文章基本信息

标题：When is the Best Time to Learn? – Evidence from an Introductory Statistics Course
本地全文：下载
作者：Till Massing ; Natalie Reckmann ; Alexander Blasberg 等
期刊名称：Open Education Studies
电子版ISSN：2544-7831
出版年度：2021
卷号：3
期号：1
页码：84-95
DOI：10.1515/edu-2020-0144
摘要：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 and the time they spend with exercises. 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.
关键词：Learning analytics; E-assessment; effective learning times