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In teaching environments, student facial expressions are a clue to the traditional classroom teacher in gauging students' level of concentration in the course. With the rapid development of information technology, e-learning will take off because students can learn anytime, anywhere and anytime they feel comfortable. And this gives the possibility of self-learning. Analyzing student concentration can help improve the learning process. When the student is working alone on a computer in an e-learning environment, this task is particularly challenging to accomplish. Due to the distance between the teacher and the students, face-to-face communication is not possible in an e-learning environment. It is proposed in this article to use transfer learning and data augmentation techniques to determine the concentration level of learners from their facial expressions in real time. We found that expressed emotions correlate with students' concentration, and we designed three distinct levels of concentration (highly concentrated, nominally concentrated, and not at all concentrated).