Proceedings ArticleDOI
Emotion Recognition for User Centred E-Learning
Akputu K. Oryina,Abiodun O. Adedolapo +1 more
- Vol. 2, pp 509-514
TLDR
A new architecture of FERT is presented, Emotion recognition of the FERT scheme employs Multiple Kernel Learning (MKL) framework which reportedly outperforms traditional classifiers, and a conceptually user centred e-learning model that has potentials for improving learning interaction is described.Abstract:
A Vision of most e-learning models is to accurately recognize learner's post (pre) learning feedbacks to improve learning interaction. Several effort towards user centred e-learning have been made in literature, but mostly concentrates on cognitive based feedbacks for learner's modelling. However, Beside cognitive factors, emotions of the learner are equally important but seldom neglected. This paper present a new architecture of FERT. A processing pipeline of the FERT is realized through results of a preliminary analysis across selected facial features descriptive techniques, namely, Gabor wavelet, Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). Further more, Emotion recognition of the FERT scheme employs Multiple Kernel Learning (MKL) framework which reportedly outperforms traditional classifiers. Experiments have been conducted on contextual emotion datasets and results shows good performances of the FERT scheme. Finally, a conceptually user centred e-learning model that has potentials for improving learning interaction is described.read more
Citations
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Journal ArticleDOI
Emotion Recognition Using Multiple Kernel Learning toward E-learning Applications
TL;DR: The potential of utilizing affect or emotion recognition research in AEH models is explored and the conceptual Emotion-based E-learning Model (EEM) with the proposed emotion recognition framework is proposed for future work.
Proceedings ArticleDOI
A study of support vector machines for emotional speech recognition
TL;DR: Efficiency comparison of Support Vector Machines (SVM) and Binary Support vector Machines (BSVM) techniques in utterance-based emotion recognition is studied, showing accuracy improvement in some emotions, such as sadness and happiness emotion.
Journal ArticleDOI
Effects of the Application of Mobile Learning to Criminal Law Education on Learning Attitude and Learning Satisfaction
TL;DR: Wang et al. as mentioned in this paper conducted a 16-week experimental research on students of a department of the university to evaluate the effect of mobile learning on learning attitude, learning attitude on learning satisfaction, and learning satisfaction on learning.
Book ChapterDOI
Emotion Recognition on E-Learning Community to Improve the Learning Outcomes Using Machine Learning Concepts: A Pilot Study
TL;DR: E-learning community, with its varied interest and expectations on its learning interface, needs more focus when it comes to providing them with the most suitable learning opportunities, hence in order to personalize the interface, simple and robust mechanisms must be framed.
Proceedings ArticleDOI
Emotion Detection Using Unobtrusive methods: An Integrated Approach
Abdul Wahid,Farecha Rasheed +1 more
TL;DR: A unique methodology to detect emotions using the unobtrusive method of keystrokes, mouse clicks, forum discussions and results of assessments if any in an E-learning scenario is proposed.
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