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.
@cite_14: Although previous meta-analyses have documented the efficacy of computer-assisted statistics instruction, the current study examined a range of specific features that presumably influence its effectiveness, such as the level of learner engagement, learner control, and the nature of feedback. In 45 experimental studies with a control condition, computer-assisted statistics instruction provided a meaningful average performance advantage (d = 0.33). Because of great methodological heterogeneity among the studies, the authors employed a conservative but appropriate mixed effects model to examine potential moderator effects. The authors’ analyses revealed three statistically significant findings. Larger effects were reported in studies in which treatment groups received more instructional time than control groups, in studies that recruited graduate students as participants, and in studies employing an embedded assessment. A newly developed second order standardized mean effect size, ddiff, reveals that additio...
@cite_13: AbstractComputer-assisted assessment (CAA) is widely used in modern university courses in many different fields. It can be used both in formative and summative assessments and with different emphas...
@cite_12: 
The overall engagement of students is indisputably one of the main covariates of academic success. For the case of mathematical statistics this has been shown on several occasions. @cite_14 show in a meta-study that the simultaneous usage of traditional classroom lectures and e-assessment has a positive effect on students' success. @cite_13 substantiate the previous result by analyzing the learning activity on the e-assessment platform JACK. The study reveals that learning effort and success, measured by the total number of (correct) submissions on JACK over the course, positively affects the final grade in the exam. @cite_12 add additional R-programming exercises to the JACK framework and show that the newly introduced exercise type helps to improve the general understanding of fundamental statistical concepts and thus ultimately yields better results in the final exam.