An Efficient Model for Detecting Learning Style Preferences in a Personalized E-Learning Management System
Improving the learning quality in e-learning environments has received considerable attention from researchers. One of the methods to improve the understanding of the students in a learning process is adapting the content to their learning styles. Adaptive Educational Systems can support different learning characteristics by building a model of the student's learning behaviour and subsequently adapting the learning environment to match different needs. In this paper, a 'Personalized E-Learning Management System (PELMS)', is developed. As the student interacts with the learning environment, the authors' predictive engine based on Na?ve Bayes classifier multinomial model, and their Efficient Detective Model (AO-EDM) predict the student's preferred learning style and adaptively customize the learning environment.