Recent research has provided solid evidence that emotions strongly affect motivation and hence play an important role in learning. In BIG-AFF, we build on the hypothesis that “it is possible to provide learners with a personalised support that enriches their learning process and experience by using low intrusive (and low cost) devices to capture affective multimodal data that include cognitive, behavioural and physiological information”.
In previous research, we have explored the viability of multimodal approaches to detect emotions and provide affective support. This research has led to the identification of open issues that need to be addressed to advance the scientific and technological knowledge regarding emotion recognition.
In particular we are dealing with information sources, context modelling, indivdual influence and data processing with various approaches, thus covering low-intrussive and low-cost in real-world settings.