1Statistics Department, University of Tabuk, Tabuk, Ksa
American Journal of Applied Mathematics and Statistics.
2020,
Vol. 8 No. 3, 90-95
DOI: 10.12691/ajams-8-3-2
Copyright © 2020 Science and Education PublishingCite this paper: Dalia Kamal Fathi Alnagar. Using Artificial Neural Network to Predicted Student Satisfaction in E-learning.
American Journal of Applied Mathematics and Statistics. 2020; 8(3):90-95. doi: 10.12691/ajams-8-3-2.
Correspondence to: Dalia Kamal Fathi Alnagar, Statistics Department, University of Tabuk, Tabuk, Ksa. Email:
Dalia_kk@hotmail.comAbstract
Multi-Layer Perceptron Artificial Neural Network constructed model was established in this study. The study suggests a model to examines the determining factors of student satisfaction in e-learning and identifying the factors that have an influence on student satisfaction using the artificial neural network for the University of Tabuk student. The study model is conducted using a questionnaire survey of 321participants were studied in the e-learning and predicted student satisfaction in e-learning depended on Instructor attitude and response, e-learning Course flexibility, interaction in the virtual classroom, diversity in assessments, the workshops and explanations prepared by the Deanship of E-Learning helped a student to use e-learning, internet quality and type of course. The model predicted student satisfaction in e-learning per correct classification rate, CCR, of (92.2%). The value of the area under ROC curve (AUC) of the model which was classified as excellent (0.990%). The results show that diversity in assessments strong determinants of learning satisfaction.
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