Predicting students' emotions using machine learning techniques

Detecting students' real-time emotions has numerous benefits, such as helping lecturers understand their students' learning behaviour and to address problems like confusion and boredom, which undermine students' engagement. One way to detect students' emotions is through their feedback about a lecture. Detecting students' emotions from their feedback, however, is both demanding and time-consuming. For this purpose, we looked at several models that could be used for detecting emotions from students' feedback by training seven different machine learning techniques using real students' feedback. The models with a single emotion performed better than those with multiple emotions. Overall, the best three models were obtained with the CNB classiffier for three emotions: amused, bored and excitement.