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Research Article

EEO. 2021; 20(2): 1832-1843

Emotion Metric Detection By Machine Learning For E-Learning System

Elmissaoui Taoufik, Agbo, Jonathan Chukwunwike, Onyedeke Obinna Cyril, Olayiwola Abisola Ayomide,Uzo Blessing Chimezie, Ikedilo Obiora Emeka, Oluwatobi Adedamola Ayilara.


Online education became fully possible with the rapid growth in technological development. The quality assurance of e-learning system is a very important factor to consider in enhancing the learning 4.0. The student emotion detection is one from the most solution that helps optimize the e-learning process. The emotion detection is based on student facial expressions recognition. This method makes it possible for the teacher to know the concentration rate of their learners. In this paper, we proposed a new system that enables the teacher follow in real time the student’s concentration. The concentration level is plotted in the teacher’s screen. Student’s facial detection parameters can help us measure the concentration rate of each learner. Our application can be used as a metric for student attention to enhance the pedagogical method. It also ensure the teacher maintains the discipline needed for the smooth running of the remote classroom with the help of our alert agent which sends a signal message to the teacher each time there is malicious acts going on showing. This application enables the teacher generate a detailed report of the learner’s attention and concentration. This report will contain a classification of the student’s concentration level and the course section classification of the student concentration number. The result of this will help the teacher optimize their course contents.

Key words: E-learning, Emotion metric, concentration level, machine learning

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