Predicting learning styles in personalized E-learning platforms

Abstract

Learning Management Systems (LMS) are popularly used in online educational systems and universities to deliver self-paced online courses. Furthermore, in the literature many educational theories have recommended that suggesting learners with suitable learning material based on their learning styles may improve learner's learning caliber. Human brains generally use different methods for grasping knowledge faster and easier. We call these methods as learning styles . Learners with diverse individual attributes, knowledge levels, backgrounds, and characteristics have distinct learning styles. Determining student's learning style improves the efficiency of the learning process. To provide personalized study material to the learner depending on his or her learning style, an accurate automated learning style identification model is required. In this work we implement an intelligent model for accurate detection of student's learning styles.