FSLSM-Based Analysis of Student Performance Information in a Blended Learning Course Using Moodle LMS

Abstract

Over the past decades, blended learning using a flipped classroom approach has become an increasingly important part of global learning development and has gradually revolutionized educational environments. However, in the online part, individual differences of learners are often not considered and treated in an equipollent way, neglecting their needs and personal characteristics, which results in a poor quality of the learning service. Thus the need to improve both the content of e-learning systems and their adaptability requires a careful examination of the direct behavior of students in distance learning platforms. Our study is based on a dataset from the FOAD_FSBM e-learning platform involved in the learning of Moroccan university students. This work is motivated by the fact that professors seek to better understand students’ learning styles, which are founded on the Felder and Silverman Learning Style Model (FSLSM) so that they can offer them adaptive and personalized courses. In this article, we first describe the basic environment of this study and the reasons that led us to choose the Moodle e-learning platform. We also present the format of the courses created to conduct our experiments, and its advantage in e-learning. Then, we highlight the development tools that we used to conduct our studies. Finally, we present the results of our statistical analyses in relation to the performance of the students.