Smart Education with artificial intelligence based determination of learning styles

TLDR

Current educational systems lack adaptivity, offering identical resources to all students, while students learn best according to individual learning styles, making adaptive content essential for efficient learning. The study proposes a framework that integrates multiple learning models and AI techniques to determine students’ learning styles. The tool compares learning models, selects the most suitable one for a given environment, and is designed for scalable cloud deployment to enable rapid determination of learning styles.

Abstract

The need of the hour in present day education environment is adaptivity. Adaptive educational systems aim to customize content and learning paths of students. These aid’s in the minimizing disorientation and cognitive overload problems; thus maximizing learning efficiency. Present learning systems are lacking adaptivity; as they offer same resources for all users irrespective of their individual needs and preferences. Students learn according to their learning styles and determining these is a crucial step in making eLearning or traditional education adaptive. To determine learning styles, learning models have been suggested in literature, but there is no readily available software tool that provides the flexibility to select and implement the most suitable learning model. To fulfil this dire need, a framework of a tool is proposed here, which takes into consideration multiple learning models and artificial intelligence techniques for determining students’ learning styles. The tool would provide the facility to compare learning models, to determine the most suitable one for a particular environment. It is suggested that this tool be deployed in a cloud environment to provide a scalable solution that offers easy and rapid determination of learning styles.