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arXiv · 1906.09864

When is the best time to learn? -- Evidence from an introductory statistics course

Till Massing·Natalie Reckmann·Alexander Blasberg·Benjamin Otto·Christoph Hanck·Michael Goedicke
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Abstract

We analyze learning data of an e-assessment platform for an introductory mathematical statistics course, more specifically the time of the day when students learn. We propose statistical models to predict students' success and to describe their behavior with a special focus on the following aspects. First, we find that learning during daytime and not at nighttime is a relevant variable for predicting success in final exams. Second, we observe that good and very good students tend to learn in the afternoon, while some students who failed our course were more likely to study at night but not successfully so. Third, we discuss the average time spent on exercises. Regarding this, students who participated in an exam spent more time doing exercises than students who dropped the course before.

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Till Massing, Natalie Reckmann, Alexander Blasberg, Benjamin Otto, Christoph Hanck, Michael Goedicke. 2019-06-24. When is the best time to learn? -- Evidence from an introductory statistics course. https://doi.org/10.1515/edu-2020-0144

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