@article{ajams2020832,
author={Alnagar, Dalia Kamal Fathi},
title={Using Artificial Neural Network to Predicted Student Satisfaction in E-learning},
journal={American Journal of Applied Mathematics and Statistics},
volume={8},
number={3},
pages={90--95},
year={2020},
url={http://pubs.sciepub.com/ajams/8/3/2},
issn={2328-7292},
abstract={In this study, a constructed multi-layer perceptron artificial neural network model was created.. This study examines the determinants of student satisfaction in E-learning and proposes a model for identifying factors that influence student satisfaction using artificial neural networks for students at Tabuk University. The study model conducting using a questionnaire survey of 321 participants who studied E-learning, attitudes, and reactions of instructors, the flexibility of E-learning courses, interaction in virtual classrooms, and E-learning according to various evaluations. It is carried out using a prediction of student satisfaction. The workshop and prepared explanations helped students in the use of E-learning, the quality of the internet, and the nature of the course E-learning in the Dean's office. The model predicted student satisfaction with E-learning per 92.2% correct classification rate (CCR). The values in the   region below the ROC curve (AUC) of the model were rated excellent (0.990%). The results show that different assessments are a powerful determinant of learning satisfaction.},
doi={10.12691/ajams-8-3-2}
publisher={Science and Education Publishing}
}