| Summary: | E-learning has become a critical factor in the academic environment due to the endless number of possibilities that it opens for the learning context. However, these platforms often suppose to increase the difficulties for the communication between teachers and students. Without having real contact between teachers and students, the former finds it harder to adapt their methods and content to their students, while the students also find complications for maintaining their focus. This paper aims to address this challenge with the use of emotion and engagement recognition techniques. We propose an emotion-aware e-learning platform architecture that recognizes students’ emotions and attention in order to improve their academic performance. The system integrates a semantic task automation system that allows users to easily create and configure their own automation rules to adapt the study environment. The main contributions of this paper are: (1) the design of an emotion-aware learning analytics architecture; (2) the integration of this architecture in a semantic task automation platform; and (3) the validation of the use of emotion recognition in the e-learning platform using partial least squares structural equation modeling (PLS-SEM) methodology.
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