Machine Learning Approach for an Adaptive E-Learning System Based on Kolb Learning Styles

Abstract

In order to effectively implement adaptive learning within E-learning systems, it is crucial to accurately define the
learner's profile that reflects the characteristics necessary for optimal learning. Traditional methods of identifying profiles often rely
on questionnaires to collect data from learners, which can be time-consuming and result in irrelevant data due to arbitrary responses.As a solution, we propose an intelligent and dynamic model for adaptive learning that takes into account the entire learning process,from diagnostic assessment to knowledge assimilation. Our approach utilizes the k-means classification algorithm to group learners based on similar characteristics, as defined by the KOLB model. To enhance the accuracy of our model, we also incorporate neural networks to automatically predict learning styles and using decision tree to propose a adaptative pedagogical content to learner. By doing so, we aim to improve the overall performance of our proposed model.