Mining Login Data For Actionable Student Insight

Student login data is a key resource for gaining insight into their learning experience. However, the scale and the complexity of this data necessitate a thorough exploration to identify potential actionable insights, thus rendering it less valuable compared to student achievement data. To compensate for the underestimation of login data importance, in this paper we performed an exploratory data analysis of a large educational dataset consisting of 100 million instances of login data from 1.5 million unique students who attempted 783 thousand assignments. The data were from a McGraw-Hill Education web-based assessment platforms called Connect. Different data mining methods were employed to answers our initial questions regarding students’ login behavior. Most of the findings were consistent with the intuitive expectations of student login patterns such as a considerable decline of activity on Saturdays, a visible peak on Sunday evenings, a high activity in September and February, and an increased activity toward later hours of the day. However, we also discovered an unexpected result while investigating the effects of the login activity, the performance scores, and the attempts. Surprisingly, this analysis showed a high positive correlation between login activity and performance scores, only up to a certain threshold. This provided us a new hypothesis on student groupings, which we explored through a cluster analysis. As a result of our exploratory efforts, a significant amount of patterns emerged that not only confirmed previously set forth expectations but also provided us new hypotheses, which can be leveraged to improve student outcomes.