This study has investigated whether it is possible to classify time series data originating from a gamified learning management system. By using the school data provided by the gamification company Insert Coin AB, the aim was to distribute the teacher’s supervision more efficiently among students who are more likely to fail. Motivating this is the possibility that the student retention and completion rate can be increased. This was done by using Long short-term memory and convolutional neural networks and Markov chain to classify time series of event data. Since the classes are balanced the classification was evaluated using only the accuracy metric. The results for the neural networks show positive results but overfitting seems to occur strongly for the convolutional network and less so for the Long short-term memory network. The Markov chain show potential but further work is needed to mitigate the problem of a strong correlation between sequence length and likelihood.