Teachers/lecturers typically adapt their teaching to respond to students' emotions, e.g. provide more examples when they think the students are confused. While getting a feel of the students' emotions is easier in small settings, it is much more difficult in larger groups. In these larger settings textual feedback from students could provide information about learning-related emotions that students experience. Prediction of emotions from text, however, is known to be a difficult problem due to language ambiguity. While prediction of general emotions from text has been reported in the literature, very little attention has been given to prediction of learning-related emotions. In this paper we report several experiments for predicting emotions related to learning using machine learning techniques and n-grams as features, and discuss their performance. The results indicate that some emotions can be distinguished more easily then others. [For complete proceedings, see ED560503.]