Online courses have transformed modern education by offering flexible and accessible learning opportunities. However, adapting course content to the diverse learning preferences of students remains a key challenge. This study introduces a data-driven approach to personalize and analyze learning content in online courses based on the Felder-Silverman Learning Styles Model (FSLSM). The concept of “learning styles” is operationalized through four FSLSM dimensions, and “cognitive profiles” refer to learners’ behavioral patterns in processing and interacting with content. An experimental study was conducted with 40 first-year economics students at the University of Guelma (Algeria), divided into control and experimental groups. Both groups accessed the same initial learning materials, while the experimental group received FSLSM-adapted content in the post-test phase. The results suggest a positive effect of the personalized content on learners’ cognitive engagement and progression. This approach demonstrates the potential of integrating learning style analysis and adaptive content delivery in online learning environments. 

Course analysis, Course design, Personalized Content, Felder and Silverman Learning style, Online Course