How to Aggregate Multimodal Features for Perceived Task Difficulty Recognition in Intelligent Tutoring Systems

Currently, a lot of research in the field of intelligent tutoring systems is concerned with recognising student’s emotions and affects. The recognition is done by extracting features from information sources like speech, typing and mouse clicking behaviour or physiological sensors. Multimodal affect recognition approaches use several information sources. Those approaches usually focus on the recognition of emotions or affects but not on how to aggregate the multimodal features in the best way to reach the best recognition performance. In this work we propose an approach which combines methods from feature selection and ensemble learning for improving the performance of perceived task difficulty recognition.