Student Behavior Analysis to Detect Learning Styles in Moodle Learning Management System

Abstract

E-learning is distance learning that uses computer technology, networks of computers and the internet. E-Learning allows students to study via computers in their respective places without having to go to study/lectures in class physically. Moodle is a Learning Management System that is used as a medium for delivering E-Learning. The problem that often arises in e-learning is that in the learning process, students interact more with e-learning media so that teachers will find it difficult to monitor student behavior when using learning media. In fact, students in some cases tend to drop out or attend lesser classes. Moodle can capture student interactions and activities while studying online using log files. From the results of student interactions and activities on e-learning, it can be used to determine their learning style. Identifying student learning styles can improve the performance of the learning process. This research suggests an approach to automatically predicting learning styles based on the Felder and Silverman learning style (FSLSM) model using the Decision Tree algorithm and the ensemble Gradient Boosted Tree method. We've used actual data sets derived from e-learning program log files to perform our work. We use precision and accuracy to assess the results. The results show that our approach is delivering excellent results.