Coding, Analysis, Interpretation, and Recognition of Facial Expressions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Affective computing
Proceedings of HCI International (the 8th International Conference on Human-Computer Interaction) on Human-Computer Interaction: Ergonomics and User Interfaces-Volume I - Volume I
The production and recognition of emotions in speech: features and algorithms
International Journal of Human-Computer Studies - Application of affective computing in humanComputer interaction
Pupil size variation as an indication of affective processing
International Journal of Human-Computer Studies - Application of affective computing in humanComputer interaction
Facial expression recognition from video sequences: temporal and static modeling
Computer Vision and Image Understanding - Special issue on Face recognition
Multimodal affect recognition in learning environments
Proceedings of the 13th annual ACM international conference on Multimedia
The FaceReader: measuring instant fun of use
Proceedings of the 4th Nordic conference on Human-computer interaction: changing roles
Real-time estimation of emotional experiences from facial expressions
Interacting with Computers
Adaptive Self-Assessment Trying to Reduce Fear
ACHI '08 Proceedings of the First International Conference on Advances in Computer-Human Interaction
Towards an Affective Aware Home
ICOST '09 Proceedings of the 7th International Conference on Smart Homes and Health Telematics: Ambient Assistive Health and Wellness Management in the Heart of the City
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Emotions are very important during learning and assessment procedures. However, measuring emotions is a very demanding task. Several tools have been developed and used for this purpose. In this paper, the efficiency of the FaceReader during a computer-based assessment (CBA) was evaluated. Instant measurements of the FaceReader were compared with the researchers' estimations regarding students' emotions. The observations took place in a properly designed room in real time. Statistical analysis showed that there are some differences between FaceReader's and researchers' estimations regarding Disgusted and Angry emotions. Results showed that FaceReader is capable of measuring emotions with an efficacy of over 87% during a CBA and that it could be successfully integrated into a computer-aided learning system for the purpose of emotion recognition. Moreover, this study provides useful results for the emotional states of students during CBA and learning procedures. This is actually the first time that student's instant emotions were measured during a CBA, based on their facial expressions. Results showed that most of the time students were experiencing Neutral, Angry, and Sad emotions. Furthermore, gender analysis highlights differences between genders' instant emotions.