A Study on Reinforcement of Self-Directed Learning using Controlling Face Emotion

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.