Learning management systems are widely used in educational organizations and universities to deliver self-paced online courses. Furthermore, educational theories have suggested that providing learners with learning material suitable for their learning styles may affect their learning performance. Learners with different individual traits, levels of knowledge, backgrounds, and characteristics are using these learning systems to enhance their learning understanding. This study is concerned with personalizing learning environments based on each learner's individual needs by designing and developing intelligent adaptive e-learning management systems. These systems behave according to the data collected in a 'learner model' from the learner to provide accurate learning material that adapts to learners' needs by changing the learning environment rapidly based on the learners' learning requirements and their learning styles.A dynamic adaptive e-learning system (DAELS) is proposed. The idea is to build an algorithm that can quickly understand an individual learner's learning styles. We propose the Similarity algorithm, which aims to adapt to the student's learning styles by taking advantage of the experience of previous students that used the same system and studied the same course. This algorithm presents the content to each student according to predictions of his/her preferred learning styles. These predictions can change during a student's progress and response to the presentation. The ID3 machine learning method was used and integrated into our Similarity algorithm. Such a method can search learners' databases efficiently and quickly by classifying learners based on their attributes. Methods and associated techniques that address these issues by use of Felder and Silverman Learning Styles Model (FSLSM) have been developed and can be built into Moodle, the learning management system, as an integral component. We then conducted experiments on students to evaluate the flexibility of the DAELS and its effect on students' learning performance.An experiment was designed and implemented to validate the proposed approach's reliability and performance on learners' scores. The proposed DAELS was compared with a static adaptive e-learning system (SAELS) and a non-adaptive e-learning system (non-AELS). The results of the empirical experiment demonstrate the effectiveness of using DAELS on student performance. On average, the dynamic adaptive group had an average increase of 60% in the post-test from pre-test, whereas the average score of the static group increased 32%, and the control group had an average increase of 8%. The results reveal that the dynamic group had the highest average scores in the post-test, and the control group had the lowest average increase in scores. The findings indicate that the developed Similarity algorithm, implemented in our DAELS for personalising learning content presentation according to students' learning styles, is appropriate in e-learning systems and can enhance learning quality.