eng Science and Education Publishing American Journal of Applied Mathematics and Statistics 2328-7292 2020-09-14 8 3 90 95 10.12691/ajams-8-3-2 AJAMS2020832 article Dalia Kamal Fathi Alnagar Dalia_kk@hotmail.com 1 Statistics Department, University of Tabuk, Tabuk, Ksa 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. http://pubs.sciepub.com/ajams/8/3/2/ajams-8-3-2.pdf artificial neural network student satisfaction E-learning correct classification rate ROC AUC