@conference {9179477,
	title = {Detecting Education level using Facial Expressions in E-learning Systems},
	booktitle = {2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE)},
	year = {2020},
	note = {n/a},
	month = {June},
	pages = {1-6},
	abstract = {<p>With the growth rate of modern technologies, Computer-based learning environment receives attention for academic goals. In this environment, a computer provides learners with a set of learning contents divided into learning levels. Usually, Computer-based learning environment research efforts detect the next level of the learner automatically based on the correct responses of the learner on a test at the end of every learning level. Different efforts use fuzzy approaches to handle the uncertainly in the learning environment. In this paper, a machine learning approach is proposed to detect the current education level of the learner based on a recorded facial expressions of the learners as well as important features of the learning environment. Several classifiers are employed to recognize the education level. The evaluation of the proposed approach on a real dataset shows that Support Vector Machine (SVM) outperforms the other classifiers and achieves accuracy of 87\%. The paper also presents a regression method to detect the learning level as a continuous value. The evaluation of the regression methods shows that the Linear Regression with mean squared error of 0.0048 outperform SVR.</p>
},
	keywords = {computer aided instruction, computer-based learning environment, Decision tree, E-Learning, e-learning systems, education level detection, Electronic learning, Emotion recognition, Face, facial expressions, Feature extraction, fuzzy logic, learning (artificial intelligence), Linear regression, Machine Learning, Machine Learning Approach, SVM, SVR},
	doi = {10.1109/ICECCE49384.2020.9179477},
	author = {Hesham, H and M. {Nabawy} and O. {Safwat} and Y. {Khalifa} and H. {Metawie} and A. Mohammed}
}
