Designing Index Learning Style for Developing Personalization Learning Management System Moodle-Based

Abstract

Differences in students' learning styles often pose challenges in online learning, particularly in personalizing learning materials to meet individual needs. This study developed an Index Learning Style (ILS) plugin based on the Felder-Silverman Learning Style Model (FSLSM) to support personalized learning on the Moodle Learning Management System (LMS). The plugin is designed to identify students' learning styles through 44 questions measuring four main dimensions: processing, perception, input, and understanding. The system development involved algorithms for learning style analysis, integration with Moodle's restricted access feature, and implementation in an Internet of Things (IoT) course. The implementation results show that the ILS plugin can effectively map students' learning styles to relevant Learning Object Materials (LOM). Moreover, personalized learning materials increase student engagement and facilitate material comprehension, particularly for those with dominant learning styles such as Active, Sensitive, Visual, and Sequential. The development of the ILS plugin provides a practical solution for enhancing the online learning experience to make it more adaptive. This plugin has the potential for widespread implementation in various technology-based education contexts to support more personal and effective learning.