E-Learning Optimization Using Supervised Artificial Neural-Network


Improving learning outcome has always been an important motivating factor in educational inquiry. In a blended learning environment where e-learning and traditional face to face class tutoring are combined, there are opportunities to explore the role of technology in improving student’s grades. A student’s performance is impacted by many factors such as engagement, self-regulation, peer interaction, tutor’s experience and tutors’ time involvement with students. Furthermore, e-course design factors such as providing personalized learning are an urgent requirement for improved learning process. In this paper, an artificial neural network model is introduced as a type of supervised learning, meaning that the network is provided with example input parameters of learning and the desired optimized and correct output for that input. We also describe, by utilizing e-learning interactions and social analytics how to use artificial neural network to produce a converging mathematical model. Then students’ performance can be efficiently predicted and so the danger of failing in an enrolled e-course should be reduced.

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Sayed, M. and Baker, F. (2015) E-Learning Optimization Using Supervised Artificial Neural-Network. Journal of Software Engineering and Applications, 8, 26-34. doi: 10.4236/jsea.2015.81004.

Conflicts of Interest

The authors declare no conflicts of interest.


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