Identification of Learning Styles in Distance Education Through the Interaction of the Student With a Learning Management System

Abstract

Greater availability and access to information and communication technologies have formed a more “connected” society. It provides more interactions between people. Also, it fosters technology-driven Distance Education (DE). In this way, new methodologies have been developed to improve teaching and learning in DE, such as artificial intelligence methods. This paper proposes an association between artificial intelligence techniques and the concepts of Learning Styles (LS). These concepts identify the learning preferences of each student. It aims at responding the following questions: Is it possible, in an automatically way, to identify the students' LS from their interactions with the Learning Management System (LMS)? What techniques could be developed to identify the LS of the course students conducted in the DE modality, so that it will improve a better academic way to student's learning? In order to answer these questions, we used some artificial intelligence algorithms to identify the relation of the students' LS with their behaviors in LMS. Results show a low relation of the LS of the students associated with their behaviors in LMS. However, this process identified a new category of LS - it is called indefinite. It corresponds to students without preference for any of the other classifications of LS identified.