  In this paper we conduct an analysis of Moodle activity data focused on
identifying early predictors of good student performance. The analysis shows
that three relevant hypotheses are largely supported by the data. These
hypotheses are: early submission is a good sign, a high level of activity is
predictive of good results and evening activity is even better than daytime
activity. We highlight some pathological examples where high levels of activity
correlates with bad results.

Computers and Society;Artificial Intelligence