Improvement of E-learning Based via Learning Management Systems (LMS) Using Artificial Neural Networks

Khaled . M. G Noama *

Department of Distance Learning, Deanship of E-Learning and Information Technology, Jazan University, KSA.

Ahmed Khalid

Department of Information System, Community College Najran University, KSA.

Arafat A. Muharram

Department of Computer Science, Faculty of Computer Science and Engineering, Hodeidah University, Yemen.

Ibrahim A. Ahmed

Department of Information System, Community College Najran University, KSA.

*Author to whom correspondence should be addressed.


Abstract

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.

Keywords: Distance learning, learning management system, artificial neural networks


How to Cite

Noama, Khaled . M. G, Ahmed Khalid, Arafat A. Muharram, and Ibrahim A. Ahmed. 2019. “Improvement of E-Learning Based via Learning Management Systems (LMS) Using Artificial Neural Networks”. Asian Journal of Research in Computer Science 4 (1):1-9. https://doi.org/10.9734/ajrcos/2019/v4i130105.

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