American Journal of Applied Mathematics and Statistics
ISSN (Print): 2328-7306 ISSN (Online): 2328-7292 Website: Editor-in-chief: Mohamed Seddeek
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American Journal of Applied Mathematics and Statistics. 2020, 8(3), 90-95
DOI: 10.12691/ajams-8-3-2
Open AccessArticle

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

Dalia Kamal Fathi Alnagar1,

1Statistics Department, University of Tabuk, Tabuk, Ksa

Pub. Date: September 14, 2020

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


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.

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

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[1]  Stefanovic, Darko, et al. "Empirical study of student satisfaction in e-learning system environment." Technics technologies education management 6.4 (2011), 1152-1164.
[2]  Arbaugh, J. B. Virtual classroom characteristics and student satisfaction with internet based MBA courses. Journal of Management Education,24(1), (2000). 32-54.
[3]  Areti, V. Satisfying distance education students of the Hellenic Open University. E-mentor, 2 (14), (2006). 1-12.
[4]  Bender, D. M., Wood, B. J., & Vredevoogd, J. D. Teaching time: Distance education versus classroom instruction. The American Journal of Distance Education, 18 (2), (2004), 103-114.
[5]  Roberts, T. G., Irani, T. A., Telg, R. W., & Lundy, L. K. The development of an instrument to evaluate distance education courses using student attitudes. The American Journal of Distance Education, 19 (1), (2005): 51- 64.
[6]  Fullerton, G., Taylor, S. "Mediating, interactive, and non-linear effects in service quality and satisfaction with services research", Canadian Journal of Administrative Sciences, Vol. 19 No.2, (2002), pp.124-36.
[7]  Eswari, J. Satya, et al. "Prediction of stenosis behaviour in artery by neural network and multiple linear regressions." Biomechanics and Modeling in Mechanobiology. (2020): 1-15.
[8]  McClelland, J.L., Rumelhart, D.E., and Hinton, G.E. The appeal of parallel distributed processing, in Parallel Distributed Processing: Explorations in the Microstructure of Cognition - Foundations, Vol.1, MIT Press, Cambridge, (1986), pp.3-44.
[9]  Leverington.. A Basic Introduction to Feedforward Backpropagation Neural Networks, (2009).
[10]  Rojas Raúl. Neural Networks: A Systematic Introduction, Springer-Verlag, Berlin, New-York. ,(1996)
[11]  IBM .Knowledge Center., (2016).
[12]  R.Ruhin kouser, J.Daphney Joann, K. Suganya, FORECASTING STUDENT ACADEMIC PERFORMANCE BY DECISION TREE LEARNING USING ARTIFICIAL NEURAL NETWORKS, International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 Volume: 03 Issue: 12 | Dec -2016.
[13]  Zacharis, Nick Z. "Predicting student academic performance in blended learning using Artificial Neural Networks." International Journal of Artificial Intelligence and Applications 7.5 (2016): 17-214.
[14]  Tufaner, Fatih, and Yavuz Demirci. "Prediction of biogas production rate from anaerobic hybrid reactor by artificial neural network and nonlinear regressions models." Clean Technologies and Environmental Policy (2020): 1-12.
[15]  Asogwa, O. C., et al. "On the Modeling of the Effects of COVID-19 Outbreak on the Welfare of Nigerian Citizens, Using Network Model." American Journal of Applied Mathematics and Statistics 8.2 (2020): 58-63.
[16]  asser, Ibrahim M., and Samy S. Abu-Naser. "Predicting Tumor Category Using Artificial Neural Networks." (2019).
[17]  O. C. Asogwa and A. V. Oladugba .Of Students Academic Performance Rates Using Artificial Neural Networks (ANNs). American Journal of Applied Mathematics and Statistics.3, (4) (2015): 151-155.
[18]  Dziuban, Charles, et al. "Student Satisfaction with Online Learning: Is It a Psychological Contract?." Online Learning 19.2 (2015): n2..
[19]  Wu, Jen-Her, Robert D. Tennyson, and Tzyh-Lih Hsia. "A study of student satisfaction in a blended E-learning system environment." Computers & Education 55.1 (2010): 155-164.