We present a novel approach for automatic report generation from time-series data, in the context of student feedback generation. Our proposed methodology treats content selection as a multi-label (ML)
classification problem, which takes as input time-series data and outputs a set of templates, while capturing the dependencies between selected templates. We show that this method generates output closer to the feedback that lecturers actually generated, achieving 3.5% higher accuracy and 15% higher F-score than multiple simple classifiers that keep a history of selected templates. Furthermore, we compare a ML classifier with a Reinforcement Learning (RL) approach in simulation and using ratings from real student users. We show that the different methods have different benefits, with ML being more
accurate for predicting what was seen in the training data, whereas RL is more exploratory and slightly preferred by the students.