Using Artificial Neural Network to Predicted Student Satisfaction in E-learning

Dalia Kamal Fathi Alnagar^1,

¹Statistics Department, University of Tabuk, Tabuk, Ksa

Cite 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

Abstract

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.

Keywords:
artificial neural network student satisfaction e-learning correct classification rate ROC AUC

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