标题： 翻转课堂在高等教育中的影响：因果机器学习分析 显示英文标题

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

摘要： 本研究使用双重/去偏机器学习来评估从以讲座为主的混合教学转向翻转课堂概念在德国一所免学费大学的一门大型必修入门统计学课程的队列比较中的影响。 我们的研究结果表明，学生的自我认知有积极的变化，并且拖延行为有所减少。 然而，我们也观察到课堂活动的乐趣有所下降。 与理论预期相反，我们没有发现考试成绩、通过率或知识保留方面的显著积极影响。 然而，与其他大多数研究不同，我们可以利用翻转课堂组的详细使用数据，包括课前视频观看的及时性和完整性，以及测验参与模式，以检查学生如何实施课程的每个部分。 我们的研究结果表明，平均而言，翻转课堂组的学生在实施教学方法方面不足，这解释了我们在考试表现和知识保留方面零结果的机制。 这突显了需要额外策略，以确保学生真正从翻转课程中受益。 显示英文摘要

摘要： This study uses double/debiased machine learning to evaluate the impact of transitioning from lecture-based blended teaching to a flipped classroom concept in a cohort comparison of a large compulsory introductory statistics course at a German tuition-free university. Our findings indicate positive changes in students' self-conception and a reduction in procrastination behaviors. However, we also observe a decline in the enjoyment of classroom sessions. Contrary to theoretical expectations, we do not find significant positive effects on exam scores, passing rates, or knowledge retention. Unlike most studies, however, we can leverage detailed usage data from the flipped cohort, including the timeliness and completeness of pre-class video watching, as well as quiz participation patterns, to check how well students implemented each part of the curriculum. Our findings suggest that, on average, students in the flipped cohort implemented the instructional approach insufficiently, explaining the mechanism of our null results in exam performance and knowledge retention. This highlights the need for additional strategies to ensure that students actually benefit from a flipped curriculum.