor Structure, and Mean-Variance Analysis on Large Asset Markets," Econometrica, Econometric Society, vol. 51(5), pages 1281-1304, September.
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