Main Article Content
E-Learning nowadays is one of the learning system which uses the latest technologies in the field of innovative learning, it has been an extension of traditional education. The effectiveness of E-Learning lies in achievement of education and improving the student's performance and its reflection on the demands of students by discovering the weaknesses and strengths of the factors affecting distance learning. In this research we have used the multilayered neural networks (feedforward neural network) with an input of five neurons which represent the five criteria (virtual class presence, Discussion during semester, Solving Quiz, Mid-term examination, Assignment), hidden layer has two neurons and the output layers have one neuron. to estimate the performance of the students attending an E-Learning course, feedforward neural network was applied to real data )400 student records (80%) are used for training data and the remaining 100 records (20%) are used as test data, performance = 0.0699), to predict the performance of the students that reflect their real grades.
Dublin L. If you only look under the street lamps or nine E-Learning Myths. The E-Learning Developers Journal. Available:http://www.eLearningguild.com
Rossi PG. Learning environment with artificial intelligence elements. Journal of E-Learning and Knowledge Society. 2009; 5(1):67-75.
European Commission. The E-Learning action plan: Designing tomorrow’s education; 2001.
Monahan T, McArdle G, Bertolotto M. Virtual reality for collaborative e- learning. Computers & Education. 2008;50(4):1339-1353.
Trelease RB. Essential E-Learning and M-learning methods for teaching anatomy. In teaching anatomy. Springer International Publishing. 2015;247-258.
Van Nuland S, Rogers K. E-Learning: Effective or Defective? The impact of commercial E-Learning tools on learner cognitive load and anatomy instruction. The FASEB Journal. 2015;29(1 Supplement):550-17.
Algahtani AF. Evaluating the effectiveness of the E-Learning experience in some universities in Saudi Arabia from male student’s perceptions, Durham theses, Durham University; 2011.
Fei T, Heng WJ, Toh KC, Qi T. Question classification for E-Learning by artificial neural network. In: ICICS-PCM; 2003.
Parminder Kaur, Kiranjit Kaur, Gurdeepak Singh. Improving E-Learning with neural networks. International Journal of Computing & Business Research, Proceedings of ‘I- Society’ at GKU, Talwandi Sabo Bathinda (Punjab); 2012.
Mota J. Using learning styles and neural networks as an approach to E-Learning content and layout adaptation. DSIE’08 – Doctoral Symposium on Informatics Engineering; 2008.
Kolb DA, Experiential learning - experience as the source of learning and development. New Jersey: Prentice hall P T R; 1984.
Petar Halachev. Prediction of E-Learning efficiency by neural networks. Cybernetics and information technologies. Sofia. 2012; 12(2).
Dekson DE, Jaichandran R. Neural network based performance monitoring system for E-Learning portal. International Conference on Recent Advancement in Mechanical Engineering and Technology (ICRAMET’ 15). Journal of Chemical and Pharmaceutical Sciences; 2015.
Mohamed Sayed, Faris Baker. E-Learning optimization using supervised artificial neural-network. Journal of software engineering and applications. 2015;8:26-34.
Villaverde JE, D Godoy_w, Amandi A. Learning styles recognition in e-learning environments with feed-forward neural networks. Journal of Computer Assisted Learning 22. 2006;197–206.
Howard Demuth, Mark Beale, Martin Hagan. Neural network toolbox™ 6 user’s guide. Copyright 1992–2009 by The Math Works, Inc;1992.
Haykin S. Neural networks a comprehensive foundation, 2nd Edition. Prentice Hall; 1999.