Design and Implementation of Affective E-Learning Strategy Based on Facial Emotion Recognition

Abstract

E Learning is emerging as a heavily learner-centric, emphasizing pervasive and personalized learning technology. Affective learning outcomes in a nutshell, involve attitudes, motivation, and values. In the same tune we can also define the affective E-learning, as a strategy, which implies recognition of learner’s emotion and selection of pedagogy in a best possible way. For the best delivery, learner’s affective state needs to be identified where the key solution is emotion recognition. Our work focuses on emotion detection using biophysical signals which further explores the evolution of emotion during learning process, to generate a feedback that can be used to improve learning experiences. Our research is deeply focused into the aspects of operative content delivery mechanism by using physiological facial signals for the detection of learner’s emotion but without detecting the face. In this paper we propose a key technique to detect learner’s facial expression, based on neural network classification and selection of appropriate learning style, which shows reasonable results in comparison with the other existing systems. The result manifests that the recognizer system is effective.