A learning style identification approach in adaptive e-learning system

Abstract

Adaptive e-learning systems are considered one of the interesting research areas in technology-based learning strategies. The main goal of these systems is to offer learns a personal and a unique learning experience based on their preferences, needs, educational background, learning style, etc. The objective of this research is to identify the learning style of the learner. The identification is based on using web Log Mining data which contain learning behavior of the learner, and then the learning styles are mapped to Felder-Silverman Learning Style Model categories using Fuzzy C means Algorithm. The learning style can be changed over a period of time therefore the system has to adapt to the changes. For this, an Artificial Neural Network Algorithm is used to predict the learning style of a learner.