Neural network application on knowledge acquisition for adaptive hypermedia learning

Abstract

Computers have been used in education for over 20 years. Computer-based training and computer aided instruction were the first such system deployed as an attempt to teach using computers. For a computer based educational system to provide individualized attention that a student would receive from a human tutor, it must reason about the domain and the learner. This has prompted research in the field of intelligent tutoring system particularly in adaptive hypermedia learning. On the other hand, in a nonlinear system, the effect depends on the values of other inputs, and the relationship is a higher-order function. Neural Network is an approach that can cater nonlinear problems, and an implementation of an algorithm inspired by research into the brain. It is a technology in which computer learns directly from data, thereby assisting in classification, function estimation, data comprehension, and similar tasks. In this paper, we present a neural network model in classifying students level of knowledge acquisition are normalized in the close interval of (0,1), and the performance of neural network model towards these data are compared to the original data.