Smart e-course recommender based on learning styles

Abstract

A student’s learning style is the approach for learning that best allows the student to gather and to understand knowledge in a specific manner. Providing students with learning materials and activities that fit to their learning styles seems to have high potential to make learning easier for them. This research aims at providing teachers with recommendations on how to best extend their existing e-courses in learning management systems to accommodate more students with different learning styles. A smart e-course recommender tool has been developed for this purpose, which analyzes the e-courses with respect to their support levels for different students’ learning styles, recommends learning objects to be added to the courses, and visualizes the recommendations and the improvement in the course support level for students’ with different learning styles. The experimental results indicate that the tool has the ability to recommend suitable learning objects that, once being added, significantly improve the course support level for accommodating more students with different learning styles.