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

TL;DR

This paper employs a causal machine learning approach to evaluate the impact of flipped classrooms in higher education, revealing effects on student attitudes but no significant improvements in exam scores or retention.

Contribution

It introduces a novel double/debiased machine learning method tailored for Likert scale data, addressing latent psychometric structures in educational research.

Findings

Effects on students' self-conception, procrastination, and enjoyment

No significant impact on exam scores or knowledge retention

Highlights need for additional teaching strategies

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.