Computer and Robot Vision
Comprehensive Learning Interactive and Group Activities as New Ways of Learning
ICCE '02 Proceedings of the International Conference on Computers in Education
SmartTutor: an intelligent tutoring system in web-based adult education
Journal of Systems and Software
Personalized e-learning system using Item Response Theory
Computers & Education
Fuzzy fusion for skin detection
Fuzzy Sets and Systems
Attentiveness assessment in learning based on fuzzy logic analysis
Expert Systems with Applications: An International Journal
An automated face reader for fatigue detection
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Determining driver visual attention with one camera
IEEE Transactions on Intelligent Transportation Systems
Self-assessment in a feasible, adaptive web-based testing system
IEEE Transactions on Education
Short communication: New results in modelling derived from Bayesian filtering
Knowledge-Based Systems
Automatic generation of emotions in tutoring agents for affective e-learning in medical education
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Incremental learning of complete linear discriminant analysis for face recognition
Knowledge-Based Systems
Face recognition using discriminant sparsity neighborhood preserving embedding
Knowledge-Based Systems
A new automatic identification system of insect images at the order level
Knowledge-Based Systems
Domain-specific knowledge representation and inference engine for an intelligent tutoring system
Knowledge-Based Systems
A fuzzy integral fusion approach in analyzing competitiveness patterns from WCY2010
Knowledge-Based Systems
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Distance learning can solve the limitations of time and space in learning. However, due to the distance, teachers cannot manage students learning behaviors, i.e. they do not know whether a student is attentive, drowsy or absent. Teachers can overcome difficulties in students' management by knowing the affective states of the students. This study adopts image recognition to capture face images of students when they are learning, and analyzes their face features to evaluate their affective states by fuzzy integrals. Test results indicate that the bad affective states are accurately identified. Teachers can monitor the students' affective states from the detection results on the system interface.