Inferring Students’ Emotions Using a Hybrid Approach that Combine Cognitive and Physical Data

There are nowadays a strong agreement in the research community that emotions directly impact learning. Then, an important feature for a software that claim to be useful for learning is to deal with students’ affective reactions. These software should be able to adapt to the users’ affective reactions, trying to get a more natural human-computer interaction. Some priors works achieved relative success in inferring students’ emotion. However, most of them, rely on intrusive, expensive or little practical sensors that track students’ physical reactions. This paper presents as its main contribution the proposal of a hybrid model for emotion inference, combining physical and cognitive elements, using cheaper and little intrusive method to collect data. First experiments with students in a traditional classroom demonstrated the feasibility of this proposal and also indicated promising results for inference of learning centered emotions. In these experiments we achieve an accuracy rate and Cohen Kappa near to 65% and 0.55, respectively, in the task of inferring five classes of learning centered emotion. Even though these results are better than some related work, we believe they can be improved in the future by incorporating new data to the model.