<![CDATA[In the digital era, adaptive e-learning has become essential in addressing students’ diverse learning preferences. This study aims to develop an adaptive e-learning system that integrates the Felder-Silverman Learning Style model (FSLSM) into Moodle using fuzzy logic and case-based reasoning. The system extracts behavioral attributes from student activity logs and classifies learning styles into four dimensions: processing, perception, input, and understanding. The experimental evaluation, conducted with and without substitution of the (ILS) questionnaire values, demonstrated varying levels of accuracy. Accuracy improved with ILS substitution as follows: processing (82.86%), perception (80.00%), input (80.00%), and understanding (74.29%). Without ILS substitution, the accuracies were as follows: processing (80.00%), perception (80.00%), input (74.29%), and understanding (62.86%). These findings confirm the system’s potential to support personalized learning by accurately identifying learning styles.]]>
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