(untitled) (International Conference on Computers in Education, 2019)
This paper discusses a machine learning approach for classifying student emotions while doing programming exercises. Detection of academic emotions in programming from face features has previously been shown to be a difficult task because people don’t tend to display as much expression as compared to more social activities. In our approach, we show that adding log features in addition to face features can improve the performance of classifiers. Furthermore, we show that identifying representative intervals of each emotion type based on human annotations can be used to build models to classify emotion over longer periods of time. We believe that our study can contribute in the development of better intelligent programming tutors that can respond to the affective state of students.