A Study on Reinforcement of Self Directed Learning using Controlling Face Emotion
Dong Hwa Kim1, Young Sung Kim2

1Prof. Dr. Dong Hwa Kim*, Researcher, Seoul National University of Science and Technology, Seoul, South Korea. 
2Prof. Dr. Young Sung Kim, Graduating School of Nano Science, Information, Design and Engineering, Seoul National University of Science and Technology, Seoul, South Korea.
Manuscript received on January 13, 2022. | Revised Manuscript received on January 15, 2022. | Manuscript published on January 30, 2022. | PP: 76-83 | Volume-10 Issue-5, January 2022. | Retrieval Number: 100.1/ijrte.E67620110522 | DOI: 10.35940/ijrte.E6762.0110522
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: This paper deals with emotion-based self-directed teaching and learning in online education. Teachers and learners cannot understand how much their communication exchanges well with each other. So, their teaching and learning efficiency decreases than their expectation. To increase teaching and learning efficiency, this paper analyzes face emotional patterns to figure out which emotion segments have dominant facts in teaching and learning through Korean women’s face data. These dominant factors are sent to control for improving self-directed learning. In the control system, deep learning compares face data with reference data and finally decides the control signal to improve self-directed learning. 
Keywords: Face Emotion, Online Education, Self-Directed Teaching and Learning, Emotion Reinforcement.
Scope of the Article: Computer Science and Engineering.