Student Emotion Recognition System (SERS) for e-learning Improvement Based on Learner Concentration Metric

Emotion originates from old Frenchesmovoir toexcite, from Latinēmovēre to disturb and movēre to move, which is also the theme of the paper. Emotions of a student during course engagement play a vital role in any learning environment whether it's in classrooms or in e-learning. We use excite, disturb and moving pattern of eyes and head to infer meaningful information to understand mood of the student while engaged in an e-learning environment. Emotion detection methods have been in focus of the researchers across various disciples to understand the user involvement, effectiveness and usefulness of the system implemented or to be implemented. Our focus is on understanding and interpolating the emotional state of the learner during a learning engagement. Evaluating the emotion of a learner can progressively help in enhancing learning experience and update the learning contents. In this paper, we propose a system that can identify and monitor emotions of the student in an e-learning environment and provide a real-time feedback mechanism to enhance the e-learning aids for a better content delivery. Detection of eyes, head movement can help us understand learner concentration level. Since our metric are captured from eyes and head movement we eliminate the need of any device usage that requires physical contact to the subject understudy. The proposed system helps to identify emotions and classify learner involvement and interest in the topic which are plotted as feedback to the instructor to improve learner experience.