A Computational Model to Embed Emotion Awareness into e-Learning Environments

Abstract

Emotions are present in any form of learning and social interaction. The realistic use of the emerging educational technologies has drawn attention to the consideration of emotions in learning environments. Towards this direction, Affective Computing is offering remarkable system implementations that detect and recognise students’ emotional states with high accuracy using machine learning algorithms, and provide feedback aiming at both students’ cognitive performance and their emotional regulation.
However, these technologies often employ expensive sensors and complex computer intelligence that require special expertise or extra resources, while they introduce obtrusiveness and invasiveness in the learning process. Moreover, research on emotions in e-learning is still rather limited while the enrichment of learning environments with emotion awareness capabilities it is still in its infancy. There is still a need for more realistic, in-context studies to investigate successful affective learning sequences that propel students’ self-motivation and engagement.
The main objective of this thesis is to investigate the importance of emotion awareness in e-learning environments. To this end, a conceptual model has been developed, including affective states and moods of interest that usually appear in e-learning settings with emphasis in Computer Supported Collaborative Learning (CSCL) scenarios. In the basis of this model, a computational model has been implemented consisting of a usable, expressive and effective multimedia interface for students to report their affective state and an affective virtual agent, employed with expressive faces to provide affectuve and task-based feedback in response to the students’ reported emotions. The integration was also enhanced with effective visualisations of students’ individual and group affective states.