  Analysis of log data generated by online educational systems is an essential
task to better the educational systems and increase our understanding of how
students learn. In this study we investigate previously unseen data from Clio
Online, the largest provider of digital learning content for primary schools in
Denmark. We consider data for 14,810 students with 3 million sessions in the
period 2015-2017. We analyze student activity in periods of one week. By using
non-negative matrix factorization techniques, we obtain soft clusterings,
revealing dependencies among time of day, subject, activity type, activity
complexity (measured by Bloom's taxonomy), and performance. Furthermore, our
method allows for tracking behavioral changes of individual students over time,
as well as general behavioral changes in the educational system. Based on the
results, we give suggestions for behavioral changes, in order to optimize the
learning experience and improve performance.
