Improving Inference of Learning Related Emotion by Combining Cognitive and Physical Information

摘要

Researches in areas such as neuroscience and psychology indicate that emotions directly impact learning. So, adapting to the learners' affective reactions became a requirement and also a challenge for building a new generation of affect aware computing learning environments. In this paper, we present a hybrid approach for inferring learning related emotion that combines cognitive and physical data, gathered using minimal or non intrusive methods. In an initial experiment with students in a real education environment it was possible to obtain promising results when comparing some usual performance metrics with correlated works. In this study we achieved accuracy rates and Cohen's Kappa near to 65% and 0.55, respectively. Furthermore, considering the open and expansible nature of this proposal, we believe that this results could be improved in the future by adding new data or new sensors to the model, for example.