A Student Learning Style Auto-Detection Model in a Learning Management System

Abstract

Learning style plays an important role in enabling students to have an efficient learning process. This paper proposes an auto-detection model of student learning styles in learning management systems based on student learning activities. A literature review was conducted to investigate the components of online learning activities. The search terms used were "online learning activities", "learning management systems", and "Felder-Silverman Learning Style Model (FSLSM)." A combination of the search terms above was also executed to enhance the search process. Based on the results of the review, eleven classes of online learning activities were identified, namely forum, chat, mail, reading materials, exam delivery time, exercises, access to examples, answer changes, learning materials, exam results, and information access. The online learning activities identified were then mapped to the Felder-Silverman model based on four model dimensions: processing, perception, input, and understanding. The proposed model shows the attributes of the online learning activities based on the dimensions in the FSLSM. The proposed model can assist educators to improve learning content according to the suitability of students and recommend appropriate learning materials to students based on their characteristics and preferences. Future studies include the use of machine learning algorithms such as decision trees to auto-detect student learning styles in learning management systems.