Automatic detection of learner's affect from conversational cues
User Modeling and User-Adapted Interaction
The relative impact of student affect on performance models in a spoken dialogue tutoring system
User Modeling and User-Adapted Interaction
UM '07 Proceedings of the 11th international conference on User Modeling
Affect-aware tutors: recognising and responding to student affect
International Journal of Learning Technology
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
Modeling mental workload using EEG features for intelligent systems
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
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Modeling learners' emotional states is a promising tool for enhancing learning outcomes and tutoring abilities. In this paper, we present a new perspective of learner emotional modeling according to two fundamental dimensions, namely mental workload and engagement. We hypothesize that analyzing results from learners' workload and engagement evolution can help Intelligent Tutoring Systems diagnose learners' emotional states and understand the learning process. We demonstrate by an experiment involving 17 participants that learners' mental workload and engagement are closely related to specific emotions with regard to different learning phases.