TITLE:
E-Learning Optimization Using Supervised Artificial Neural-Network
AUTHORS:
Mohamed Sayed, Faris Baker
KEYWORDS:
Artificial Neural Networks, E-Learning, Prediction Models, Supervised Learning
JOURNAL NAME:
Journal of Software Engineering and Applications,
Vol.8 No.1,
January
21,
2015
ABSTRACT: 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.