A semantic approach to enhance moodle with personalization

Abstract

© 2018 Wiley Periodicals, Inc. The purpose of this paper is to present a framework for an adaptive mechanism implemented in Moodle in order to improve learning outcomes and students' satisfaction with the learning process. The proposed mechanism adapts the learning content within the course according to students' characteristics expressed by their learning style. In our study, student's learning style is dynamically determined by monitoring students’ actions and activities during the learning process, and detecting patterns of behavior that correspond to specific learning style. Semantic web technologies are in the background of the entire adaptive system. In order to examine the effectiveness of the proposed model and students' feedback, an evaluation study was conducted on two groups of students. Students from the control group had access to standard Moodle course, while students from experimental group had access to personalized learning content. The results indicated that students' performance was improved by using the proposed framework, while the student's feedback from regarding its usefulness was positive.