Multilayer feedforward networks are universal approximators
Neural Networks
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
The nature of statistical learning theory
The nature of statistical learning theory
The Aplusix-Editor: A New Kind of Software for the Learning of Algebra
ITS '02 Proceedings of the 6th International Conference on Intelligent Tutoring Systems
Automatic prediction of frustration
International Journal of Human-Computer Studies
Affective learning companions: strategies for empathetic agents with real-time multimodal affective sensing to foster meta-cognitive and meta-affective approaches to learning, motivation, and perseverance
Learning from Data: Concepts, Theory, and Methods
Learning from Data: Concepts, Theory, and Methods
UM '07 Proceedings of the 11th international conference on User Modeling
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
Predicting Stress Level Variation from Learner Characteristics and Brainwaves
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
User Modeling and User-Adapted Interaction
IEEE Transactions on Information Technology in Biomedicine - Special section on affective and pervasive computing for healthcare
KSE '11 Proceedings of the 2011 Third International Conference on Knowledge and Systems Engineering
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Brainwaves EEG signals and mouse behavior information are shown to be useful in predicting academic emotions, such as confidence, excitement, frustration and interest. Twenty five college students were asked to use the Aplusix math learning software while their brainwaves signals and mouse behavior number of clicks, duration of each click, distance traveled by the mouse were automatically being captured. It is shown that by combining the extracted features from EEG signals with data representing mouse click behavior, the accuracy in predicting academic emotions substantially increases compared to using only features extracted from EEG signals or just mouse behavior alone. Furthermore, experiments were conducted to assess the prediction accuracy of the system at points during the learning session where several of the extracted features significantly deviate in value from their mean. The experiments confirm that the prediction performance increases as the number of feature values that deviate significantly from the mean increases.